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I finally understood that the popular Shakespearean quote: "To be or not to be" actually means "to live or not to live." The man was contemplating suicide!!!!!! #literature #freecompliments #cent
"I have noted how many newcomers to WEB3 and SocialFi apply themselves for a few days and then expect to see results. Unfortunately, it’s not merely a case of effort, but an ongoing effort over months and even years. This is the bitter pill that many are unable to swallow. Eventually, it becomes easier to extract value. This is primarily due to the increasing value of working capital and your influence." - Sapphire Crypto #cent #WEB3
$BTC is above $58K and it’s Friday! Can we get a weekend pump? That is the question. Generally, weekends are comprised of sideways chop and low volume. However, it’s not always the case. Hopefully, this weekend is one of those!😎 #cent
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UN reports that the victims of the congo prison break are afraid to speak up. Where is freedom of speech at times like this? #dailydook #cent #newsonleo
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I was working.. I'm the one who had to fix all those problems 😂
She's doing great, currently at a birthday party at a twin pair in her class 🥰
How's everything on your end?
Daily screenshot of my share in the LEO/CACAO Pool. The USD value is $40.11 today. The good part is that the amount of $LEO is lower, which means each token has a higher LEO value compared to CACAO value than before. #cent #leodex #freecompliments
2024 FIDE #Chess Olympiad, round 2:
Magnus was missing in round 2 as well what turned out a bad decision: His team Norway was the one top team not to win but held to a 2-2 draw by 50th seed Canada!
Here is the daily technology #threadcast for 9/13/24. We aim to educate people about this crucial area along with providing information of what is taking place.
Drop all question, comments, and articles relating to #technology and the future. The goal is make it a technology center.
Meta’s Big Privacy Admission: 17 Years of Data Scraping❗️
Did you know Meta (the company behind Facebook and Instagram) has been quietly scraping public posts, photos, and comments for the last 17 years? Yep, an Australian investigation uncovered that Meta’s been using all this data to train its AI models. Whether it’s your Facebook posts or Insta pics, they’ve been collecting it all. This isn’t just recent — it’s been happening since 2006! Definitely a reminder to keep an eye on what you’re sharing online.
Scientists Found a 520-Million-Year-Old Miracle: a Fossil With Brains and Guts Intact
A remarkable fossilized larva has been discovered by scientists with its brain and guts still intact.
The fossilized creature is one of the earliest ancestors of a group known as arthropods, which includes insects, crabs, and lobsters.
A unique window into the past, the ancient critter has allowed experts a chance to better understand evolutionary links between the arthropods of the pasta and those of the present day.
Google DeepMind just dropped some exciting news! They’ve developed a system called ALOHA Unleashed that teaches robots to perform tricky tasks simply by watching humans. Paired with their simulation tool, DemoStart, these robots are picking up skills from visual demos, like assembling objects or handling delicate tasks. It’s like teaching a robot by showing it a YouTube tutorial! Want to see it in action? Videos of these trained robots are contained in the source article.
🚗✨ Big news for #AutonomousMobility! Uber and Waymo are teaming up to bring driverless rides to Austin and Atlanta by early 2025. The future of transportation is here! 🚀 #UberWaymo #technology
State-of-the-art technology that improves detection rates for cancer and other diseases will be officially unveiled at an East Yorkshire hospital on Friday.
SpaceX's First Commercial Spacewalk Just Happened! 🚀
In a historic moment, private astronauts with SpaceX completed the first-ever commercial spacewalk on Thursday, kicking off just after 6 am ET. Jared Isaacman, a tech billionaire, led the charge, stepping out of the SpaceX Crew Dragon for mobility tests. He was outside for about an hour, fully exposed to the vacuum of space. What’s even more wild? They did this using SpaceX-designed spacesuits that had never been tested in orbit before. Curious to see it in action? There's a clip of the livestream showing the entire adventure.
My favorite quote so far from the Harvard Business Review Special Issue on How To Thrive In A GenAI World,
People that don't find it useful simply haven't really understood how to use it...The 5% or whatever who use it effectively are going to smoke the others."
Starting next week, Meta is tweaking how it flags AI-edited content on Instagram, Facebook, and Threads. Instead of showing an "AI Info" label under usernames, you'll now find it tucked away in a menu at the top-right of images or videos. You can use tools like Adobe's Content Credentials or Google's SynthID to spot AI changes. This shift is in response to complaints that genuine photos were being mislabeled as AI-generated. So, keep an eye out for the new system—it’s designed to be more accurate and less intrusive.
Uber users in Austin and Atlanta will be able to hail Waymo robotaxis through the app in early 2025 as part of a partnership between the two companies.
Uber users in Austin and Atlanta will be able to hail Waymo robotaxis through the app in early 2025 as part of an expanded partnership between the two companies.
Waymo’s autonomous vehicles have been available on the Uber app in Phoenix since October 2023. Uber has been snatching up self-driving partnerships across its ride-hail and delivery verticals, and it last month said it was partnering with GM’s Cruise and the U.K.’s Wayve as well.
Waymo runs its own autonomous ride-hailing service, Waymo One, in San Francisco, Phoenix, and Los Angeles, and provides around 100,000 trips per week, according to the company. The Alphabet-owned AV company began testing robotaxis in Atlanta and shuttling its own employees around Austin earlier this year — usually the first steps Waymo takes before it begins offering its ride-hailing service in new markets.
Still, Waymo said only Uber users would be able to hail its fleet of Jaguar I-PACE AVs in Austin and Atlanta.
Waymo said a limited number of users will be able to access rides in Austin and Atlanta via the Waymo One app in the coming weeks.
Waymo did not mention how many vehicles it would dispatch in the two cities, but said its fleet would “grow to hundreds of vehicles over time.” Uber will handle fleet management, like cleaning and repairing the cars, and Waymo will oversee testing and operation of Waymo Driver, as well as offering roadside assistance and other rider support functions.
OpenAI released its new o1 models on Thursday, giving ChatGPT users their first chance to try AI models that pause to "think" before they answer.
OpenAI released its new o1 models on Thursday, giving ChatGPT users their first chance to try AI models that pause to “think” before they answer. There’s been a lot of hype building up to these models, codenamed “Strawberry” inside OpenAI. But does Strawberry live up to the hype?
Compared to GPT-4o, the o1 models feel like one step forward and two steps back. ChatGPT o1 excels at reasoning and answering complex questions, but the model is roughly four times more expensive to use than GPT-4o. OpenAI’s latest model lacks the tools, multimodal capabilities, and speed that made GPT-4o so impressive. In fact, OpenAI even admits that “GPT-4o is still the best option for most prompts” on its help page, and notes elsewhere that GPT o1 struggles at simpler tasks.
“It’s impressive, but I think the improvement is not very significant,” said Ravid Shwartz Ziv, an NYU professor who studies AI models. “It’s better at certain problems, but you don’t have this across-the-board improvement.”
For all of these reasons, it’s important to use GPT o1 only for the questions it’s truly designed to help with: big ones. To be clear, most people are not using generative AI to answer these kinds of questions today, largely because today’s AI models are not very good at it. However, o1 is a tentative step in that direction.
ChatGPT o1 is unique because it “thinks” before answering, breaking down big problems into small steps and attempting to identify when it gets one of those steps right or wrong. This “multi-step reasoning” isn’t entirely new (researchers have proposed it for years, and You.com uses it for complex queries), but it hasn’t been practical until recently.
“There’s a lot of excitement in the AI community,” said Workera CEO and Stanford professor Kian Katanforoosh, who teaches classes on machine learning, in an interview. “If you can train a reinforcement learning algorithm paired with some of the language model techniques that OpenAI has, you can technically create step-by-step thinking and allow the AI model to walk backwards from big ideas you’re trying to work through.”
ChatGPT o1 is also uniquely pricey. In most models, you pay for input tokens and output tokens. However, ChatGPT o1 adds a hidden process (the small steps the model breaks big problems into), which adds a large amount of compute you never fully see. OpenAI is hiding some details of this process to maintain its competitive advantage. That said, you still get charged for these in the form of “reasoning tokens.” This further emphasizes why you need to be careful about using ChatGPT o1, so you don’t get charged a ton of tokens for asking where the capital of Nevada is.
The idea of an AI model that helps you “walk backwards from big ideas” is powerful, though. In practice, the model is pretty good at that.
In one example, I asked ChatGPT o1 preview to help my family plan Thanksgiving, a task that could benefit from a little unbiased logic and reasoning. Specifically, I wanted help figuring out if two ovens would be sufficient to cook a Thanksgiving dinner for 11 people and wanted to talk through whether we should consider renting an Airbnb to get access to a third oven.
After 12 seconds of “thinking,” ChatGPT wrote me out a 750+ word response ultimately telling me that two ovens should be sufficient with some careful strategizing, and will allow my family to save on costs and spend more time together. But it broke down its thinking for me at each step of the way and explained how it considered all of these external factors, including costs, family time, and oven management.
ChatGPT o1 told me how to prioritize oven space at the house that is hosting the event, which was smart. Oddly, it suggested I consider renting a portable oven for the day. That said, the model performed much better than GPT-4o, which required multiple follow-up questions about what exact dishes I was bringing, and then gave me bare-bones advice I found less useful.
Asking about Thanksgiving dinner may seem silly, but you could see how this tool would be helpful for breaking down complicated tasks.
I also asked ChatGPT o1 to help me plan out a busy day at work, where I needed to travel between the airport, multiple in-person meetings in various locations, and my office. It gave me a very detailed plan, but maybe was a little bit much. Sometimes, all the added steps can be a little overwhelming.
People presume that future activity will come from people. Because of this, they are focused upon building for humans. This might not be the case, especially for a platform for Leo.
Oprah Winfrey hosted a special on AI. Guests included OpenAI CEO Sam Altman, Marques Brownlee and FBI director Christopher Wray.
Late Thursday evening, Oprah Winfrey aired a special on AI, appropriately titled “AI and the Future of Us.” Guests included OpenAI CEO Sam Altman, tech influencer Marques Brownlee, and current FBI director Christopher Wray.
The dominant tone was one of skepticism — and wariness.
Oprah noted in prepared remarks that the AI genie is out of the bottle, for better or worse, and that humanity will have to learn to live with the consequences.
“AI is still beyond our control and to a great extent…our understanding,” she said. “But it is here, and we’re going to be living with technology that can be our ally as well as our rival … We are this planet’s most adaptable creatures. We will adapt again. But keep your eyes on what’s real. The stakes could not be higher.”
Sam Altman overpromises
Altman, Oprah’s first interview of the night, made the questionable case that today’s AI is learning concepts within the data it’s trained on.
“We are showing the system a thousand words in a sequence and asking it to predict what comes next,” he told Oprah. “The system learns to predict, and then in there, it learns the underlying concepts.”
Sam Altman overpromises
Altman, Oprah’s first interview of the night, made the questionable case that today’s AI is learning concepts within the data it’s trained on.
“We are showing the system a thousand words in a sequence and asking it to predict what comes next,” he told Oprah. “The system learns to predict, and then in there, it learns the underlying concepts.”
While Altman possibly overstated the capabilities of today’s AI systems, he underlined the importance of figuring out how to safety-test those same systems.
“One of the first things we need to do — and this is now happening — is to get the government to start figuring out how to do safety testing of these systems, like we do for aircraft or new medicines,” he said. “I personally, probably have a conversation with someone in the government every few days.”
Three and Vodafone’s planned $19 billion merger hits the skids as UK rules the deal is likely to reduce competition.
The U.K.’s antitrust regulator has delivered its provisional ruling in a longstanding battle to combine two of the country’s major telecommunication operators.
The Competition and Markets Authority (CMA) says that Three and Vodafone’s planned $19 billion merger — announced 15 months ago — could lead to higher prices for consumers, diminished service such as smaller data packages in contracts, and reduced investment in U.K. mobile networks.
The CMA also took aim at the market for mobile virtual network operators (MVNOs) — a set up aimed at increasing competition by enabling new carriers to set up and offer services without building all of their own costly communications infrastructure. Both Three and Vodafone supply network to MVNOs, with the list including iD Mobile and Lebara. The CMA said that a merger might make it more difficult for MVNOs to access reasonable wholesale deals, in turn making services more expensive for customers.
Competition concerns aside, there was at least one other potential roadblock to this merger. Three is owned by CK Hutchison Holdings, a Hong Kong conglomerate subject to a national security law introduced by China in 2020, leading some to argue that Three could be compelled to share sensitive data with the Chinese state. The U.K. had introduced the National Security and Investment Act back in 2022 to cover such scenarios, and the government had previously used this law to block other deals between U.K. entities and Chinese companies.
However, back in May the U.K. government greenlighted the Three / Vodafone merger on security grounds, with some provisions, leaving the remaining regulatory concerns firmly in the CMA’s domain.
It was a matter of time, but Apple is going to allow third-party app stores on the iPad starting next week, on September 16.
It was a matter of time, but Apple is going to allow third-party app stores on the iPad starting next week, on September 16. This change will occur with the next major release of iPadOS, the operating system specifically designed for the iPad.
The move is related to the European Union’s Digital Markets Act (DMA), a set of market fairness and pro-competition rules. Last fall, the Commission shared a list of six tech companies that have been designated “gatekeepers”, as they operate so called “core platform services” (CPS). It’s since added a seventh.
For Apple, the Commission initially designated three products and services as CPS: its mobile operating system iOS, its app distribution marketplace the App Store, and its web browser Safari. Then, in April, it announced that it was adding Apple’s iPadOS to the list.
While iPadOS user numbers did not meet the threshold to be in scope of the DMA, the Commission has some leeway in designations and said it considered that there are strong locked-in effects for business users in particular.
Apple had six months to update iPadOS and make sure that it is compliant with the DMA. Which brings us to Friday’s announcement: Starting with iPadOS 18, users in the EU will be able to install alternative app stores. Similarly, web browser developers will be able to release browsers for the iPad with their own browser engines.
Given the different DMA compliance timeline for iOS, recent changes to iOS in the EU can be an indication of what’s going to happen for iPad users in the EU.
There are five third-party app stores that are now available for iOS in the EU. One example, the AltStore PAL, was the first alternative app marketplace made available on iOS in the EU. You can use it to download video game emulator app Delta, virtual machine app UTM, torrenting app iTorrent, and more.
Apps are notarized by Apple for security purposes before they can be released on alternative app stores. App developers also have to sign new business terms with Apple — and pay a controversial “Core Technology Fee” above a certain threshold.
Epic Games also launched its alternative iOS app store in the EU so that people can download and play Fortnite, Rocket League Sideswipe and Fall Guys on their iPhones. The company has already said that it plans to bring Fortnite and its other games to the iPad.
A crew of four private astronauts made history in the early hours of Thursday when they opened the hatch of their SpaceX Dragon capsule
A crew of four private astronauts made history in the early hours of Thursday when they opened the hatch of their SpaceX Dragon capsule and conducted the first commercial spacewalk.
The spacewalk, the riskiest part of the five-day Polaris Dawn mission, kicked off at 6:12 a.m. ET when oxygen started flowing into the astronauts’ spacesuits. Only two of the four crew members actually exited the vehicle, but all four had to don the new SpaceX-made suits because the Dragon capsule doesn’t have an airlock. That meant the entire spacecraft had to be depressurized.
A spacewalk — sometimes called extravehicular activity — is when astronauts leave the relative safety of their spacecraft for the vacuum of space. In the history of human spaceflight, spacewalks have only ever been performed by government astronauts, who use them as an opportunity to do repairs, perform maintenance, or to conduct scientific experiments. Spacewalks performed by NASA astronauts typically last between five and eight hours.
Billionaire entrepreneur and mission leader Jared Isaacman was the first to exit the Dragon capsule; after he returned, SpaceX engineer Sarah Gillis took a turn in the vacuum of space. They used a special ladder mobility aid dubbed a “skywalker,” which SpaceX added to the Dragon just for this purpose, to assist them outside the capsule. The pair was connected to the spacecraft by umbilical cords and they kept contact with the ladder at all times. The spacewalk was very quick, with each person outside the spacecraft for less than ten minutes. During that time, Isaacman and Gillis performed a series of movements to test the suits’ mobility and performance.
Rajeev Behera’s new all-on-one HR startup, dubbed Every, is either brilliant or crazy.
Rajeev Behera’s new all-in-one HR startup, dubbed Every, is either brilliant or crazy.
Crazy because multi-module HR software that does payroll, onboarding, and spend management for small businesses is already a jam-packed market. Competitors include unicorn startups Gusto, Rippling, and Deel; incumbents that are strong in one area and are expanding into others like Mercury and Brex; and many smaller startups like Finally, Paylocity, and AccountsIQ.
Every’s investors clearly think Behera’s particular take on the idea is brilliant. Every just raised a $22.5 million Series A, led by Redpoint Ventures’ Alex Bard, with participation from Y Combinator, Okta Ventures, and Base10 Partners’ Rexhi Dollaku, TechCrunch can exclusively report.
Behera’s unique — and possibly brilliant — game plan revolves around his target customers and what he’s offering to hook them.
He and his co-founder, Barry Peterson, aimed Every at very early-stage tech startups and will help them do their incorporation documents for free, then set them up with a business bank account as well as other back-office essentials. Every makes its money by charging monthly SaaS fees for other modules, like accounting, and interchange fees.
“We spent all this time building pretty advanced expense management, banking, payroll, all that stuff. Now we will release incorporation for founders, and we’re going to just give it away for free,” Behera said.
After a 30-minute, white-glove onboarding session, startups get an integrated suite of banking, payroll, HR onboarding, HR benefits, bookkeeping, taxes, state compliance, and so on. (As we recently reported, the state compliance stuff is particularly tricky for startups.) Every’s customers also get a Slack channel where they can commiserate with other founders.
Delhivery claims Ecom Express has inaccurately represented Delhivery's business metrics when drawing comparisons in its IPO filing.
Indian logistics firm Delhivery has publicly contested the accuracy of the metrics presented by competitor Ecom Express in its draft initial public offering prospectus, a rare confrontation in the lead-up to the latter’s market debut.
Delhivery, backed by SoftBank and already publicly listed, claims Ecom Express has inaccurately represented Delhivery’s business metrics when drawing comparisons in its IPO filing.
The 442-page draft prospectus (PDF) submitted by Ecom Express last month said the startup had shipped 514.41 million packages in the fiscal year ended March 2024, while Delhivery handled 740 million during the same period.
Delhivery alleged in a filing to the stock exchanges on Friday that this comparison was flawed, asserting that what it considers a single shipment is counted as two by its rivals, suggesting that Ecom Express’ volume figures are potentially inflated. Delhivery said that its rival counts returned orders as two shipments.
Delhivery also called out Ecom Express’ cost per shipment (CPS) calculations, citing disparities in accounting methods and alleging inflated shipment figures.
The SoftBank-backed firm also pointed out that Ecom Express’ claim that it offers its services in 27,000 zipcodes isn’t accurate, as India has fewer than 19,500 unique zip codes.
This public dispute comes less than a month after Ecom Express, which counts Warburg Pincus, Partners Group and British International Investment among its backers, filed for an IPO, aiming to raise $310 million.
Delhivery has also questioned Ecom Express’ presentation of service EBITDA and corporate costs, citing a lack of consistent definitions for these metrics in the prospectus.
Antonio Moraes, the grandson of a late prominent Brazilian billionaire, was never interested in joining the family-owned conglomerate
Antonio Moraes, the grandson of a late prominent Brazilian billionaire, was never interested in joining the family-owned conglomerate of construction companies and a bank. Shortly after graduating from college, he founded one of Brazil’s first impact funds, which invested primarily in companies that made healthcare more accessible and affordable.
But while attending Stanford University, where Moraes received a master’s degree in business administration and healthcare policy, he realized that instead of investing in impactful companies, he wanted to start his own.
As a part of an entrepreneurship class, Moraes and his co-founder, an engineering grad student, James Wong, visited multiple eyeglass manufacturing factories in China. They discovered that designer frames that sell for as much as $600 in the U.S. cost only about $10 to produce. “We thought there’s something very wrong with these markups,” Moraes told TechCrunch.
Because vision care and eyeglasses are expensive, many employees buy frames with their vision insurance, but the benefits typically don’t cover all the costs, Moraes said. “With vision insurance, people expect not to pay anything, but then they leave the optician’s office with a $300 out-of-pocket bill.”
Moraes and Wong started XP Health in late 2018, but during the pandemic, they shifted the startup’s focus to a digital-first, AI-driven platform that offers employees eye exams and eyewear benefits at significantly lower costs than existing vision insurance plans.
On Thursday, XP Health announced a $33.2 million Series B led by QED Investors with participation from Canvas Ventures, American Family Ventures, HC9 Ventures, Valor Capital Group and Manchester Story. The round comes less than two years after XP Health’s $17.1 million Series A.
XP Health members who buy eyeglasses virtually can save as much as 69% off the retail price, Moraes said. The company claims not to mark up the frames or lenses sourced directly from factories in Asia. Instead, XP Health generates its revenue through recurring membership fees.
Cord-Cutting News Roundup: ATSC 3.0, Fubo TV Lawsuit, MLB TV Rights, and More
In the rapidly evolving world of cord-cutting and streaming media, several significant developments have emerged. This article summarizes the key points from a recent "Cord Cutting Today" news roundup.
The transition to ATSC 3.0, also known as NextGen TV, is facing significant challenges that could jeopardize its future:
A patent lawsuit against LG, a leading manufacturer of TVs with ATSC 3.0 tuners, has resulted in LG halting production of these TVs.
Other manufacturers have also reduced their production of TVs with ATSC 3.0 tuners.
Pearl TV, the group behind ATSC 3.0, has filed a supportive brief in LG's appeal, warning that if the Verdict isn't overturned, it could threaten the future of over-the-air television.
Unlike with ATSC 1.0, the FCC did not create a patent pool for ATSC 3.0, leading to conflicts with patent holders.
If LG loses its appeal, there are concerns that TV manufacturers may widely drop ATSC 3.0 tuners to avoid potential lawsuits.
Despite these challenges, ATSC 3.0 offers benefits such as improved coverage area and the potential for more channels. However, criticisms persist regarding the implementation of digital Rights Management (DRM) in the standard.
Fubo TV to Release Confidential Carriage Agreements
In a significant move for transparency in the streaming industry:
Fubo TV has announced its intention to release confidential documents related to carriage deals with major networks like Disney, Fox, and Warner Bros. Discovery.
This release could provide unprecedented insight into how these deals are structured and negotiated.
The league faces challenges in balancing this goal with the profitability of existing TV contracts.
It remains to be seen how MLB will navigate the transition while maintaining its revenue streams.
Other Notable Updates
Pluto TV has added five new channels, including several Spanish-language options.
MeTV is bringing back its annual Halloween programming event.
DirecTV Stream is rolling out an enhanced user experience, including custom profiles with unique DVRs and content recommendations for each household member.
These developments highlight the ongoing evolution of the cord-cutting landscape, with changes in technology, legal battles, and content distribution strategies aLL playing significant roles in shaping the future of television and streaming services.
Arkansas is becoming a key player in U.S. lithium production, but the state faces challenges like volatile prices and unproven technology.
The future of lithium production in the U.S. is gaining momentum in Arkansas, as companies like ExxonMobil, Albemarle, and Standard Lithium make significant investments in the state.
This comes at a time when global demand for lithium, driven by electric vehicles and energy-storage needs, continues to grow. In 2023, global lithium consumption reached 180,000 metric tons, up from 142,000 metric tons in 2022, according to the United States Geological Survey. But the U.S. produces less than 1% of the world's supply.
While most of the world's lithium still comes from countries like Australia, Chile and China, Arkansas could change that.
The state is home to the Smackover Formation, a geological formation rich in lithium brine.
"Lithium resource quality is really what makes this a great region," said Wesley Hamilton, CTO and vice president of research and technology at Albemarle, the world's top lithium producer. "It comes down to two things: the concentration of lithium and the ability to extract it efficiently from the brine."
Arkansas has long been a producer of bromine, which is extracted from the same brines now being tapped for lithium. The formation holds over 4 million metric tons of lithium, which is enough to power millions of EVs and devices, according to Galvanic Energy. That has attracted a rush of interest from companies looking to capitalize on the formation's potential.
Exxon Mobil, for example, acquired 120,000 acres in the Smackover Formation in 2023 and aims to start producing battery-grade lithium by 2027. The company said it will produce enough lithium to supply the manufacturing more than 1 million EVs per year by 2030. Standard Lithium, which has operated in Arkansas since 2020, is also expanding its Direct Lithium Extraction (DLE) facility in El Dorado, thanks to a $100 million investment from Koch Strategic Platforms. DLE is touted as a more eco-friendly extraction method, using advanced filters to reduce energy and water usage.
DLE technology, while promising, has yet to be proven on a large scale, and lithium prices have dropped sharply from over $80,000 per metric ton in 2022 to around $10,600 today. That's due to oversupply, slower-than-expected EV growth and new battery technologies, according to Benchmark.
The U.K. government has had "constructive" talks with X over the spread of misinformation and other harmful content, technology minister Peter Kyle told CNBC.
As the riots raged in the U.K., Elon Musk began making incendiary comments about the situation, including the statement: "Civil war is inevitable." Musk is the owner of X, the social media platform formerly known as X.
The U.K. government has had "constructive" talks with Elon Musk's social media site X over the spread of misinformation and other harmful content, technology minister Peter Kyle told CNBC Friday.
Kyle told CNBC's Arabile Gumede that the government had been in contact with all the major social media platforms — including Musk's X — over the summer about misinformation and the role they have in propagating harmful material.
The minister said that, although he hasn't had direct contact with Musk himself, he is "in touch often with his local chief executives here in the United Kingdom."
"So far, it has been a constructive set of conversations," he said, adding that, though there are "differences" in views between the two parties, they talk them through.
Citizens and governments around the world have higher expectations about social media platforms today and the role they play in keeping people safe and mitigating potential harms stemming from their products, Kyle said.
"It is a privilege having access to the British economy and society. And I just expect any company that comes to work here and aspires to sell products and services into our country to respect that," he added.
Kyle's comments to CNBC come after misinformation spread online after a knife attack at a Taylor Swift-themed dance class in northwest England sparked far-right, anti-immigration riots — with shops and mosques being attacked in towns across the country.
Keith Rabois, managing director of Khosla Ventures, was having dinner with a “very successful CEO” in October 2018
Keith Rabois, managing director of Khosla Ventures, was having dinner with a “very successful CEO” in October 2018 when the CEO asked him a question: How many people does it take to create a whole new Silicon Valley? Is it 10,000? 100,000?
Rabois didn’t know, but he decided to accept the challenge and set about trying to make Miami the next Valley.
And despite other big name investors like Andreessen Horowitz decamping and shutting down its office in Miami a mere two years after setting up shop, Rabois said he’s still bullish on the South Florida city.
At Primary Venture Partners’ NYC Summit on Thursday, Rabois claimed that 11% of all seed investments in the United States have come out of Miami, which is “up basically from zero,” and that he hopes to raise that to 20%.
According to PitchBook data, seed investments into Miami startups to date this year accounted for only 2.6% of total U.S. seed investment. In 2023, they accounted for 3.5%.
“And the stats you should be looking at if you care about the future of tech are [the] fraction of seed investments, where do they happen?” Rabois said, adding that later-stage investment reveals less about the future of technology.
Rabois also said Khosla Ventures was gearing up to invest in its fifth company in Miami that will “reinvent education,” but didn’t provide specifics.
In April, Khosla and Founders Fund, where Rabois worked from 2019 until January, led the $150 million investment into spend management startup Ramp. Rabois said that Ramp, which is based in New York, has an office in Miami, which adds to the city’s appeal.
After years of rumour, speculation and hype, Sony has confirmed it is launching a more powerful - and much more expensive - version of its hugely popular PlayStation 5 console
Meta is changing the way it labels content that has been edited or modified by AI tools on Instagram, Facebook and Threads.
Meta is changing the way it labels content that has been edited or modified by AI tools on Instagram, Facebook, and Threads. For this type of content, Meta is moving the “AI info” label to the post’s menu. In the past, the label would appear directly under the user’s name.
The company says the label will still appear under content that it has detected was generated by an AI tool. This means that although the label is being hidden for content that was changed or edited by AI tools, it will still be prominently displayed under content that was fully generated by an AI prompt.
For content that was generated by AI, Meta will “share whether the content is labeled because of industry-shared signals or because someone self-disclosed,” the company says.
Meta says the change, which is rolling out next week, will “better reflect the extent of AI used in content” on its platforms.
By making the AI info label harder to find, it might be easier for users to be deceived by content that was edited with AI, especially as editing tools become more and more advanced.
Given that generative AI is a relatively newer technology, this isn’t the first time that Meta has changed how it labels such content on its platforms. In July, the company changed its AI label from “Made with AI” to “AI info” after Meta received complaints from photographers who said the label was being added to real photos.
The strike in Samsung India follows recent wage protests in South Korea where 36,500 members from its biggest workers' union went on strike in July and August.
Shares of Samsung Electronics fell as much as 3% on Friday, as workers at its southern Indian plant continued to strike, disrupting production at the consumer electronics unit for a fifth day.
Worker union's representatives, Samsung's management and the state's labor officials failed to reach an agreement over pay and working conditions among other things, on Thursday.
Hundreds of workers have been on strike since Monday, demanding the electronics conglomerate to recognize their union, raise wages and reduce working hours. It is one of the biggest such strikes in recent years in India, Reuters reported.
The plant, located in the city of Chennai in southern India, makes electronic appliances including televisions, refrigerators and washing machines.
It's one of the two factories that Samsung runs in India and can account for up to 30% of the group's $12 billion annual revenue in the country, Reuters reported.
Samsung Electronics is one of the leading players in India's smartphone and electronic appliances market. The major appliances sector-wide 2024 revenue in India is pegged at $38.2 billion, according to Statista.
The workers will continue to strike until their demands for better wages and working conditions are met, union leader E. Muthukumar told Reuters, "Samsung management asked us to stop striking but wouldn't recognize the union or talk to us, so the strike continues."
The U.S. has imposed restrictions on exports of the chips out of concerns they could be accessed by China, which is Saudi Arabia's top trading partner.
Saudi Arabia is optimistic about gaining access to U.S. chipmaker Nvidia's high-performance chips, which would enable it to develop and operate the most advanced artificial intelligence models.
Speaking to CNBC on Thursday, a top official at the Saudi Data and AI Authority, Abdulrahman Tariq Habib, said the kingdom expected to make such a stride in the next year.
"I think within the next year," Habib, Deputy CEO of SDAIA's strategy management office, told CNBC's Dan Murphy after being asked about a potential timeline. It's a significant expectation given that the United States' strict export controls have thus far prevented the chips' export to the kingdom. Habib made the comments on the sidelines of GAIN, Saudi Arabia's international AI summit, which took place in Riyadh this week.
It "will mean a lot" for Saudi Arabia to have access to the chips, Habib said — in this case, the Nvidia H200s, the firm's most powerful chips, which are used in OpenAI's GPT-4o.
"It will ease business between Saudi and U.S.," he said. "It will also open a lot of doors for building the capability, the computational capabilities, in the kingdom. But most importantly, it's not only the computational capability that's important. We worked hard in the past three years in building capacity, in human capacity, we also build data capacity as well. So we are working and collaborating with all [of the] international community and contributing [to] be one of the top active countries in data analysis."
Saudi Arabia is pouring considerable investment into developing a robust AI ecosystem in the kingdom, disclosing in a report by SDAIA that it aims to have AI make up 12% of its gross domestic product by 2030. According to the report, published on Sept. 9, the kingdom's $925 billion Public Investment Fund will lead the investment.
Cohost, a would-be X rival launched to the public in June 2022, is shutting down, the company announced via the social network's staff account earlier
Cohost, a would-be X rival launched to the public in June 2022, is shutting down, the company announced via the social network’s staff account earlier this week. The service had operated much like Twitter, offering users the ability to follow others, view posts in a feed, and like and repost content shared by others. However, Cohost differentiated itself by focusing on a chronological feed without trending topics, support for long-form posts, and pursuing a business model that didn’t rely on advertising.
The startup’s premium subscription, Cohost Plus, offered advanced features like an increased file size limit on uploads, with plans to add support for creator tools like tips and the ability to sell subscriptions, among other things.
Founded by a not-for-profit software company, Anti Software Software Club, with a small handful of developers, Cohost’s manifesto had anti-capitalist and anti-Big Tech leanings.
“[We] have watched the world buy into the lies of people who ‘believe in the disruptive potential of technology,’ and who think the best way to realize that potential is to build for-profit businesses that enable a creative-class petit bourgeois to make it through their day without acknowledging another human being,” the founders, Colin Bayer and Jae Kaplan, stated back in 2020. “We think we can do better, by building tools that focus on fair dealing and sustainable growth rather than market dominance,” their manifesto read.
Despite Cohost’s ambition to disrupt the tech giants, it faced increased competition not only from X (formerly Twitter) but soon Meta as well, which launched its Twitter-like service Threads. Users who favored decentralized social networking on an open social web had various options, too, including Mastodon and Bluesky, among others.
As a result, Cohost will no longer be able to continue.
The company cited “lack of funding and burnout” as reasons for the shutdown, currently planned for the end of 2024.
“As of today, none of us are being paid for our labor,” the company shared in a post on its staff account, possibly an attempt to dispel rumors that staff salaries had eaten up the funds. “All of our money in the bank, and any money coming in from people who buy our merch or don’t cancel cohost plus, is going towards servers and operations — paying the bills so we can turn the lights off with as little disruption as possible.”
Alibaba's Taobao app topped Singapore's Apple Store charts after launching an AI-powered English version on Tuesday, boosting accessibility for users.
Chinese e-commerce giant Alibaba's Taobao shopping app topped the Apple App Store charts in Singapore after releasing an English version on Tuesday — thanks to translations powered by artificial intelligence.
That's according to Sensor Tower, a market intelligence firm whose data shows Taobao shot to first place in Apple's Singapore App Store across all categories, as of Sept. 11. On Tuesday, the day the English-language version was announced, the app rose from fifth to first place in the shopping category.
Prior to this, the Taobao app had still enjoyed relative popularity and was consistently ranked in the top ten shopping apps for iPhone users from mid-August onwards, according to Sensor Tower.
The new update "highlights Taobao's dedication to serving its Singapore users, who have shown a strong desire for an English-language interface, reflecting their diverse language fluency," Alibaba said in a press release Tuesday. It did not elaborate on the AI translation features. The company has its own AI model.
The release said the new platform "enhances accessibility for non-Chinese users, eliminating their need for manual translations that previously made shopping less convenient for them."
Taobao and Tmall are Alibaba's biggest source of revenue by far, but to date have primarily sold to people in China using a Chinese-language interface. Taobao and Tmall Group's revenue for the quarter ended June 30 was 26.55 billion yuan ($3.65 billion), a 6% increase year-on-year.
Alibaba has in recent years has also sought to ramp up its overseas e-commerce business with platforms such as Alibaba.com and AliExpress.
At the MTV Video Music Awards (VMAs) on Wednesday night, new technology allowed fans to shop their favorite artists' styles as they appeared on the Thanks to a partnership with Shopsense AI and Paramount, viewers watching the VMAs could purchase similar outfits to replicate their favorite artist's style.
At the MTV Video Music Awards (VMAs) on Wednesday night, new technology allowed fans to shop their favorite artists’ styles as they appeared on the screen.
Though the drama from last night’s event focused on Chappell Roan confronting a rude paparazzi and Sabrina Carpenter‘s onstage kiss with an alien, fans were also raving about the extravagant and intricate outfits worn by the industry’s most-loved singers.
Thanks to a partnership between Paramount and technology company Shopsense AI, viewers had the opportunity to purchase similar outfits from the service’s suggestions.
Launched in January, Shopsense AI offers software that allows viewers to capture images of their preferred looks as they appear live on screen and then explore comparable options suggested by Shopsense’s detection model. The “AI” in this case refers to a sort of computer vision technology that matches on-screen looks with a database of clothing from online retailers.
Currently, Shopsense recognizes more than 1 billion items from over 1,000 retailers, including AllSaints, Macy’s, Nordstrom, Urban Outfitters, Revolve, and more.
Viewers can go to shop.mtvvmas.com/vmas and upload a photo of their favorite look from the VMAs or any outfit of their choosing using their phone camera. For Roan’s medieval warrior-inspired outfit, the software recommends a $500 AllSaints maxi dress or the more affordable $56 Boohoo milkmaid dress. It’s worth noting that Roan’s outfit comes from the Y/Project Fall 2024 collection, which is quite expensive, which makes having an affordable alternative a nice option.
The online storefront doesn’t have a built-in checkout feature. Instead, it uses direct links for each product, which allows brands to keep traffic on their respective platforms.
Shopsense’s technology still has some issues to resolve, we found.
During our testing, the suggestions were black dresses instead of the actual deep merlot color. There were also some outliers that didn’t seem to match, such as a metallic dress that Shopsense may have pulled from Roan’s acrylic nails, which resembled metal armor. However, the company points out that some items are meant to only match the “aesthetic” of the initial look.
Fast answers aren't always the best, which might be the key takeaway from the arrival of OpenAI Strawberry – now called o1-preview – a new ChatGPT reasoning model that takes longer to give you what might be vastly better answers.
OpenAI announced the preview release on Thursday in a blog post, saying that it will arrive in ChatGPT and the generative AI company's API. I can confirm that the o1-preview and a faster, cheaper model o1-mini are both live in our ChatGPT Plus account. The new models will not yet appear in the free ChatGPT accounts, though.
Strawberry has been eagerly anticipated because of its possible human-like-thinking capabilities. In the weeks before this announcement, OpenAI CEO Sam Altman has teased us with numerous cheeky fruit references, but has also made it clear in recent months that generative AI was set to make a significant leap forward.
In the blog post, OpenAI explained, "We trained these models to spend more time thinking through problems before they respond, much like a person would. Through training, they learn to refine their thinking process, try different strategies, and recognize their mistakes."
OpenAI claims this more powerful o1-preview has performed "similarly to PhD students on challenging benchmark tasks in physics, chemistry, and biology." And that's key here. o1-preview is a generative model that might have the greatest application in academia, not for helping you write an engaging prom-posal.
One example given in a video accompanying the blog is gene sequencing. In it, a scientist notes that while humans can't keep track of everything in gene sequencing, an AI can. The scientist refers to the new model as "chat with reasoning" and shows how when she types in a question, there's a moment where o1-preview says "Thinking." The value of it is that it keeps her from rabbit-holing into the wrong part of gene theory.
However, o1-preview is not a replacement for ChatGPT-4o, which is barely a month old. The new model isn't searching the web or capable of ingesting files and images. Though, that will likely show up at some point.
If biology and math are not your thing, the lighter and slightly more agile o1-mini might be for you, and is also live in ChatGPT Plus now. It's particularly adept at coding.
You can try out the new models in ChatGPT Plus ($20 a month) by logging in and then selecting the model drop-down menu. You'll see o1-preview, and o1-mini have been added to the list as of this story publishing.
The company revealed that this plant has incorporated direct lithium extraction (DLE), concentration, and conversion technologies to produce lithium sustainably at a large scale.
By integrating these various technologies, SLB has created a complete solution for producing lithium, while minimizing environmental impact and maximizing efficiency.
“Lithium is a key enabler of electrification, so we must find ways to accelerate its production without adversely affecting the environment,” said Gavin Rennick, president of SLB’s New Energy business.
SLB’s integrated solution is a comprehensive process designed to extract lithium from brine and produce high-purity lithium compounds. The process involves direct lithium extraction, concentration, and conversion.
As per the press release, the new tech combines SLB’s subsurface and surface expertise, including DLE.
Interestingly, this new integrated tech produces lithium from brine 500 times faster than other known techniques. Moreover, this technology is more sustainable because it requires only 10% of the land along with less water, energy, and chemical reagents.
“Operating at approximately one-tenth the size of a commercial-scale facility, the plant reached a verified recovery rate of 96 percent lithium from brine,” the press release noted.
This method produces high-purity lithium carbonate or hydroxide, which are key elements in the production of batteries and other energy storage devices.
“SLB’s demonstration plant in Clayton Valley proves our unique integrated approach to produce scalable quantities of lithium in the fastest, most economical and sustainable way for today’s market. This accelerates deployment of viable commercial-scale facilities for high-quality lithium products that are the backbone of our electrification economy,” Rennick said.
United will start rolling out the high-speed Wi-Fi to passenger flights in 2025.
United Airlines’ in-flight Wi-Fi is getting a big upgrade on all its jets thanks to SpaceX’s Starlink satellites. After teasing “something big” for the skies, United says that it will start testing Starlink’s fast Wi-Fi service in early 2025, with the first passenger flights expected later next year.
United is installing Starlink Wi-Fi into all of its aircraft, more than 1,000 planes, over the next several years, and the service will be free for passengers. “Everything you can do on the ground, you’ll soon be able to do onboard a United plane at 35,000 feet, just about anywhere in the world,” says United CEO Scott Kirby.
One Mile at a Time reports that United currently has four different Wi-Fi providers, with regional jets utilizing Intelsat (formerly Gogo) and most wide-body jets using Panasonic Wi-Fi. United also uses Viasat Wi-Fi on most of its 737 Max aircraft, some A319s, and A321neos. Viasat is the best of the bunch in terms of speeds and is commonly found on American and Delta flights.
The announcement is a major one for travelers, as onboard Wi-Fi is often unreliable and slow right now. The Wall Street Journal recently showed how Starlink and others are about to change that, achieving speeds over 100Mbps on a shared Starlink connection with latency under 100ms on a real-world flight. That allows for uninterrupted Netflix streams and even the ability to join video conference calls. Starlink says it can offer speeds of up to 220Mbps per plane.
he high-speed Starlink service is only currently available on JSX or Hawaiian Airlines in the US, so an expansion to United will undoubtedly put the pressure on rivals to improve their in-flight Wi-Fi. A number of international airlines have also announced plans to install Starlink Wi-Fi in recent months, with WestJet planning to use Starlink onboard some of its aircraft starting in December and Qatar Airways planning to introduce free Starlink Wi-Fi on three of its Boeing 777-300 aircraft later this year. Air New Zealand is aiming to roll out Starlink in its domestic fleet in 2025.
News of United’s Starlink deal comes the same week that Jessica Rosenworcel, chair of the Federal Communications Commission, said she wanted to see more competition to SpaceX’s Starlink. Elon Musk’s Starlink has launched around 7,000 satellites into orbit since 2018, with SpaceX controlling “almost two-thirds of the satellites that are in space right now,” according to Rosenworcel. “Our economy doesn’t benefit from monopolies. So we’ve got to invite many more space actors in, many more companies that can develop constellations and innovations in space.”
T-Mobile also announced this week that it had recently tested an emergency alert successfully via a Starlink satellite. In 2022, T-Mobile and SpaceX announced a partnership that would let people text, make calls, and use their T-Mobile phones through Starlink satellites. AT&T and Verizon are also building out similar satellite-to-smartphone services, with Apple and Google offering satellite services for their latest smartphones.
If you’re yearning for a fistfight with an artist, one simple phrase should do the trick: AI can do what you do.
The recent explosion of chatbots and text-to-image generators has prompted consternation from writers, illustrators, and musicians. AI tools like ChatGPT and DALL-E are extraordinary technical accomplishments, yet they seem increasingly purpose-built for producing bland content sludge. Artists fear both monetary loss and a devaluing of the creative process, and in a world where “AI” is coming to mean ubiquitous aesthetic pink slime, it’s not hard to see the source of the concern.
But even as their output tends to be disappointing, AI tools have become the internet’s favorite game — not because they often produce objectively great things but because people seem to love the process of producing and sharing them. Few things are more satisfying than tricking (or watching someone trick) a model into doing something naughty or incompetent: just look at the flurry of interest when xAI released an image generator that could make Disney characters behave badly or when ChatGPT persistently miscounted the letter “r” in “strawberry.” One of the first things people do with AI tools is mash together styles and ideas: Kermit the Frog as the Girl With a Pearl Earring, a Bible passage about removing a sandwich from a VCR, any movie scene directed by Michael Bay.
Despite artists’ concerns about being replaced by bad but cheap AI software, a lot of these words and images clearly weren’t made to avoid paying a writer or illustrator — or for commercial use at all. The back-and-forth of creating them is the point. And unlike promises that machines can replace painters or novelists, that back-and-forth offers a compelling vision of AI-based art.
rt by algorithm has an extensive history, from Oulipo literature of the 1960s to the procedural generation of video games like No Man’s Sky. In the age of generative AI, some people are creating interesting experiments or using tools to automate parts of the conventional artistic process. The platform Artbreeder, which predates most modern AI image generators, appealed directly to artists with intriguing tools for collaboration and fine-grained control. But so far, much of the AI-generated media that spreads online does so through sheer indifference or the novelty factor. It’s funny when a product like xAI’s Grok or Microsoft’s Bing spits out tasteless or family-unfriendly pictures, but only because it’s xAI or Microsoft — any half-decent artist can make Mickey Mouse smoke pot.
Annapurna Interactive, the game company famous for publishing indie hits like Stray, Outer Wilds, Gorogoa, Neon White, What Remains of Edith Finch, and many more, may not be the same company anymore.
Bloomberg reports that the entire staff of Annapurna Interactive, the gaming division of Megan Ellison’s Annapurna, has resigned after failing to convince Ellison to let them spin off its games division into a new company. IGN is corroborating the report.
“All 25 members of the Annapurna Interactive team collectively resigned,’’ former president Nathan Gary and staffers told Bloomberg. “This was one of the hardest decisions we have ever had to make and we did not take this action lightly.”
An Annapurna spokesperson told Bloomberg that existing games and projects will remain under the company. Annapurna didn’t immediately reply to a request for comment from The Verge.
Last week, The Hollywood Reporter said that Gary and the coheads of Annapurna Interactive, Deborah Mars and Nathan Vella, would be leaving. THR also reported that Annapurna planned to “integrate its in-house gaming operations with the rest of Annapurna’s divisions, which include film, TV and theater.” Hector Sanchez, who most recently headed up the Unreal Engine games business at Epic Games and is an Annapurna Interactive cofounder, announced last month that he would be president of interactive and new media at Annapurna.
For one of PlayStation's most prized and lauded development studios, Naughty Dog appears to have made some sloppy mistakes in creating The Last of Us. First came criticism from an unappreciative Ellen Page regarding Elle's likeness to the actress. Then earlier this week, the developer came under fire for using a Boston subway map as in-game artwork without proper attribution. Having smoothed that situation over, Naughty Dog now finds itself attached to a more risqué controversy. It turns out the phone number displayed on another piece of in-game background art — a billboard advertisement for a pest control company — belongs to an exotic phone sex hotline. The Verge can confirm that dialing the number for The Last of Us' fictional ABC Quality Pest Control connects you to a very real adult service.
Speaking to Kotaku, creative director Neil Druckmann explained that the slipup was "an honest mistake" by one of the game's artists. "What happened was, they put some phone numbers in the game and then they thought they could just change the area code to 555, then it's invalid because it's what they do in movies," Druckmann said. "But I guess that doesn't work when you have a 1-800 in front of it." Naughty Dog says it's currently working to remove the inappropriate number, presumably through a forthcoming patch.
OpenAI's 01 Model: A New Benchmark in AI Performance
OpenAi has recently unveiled its latest language model, dubbed "01," which appears to be setting new standards in artificial intelligence capabilities. This article summarizes a detailed test of the 01 model, highlighting its impressive performance across various tasks and comparing it to previous AI models.
Improved Thinking Process: The 01 model demonstrates a more sophisticated thinking process, with visible "thoughts" displayed during task completion. This allows users to see a summary of the model's reasoning, although the full chain of thought remains hidden.
Faster Processing: Compared to previous iterations, 01 shows significantly reduced thinking time. For instance, a coding task that previously took 90+ seconds of thinking now only requires about 35 seconds.
Enhanced Code Generation: The model successfully created a fully functional Tetris game in Python on the first attempt, demonstrating superior code generation abilities.
Nuanced Problem-Solving: 01 excels at understanding and addressing nuances in complex problems, often considering aspects that other models overlook.
Improved Accuracy: The model consistently provided accurate answers to a wide range of questions, from mathematical problems to logical reasoning tasks.
Coding Task: 01 generated a working Tetris game in Python within 35 seconds of thinking time, improving upon previous attempts both in speed and functionality.
Logical Reasoning: The model correctly solved a problem about envelope dimensions for mailing, considering the possibility of rotation - a nuance often missed by other models.
Self-Referential Tasks: 01 accurately counted the number of words in its own response, demonstrating strong self-awareness and precision.
Complex Scenarios: In a question about "killers in a room," the model showed exceptional reasoning, considering multiple perspectives and nuances that other AIs typically miss.
Scientific Understanding: For the classic "chicken or egg" question, 01 provided a well-reasoned answer based on evolutionary biology.
Areas for Improvement
Despite its impressive performance, 01 still faces challenges with certain types of problems:
Geometric Reasoning: The model struggled with a complex geometric problem involving walking patterns from the North Pole, which aligns with observations that language models often find such spatial reasoning tasks difficult.
OpenAI's 01 model represents a significant leap forward in AI capabilities. Its improved thinking process, faster processing times, and ability to handle nuanced problems set it apart from previous models. While it still faces challenges with certain types of reasoning, its overall performance suggests that AI is moving closer to human-like problem-solving abilities across a wide range of tasks.
As AI continues to evolve, models like 01 are likely to play an increasingly important role in various fields, from coding and data analysis to complex problem-solving and decision-making processes.
TuSimple, once a buzzy startup considered a leader in self-driving trucks, is trying to move its assets to China to fund a new AI-generated animation
TuSimple, once a buzzy startup considered a leader in self-driving trucks, is trying to move its assets to China to fund a new AI-generated animation and video game business. The pivot has not only puzzled and enraged several shareholders, but also threatens to pull the company back into a legal morass mere weeks after reaching a preliminary settlement in a class action lawsuit.
Now, a fight is brewing over roughly $450 million in funds, the bulk of which remains in the United States, TechCrunch has learned. And arguments over the company’s mission lie at the center of it.
Before the company formally disclosed its new business segment in August, a group of shareholders who got wind of the change sent a letter to the company’s board of directors. The letter, viewed by TechCrunch, alleges “potentially fraudulent activities” and asks the board to investigate whether funds were being misappropriated “to facilitate the growth of private ventures” established by Mo Chen, TuSimple’s co-founder and chairman.
Shareholders also complained the company failed to disclose its pursuit of AI animation; the board would eventually publicly announce a new AI animation and gaming business.
The group, which sent the letter anonymously in July, threatened litigation. However, at the time of this writing, no suits have been filed.
TuSimple’s new business segment, which is developing an animated feature film and video game based on the science fiction series “The Three-Body Problem,” is a startling change from its origins.
Imagine that — being able to freely rearrange and resize the apps on your screen.
Google is testing a new feature for Android tablets that will let you resize apps freely and arrange them on your screen at will, making it easier to juggle multiple tasks. The “desktop windowing” feature is now available as a developer preview, and for apps that support it, you could even have more than one instance open.
Currently, apps on Android tablets open in full-screen by default. When the new mode is enabled, each app will appear in a window with controls that allow you to reposition, maximize, or close the app. You’ll also see a taskbar at the bottom of your screen with your running apps.
It sounds a lot like the iPad’s Stage Manager feature that similarly lets you resize and move windows around your screen or pretty much any desktop operating system. Samsung has also offered its DeX experience for years, bringing desktop-like windows management to Android apps on Galaxy phones and tablets.
Once the feature is rolled out to everyone, you can turn it on by pressing and holding the window handle at the top of an app’s screen. If you have a keyboard attached, you can also use the shortcut meta key (Windows, Command, or Search) + Ctrl + Down to activate desktop mode. (You can exit the mode by closing all your active apps or by dragging a window and dragging it to the top of your screen.)
Google notes that apps locked to portrait orientation are still resizable, which might make things look a bit weird if certain apps aren’t optimized. However, Google plans to address this in a future update by scaling the UI of non-resizable apps while maintaining their aspect ratio.
THE IMPACT OF ARTIFICIAL INTELLIGENCE ON INNOVATION
Iain M. Cockburn
Rebecca Henderson
Scott Stern
Working Paper 24449 http://www.nber.org/papers/w24449
NATIONAL BUREAU OF ECONOMIC RESEARCH
1050 Massachusetts Avenue
Cambridge, MA 02138
March 2018
The authors would like to thank the organizers and participants at the first NBER conference on
the Economics of Artificial Intelligence, and in particular our discussant Matthew Mitchell for
many helpful suggestions and ideas. Michael Kearney provided extraordinary research assistance.
The views expressed herein are those of the authors and do not necessarily reflect the views of the
National Bureau of Economic Research. Funding for this paper was provided by the MIT Sloan
School of Management, by the HBS Division of Research and by the Questrom School of
Management.
At least one co-author has disclosed a financial relationship of potential relevance for this
research. Further information is available online at http://www.nber.org/papers/w24449.ack
NBER working papers are circulated for discussion and comment purposes. They have not been
peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies
official NBER publications.
ABSTRACT
Artificial intelligence may greatly increase the efficiency of the existing economy. But it may
have an even larger impact by serving as a new general-purpose “method of invention” that can
reshape the nature of the innovation process and the organization of R&D. We distinguish
between automation-oriented applications such as robotics and the potential for recent
developments in “deep learning” to serve as a general-purpose method of invention, finding
strong evidence of a “shift” in the importance of application-oriented learning research since
We suggest that this is likely to lead to a significant substitution away from more routinized
labor-intensive research towards research that takes advantage of the interplay between passively
generated large datasets and enhanced prediction algorithms. At the same time, the potential
commercial rewards from mastering this mode of research are likely to usher in a period of
racing, driven by powerful incentives for individual companies to acquire and control critical
large datasets and application-specific algorithms. We suggest that policies which encourage
transparency and sharing of core datasets across both public and private actors may be critical
tools for stimulating research productivity and innovation-oriented competition going forward.
Rapid advances in the field of artificial intelligence have profound implications for the
economy as well as society at large. These innovations have the potential to directly influence
both the production and the characteristics of a wide range of products and services, with
important implications for productivity, employment, and competition. But, as important as
these effects are likely to be, artificial intelligence also has the potential to change the innovation
process itself, with consequences that may be equally profound, and which may, over time, come
to dominate the direct effect.
Consider the case of Atomwise, a startup firm which is developing novel technology for
identifying potential drug candidates (and insecticides) by using neural networks to predict the
bioactivity of candidate molecules. The company reports that its deep convolutional neural
networks “far surpass” the performance of conventional “docking” algorithms. After appropriate
training on vast quantities of data, the company’s AtomNet product is described as being able to
“recognize” foundational building blocks of organic chemistry, and is capable of generating
highly accurate predictions of the outcomes of real-world physical experiments (Wallach et al.,
2015).
Such breakthroughs hold out the prospect of substantial improvements in the productivity
of early stage drug screening. Of course, Atomwise’s technology (and that of other companies
leveraging artificial intelligence to advance drug discovery or medical diagnosis) is still at an
early stage: though their initial results seem to be promising, no new drugs have actually come
to market using these new approaches. But whether or not Atomwise delivers fully on its
promise, its technology is representative of the ongoing attempt to develop a new innovation
“playbook”, one that leverages large datasets and learning algorithms to engage in precise
prediction of biological phenomena in order to guide design effective interventions. Atomwise,
for example, is now deploying this approach to the discovery and development of new pesticides
and agents for controlling crop diseases.
Atomwise’s example illustrates two of the ways in which advances in artificial intelligence
have the potential to impact innovation. First, though the origins of artificial intelligence are
broadly in the field of computer science, and its early commercial applications have been in
relatively narrow domains such as robotics, the learning algorithms that are now being developed
suggest that artificial intelligence may ultimately have applications across a very wide range.
From the perspective of the economics of innovation (among others, Bresnahan and Trajtenberg
(1995)), there is an important distinction between the problem of providing innovation incentives
to develop technologies with a relatively narrow domain of application, such robots purposebuilt for narrow tasks, versus technologies with a wide—advocates might say almost limitless—
domain of application, as may be true of the advances in neural networks and machine learning
often referred to as “deep learning.” As such, a first question to be asked is the degree to which
developments in artificial intelligence are not simply examples of new technologies, but rather
may be the kinds of “general purpose technologies” (hereafter GPTs) that have historically been
such influential drivers of long-term technological progress.
Second, while some applications of artificial intelligence will surely constitute lower-cost or
higher-quality inputs into many existing production processes (spurring concerns about the
potential for large job displacements), others, such as deep learning, hold out the prospect of not
only productivity gains across a wide variety of sectors but also changes in the very nature of the
innovation process within those domains. As articulated famously by Griliches (1957), by
enabling innovation across many applications, the “invention of a method of invention” has the
potential to have much larger economic impact than development of any single new product.
Here we argue that recent advances in machine learning and neural networks, through their
ability to improve both the performance of end use technologies and the nature of the innovation
process, are likely to have a particularly large impact on innovation and growth. Thus the
incentives and obstacles that may shape the development and diffusion of these technologies are
an important topic for economic research, and building an understanding of the conditions under
which different potential innovators are able to gain access to these tools and to use them in a
pro-competitive way is a central concern for policy.
This essay begins to unpack the potential impact of advances in artificial intelligence on
innovation, and to identify the role that policy and institutions might play in providing effective
incentives for innovation, diffusion, and competition in this area. We begin in Section II by
highlighting the distinctive economics of research tools, of which deep learning applied to R&D
problems is such an intriguing example. We focus on the interplay between the degree of
generality of application of a new research tool and the role of research tools not simply in
enhancing the efficiency of research activity but in creating a new “playbook” for innovation
itself. We then turn in Section III to briefly contrasting three key technological trajectories
within AI—robotics, symbolic systems, and deep learning. We propose that these often
conflated fields will likely play very different roles in the future of innovation and technical
change. Work in symbolic systems appears to have stalled and is likely to have relatively little
impact going forwards. And while developments in robotics have the potential to further displace
human labor in the production of many goods and services, innovation in robotics technologies
per se has relatively low potential to change the nature of innovation itself. By contrast, deep
learning seems to be an area of research that is highly general-purpose and that has the potential
to change the innovation process itself.
We explore whether this might indeed be the case through an examination of some
quantitative empirical evidence on the evolution of different areas artificial intelligence in terms
of scientific and technical outputs of AI researchers as measured (imperfectly) by the publication
of papers and patents from 1990 through 2015. In particular, we develop what we believe is the
first systematic database that captures the corpus of scientific paper and patenting activity in
artificial intelligence, broadly defined, and divides these outputs into those associated with
robotics, symbolic systems, and deep learning. Though preliminary in nature (and inherently
imperfect given that key elements of research activity in artificial intelligence may not be
observable using these traditional innovation metrics), we find striking evidence for a rapid and
meaningful shift in the application orientation of learning-oriented publications, particularly after
The timing of this shift is informative, since it accords with qualitative evidence about the
surprisingly strong performance of so-called “deep learning” multi-layered neural networks in a
range of tasks including computer vision and other prediction tasks. Supplementary evidence
(not reported here) based on the citation patterns to authors such as Geoffrey Hinton who are
leading figures in deep learning suggests a striking acceleration of work in just the last few years
that builds on a small number of algorithmic breakthroughs related to multi-layered neural
networks.
Developments in
neural networks and machine learning thus raise the question of, even if the underlying scientific
approaches (i.e., the basic multi-layered neural networks algorithms) are open, prospects for
continued progress in this field—and commercial applications thereof—are likely to be
significantly impacted by terms of access to complementary data. Specifically, if there are
increasing returns to scale or scope in data acquisition (there is more learning to be had from the
“larger” dataset), it is possible that early or aggressive entrants into a particular application area
may be able to create a substantial and long-lasting competitive advantage over potential rivals
merely through the control over data rather than through formal intellectual property or demandside network effects.
Strong incentives to maintain data privately has the additional potential
downside that data is not being shared across researchers, thus reducing the ability of all
researchers to access an even larger set of data that would arise from public aggregation. As the
competitive advantage of incumbents is reinforced, the power of new entrants to drive
technological change may be weakened. Though this is an important possibility, it is also the
case that, at least so far, there seems to be a significant amount of entry and experimentation
across most key application sectors.
In contrast to technological
progress in relatively narrow domains, such as traditional automation and industrial robots, we
argue that those areas of artificial intelligence evolving most rapidly—such as deep learning—
are likely to raise serious challenges in both dimensions.
First, consider the challenge in providing appropriate innovation incentives when an
innovation has potential to drive technological and organizational change across a wide number
of distinct applications. Such “general purpose technologies” (David, 1990; Bresnahan and
Trajtenberg, 1995) often take the form of core inventions that have the potential to significantly
enhance productivity or quality across a wide number of fields or sectors. David’s (1990)
foundational study of the electric motor showed that this invention brought about enormous
technological and organizational change across sectors as diverse as manufacturing, agriculture,
retail, and residential construction.
As emphasized by Bresnahan and Trajtenberg (1995), the presence of a general-purpose
technology gives rise to both vertical and horizontal externalities in the innovation process that
can lead not just to underinvestment but also to distortions in the direction of investment,
depending on the degree to which private and social returns diverge across different application
sectors. Most notably, if there are “innovation complementarities” between the general purpose
technology and each of the application sectors, lack of incentives in one sector can create an
indirect externality that results in a system-wide reduction in innovative investment itself. While
the private incentives for innovative investment in each application sector depend on its the
market structure and appropriability conditions, that sector’s innovation enhances innovation in
the GPT itself, which then induces subsequent demand (and further innovation) in other
downstream application sectors.
Various aspects of artificial intelligence can certainly be understood as a GPT, and learning from
examples such as the microprocessor are likely to be a useful foundation for thinking about both
the magnitude of their impact on the economy, and associated policy challenges.
A second conceptual framework for thinking about AI is the economics of research tools.
Within the research sectors, some innovations open up new avenues of inquiry, or simply
improve productivity “within the lab”. Some of these advances appear to have great potential
across a broad set of domains, beyond their initial application: as highlighted by Griliches (1957)
in his classic studies of hybrid corn, some new research tools are inventions that do not just
create or improve a specific product—instead they constitute a new way of creating new
products, with much broader application. In Griliches’ famous construction, the discovery of
double-cross hybridization “was the invention of a method of inventing.” (Hereinafter, “IMI”.)
Rather than being a means of creating a single a new corn variety, hybrid corn represented a
widely applicable method for breeding many different new varieties. When applied to the
challenge of creating new varieties optimized for many different localities (and even more
broadly, to other crops) the invention of double-cross hybridization had a huge impact on
agricultural productivity.
Advances in machine learning and neural networks appear to have great potential as a
research tool in problems of classification and prediction. These are both important limiting
factors in a variety of research tasks, and, as exemplified by the Atomwise example, application
of “learning” approaches to AI hold out the prospect of dramatically lower costs and improved
performance in R&D projects where these are significant challenges. But as with hybrid corn,
AI based learning may be more usefully understood as an IMI than as a narrowly limited solution
to a specific problem. One the one hand, AI based learning may be able to substantially
“automate discovery” across many domains where classification and prediction tasks play an
important role.
On the other, they may also “expand the playbook” is the sense of opening up
the set of problems that can be feasibly addressed, and radically altering scientific and technical
communities’ conceptual approaches and framing of problems. The invention of optical lenses
in the 17th century had important direct economic impact in applications such as spectacles. But
optical lenses in the form of microscopes and telescopes also had enormous and long-lasting
indirect effects on the progress of science, technological change, growth, and welfare: by making
very small or very distant objects visible for the first time, lenses opened up entirely new
domains of inquiry and technological opportunity. Leung et al. (2016), for example, evocatively
characterize machine learning as an opportunity to “learn to read the genome” in ways that
human cognition and perception cannot.
Of course, many research tools are neither IMIs nor GPTs, and their primary impact is to
reduce the cost or enhance the quality of an existing innovation process. For example, in the
pharmaceutical industry, new kinds of materials promise to enhance the efficiency of specific
research processes. Other research tools can indeed be thought of as IMIs but are nonetheless
relatively limited in application. For example, the development of genetically engineered
research mice (such as the Oncomouse) is an IMI that has had a profound impact on the conduct
and “playbook” of biomedical research, but has no obvious relevance to innovation in areas such
as information technology, energy, or aerospace. The challenge presented by advances in AI is
that they appear to be research tools that not only have the potential to change the method of
innovation itself but also have implications across an extraordinarily wide range of fields.
From a policy perspective, a further important feature of research tools is that it may be
particularly difficult to appropriate their benefits. As emphasized by Scotchmer (1990),
providing appropriate incentives for an upstream innovator that develops only the first “stage” of
an innovation (such as a research tool) can be particularly problematic when contracting is
imperfect and the ultimate application of the new products whose development is enabled by the
upstream innovation is uncertain. Scotchmer and her co-authors emphasized a key point about a
multi-stage research process:
when the ultimate innovation that creates value requires multiple
steps, providing appropriate innovation incentives are not only a question of whether and how to
provide property rights in general, but also of how best to distribute property rights and
incentives across the multiple stages of the innovation process. Lack of incentives for earlystage innovation can therefore mean that the tools required for subsequent innovation do not
even get invented; strong early-stage property rights without adequate contracting opportunities
may result in “hold-up” for later-stage innovators and so reduce the ultimate impact of the tool in
terms of commercial application.
The vertical research spillovers created by new research tools (or IMIs) are not just a
challenge for designing appropriate intellectual property policy.1 They are also exemplars of the
core innovation externality highlighted by endogenous growth theory (Romer, 1990; Aghion and
Howitt, 1992); a central source of underinvestment in innovation is the fact that the intertemporal
spillovers from innovators today to innovators tomorrow cannot be easily captured. While
tomorrow’s innovators benefit from “standing on the shoulders of giants,” their gains are not
easily shared with their predecessors. This is not simply a theoretical idea: an increasing body of
evidence suggests that research tools and the institutions that support their development and
Challenges presented by AI-enabled invention for legal doctrine and the patent process are beyond the scope of
this essay.
diffusion play an important role in generating intertemporal spillovers (among others, Furman
and Stern, 2011; Williams, 2014). A central insight of this work is that control—both in the
form of physical exclusivity as well as in the form of formal intellectual property rights—over
tools and data can shape both the level and direction of innovative activity, and that rules and
institutions governing control over these areas has a powerful influence on the realized amount
and nature of innovation.
Of course, these frameworks cover only a subset of the key informational and
competitive distortions that might arise when considering whether and how to provide optimal
incentives for the type of technological change represented by some areas of AI. But these two
areas in particular seem likely to be important for understanding the implications of the current
dramatic advances in AI supported learning. We therefore turn in the next section to a brief
outline of the ways in which AI is changing, with an eye towards bringing the framework here to
bear on how we might outline a research agenda exploring the innovation policy challenges that
they create.
The Evolution of Artificial Intelligence: Robotics, Symbolic Systems, and Neural
Networks
In his omnibus historical account of AI research, Nilsson (2010) defines AI as “that
activity devoted to making machines intelligent, and intelligence is that quality that enables an
entity to function appropriately and with foresight in its environment.” His account details the
contributions of multiple fields to achievements in AI, including but not limited to biology,
linguistics, psychology and cognitive sciences, neuroscience, mathematics, philosophy and logic,
engineering and computer science. And, of course, regardless of their particular approach,
artificial intelligence research has been united by from the beginning by its engagement with
Turing (1950), and his discussion of the possibility of mechanizing intelligence.
Although early pioneers such as Turing had emphasized the importance of teaching a
machine as one might a child (i.e., emphasizing AI as a learning process), the “symbol
processing hypothesis” (Newell, Shaw, and Simon, 1958; Newell and Simon, 1976) was
premised on the attempt to replicate the logical flow of human decision making through
processing symbols. Early attempts to instantiate this approach yielded striking success in
demonstration projects, such as the ability of a computer to navigate elements of a chess game
(or other board games) or engage in relatively simple conversations with humans by following
specific heuristics and rules embedded into a program. However, while research based on the
concept of a “general problem solver” has continued to be an area of significant academic
interest, and there have been periodic explosions of interest in the use of such approaches to
assist human decision-making (e.g., in the context of early-stage expert systems to guide medical
diagnosis), the symbolic systems approach has been heavily criticized for its inability to
meaningfully impact real-world processes in a scalable way. It is of course possible that this
field will see breakthroughs in the future, but it is fair to say that, while symbolic systems
continues to be an area of academic research, it has not been central to the commercial
application of AI. Nor is it at the heart of the recent reported advances in AI that are associated
with the area of machine learning and prediction.
A second influential trajectory in AI has been broadly in the area of robotics. While the
concepts of “robots” as machines that can perform human tasks dates back at least to the 1940s,
the field of robotics began to meaningfully flourish from the 1980s onwards through a
combination of the advances in numerically controlled machine tools and the development of
more adaptive but still rules-based robotics that rely on the active sensing of a known
environment. Perhaps the most economically consequential application of AI to date has been in
this area, with large scale deployment of “industrial robots” in manufacturing applications.
These machines are precisely programmed to undertake a given task in a highly controlled
environment.
Often located in “cages” within highly specialized industrial processes (most
notably automobile manufacturing), these purpose-built tools are perhaps more aptly described
as highly sophisticated numerically controlled machines rather than as robots with significant AI
content. Over the past twenty years, innovation in robotics has had an important impact on
manufacturing and automation, most notably through the introduction of more responsive robots
that rely on programmed response algorithms that can respond to a variety of stimuli. This
approach, famously pioneered by Rod Brooks (1990), focused the commercial and innovation
orientation of AI away from the modeling of human-like intelligence towards providing feedback
mechanisms that would allow for practical and effective robotics for specified applications. This
insight led, among other applications, to the Roomba and to other adaptable industrial robots that
could interact with humans such as Rethink Robotics’ Baxter). Continued innovation in robotics
technologies (particularly in the ability of robotic devices to sense and interact with their
environment) may lead to wider application and adoption outside industrial automation.
These advances are important, and the most advanced robots continue to capture public
imagination when the term AI is invoked. But innovations in robotics are not, generally
speaking, IMIs.
The increasing automation of laboratory equipment certainly improves research
productivity, but advances in robotics are not (yet) centrally connected to the underlying ways in
which researchers themselves might develop approaches to undertake innovation itself across
multiple domains. There are of course counterexamples to this proposition: robotic space
probes have been a very important research tool in planetary science, and the ability of
automated remote sensing devices to collect data at very large scale or in challenging
environments may transform some fields of research. But robots continue to be used principally
in specialized end-use “production” applications.
Finally, a third stream of research that has been a central element of AI since its founding
can be broadly characterized as a “learning” approach. Rather than being focused on symbolic
logic, or precise sense-and-react systems, the learning approach attempts to create reliable and
accurate methods for the prediction of particular events (either physical or logical) in the
presence of particular inputs. The concept of a neural network has been particularly important
in this area. A neural network is a program that uses a combination of weights and thresholds to
translate a set of inputs into a set of outputs, measures the “closeness” of these outputs to reality,
and then adjusts the weights it uses to narrow the distance between outputs and reality. In this
way, neural networks can learn as they are fed more inputs (Rosenblatt, 1958; 1963).
Over the
course of the 1980s, Hinton and his co-authors further advanced the conceptual framework on
which neural networks are based through the development of “back-propagating multi-layer”
techniques that further enhance their potential for supervised learning.
After being initially heralded as having significant promise, the field of neural networks
has come in and out of fashion, particularly within the United States. From the 1980s through
the mid-2000s, their challenge seemed to be that there were significant limitations to the
technology that could not be easily fixed by using larger training datasets or through the
introduction of additional layers of “neurons.” However, in the mid-2000s, a small number of
new algorithmic approaches demonstrated the potential to enhance prediction through back
propagation through multiple layers. These neural networks increased their predictive power as
they were applied to larger and larger datasets, and were able to scale to an arbitrary level
(among others, a key reference here is Hinton and Salakhutdinov (2006)).
These advances
exhibited a “surprising” level of performance improvement, notably in the context of the
ImageNet visual recognition project competition pioneered by Fei-Fei Li at Stanford
(Krizhevsky, Sutskever and Hinton, 2012).
How Might Different Fields within Artificial Intelligence Impact Innovation?
Distinguishing between these three streams of AI is a critical first step towards
developing a better understanding of how AI is likely to influence the innovation process going
forward, since the three differ significantly in their potential to be either GPTs or IMIs—or both.
First, though a significant amount of public discussion of AI focuses on the potential for
AI to achieve super-human performance over a wide range of human cognitive capabilities, it is
important to note that, at least so far, the significant advances in AI have not been in the form of
the “general problem solver” approaches that were at the core of early work in symbolic systems
(and that were the motivation for considerations of human reasoning such as the Turing test).
Instead, recent advances in both robotics and in deep learning are by and large innovations that
require a significant level of human planning and that apply to a relatively narrow domain of
problem-solving (e.g., face recognition, playing Go, picking up a particular object, etc.) While it
is of course possible that further breakthroughs will lead to a technology that can meaningfully
mimic the nature of human subjective intelligence and emotion, the recent advances that have
attracted scientific and commercial attention are well removed from these domains.
Second, though most economic and policy analysis of AI draws out consequences from
the last two decades of automation to consider the future economic impact of AI (e.g., in job
displacement for an ever-increasing number of tasks), it is important to emphasize that there is a
sharp difference between the advances in robotics that were a primary focus of applications of AI
research during the 2000s and the potential applications of deep learning which have come to the
fore over the last few years.
As we suggested above, current advances in robotics are by and large associated with
applications that are highly specialized and that are focused on end-user applications rather than
on the innovation process itself and these advances do not seem as of yet to have translated to a
more generally applicable IMI. Robotics is therefore an area where we might focus on the
impact of innovation (improved performance) and diffusion (more widespread application) in
terms of job displacement versus job enhancement. We see limited evidence as yet of
widespread applications of robotics outside industrial automation, or of the scale of
improvements in the ability to sense, react to, and manipulate the physically environment that the
use of robotics outside manufacturing probably requires.
But there are exceptions: developments
in the capabilities of “pick and place” robots and rapid progress in autonomous vehicles point to
the possibility for robotics to escape manufacturing and become much more broadly used.
Advances in robotics may well reveal this area of AI be a GPT, as defined by the classic criteria.
Some research tools/IMIs based on algorithms have transformed the nature of research in
some fields, but have lacked generality. These types of algorithmic research tools, based on a
static set of program instructions, are a valuable IMI, but do not appear to have wide
applicability outside a specific domain and do not qualify as GPTs. For example, while far from
perfect, powerful algorithms to scan brain images (so-called functional MRI imaging) have
transformed our understanding of the human brain, not only through the knowledge they have
generated but also by establishing an entirely new paradigm and protocol for brain research.
However, despite its role as a powerful IMI, fMRI lacks the type of general-purpose applicability
that has been associated with the most important GPTs. In contrast, the latest advances in deep
learning have the potential to be both a general-purpose IMI and a classic GPT.
Rather than focusing on small well-characterized datasets or testing settings, it is now
possible to proceed by identifying large pools of unstructured data which can be used to
dynamically develop highly accurate predictions of technical and behavioral phenomena. In
pioneering an unstructured approach to predictive drug candidate selection that brings together a
vast array of previously disparate clinical and biophysical data, for example, Atomwise may
fundamentally reshape the “ideas production function” in drug discovery.
If advances in deep learning do represent the arrival of a general-purpose IMI, it is clear
that there are likely to be very significant long-run economic, social, and technological
consequence.
First, as this new IMI diffuses across many application sectors, the resulting
explosion in technological opportunities and increased productivity of R&D seem likely to
generate economic growth that can eclipse any near-term impact of AI on jobs, organizations,
and productivity. A more subtle implication of this point is that “past is not prologue”: even if
automation over the recent past has resulted in job displacement (e.g., Acemoglu and Restrepo,
2017a), AI is likely to have at least as important an impact through its ability to enhance the
potential for “new tasks” (as in Acemoglu and Restrepo, 2017b).
Finally, if deep learning does indeed prove to be a general-purpose IMI, it will be
important to develop institutions and a policy environment that is conductive to enhancing
innovation through this approach, and to do so in a way that promotes competition and social
welfare. A central concern here may be the interplay between a key input required for deep
learning—large unstructured databases that provide information about physical or logical
events—and the nature of competition. While the underlying algorithms for deep learning are in
the public domain (and can and are being improved on rapidly), the data pools that are essential
to generate predictions may be public or private, and access to them will depend on
organizational boundaries, policy and institutions.
Because the performance of deep learning
algorithms depends critically on the training data that they are created from, it may be possible,
in a particular application area, for a specific company (either an incumbent or start-up) gain a
significant, persistent innovation advantage through their control over data that is independent of
traditional economies of scale or demand-side network effects. This “competition for the
market” is likely to have several consequences. First, it creates incentives for duplicative racing
to establish a data advantage in particular application sectors (say, search, autonomous driving,
or cytology) followed by the establishment of durable barriers to entry that may be of significant
concern for competition policy.
Perhaps even more importantly, this kind of behavior could
result in a balkanization of data within each sector, not only reducing innovative productivity
within the sector, but also reducing spillovers back to the deep learning GPT sector, and to other
application sectors. This suggests that the proactive development of institutions and policies that
encourage competition, data sharing, and openness is likely to be an important determinant of
economic gains from the development and application of deep learning.
Data
This analysis draws upon two distinct datasets, one that captures a set of AI publications
from Thompson Reuters Web of Science, and another that identifies a set of AI patents issued by
the U.S. Patent and Trademark Office. In this section, we provide detail on the assembly of these
datasets and summary statistics for variables in the sample.
. As previously discussed, peer-reviewed and public-domain literature on AI points to the
existence of three distinct fields within AI: robotics, learning systems and symbol systems, each
comprised of numerous subfields. To track development of each of these using this data, we
began by identifying the publications and patents falling into each of these three fields based on
keywords. Appendix 1 lists the terms we used to define each field and identify the papers and
patents belonging to it. .2 In short, the robotics field includes approaches in which a system
engages with and responds to environmental conditions; the symbolic systems field attempts to
represent complex concepts through logical manipulation of symbolic representations, and the
learning systems field processes data through analytical programs modeled on neurologic
systems.
Publication Sample and Summary Statistics
Our analysis focuses on journal articles and book publications through the Web of
Science from 1955 to 2015. We conducted a keyword search utilizing the keywords described in
Appendix A (we tried several variants of these keywords and alternative algorithmic approaches
but this did not result in a meaningful difference in the publication set). We are able to gather
detailed information about each publication, including publication year, journal information,
topical information, as well as author and institutional affiliations.
Deep Learning as a General-Purpose Invention in the Method of Invention:
Considerations for Organizations, Institutions and Policy
With these results in mind, we now consider the potential implications for innovation and
innovation policy if deep learning is indeed a general-purpose technology (GPT) and/or a
general-purpose invention in the method of invention (IMI). If deep learning is merely a GPT, it
is likely to generate innovation across a range of applications (with potential for spillovers both
back to the learning GPT and also to other application sectors) but will not itself change the
nature of the innovation production function. If it is also a general purpose IMI, we would
expect it to have an even larger impact on economy-wide innovation, growth, and productivity as
dynamics play out—and to trigger even more severe short run disruptions of labor markets and
the internal structure of organizations.
The Management and Organization of Innovation
Perhaps most immediately, the rise of general-purpose predictive analytics using large
datasets seems likely to result in a substitution towards capital and away from labor in the
research production process. Many types of R&D and innovation more generally are effectively
problems of labor-intensive search with high marginal cost per search (Evenson and Kislev,
1975, among others). The development of deep learning holds out the promise of sharply
reduced marginal search costs, inducing R&D organizations to substitute away from highlyskilled labor towards fixed cost investments in AI. These investments are likely to improve
performance in existing “search intensive” research projects, as well as to open up new
opportunities to investigate social and physical phenomena that have previously been considered
intractable or even as beyond the domain of systematic scientific and empirical research.
It is possible that the ability to substitute away from specialized labor and towards capital
(that in principle could be rented or shared) may lower the “barriers to entry” in certain scientific
or research fields—particularly those in which the necessary data and algorithms are freely
available—while erecting new barriers to entry in other areas (e.g. by restricting access to data
and algorithms). As of yet, there are few if any organized markets for “trained” research tools or
services based on deep learning, and few standards to evaluate alternatives. Our analysis
suggests that the development of markets for shared AI services and the widespread availability
of relevant data may be a necessary precursor to the broad adoption and dissemination of deep
learning.
There is also the possibility that the large scale replacement of
skilled technical labor in the research sector by AI will “break science” in some fields by
disrupting the career ladders and labor markets that support the relatively long periods of training
and education required in many scientific and technical occupations.
Finally, it is possible that deep learning will change the nature of scientific and technical
advance itself. Many fields of science and engineering are driven by a mode of inquiry that
focuses on identifying a relatively small number of causal drivers of underlying phenomena built
upon an underlying theory (the parsimony principle as restated by Einstein states that theory
should be “as simple as possible but no simpler.”) However, deep learning offers an alternative
paradigm based on the ability to predict complex multi-causal phenomena using a “black box”
approach that abstracts away from underlying causes but that does allow for a singular prediction
index that can yield sharp insight.
De-emphasizing the understanding of causal mechanisms and
abstract relationships may come at a cost: many major steps forward in science involve the
ability to leverage an understanding of “big picture” theoretical structure to make sense of, of
recognize the implications of, smaller discoveries. For example, it is easy to imagine a deep
learning system trained on a large amount of x-ray diffraction data quickly “discovering” the
double helix structure of DNA at very low marginal cost, but it would likely require human
judgment and insight about a much broader biological context to notice that the proposed
structure suggests a direct mechanism for heredity.
But it is useful to emphasize that there is likely to be a significant gap between the
private and social incentives to share and aggregate data—even among academic researchers or
private sector research communities. One implication of this divergence may be that to the
degree any single research result depends on the aggregation of data from many sources, it will
be important to develop rules of credit and attribution, as well as to develop mechanisms to
replicate the results.
This implies that it will be particularly important to pay attention to the design and
enforcement of formal intellectual property rights. On the one hand it will be important to think
carefully about the laws that currently surround the ownership of data. Should the data about e.g.
my shopping and travel behavior belong to me or to the search engine or ride sharing company
that I use? Might consumers have a strong collective interest in ensuring that these data (suitably
blinded, of course) are in the public domain, so that many companies can use them in the pursuit
of innovation?
On the other, the advent of deep learning has significant implications for the patent
system. Though there has so far been relatively little patenting of deep learning innovations,
historical episodes such as the discovery and attempted wholesale patenting of express sequence
tags and other kinds of genetic data suggests that breakthroughs in research tools—often
combined with a lack of capacity at patent offices and conflicting court decisions—can result in
long periods of uncertainty that has hampered the issuing of new patents, and this in turn has led
to lower research productivity and less competition. Deep learning also presents difficult
questions of legal doctrine for patent systems that have been built around the idea of creative
authors and inventors. For example, “inventorship” has a specific meaning in patent law, with
very important implications for ownership and control of the claimed invention.
In addition to these traditional innovation policy questions, the prospect for deep learning
raises a wide variety of other issues, including issues relating to privacy, the potential for bias
(deep learning has been found to reinforce stereotypes already present in society), and consumer
protection (related to areas such as search, advertising, and consumer targeting and monitoring).
The key is that, to the extent that deep learning is general-purpose, the issues that arise across
each of these domains (and more) will play out across a wide variety of sectors and contexts and
at a global rather than local level. Little analysis has been conducted that can help design
institutions that will be responsive at the level of application sectors that also internalize the
potential issues that may arise with the fact that deep learning is likely to be a GPT.
Finally, the broad applicability of deep learning (and possibly robotics) across many
sectors is likely to engender a race within each sector to establish a proprietary advantage that
leverages these new approaches. As such, the arrival of deep learning raises issues for
competition policy. In each application sector, there is the possibility that firms that are able to
establish an advantage at an early stage, and in doing so position themselves to be able to
generate more data (about their technology, about customer behavior, about their organizational
processes) will be able to erect a deep-learning-driven barrier to entry that will ensure market
dominance over at least the medium term.
This suggests that rules ensuring data accessibility are
not only a matter of research productivity or aggregation, but also speak to the potential to guard
against lock-in and anticompetitive conduct. At the present moment there seem to be a large
number of individual companies attempting to take advantage of AI across a wide variety of
domains (e.g., there are probably more than 20 firms engaging in significant levels of research in
autonomous vehicles, and no firm has yet to show a decisive advantage), but this high level of
activity likely reflects an expectation for the prospects for significant market power in the future.
Ensuring that deep learning does not enhance monopolization and increase barriers to entry
across a range of sectors will be a key topic going forward.
The purpose of this exploratory essay has not been to provide a systematic account or
prediction of the likely impact of AI on innovation, nor clear guidance for policy or the
management of innovation. Instead, our goal has been to raise a specific possibility—that deep
learning represents a new general-purpose invention of a method of invention—and to draw out
some preliminary implications of that hypothesis for management, institutions, and policy.
Our preliminary analysis highlights a few key ideas that have not been central to the
economics and policy discussion so far. First, at least from the perspective of innovation, it is
useful to distinguish between the significant and important advances in fields such as robotics
from the potential of a general-purpose method of invention based on application of multilayered neural networks to large amounts of digital data to be an “invention in the method of
invention”.
Both the existing qualitative evidence and our preliminary empirical analysis
documents a striking shift since 2009 towards deep learning based application-oriented research
that is consistent with this possibility. Second, and relatedly, the prospect of a change in the
innovation process raises key issues for a range of policy and management areas, ranging from
how to evaluate this new type of science to the potential for prediction methods to induce new
barriers to entry across a wide range of industries. Proactive analysis of the appropriate private
and public policy responses towards these breakthroughs seems like an extremely promising area
for future research.
Fei-Fei Li, former Google Cloud AI director and co-director of the Stanford Institute for Human-Centered Artificial Intelligence, announced the launch of “World Labs” after two successful funding rounds.
Artificial intelligence startup World Labs announced its official launch on Sept. 13. The firm is reportedly valued at over $1 billion and has raised more than $230 million in funding to build “spatial intelligence” systems.
World Labs was co-founded by Fei-Fei-Li, the former Google Cloud AI boss. Due to her involvement in much of the foundational research behind modern generative artificial intelligence, Fei-Fei-Li is often referred to as the “Godmother of AI.” She’s also the current co-director of the Stanford Institute for Human-Centered Artificial Intelligence.
World Labs describes its primary product as “Large World Models” (LWMs). In a blog post announcing the company’s launch, it pointed out that current generative AI models can only interact with the world through text, audio, and video.
Humans, on the other hand, experience the world as a three-dimensional space with physics that relates to the passage of time.
“To advance beyond the capabilities of today’s models,” the World Labs team wrote, “we need spatially intelligent AI that can model the world and reason about objects, places, and interactions in 3D space and time.”
World Labs aims to bridge the gap between AI models that interpret the world through a 2D lens and artificial agents capable of perceiving 3D worlds by “creating and editing virtual spaces complete with physics, semantics, and control.”
The first time I saw my future Wife was at the local Zoo. I just knew she was a keeper. Credit: reddit @taskmaster4450le, I sent you an $LOLZ on behalf of barski
(6/10) Delegate Hive Tokens to Farm $LOLZ and earn 110% Rewards. Learn more.
It's quite elusive to grasp how some people can oppose big government, advocating for minimal interference in personal lives, yet when it comes to abortion, they push for extreme measures like proposing an office to track women's pregnancies.
It's a reality we have to accept that when it concerns people's interest they go for big government. They only minimal government when it affects them personally in a negative way. #cent #pob #inleo
Though I cannot do away with it completely for I am also part of an institution that loves big government when it comes to dole out, personally, it's against my principle and policy. #cent #pob #inleo
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🧵 1. "12 Years a Slave" revisited: A brutal journey from freedom to captivity, showcasing the harrowing realities of slavery through Solomon Northup's ordeal. #cinema
🧵 5. As Northup grapples with his newfound reality, the film delves into the darkness of slavery, revealing the resilience and suffering of those affected.
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The dollar is weakening ahead of the Fed's highly anticipated interest rate decision. Uncertainty surrounding the size of the cut, with recent economic indicators, has led to increased speculation for a more significant rate reduction.
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Trying to motivate myself to look at my #splinterlands soulbound cards and decide if I want to level some up before the price goes up. Iziar seems to be a must have. Any others?
Hello foodie Lions 🦁! Happy Friday. Welcome to the first session of today's show. 🥗🍲🫕
This is the #threadcast for Day 80 of the #foodtalk on Leo, 13/9/2024 for 12/9/2024. It's time for some meal inspirations and food conversation. Let's get into it, learn and connect. Don't forget to use #foodtalk in your comments. Discussion
Share your meals in this threadcast, photos or videos.
Are there foods you eat on daily or weekly basis? Let's talk.
How do you store your leftover foods?
What cuisines are you familiar with? You can share one or two dishes with us.
Share other food related content here and ask questions about food. Let's engage.
More about food with tips and tricks will be dropped in the threadcast. Upvote the comments you find interesting, engage and connect with others. Let's have fun. #foodie
I can see pineapple, pawpaw, avocado and the other fruits, so is it the pastry that is called empanadas or the entire meal?
The threadcast hashtag is used when you want to set up a threadcast about any topic and when you have up to 15 comments underneath, your icon will be at the top of the page and any one can click on it to engage.
Nice one, please don't forget to snap pictures of your food, I want to feed my eyes and enjoy the food and I'm sure @taskmaster4450le would not want to miss a good roast pork and barbecue from you.
Now that reminds me, I have not posted my birthday cake. I should make a short video about it soon.
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I store mine in the freezer for more shelf life just as I store my freshly cooked foods.
I allow the food to cool to room temperature then put in an airtight sizeable plastic container and into the freezer.
It is not all foods that are stored in the freezer, some are best stored in the fridge and then consumed within a short period. #foodtalk #foodstorage #foodie
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I Sabrina vow to challenge the status quo, to make the glass ceiling my floor. To put put my magic into the world courageously. To use naysayers as gasoline for my fire. #freecompliments #dailydook #cent
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This is the #selfie & #memory #threadcast. You are your brand, your face is your brand. Post a selfie of yourself, or yourself with friends, pets, food, or just what ever you are doing. Or just a memory. Or a Leo Short.
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🧵 3. The film's captivating opening immerses viewers with a medieval fair-like atmosphere, beckoning with the question, "Wouldn't you like to see something strange?"
🧵 4. From devilish chants to whimsical characters, The Nightmare Before Christmas continues to transport audiences to a fantastical and delightfully creepy realm.
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"Balance is not something you find, it's something you create."
Well done and congratulations 👏
#freecompliments !DOOK
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A breakout is expected based on technical analysis , but the market can sometimes do the opposite of our analysis and eexpectations
#carplates in Vilnius
Brazzers?? Jeez my mind is poisoned with porn..lmao
With good morning worlds.
#freecompliments #inleo #threadcast #photography #photographers
Good morning, today the sun finally came out in #argentina, we have a nice morning.
#freecompliments #inleo #life #sunrise #threadcast
How nice, I am very happy for you, I also share with you my sunrise, where the sun fills my life with energy.
#threadcast #freecompliments #inleo #life #sunrise
I finally understood that the popular Shakespearean quote: "To be or not to be" actually means "to live or not to live." The man was contemplating suicide!!!!!! #literature #freecompliments #cent
May we not get to that stage in life, where we consider suicide as an option.
#cheers
"I have noted how many newcomers to WEB3 and SocialFi apply themselves for a few days and then expect to see results. Unfortunately, it’s not merely a case of effort, but an ongoing effort over months and even years. This is the bitter pill that many are unable to swallow. Eventually, it becomes easier to extract value. This is primarily due to the increasing value of working capital and your influence." - Sapphire Crypto #cent #WEB3
Agree with you. it's a constant and steady effort.
Yep, and for a lengthy period too!
$BTC is above $58K and it’s Friday! Can we get a weekend pump? That is the question. Generally, weekends are comprised of sideways chop and low volume. However, it’s not always the case. Hopefully, this weekend is one of those!😎 #cent
Our seven universal needs:
Autonomy, security, health, leisure, purpose, connection and esteem.
Which one of these are you lacking?
#dailydook #inleo #cent
None ;)
I can bet there's purpose 🥺
#freecompliments !DOOK
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Oh, I have lots of purpose :) !BBH !DOOK
You see??? You didn't have to answer "none". I caught you 😁
#freecompliments !DOOK
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lol. got me
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@sabrinah! @bradleyarrow likes your content! so I just sent 1 BBH to your account on behalf of @bradleyarrow. (8/100)
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got all. Grateful hearts.
Live that for you. You're ready to take in the world. #freecompliments !DOOK
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I thought I had my vote for @vsc.network before, but I realized I wasn't so I had to fix that and upvote them! #freecompliments #cent
It is good to support one another, however we can.
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UN reports that the victims of the congo prison break are afraid to speak up. Where is freedom of speech at times like this? #dailydook #cent #newsonleo
Nowhere.
Such a pity 😪
#freecompliments !DOOK
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No freedom of speech again or you get yourself into trouble the more
Yesterday at the office: our backend systems had like 8 different errors, showshowing up no matter what we did.
Today: all errors have been fixed
Nice!!
#pob #cent #work
Oh, that's a relief. Back to work then. How's your princess? 🥰
#freecompliments !DOOK
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I was working.. I'm the one who had to fix all those problems 😂
She's doing great, currently at a birthday party at a twin pair in her class 🥰
How's everything on your end?
Price $XRP #hive #threads #cent #freecompliments
#fundtheflywheel #feedback
Daily screenshot of my share in the LEO/CACAO Pool. The USD value is $40.11 today. The good part is that the amount of $LEO is lower, which means each token has a higher LEO value compared to CACAO value than before. #cent #leodex #freecompliments
Finally Friday. I hope everyone has a great weekend!
You also :)
Friday is one of the most awaited days hehehehe. Happy weekend.
#threadcast
2024 FIDE #Chess Olympiad, round 2:
Magnus was missing in round 2 as well what turned out a bad decision: His team Norway was the one top team not to win but held to a 2-2 draw by 50th seed Canada!
Here is the daily technology #threadcast for 9/13/24. We aim to educate people about this crucial area along with providing information of what is taking place.
Drop all question, comments, and articles relating to #technology and the future. The goal is make it a technology center.
Meta’s Big Privacy Admission: 17 Years of Data Scraping❗️
Did you know Meta (the company behind Facebook and Instagram) has been quietly scraping public posts, photos, and comments for the last 17 years? Yep, an Australian investigation uncovered that Meta’s been using all this data to train its AI models. Whether it’s your Facebook posts or Insta pics, they’ve been collecting it all. This isn’t just recent — it’s been happening since 2006! Definitely a reminder to keep an eye on what you’re sharing online.
> S👁️URCE <
Is anyone really surprised? Even Elon said "is there anyone who hasnt scraped the data from X?
not me …
Scientists Found a 520-Million-Year-Old Miracle: a Fossil With Brains and Guts Intact
A remarkable fossilized larva has been discovered by scientists with its brain and guts still intact.
The fossilized creature is one of the earliest ancestors of a group known as arthropods, which includes insects, crabs, and lobsters.
A unique window into the past, the ancient critter has allowed experts a chance to better understand evolutionary links between the arthropods of the pasta and those of the present day.
Robots Are Learning by Watching Us! 🤖👀
Google DeepMind just dropped some exciting news! They’ve developed a system called ALOHA Unleashed that teaches robots to perform tricky tasks simply by watching humans. Paired with their simulation tool, DemoStart, these robots are picking up skills from visual demos, like assembling objects or handling delicate tasks. It’s like teaching a robot by showing it a YouTube tutorial! Want to see it in action? Videos of these trained robots are contained in the source article.
> S👁️URCE <
https://www.bbc.com/news/articles/c049kn7wlgxo
#youtube #cent
OpenAI Announces a New AI Model, Code-Named Strawberry, That Solves Difficult Problems Step by Step
https://www.wired.com/story/openai-o1-strawberry-problem-reasoning/
#tech #news
🚗✨ Big news for #AutonomousMobility! Uber and Waymo are teaming up to bring driverless rides to Austin and Atlanta by early 2025. The future of transportation is here! 🚀 #UberWaymo #technology
🔗Source in comments
LOL they are going to be trailing big time. Geofencing isnt really the way to go in my opinion.
@taskmaster4450le - As of today I would not get on any of those fucking things lol.
Tesla is going to shock everyone when, suddenly, one day, autonomous driving is there. It might take another year but it will be here soon enough.
It’s inevitable Task you are definitely right.
🔗Source:
~~~ embed:1834577946415432027?s=46&t=nxMrQtJGKh6vmosUkhbWLw twitter metadata:d2F5bW98fGh0dHBzOi8vdHdpdHRlci5jb20vd2F5bW8vc3RhdHVzLzE4MzQ1Nzc5NDY0MTU0MzIwMjd8 ~~~
https://www.bbc.com/worklife/article/20240905-microsoft-ai-interview-bbc-executive-lounge
https://www.bbc.com/news/articles/c93pr5ewr22o
SpaceX's First Commercial Spacewalk Just Happened! 🚀
In a historic moment, private astronauts with SpaceX completed the first-ever commercial spacewalk on Thursday, kicking off just after 6 am ET. Jared Isaacman, a tech billionaire, led the charge, stepping out of the SpaceX Crew Dragon for mobility tests. He was outside for about an hour, fully exposed to the vacuum of space. What’s even more wild? They did this using SpaceX-designed spacesuits that had never been tested in orbit before. Curious to see it in action? There's a clip of the livestream showing the entire adventure.
> S👁️URCE <
@bradleyarrow is very jealous.
Maybe he can be one of the first ten private humans to walk in space.
Very jealous. Oh man, Thread while I am on a space walk. Can you imagine :)
Just think of the number of shorts you could make while doing that.
Are shorts allowed in space?
So far space is free although government tyranny is coming.
I could do that now, all blackness. lol
Well I would say on the spacewalk point it to things that arent black like the spacecraft or the planet.
If too dark bring a flashlight.
🚀👩🏼🚀👍🏽
https://www.euractiv.com/section/artificial-intelligence/news/irish-data-protection-watchdog-probes-googles-processing-of-personal-data-for-ai-training/
OpenAI’s newly launched o1 model can do a better job of writing code nd solving maths problems
https://indianexpress.com/article/technology/artificial-intelligence/openai-unveils-o1-new-ai-model-trained-reasoning-9565662/
#news #cent #ai
My favorite quote so far from the Harvard Business Review Special Issue on How To Thrive In A GenAI World,
https://edition.cnn.com/2024/09/10/tech/india-samsung-strike-intl-hnk/index.html
#jobs #cent #news
https://www.reuters.com/business/finance/microstrategy-continues-bitcoin-buying-spree-lifting-holdings-945-bln-2024-09-13/
#bitcoin #cent #nees
Meta's AI Update: What You Need to Know
Starting next week, Meta is tweaking how it flags AI-edited content on Instagram, Facebook, and Threads. Instead of showing an "AI Info" label under usernames, you'll now find it tucked away in a menu at the top-right of images or videos. You can use tools like Adobe's Content Credentials or Google's SynthID to spot AI changes. This shift is in response to complaints that genuine photos were being mislabeled as AI-generated. So, keep an eye out for the new system—it’s designed to be more accurate and less intrusive.
> S👁️URCE <
https://www.reuters.com/legal/tiktok-faces-crucial-court-hearing-that-could-decide-fate-us-2024-09-13/
#tiktok #socialmedia #cent
Waymo robotaxis to become available on Uber in Austin, Atlanta in early 2025
Uber users in Austin and Atlanta will be able to hail Waymo robotaxis through the app in early 2025 as part of a partnership between the two companies.
Uber users in Austin and Atlanta will be able to hail Waymo robotaxis through the app in early 2025 as part of an expanded partnership between the two companies.
#uber #technology #newsonleo #waymo
Waymo’s autonomous vehicles have been available on the Uber app in Phoenix since October 2023. Uber has been snatching up self-driving partnerships across its ride-hail and delivery verticals, and it last month said it was partnering with GM’s Cruise and the U.K.’s Wayve as well.
Waymo runs its own autonomous ride-hailing service, Waymo One, in San Francisco, Phoenix, and Los Angeles, and provides around 100,000 trips per week, according to the company. The Alphabet-owned AV company began testing robotaxis in Atlanta and shuttling its own employees around Austin earlier this year — usually the first steps Waymo takes before it begins offering its ride-hailing service in new markets.
Still, Waymo said only Uber users would be able to hail its fleet of Jaguar I-PACE AVs in Austin and Atlanta.
Waymo said a limited number of users will be able to access rides in Austin and Atlanta via the Waymo One app in the coming weeks.
Waymo did not mention how many vehicles it would dispatch in the two cities, but said its fleet would “grow to hundreds of vehicles over time.” Uber will handle fleet management, like cleaning and repairing the cars, and Waymo will oversee testing and operation of Waymo Driver, as well as offering roadside assistance and other rider support functions.
First impressions of ChatGPT o1: An AI designed to overthink it
OpenAI released its new o1 models on Thursday, giving ChatGPT users their first chance to try AI models that pause to "think" before they answer.
OpenAI released its new o1 models on Thursday, giving ChatGPT users their first chance to try AI models that pause to “think” before they answer. There’s been a lot of hype building up to these models, codenamed “Strawberry” inside OpenAI. But does Strawberry live up to the hype?
Sort of.
#openai #strawberry #ai #llm
Compared to GPT-4o, the o1 models feel like one step forward and two steps back. ChatGPT o1 excels at reasoning and answering complex questions, but the model is roughly four times more expensive to use than GPT-4o. OpenAI’s latest model lacks the tools, multimodal capabilities, and speed that made GPT-4o so impressive. In fact, OpenAI even admits that “GPT-4o is still the best option for most prompts” on its help page, and notes elsewhere that GPT o1 struggles at simpler tasks.
“It’s impressive, but I think the improvement is not very significant,” said Ravid Shwartz Ziv, an NYU professor who studies AI models. “It’s better at certain problems, but you don’t have this across-the-board improvement.”
For all of these reasons, it’s important to use GPT o1 only for the questions it’s truly designed to help with: big ones. To be clear, most people are not using generative AI to answer these kinds of questions today, largely because today’s AI models are not very good at it. However, o1 is a tentative step in that direction.
Thinking through big ideas
ChatGPT o1 is unique because it “thinks” before answering, breaking down big problems into small steps and attempting to identify when it gets one of those steps right or wrong. This “multi-step reasoning” isn’t entirely new (researchers have proposed it for years, and You.com uses it for complex queries), but it hasn’t been practical until recently.
“There’s a lot of excitement in the AI community,” said Workera CEO and Stanford professor Kian Katanforoosh, who teaches classes on machine learning, in an interview. “If you can train a reinforcement learning algorithm paired with some of the language model techniques that OpenAI has, you can technically create step-by-step thinking and allow the AI model to walk backwards from big ideas you’re trying to work through.”
ChatGPT o1 is also uniquely pricey. In most models, you pay for input tokens and output tokens. However, ChatGPT o1 adds a hidden process (the small steps the model breaks big problems into), which adds a large amount of compute you never fully see. OpenAI is hiding some details of this process to maintain its competitive advantage. That said, you still get charged for these in the form of “reasoning tokens.” This further emphasizes why you need to be careful about using ChatGPT o1, so you don’t get charged a ton of tokens for asking where the capital of Nevada is.
The idea of an AI model that helps you “walk backwards from big ideas” is powerful, though. In practice, the model is pretty good at that.
In one example, I asked ChatGPT o1 preview to help my family plan Thanksgiving, a task that could benefit from a little unbiased logic and reasoning. Specifically, I wanted help figuring out if two ovens would be sufficient to cook a Thanksgiving dinner for 11 people and wanted to talk through whether we should consider renting an Airbnb to get access to a third oven.
After 12 seconds of “thinking,” ChatGPT wrote me out a 750+ word response ultimately telling me that two ovens should be sufficient with some careful strategizing, and will allow my family to save on costs and spend more time together. But it broke down its thinking for me at each step of the way and explained how it considered all of these external factors, including costs, family time, and oven management.
ChatGPT o1 told me how to prioritize oven space at the house that is hosting the event, which was smart. Oddly, it suggested I consider renting a portable oven for the day. That said, the model performed much better than GPT-4o, which required multiple follow-up questions about what exact dishes I was bringing, and then gave me bare-bones advice I found less useful.
Asking about Thanksgiving dinner may seem silly, but you could see how this tool would be helpful for breaking down complicated tasks.
I also asked ChatGPT o1 to help me plan out a busy day at work, where I needed to travel between the airport, multiple in-person meetings in various locations, and my office. It gave me a very detailed plan, but maybe was a little bit much. Sometimes, all the added steps can be a little overwhelming.
People presume that future activity will come from people. Because of this, they are focused upon building for humans. This might not be the case, especially for a platform for Leo.
Oprah just had an AI special with Sam Altman and Bill Gates — here are the highlights
Oprah Winfrey hosted a special on AI. Guests included OpenAI CEO Sam Altman, Marques Brownlee and FBI director Christopher Wray.
Late Thursday evening, Oprah Winfrey aired a special on AI, appropriately titled “AI and the Future of Us.” Guests included OpenAI CEO Sam Altman, tech influencer Marques Brownlee, and current FBI director Christopher Wray.
The dominant tone was one of skepticism — and wariness.
#technology #newsonleo #oprah
Oprah noted in prepared remarks that the AI genie is out of the bottle, for better or worse, and that humanity will have to learn to live with the consequences.
“AI is still beyond our control and to a great extent…our understanding,” she said. “But it is here, and we’re going to be living with technology that can be our ally as well as our rival … We are this planet’s most adaptable creatures. We will adapt again. But keep your eyes on what’s real. The stakes could not be higher.”
Sam Altman overpromises
Altman, Oprah’s first interview of the night, made the questionable case that today’s AI is learning concepts within the data it’s trained on.
“We are showing the system a thousand words in a sequence and asking it to predict what comes next,” he told Oprah. “The system learns to predict, and then in there, it learns the underlying concepts.”
Sam Altman overpromises
Altman, Oprah’s first interview of the night, made the questionable case that today’s AI is learning concepts within the data it’s trained on.
“We are showing the system a thousand words in a sequence and asking it to predict what comes next,” he told Oprah. “The system learns to predict, and then in there, it learns the underlying concepts.”
While Altman possibly overstated the capabilities of today’s AI systems, he underlined the importance of figuring out how to safety-test those same systems.
“One of the first things we need to do — and this is now happening — is to get the government to start figuring out how to do safety testing of these systems, like we do for aircraft or new medicines,” he said. “I personally, probably have a conversation with someone in the government every few days.”
Article
Three and Vodafone’s $19B merger hits the skids as UK rules the deal would adversely impact customers and MVNOs
Three and Vodafone’s planned $19 billion merger hits the skids as UK rules the deal is likely to reduce competition.
The U.K.’s antitrust regulator has delivered its provisional ruling in a longstanding battle to combine two of the country’s major telecommunication operators.
#vodaphone #merger #business #newsonleo #technology #three
The Competition and Markets Authority (CMA) says that Three and Vodafone’s planned $19 billion merger — announced 15 months ago — could lead to higher prices for consumers, diminished service such as smaller data packages in contracts, and reduced investment in U.K. mobile networks.
The CMA also took aim at the market for mobile virtual network operators (MVNOs) — a set up aimed at increasing competition by enabling new carriers to set up and offer services without building all of their own costly communications infrastructure. Both Three and Vodafone supply network to MVNOs, with the list including iD Mobile and Lebara. The CMA said that a merger might make it more difficult for MVNOs to access reasonable wholesale deals, in turn making services more expensive for customers.
Competition concerns aside, there was at least one other potential roadblock to this merger. Three is owned by CK Hutchison Holdings, a Hong Kong conglomerate subject to a national security law introduced by China in 2020, leading some to argue that Three could be compelled to share sensitive data with the Chinese state. The U.K. had introduced the National Security and Investment Act back in 2022 to cover such scenarios, and the government had previously used this law to block other deals between U.K. entities and Chinese companies.
However, back in May the U.K. government greenlighted the Three / Vodafone merger on security grounds, with some provisions, leaving the remaining regulatory concerns firmly in the CMA’s domain.
Article
Alternative app stores will be allowed on Apple iPad in the EU from September 16
It was a matter of time, but Apple is going to allow third-party app stores on the iPad starting next week, on September 16.
It was a matter of time, but Apple is going to allow third-party app stores on the iPad starting next week, on September 16. This change will occur with the next major release of iPadOS, the operating system specifically designed for the iPad.
#newsonleo #apple #appstore #ios #technology
The move is related to the European Union’s Digital Markets Act (DMA), a set of market fairness and pro-competition rules. Last fall, the Commission shared a list of six tech companies that have been designated “gatekeepers”, as they operate so called “core platform services” (CPS). It’s since added a seventh.
For Apple, the Commission initially designated three products and services as CPS: its mobile operating system iOS, its app distribution marketplace the App Store, and its web browser Safari. Then, in April, it announced that it was adding Apple’s iPadOS to the list.
While iPadOS user numbers did not meet the threshold to be in scope of the DMA, the Commission has some leeway in designations and said it considered that there are strong locked-in effects for business users in particular.
Apple had six months to update iPadOS and make sure that it is compliant with the DMA. Which brings us to Friday’s announcement: Starting with iPadOS 18, users in the EU will be able to install alternative app stores. Similarly, web browser developers will be able to release browsers for the iPad with their own browser engines.
Given the different DMA compliance timeline for iOS, recent changes to iOS in the EU can be an indication of what’s going to happen for iPad users in the EU.
There are five third-party app stores that are now available for iOS in the EU. One example, the AltStore PAL, was the first alternative app marketplace made available on iOS in the EU. You can use it to download video game emulator app Delta, virtual machine app UTM, torrenting app iTorrent, and more.
Apps are notarized by Apple for security purposes before they can be released on alternative app stores. App developers also have to sign new business terms with Apple — and pay a controversial “Core Technology Fee” above a certain threshold.
Epic Games also launched its alternative iOS app store in the EU so that people can download and play Fortnite, Rocket League Sideswipe and Fall Guys on their iPhones. The company has already said that it plans to bring Fortnite and its other games to the iPad.
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Polaris Dawn astronauts perform historic private spacewalk while wearing SpaceX-made suits
A crew of four private astronauts made history in the early hours of Thursday when they opened the hatch of their SpaceX Dragon capsule
A crew of four private astronauts made history in the early hours of Thursday when they opened the hatch of their SpaceX Dragon capsule and conducted the first commercial spacewalk.
#space #technology #newsloneo #polaris #spacewalk
The spacewalk, the riskiest part of the five-day Polaris Dawn mission, kicked off at 6:12 a.m. ET when oxygen started flowing into the astronauts’ spacesuits. Only two of the four crew members actually exited the vehicle, but all four had to don the new SpaceX-made suits because the Dragon capsule doesn’t have an airlock. That meant the entire spacecraft had to be depressurized.
A spacewalk — sometimes called extravehicular activity — is when astronauts leave the relative safety of their spacecraft for the vacuum of space. In the history of human spaceflight, spacewalks have only ever been performed by government astronauts, who use them as an opportunity to do repairs, perform maintenance, or to conduct scientific experiments. Spacewalks performed by NASA astronauts typically last between five and eight hours.
Billionaire entrepreneur and mission leader Jared Isaacman was the first to exit the Dragon capsule; after he returned, SpaceX engineer Sarah Gillis took a turn in the vacuum of space. They used a special ladder mobility aid dubbed a “skywalker,” which SpaceX added to the Dragon just for this purpose, to assist them outside the capsule. The pair was connected to the spacecraft by umbilical cords and they kept contact with the ladder at all times. The spacewalk was very quick, with each person outside the spacecraft for less than ten minutes. During that time, Isaacman and Gillis performed a series of movements to test the suits’ mobility and performance.
“Back at home, we all have a lot of work to do, but from here — looks like a perfect world,” Isaacman said.
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Why Y Combinator companies are flocking to banking and HR startup Every
Rajeev Behera’s new all-on-one HR startup, dubbed Every, is either brilliant or crazy.
Rajeev Behera’s new all-in-one HR startup, dubbed Every, is either brilliant or crazy.
Crazy because multi-module HR software that does payroll, onboarding, and spend management for small businesses is already a jam-packed market. Competitors include unicorn startups Gusto, Rippling, and Deel; incumbents that are strong in one area and are expanding into others like Mercury and Brex; and many smaller startups like Finally, Paylocity, and AccountsIQ.
#newsonleo #ycombinator #technology #every
Every’s investors clearly think Behera’s particular take on the idea is brilliant. Every just raised a $22.5 million Series A, led by Redpoint Ventures’ Alex Bard, with participation from Y Combinator, Okta Ventures, and Base10 Partners’ Rexhi Dollaku, TechCrunch can exclusively report.
Behera’s unique — and possibly brilliant — game plan revolves around his target customers and what he’s offering to hook them.
He and his co-founder, Barry Peterson, aimed Every at very early-stage tech startups and will help them do their incorporation documents for free, then set them up with a business bank account as well as other back-office essentials. Every makes its money by charging monthly SaaS fees for other modules, like accounting, and interchange fees.
“We spent all this time building pretty advanced expense management, banking, payroll, all that stuff. Now we will release incorporation for founders, and we’re going to just give it away for free,” Behera said.
After a 30-minute, white-glove onboarding session, startups get an integrated suite of banking, payroll, HR onboarding, HR benefits, bookkeeping, taxes, state compliance, and so on. (As we recently reported, the state compliance stuff is particularly tricky for startups.) Every’s customers also get a Slack channel where they can commiserate with other founders.
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India's Delhivery contests metrics in rival Ecom Express' IPO filing
Delhivery claims Ecom Express has inaccurately represented Delhivery's business metrics when drawing comparisons in its IPO filing.
Indian logistics firm Delhivery has publicly contested the accuracy of the metrics presented by competitor Ecom Express in its draft initial public offering prospectus, a rare confrontation in the lead-up to the latter’s market debut.
Delhivery, backed by SoftBank and already publicly listed, claims Ecom Express has inaccurately represented Delhivery’s business metrics when drawing comparisons in its IPO filing.
#ipo #delhivery #technology #newsonleo
The 442-page draft prospectus (PDF) submitted by Ecom Express last month said the startup had shipped 514.41 million packages in the fiscal year ended March 2024, while Delhivery handled 740 million during the same period.
Delhivery alleged in a filing to the stock exchanges on Friday that this comparison was flawed, asserting that what it considers a single shipment is counted as two by its rivals, suggesting that Ecom Express’ volume figures are potentially inflated. Delhivery said that its rival counts returned orders as two shipments.
Delhivery also called out Ecom Express’ cost per shipment (CPS) calculations, citing disparities in accounting methods and alleging inflated shipment figures.
The SoftBank-backed firm also pointed out that Ecom Express’ claim that it offers its services in 27,000 zipcodes isn’t accurate, as India has fewer than 19,500 unique zip codes.
This public dispute comes less than a month after Ecom Express, which counts Warburg Pincus, Partners Group and British International Investment among its backers, filed for an IPO, aiming to raise $310 million.
Delhivery has also questioned Ecom Express’ presentation of service EBITDA and corporate costs, citing a lack of consistent definitions for these metrics in the prospectus.
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XP Health grabs $32M to bring employees more affordable vision care
Antonio Moraes, the grandson of a late prominent Brazilian billionaire, was never interested in joining the family-owned conglomerate
Antonio Moraes, the grandson of a late prominent Brazilian billionaire, was never interested in joining the family-owned conglomerate of construction companies and a bank. Shortly after graduating from college, he founded one of Brazil’s first impact funds, which invested primarily in companies that made healthcare more accessible and affordable.
#technology #newsonleo #xphealth
But while attending Stanford University, where Moraes received a master’s degree in business administration and healthcare policy, he realized that instead of investing in impactful companies, he wanted to start his own.
As a part of an entrepreneurship class, Moraes and his co-founder, an engineering grad student, James Wong, visited multiple eyeglass manufacturing factories in China. They discovered that designer frames that sell for as much as $600 in the U.S. cost only about $10 to produce. “We thought there’s something very wrong with these markups,” Moraes told TechCrunch.
Because vision care and eyeglasses are expensive, many employees buy frames with their vision insurance, but the benefits typically don’t cover all the costs, Moraes said. “With vision insurance, people expect not to pay anything, but then they leave the optician’s office with a $300 out-of-pocket bill.”
Moraes and Wong started XP Health in late 2018, but during the pandemic, they shifted the startup’s focus to a digital-first, AI-driven platform that offers employees eye exams and eyewear benefits at significantly lower costs than existing vision insurance plans.
On Thursday, XP Health announced a $33.2 million Series B led by QED Investors with participation from Canvas Ventures, American Family Ventures, HC9 Ventures, Valor Capital Group and Manchester Story. The round comes less than two years after XP Health’s $17.1 million Series A.
XP Health members who buy eyeglasses virtually can save as much as 69% off the retail price, Moraes said. The company claims not to mark up the frames or lenses sourced directly from factories in Asia. Instead, XP Health generates its revenue through recurring membership fees.
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The End of Free Over The Air TV ATSC 3.0 Nextgen TV?
#streaming #television #technology #entertainment
Cord-Cutting News Roundup: ATSC 3.0, Fubo TV Lawsuit, MLB TV Rights, and More
In the rapidly evolving world of cord-cutting and streaming media, several significant developments have emerged. This article summarizes the key points from a recent "Cord Cutting Today" news roundup.
ATSC 3.0 (NextGen TV) Faces Uncertain Future
The transition to ATSC 3.0, also known as NextGen TV, is facing significant challenges that could jeopardize its future:
Despite these challenges, ATSC 3.0 offers benefits such as improved coverage area and the potential for more channels. However, criticisms persist regarding the implementation of digital Rights Management (DRM) in the standard.
Fubo TV to Release Confidential Carriage Agreements
In a significant move for transparency in the streaming industry:
Major League Baseball Seeks Wider National Availability
Amid the bankruptcy of regional sports networks, MLB is looking to change its approach to TV rights:
Other Notable Updates
These developments highlight the ongoing evolution of the cord-cutting landscape, with changes in technology, legal battles, and content distribution strategies aLL playing significant roles in shaping the future of television and streaming services.
How oil-rich Arkansas became a hotbed of lithium mining
Arkansas is becoming a key player in U.S. lithium production, but the state faces challenges like volatile prices and unproven technology.
The future of lithium production in the U.S. is gaining momentum in Arkansas, as companies like ExxonMobil, Albemarle, and Standard Lithium make significant investments in the state.
#newsonleo #technology #arkansas #lithium #mining
This comes at a time when global demand for lithium, driven by electric vehicles and energy-storage needs, continues to grow. In 2023, global lithium consumption reached 180,000 metric tons, up from 142,000 metric tons in 2022, according to the United States Geological Survey. But the U.S. produces less than 1% of the world's supply.
While most of the world's lithium still comes from countries like Australia, Chile and China, Arkansas could change that.
The state is home to the Smackover Formation, a geological formation rich in lithium brine.
"Lithium resource quality is really what makes this a great region," said Wesley Hamilton, CTO and vice president of research and technology at Albemarle, the world's top lithium producer. "It comes down to two things: the concentration of lithium and the ability to extract it efficiently from the brine."
Arkansas has long been a producer of bromine, which is extracted from the same brines now being tapped for lithium. The formation holds over 4 million metric tons of lithium, which is enough to power millions of EVs and devices, according to Galvanic Energy. That has attracted a rush of interest from companies looking to capitalize on the formation's potential.
Exxon Mobil, for example, acquired 120,000 acres in the Smackover Formation in 2023 and aims to start producing battery-grade lithium by 2027. The company said it will produce enough lithium to supply the manufacturing more than 1 million EVs per year by 2030. Standard Lithium, which has operated in Arkansas since 2020, is also expanding its Direct Lithium Extraction (DLE) facility in El Dorado, thanks to a $100 million investment from Koch Strategic Platforms. DLE is touted as a more eco-friendly extraction method, using advanced filters to reduce energy and water usage.
However, the road ahead isn't without challenges.
DLE technology, while promising, has yet to be proven on a large scale, and lithium prices have dropped sharply from over $80,000 per metric ton in 2022 to around $10,600 today. That's due to oversupply, slower-than-expected EV growth and new battery technologies, according to Benchmark.
British government had 'constructive' talks with Musk's X over disinformation, minister says
The U.K. government has had "constructive" talks with X over the spread of misinformation and other harmful content, technology minister Peter Kyle told CNBC.
As the riots raged in the U.K., Elon Musk began making incendiary comments about the situation, including the statement: "Civil war is inevitable." Musk is the owner of X, the social media platform formerly known as X.
#britian #x #technology #newsonleo
The U.K. government has had "constructive" talks with Elon Musk's social media site X over the spread of misinformation and other harmful content, technology minister Peter Kyle told CNBC Friday.
Kyle told CNBC's Arabile Gumede that the government had been in contact with all the major social media platforms — including Musk's X — over the summer about misinformation and the role they have in propagating harmful material.
The minister said that, although he hasn't had direct contact with Musk himself, he is "in touch often with his local chief executives here in the United Kingdom."
"So far, it has been a constructive set of conversations," he said, adding that, though there are "differences" in views between the two parties, they talk them through.
Citizens and governments around the world have higher expectations about social media platforms today and the role they play in keeping people safe and mitigating potential harms stemming from their products, Kyle said.
"It is a privilege having access to the British economy and society. And I just expect any company that comes to work here and aspires to sell products and services into our country to respect that," he added.
Kyle's comments to CNBC come after misinformation spread online after a knife attack at a Taylor Swift-themed dance class in northwest England sparked far-right, anti-immigration riots — with shops and mosques being attacked in towns across the country.
Keith Rabois says Miami is still a great place for startups, even as a16z leaves
Keith Rabois, managing director of Khosla Ventures, was having dinner with a “very successful CEO” in October 2018
Keith Rabois, managing director of Khosla Ventures, was having dinner with a “very successful CEO” in October 2018 when the CEO asked him a question: How many people does it take to create a whole new Silicon Valley? Is it 10,000? 100,000?
#miami #technology #startups
Rabois didn’t know, but he decided to accept the challenge and set about trying to make Miami the next Valley.
And despite other big name investors like Andreessen Horowitz decamping and shutting down its office in Miami a mere two years after setting up shop, Rabois said he’s still bullish on the South Florida city.
At Primary Venture Partners’ NYC Summit on Thursday, Rabois claimed that 11% of all seed investments in the United States have come out of Miami, which is “up basically from zero,” and that he hopes to raise that to 20%.
According to PitchBook data, seed investments into Miami startups to date this year accounted for only 2.6% of total U.S. seed investment. In 2023, they accounted for 3.5%.
“And the stats you should be looking at if you care about the future of tech are [the] fraction of seed investments, where do they happen?” Rabois said, adding that later-stage investment reveals less about the future of technology.
Rabois also said Khosla Ventures was gearing up to invest in its fifth company in Miami that will “reinvent education,” but didn’t provide specifics.
In April, Khosla and Founders Fund, where Rabois worked from 2019 until January, led the $150 million investment into spend management startup Ramp. Rabois said that Ramp, which is based in New York, has an office in Miami, which adds to the city’s appeal.
Article
https://www.bbc.com/news/articles/cx29r65ygdqo
#news #cent
Meta is making its AI info label less visible on content edited or modified by AI tools
Meta is changing the way it labels content that has been edited or modified by AI tools on Instagram, Facebook and Threads.
Meta is changing the way it labels content that has been edited or modified by AI tools on Instagram, Facebook, and Threads. For this type of content, Meta is moving the “AI info” label to the post’s menu. In the past, the label would appear directly under the user’s name.
#facebook #ai #meta #tools #technology
The company says the label will still appear under content that it has detected was generated by an AI tool. This means that although the label is being hidden for content that was changed or edited by AI tools, it will still be prominently displayed under content that was fully generated by an AI prompt.
For content that was generated by AI, Meta will “share whether the content is labeled because of industry-shared signals or because someone self-disclosed,” the company says.
Meta says the change, which is rolling out next week, will “better reflect the extent of AI used in content” on its platforms.
By making the AI info label harder to find, it might be easier for users to be deceived by content that was edited with AI, especially as editing tools become more and more advanced.
Given that generative AI is a relatively newer technology, this isn’t the first time that Meta has changed how it labels such content on its platforms. In July, the company changed its AI label from “Made with AI” to “AI info” after Meta received complaints from photographers who said the label was being added to real photos.
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Samsung shares fall as workers' strike at India plant continues after talks fall through
The strike in Samsung India follows recent wage protests in South Korea where 36,500 members from its biggest workers' union went on strike in July and August.
Shares of Samsung Electronics fell as much as 3% on Friday, as workers at its southern Indian plant continued to strike, disrupting production at the consumer electronics unit for a fifth day.
Worker union's representatives, Samsung's management and the state's labor officials failed to reach an agreement over pay and working conditions among other things, on Thursday.
#swamsung #technology #strike #india
Hundreds of workers have been on strike since Monday, demanding the electronics conglomerate to recognize their union, raise wages and reduce working hours. It is one of the biggest such strikes in recent years in India, Reuters reported.
The plant, located in the city of Chennai in southern India, makes electronic appliances including televisions, refrigerators and washing machines.
It's one of the two factories that Samsung runs in India and can account for up to 30% of the group's $12 billion annual revenue in the country, Reuters reported.
Samsung Electronics is one of the leading players in India's smartphone and electronic appliances market. The major appliances sector-wide 2024 revenue in India is pegged at $38.2 billion, according to Statista.
The workers will continue to strike until their demands for better wages and working conditions are met, union leader E. Muthukumar told Reuters, "Samsung management asked us to stop striking but wouldn't recognize the union or talk to us, so the strike continues."
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Saudi Arabia expects to get access to Nvidia's high performance chips 'within the next year'
The U.S. has imposed restrictions on exports of the chips out of concerns they could be accessed by China, which is Saudi Arabia's top trading partner.
Saudi Arabia is optimistic about gaining access to U.S. chipmaker Nvidia's high-performance chips, which would enable it to develop and operate the most advanced artificial intelligence models.
Speaking to CNBC on Thursday, a top official at the Saudi Data and AI Authority, Abdulrahman Tariq Habib, said the kingdom expected to make such a stride in the next year.
#saudiarabia #nvidia #semiconductors #ai
"I think within the next year," Habib, Deputy CEO of SDAIA's strategy management office, told CNBC's Dan Murphy after being asked about a potential timeline. It's a significant expectation given that the United States' strict export controls have thus far prevented the chips' export to the kingdom. Habib made the comments on the sidelines of GAIN, Saudi Arabia's international AI summit, which took place in Riyadh this week.
It "will mean a lot" for Saudi Arabia to have access to the chips, Habib said — in this case, the Nvidia H200s, the firm's most powerful chips, which are used in OpenAI's GPT-4o.
"It will ease business between Saudi and U.S.," he said. "It will also open a lot of doors for building the capability, the computational capabilities, in the kingdom. But most importantly, it's not only the computational capability that's important. We worked hard in the past three years in building capacity, in human capacity, we also build data capacity as well. So we are working and collaborating with all [of the] international community and contributing [to] be one of the top active countries in data analysis."
Saudi Arabia is pouring considerable investment into developing a robust AI ecosystem in the kingdom, disclosing in a report by SDAIA that it aims to have AI make up 12% of its gross domestic product by 2030. According to the report, published on Sept. 9, the kingdom's $925 billion Public Investment Fund will lead the investment.
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Cohost, the X rival founded with an anti-Big Tech manifesto, is running out of money and will shut down
Cohost, a would-be X rival launched to the public in June 2022, is shutting down, the company announced via the social network's staff account earlier
Cohost, a would-be X rival launched to the public in June 2022, is shutting down, the company announced via the social network’s staff account earlier this week. The service had operated much like Twitter, offering users the ability to follow others, view posts in a feed, and like and repost content shared by others. However, Cohost differentiated itself by focusing on a chronological feed without trending topics, support for long-form posts, and pursuing a business model that didn’t rely on advertising.
#x #cohost #technology #newsonleo
The startup’s premium subscription, Cohost Plus, offered advanced features like an increased file size limit on uploads, with plans to add support for creator tools like tips and the ability to sell subscriptions, among other things.
Founded by a not-for-profit software company, Anti Software Software Club, with a small handful of developers, Cohost’s manifesto had anti-capitalist and anti-Big Tech leanings.
“[We] have watched the world buy into the lies of people who ‘believe in the disruptive potential of technology,’ and who think the best way to realize that potential is to build for-profit businesses that enable a creative-class petit bourgeois to make it through their day without acknowledging another human being,” the founders, Colin Bayer and Jae Kaplan, stated back in 2020. “We think we can do better, by building tools that focus on fair dealing and sustainable growth rather than market dominance,” their manifesto read.
Despite Cohost’s ambition to d
Despite Cohost’s ambition to disrupt the tech giants, it faced increased competition not only from X (formerly Twitter) but soon Meta as well, which launched its Twitter-like service Threads. Users who favored decentralized social networking on an open social web had various options, too, including Mastodon and Bluesky, among others.
As a result, Cohost will no longer be able to continue.
The company cited “lack of funding and burnout” as reasons for the shutdown, currently planned for the end of 2024.
“As of today, none of us are being paid for our labor,” the company shared in a post on its staff account, possibly an attempt to dispel rumors that staff salaries had eaten up the funds. “All of our money in the bank, and any money coming in from people who buy our merch or don’t cancel cohost plus, is going towards servers and operations — paying the bills so we can turn the lights off with as little disruption as possible.”
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Alibaba’s Taobao shopping app launches AI-powered English version in Singapore, jumps to first place in Apple’s App Store
Alibaba's Taobao app topped Singapore's Apple Store charts after launching an AI-powered English version on Tuesday, boosting accessibility for users.
Chinese e-commerce giant Alibaba's Taobao shopping app topped the Apple App Store charts in Singapore after releasing an English version on Tuesday — thanks to translations powered by artificial intelligence.
#alibaba #taobao #singapore #technology
That's according to Sensor Tower, a market intelligence firm whose data shows Taobao shot to first place in Apple's Singapore App Store across all categories, as of Sept. 11. On Tuesday, the day the English-language version was announced, the app rose from fifth to first place in the shopping category.
Prior to this, the Taobao app had still enjoyed relative popularity and was consistently ranked in the top ten shopping apps for iPhone users from mid-August onwards, according to Sensor Tower.
The new update "highlights Taobao's dedication to serving its Singapore users, who have shown a strong desire for an English-language interface, reflecting their diverse language fluency," Alibaba said in a press release Tuesday. It did not elaborate on the AI translation features. The company has its own AI model.
The release said the new platform "enhances accessibility for non-Chinese users, eliminating their need for manual translations that previously made shopping less convenient for them."
Taobao and Tmall are Alibaba's biggest source of revenue by far, but to date have primarily sold to people in China using a Chinese-language interface. Taobao and Tmall Group's revenue for the quarter ended June 30 was 26.55 billion yuan ($3.65 billion), a 6% increase year-on-year.
Alibaba has in recent years has also sought to ramp up its overseas e-commerce business with platforms such as Alibaba.com and AliExpress.
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The Guardian: Parents ‘don’t use’ parental controls on Facebook and Instagram, says Nick Clegg
https://www.theguardian.com/technology/2024/sep/12/parental-controls-facebook-instagram-meta-nick-clegg
The Guardian: AI can change belief in conspiracy theories, study finds
https://www.theguardian.com/science/2024/sep/12/ai-can-change-belief-in-conspiracy-theories-study-finds
CNBC: British government had ‘constructive’ talks with Musk’s X over disinformation, minister says
https://www.cnbc.com/2024/09/13/britain-had-constructive-talks-with-elon-musks-x-over-disinformation.html
Shopsense AI lets music fans buy dupes inspired by red-carpet looks at the VMAs
At the MTV Video Music Awards (VMAs) on Wednesday night, new technology allowed fans to shop their favorite artists' styles as they appeared on the Thanks to a partnership with Shopsense AI and Paramount, viewers watching the VMAs could purchase similar outfits to replicate their favorite artist's style.
At the MTV Video Music Awards (VMAs) on Wednesday night, new technology allowed fans to shop their favorite artists’ styles as they appeared on the screen.
#technology #ama #shopsense #newsonleo
Though the drama from last night’s event focused on Chappell Roan confronting a rude paparazzi and Sabrina Carpenter‘s onstage kiss with an alien, fans were also raving about the extravagant and intricate outfits worn by the industry’s most-loved singers.
Thanks to a partnership between Paramount and technology company Shopsense AI, viewers had the opportunity to purchase similar outfits from the service’s suggestions.
Launched in January, Shopsense AI offers software that allows viewers to capture images of their preferred looks as they appear live on screen and then explore comparable options suggested by Shopsense’s detection model. The “AI” in this case refers to a sort of computer vision technology that matches on-screen looks with a database of clothing from online retailers.
Currently, Shopsense recognizes more than 1 billion items from over 1,000 retailers, including AllSaints, Macy’s, Nordstrom, Urban Outfitters, Revolve, and more.
Viewers can go to shop.mtvvmas.com/vmas and upload a photo of their favorite look from the VMAs or any outfit of their choosing using their phone camera. For Roan’s medieval warrior-inspired outfit, the software recommends a $500 AllSaints maxi dress or the more affordable $56 Boohoo milkmaid dress. It’s worth noting that Roan’s outfit comes from the Y/Project Fall 2024 collection, which is quite expensive, which makes having an affordable alternative a nice option.
The online storefront doesn’t have a built-in checkout feature. Instead, it uses direct links for each product, which allows brands to keep traffic on their respective platforms.
Shopsense’s technology still has some issues to resolve, we found.
During our testing, the suggestions were black dresses instead of the actual deep merlot color. There were also some outliers that didn’t seem to match, such as a metallic dress that Shopsense may have pulled from Roan’s acrylic nails, which resembled metal armor. However, the company points out that some items are meant to only match the “aesthetic” of the initial look.
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CNBC: How oil-rich Arkansas became a hotbed of lithium mining
https://www.cnbc.com/2024/09/13/how-oil-rich-arkansas-became-a-hotbed-of-lithium-mining.html
CNN: ChatGPT maker says its new AI model can reason and think ‘much like a person’
https://edition.cnn.com/2024/09/13/tech/chatgpt-openai-o1-human-reasoning/index.html
Wired: OpenAI Announces a New AI Model, Code-Named Strawberry, That Solves Difficult Problems Step by Step
https://www.wired.com/story/openai-o1-strawberry-problem-reasoning/
Is this the same model "o1" that I got an email about?
*Checks the link... Yeah, it is!
Thanks for sharing!
BBC: The 12-day flight across the world in a 60-year-old plane
https://www.bbc.com/future/article/20240912-the-12-stop-flight-across-the-world-in-a-60-year-old-plane
BBC: How much will AI help in the next pandemic?
https://www.bbc.com/news/articles/c1epnnd3l5jo
Here's day 3 threadcast for Conversations with AI. If you're interested in feeding #leoai some data, put them there:
https://inleo.io/threads/view/ahmadmanga/re-leothreads-26rfswpsb
BBC: New beanless 'coffee' emerges but does it taste any good?
https://www.bbc.com/news/articles/c4gv0rvx0dvo
BBC: Billionaire completes first private spacewalk
https://www.bbc.com/news/articles/c86l6j2w865o
Reuters: TikTok faces crucial court hearing that could decide fate in US
https://www.reuters.com/legal/tiktok-faces-crucial-court-hearing-that-could-decide-fate-us-2024-09-13/
Reuters: 'AI godmother' Fei-Fei Li raises $230 million to launch AI startup
https://www.reuters.com/technology/artificial-intelligence/ai-godmother-fei-fei-li-raises-230-million-launch-ai-startup-2024-09-13/
Reuters: Poland's Intel plant gets EU green light for $1.9 bln in state support
https://www.reuters.com/technology/polands-intel-plant-gets-eu-green-light-19-bln-state-support-2024-09-13/
Reuters: India lawmaker, trade group seek suspension of Amazon, Flipkart operations after antitrust breaches
https://www.reuters.com/business/retail-consumer/india-lawmaker-trade-group-seek-suspension-amazon-flipkart-operations-after-2024-09-13/
Reuters: China amends statistics law to combat data fraud
https://www.reuters.com/world/china/chinas-top-legislative-body-approves-amended-statistics-law-combat-data-fraud-2024-09-13/
Reuters: As storm Bebinca approaches, Taiwan uses AI to predict typhoon paths
https://www.reuters.com/technology/artificial-intelligence/storm-bebinca-approaches-taiwan-uses-ai-predict-typhoon-paths-2024-09-13/
Reuters: FDA authorizes first OTC hearing aid software to be used in Apple's AirPods Pro
https://www.reuters.com/business/healthcare-pharmaceuticals/fda-authorizes-first-otc-hearing-aid-software-be-used-airpods-pro-headphones-2024-09-12/
OpenAI Strawberry is here - it's called o1-preview and it might be the most human ChatGPT ever
Fast answers aren't always the best, which might be the key takeaway from the arrival of OpenAI Strawberry – now called o1-preview – a new ChatGPT reasoning model that takes longer to give you what might be vastly better answers.
OpenAI announced the preview release on Thursday in a blog post, saying that it will arrive in ChatGPT and the generative AI company's API. I can confirm that the o1-preview and a faster, cheaper model o1-mini are both live in our ChatGPT Plus account. The new models will not yet appear in the free ChatGPT accounts, though.
#openai #strawberry #technology #newsonleo
Strawberry has been eagerly anticipated because of its possible human-like-thinking capabilities. In the weeks before this announcement, OpenAI CEO Sam Altman has teased us with numerous cheeky fruit references, but has also made it clear in recent months that generative AI was set to make a significant leap forward.
In the blog post, OpenAI explained, "We trained these models to spend more time thinking through problems before they respond, much like a person would. Through training, they learn to refine their thinking process, try different strategies, and recognize their mistakes."
Ph.D intelligence
OpenAI claims this more powerful o1-preview has performed "similarly to PhD students on challenging benchmark tasks in physics, chemistry, and biology." And that's key here. o1-preview is a generative model that might have the greatest application in academia, not for helping you write an engaging prom-posal.
One example given in a video accompanying the blog is gene sequencing. In it, a scientist notes that while humans can't keep track of everything in gene sequencing, an AI can. The scientist refers to the new model as "chat with reasoning" and shows how when she types in a question, there's a moment where o1-preview says "Thinking." The value of it is that it keeps her from rabbit-holing into the wrong part of gene theory.
However, o1-preview is not a replacement for ChatGPT-4o, which is barely a month old. The new model isn't searching the web or capable of ingesting files and images. Though, that will likely show up at some point.
If biology and math are not your thing, the lighter and slightly more agile o1-mini might be for you, and is also live in ChatGPT Plus now. It's particularly adept at coding.
You can try out the new models in ChatGPT Plus ($20 a month) by logging in and then selecting the model drop-down menu. You'll see o1-preview, and o1-mini have been added to the list as of this story publishing.
Article
New plant produces lithium 500 times faster with 96% recovery rate from brine
Energy tech company, SLB, has successfully demonstrated sustainable lithium production at its demonstration plant in Clayton Valley, Nevada.
The world is gradually moving towards a cleaner, more sustainable future, and lithium is a crucial element in this transition.
Energy tech company, SLB, has successfully demonstrated sustainable lithium production at its demonstration plant in Clayton Valley, Nevada.
SLB has announced that the results from their demonstration plant confirm a recovery rate of “96% lithium from brine at operational rates.”
#lithium #nevada #technology
The company revealed that this plant has incorporated direct lithium extraction (DLE), concentration, and conversion technologies to produce lithium sustainably at a large scale.
By integrating these various technologies, SLB has created a complete solution for producing lithium, while minimizing environmental impact and maximizing efficiency.
“Lithium is a key enabler of electrification, so we must find ways to accelerate its production without adversely affecting the environment,” said Gavin Rennick, president of SLB’s New Energy business.
SLB’s integrated solution is a comprehensive process designed to extract lithium from brine and produce high-purity lithium compounds. The process involves direct lithium extraction, concentration, and conversion.
As per the press release, the new tech combines SLB’s subsurface and surface expertise, including DLE.
Interestingly, this new integrated tech produces lithium from brine 500 times faster than other known techniques. Moreover, this technology is more sustainable because it requires only 10% of the land along with less water, energy, and chemical reagents.
“Operating at approximately one-tenth the size of a commercial-scale facility, the plant reached a verified recovery rate of 96 percent lithium from brine,” the press release noted.
This method produces high-purity lithium carbonate or hydroxide, which are key elements in the production of batteries and other energy storage devices.
“SLB’s demonstration plant in Clayton Valley proves our unique integrated approach to produce scalable quantities of lithium in the fastest, most economical and sustainable way for today’s market. This accelerates deployment of viable commercial-scale facilities for high-quality lithium products that are the backbone of our electrification economy,” Rennick said.
Article
United Airlines is adding free Starlink Wi-Fi to all of its planes
United will start rolling out the high-speed Wi-Fi to passenger flights in 2025.
United Airlines’ in-flight Wi-Fi is getting a big upgrade on all its jets thanks to SpaceX’s Starlink satellites. After teasing “something big” for the skies, United says that it will start testing Starlink’s fast Wi-Fi service in early 2025, with the first passenger flights expected later next year.
#united #starliink #wifi #business
United is installing Starlink Wi-Fi into all of its aircraft, more than 1,000 planes, over the next several years, and the service will be free for passengers. “Everything you can do on the ground, you’ll soon be able to do onboard a United plane at 35,000 feet, just about anywhere in the world,” says United CEO Scott Kirby.
One Mile at a Time reports that United currently has four different Wi-Fi providers, with regional jets utilizing Intelsat (formerly Gogo) and most wide-body jets using Panasonic Wi-Fi. United also uses Viasat Wi-Fi on most of its 737 Max aircraft, some A319s, and A321neos. Viasat is the best of the bunch in terms of speeds and is commonly found on American and Delta flights.
The announcement is a major one for travelers, as onboard Wi-Fi is often unreliable and slow right now. The Wall Street Journal recently showed how Starlink and others are about to change that, achieving speeds over 100Mbps on a shared Starlink connection with latency under 100ms on a real-world flight. That allows for uninterrupted Netflix streams and even the ability to join video conference calls. Starlink says it can offer speeds of up to 220Mbps per plane.
he high-speed Starlink service is only currently available on JSX or Hawaiian Airlines in the US, so an expansion to United will undoubtedly put the pressure on rivals to improve their in-flight Wi-Fi. A number of international airlines have also announced plans to install Starlink Wi-Fi in recent months, with WestJet planning to use Starlink onboard some of its aircraft starting in December and Qatar Airways planning to introduce free Starlink Wi-Fi on three of its Boeing 777-300 aircraft later this year. Air New Zealand is aiming to roll out Starlink in its domestic fleet in 2025.
News of United’s Starlink deal comes the same week that Jessica Rosenworcel, chair of the Federal Communications Commission, said she wanted to see more competition to SpaceX’s Starlink. Elon Musk’s Starlink has launched around 7,000 satellites into orbit since 2018, with SpaceX controlling “almost two-thirds of the satellites that are in space right now,” according to Rosenworcel. “Our economy doesn’t benefit from monopolies. So we’ve got to invite many more space actors in, many more companies that can develop constellations and innovations in space.”
T-Mobile also announced this week that it had recently tested an emergency alert successfully via a Starlink satellite. In 2022, T-Mobile and SpaceX announced a partnership that would let people text, make calls, and use their T-Mobile phones through Starlink satellites. AT&T and Verizon are also building out similar satellite-to-smartphone services, with Apple and Google offering satellite services for their latest smartphones.
Article
Don’t ask if AI can make art — ask how AI can be art
Debates over AI’s artistic value have focused on its generative output. But so far, interactive systems have proved far more interesting.
#ai #technology #art
If you’re yearning for a fistfight with an artist, one simple phrase should do the trick: AI can do what you do.
The recent explosion of chatbots and text-to-image generators has prompted consternation from writers, illustrators, and musicians. AI tools like ChatGPT and DALL-E are extraordinary technical accomplishments, yet they seem increasingly purpose-built for producing bland content sludge. Artists fear both monetary loss and a devaluing of the creative process, and in a world where “AI” is coming to mean ubiquitous aesthetic pink slime, it’s not hard to see the source of the concern.
But even as their output tends to be disappointing, AI tools have become the internet’s favorite game — not because they often produce objectively great things but because people seem to love the process of producing and sharing them. Few things are more satisfying than tricking (or watching someone trick) a model into doing something naughty or incompetent: just look at the flurry of interest when xAI released an image generator that could make Disney characters behave badly or when ChatGPT persistently miscounted the letter “r” in “strawberry.” One of the first things people do with AI tools is mash together styles and ideas: Kermit the Frog as the Girl With a Pearl Earring, a Bible passage about removing a sandwich from a VCR, any movie scene directed by Michael Bay.
Despite artists’ concerns about being replaced by bad but cheap AI software, a lot of these words and images clearly weren’t made to avoid paying a writer or illustrator — or for commercial use at all. The back-and-forth of creating them is the point. And unlike promises that machines can replace painters or novelists, that back-and-forth offers a compelling vision of AI-based art.
rt by algorithm has an extensive history, from Oulipo literature of the 1960s to the procedural generation of video games like No Man’s Sky. In the age of generative AI, some people are creating interesting experiments or using tools to automate parts of the conventional artistic process. The platform Artbreeder, which predates most modern AI image generators, appealed directly to artists with intriguing tools for collaboration and fine-grained control. But so far, much of the AI-generated media that spreads online does so through sheer indifference or the novelty factor. It’s funny when a product like xAI’s Grok or Microsoft’s Bing spits out tasteless or family-unfriendly pictures, but only because it’s xAI or Microsoft — any half-decent artist can make Mickey Mouse smoke pot.
Article
The entire staff of beloved game publisher Annapurna Interactive has reportedly resigned
The staff reportedly tried to spin out the company into an independent entity, but negotiations broke down.
#newsonleo #annapuma #gaming #technology
Annapurna Interactive, the game company famous for publishing indie hits like Stray, Outer Wilds, Gorogoa, Neon White, What Remains of Edith Finch, and many more, may not be the same company anymore.
Bloomberg reports that the entire staff of Annapurna Interactive, the gaming division of Megan Ellison’s Annapurna, has resigned after failing to convince Ellison to let them spin off its games division into a new company. IGN is corroborating the report.
“All 25 members of the Annapurna Interactive team collectively resigned,’’ former president Nathan Gary and staffers told Bloomberg. “This was one of the hardest decisions we have ever had to make and we did not take this action lightly.”
An Annapurna spokesperson told Bloomberg that existing games and projects will remain under the company. Annapurna didn’t immediately reply to a request for comment from The Verge.
Last week, The Hollywood Reporter said that Gary and the coheads of Annapurna Interactive, Deborah Mars and Nathan Vella, would be leaving. THR also reported that Annapurna planned to “integrate its in-house gaming operations with the rest of Annapurna’s divisions, which include film, TV and theater.” Hector Sanchez, who most recently headed up the Unreal Engine games business at Epic Games and is an Annapurna Interactive cofounder, announced last month that he would be president of interactive and new media at Annapurna.
Article
Phone sex hotline accidentally featured in 'The Last of Us'
For one of PlayStation's most prized and lauded development studios, Naughty Dog appears to have made some sloppy mistakes in creating The Last of Us. First came criticism from an unappreciative Ellen Page regarding Elle's likeness to the actress. Then earlier this week, the developer came under fire for using a Boston subway map as in-game artwork without proper attribution. Having smoothed that situation over, Naughty Dog now finds itself attached to a more risqué controversy. It turns out the phone number displayed on another piece of in-game background art — a billboard advertisement for a pest control company — belongs to an exotic phone sex hotline. The Verge can confirm that dialing the number for The Last of Us' fictional ABC Quality Pest Control connects you to a very real adult service.
#gaming
Hilarious, I bet some players had some unexpected fun! 🤣
Yes. Nothing like adding some unexpected spice to the games.
Speaking to Kotaku, creative director Neil Druckmann explained that the slipup was "an honest mistake" by one of the game's artists. "What happened was, they put some phone numbers in the game and then they thought they could just change the area code to 555, then it's invalid because it's what they do in movies," Druckmann said. "But I guess that doesn't work when you have a 1-800 in front of it." Naughty Dog says it's currently working to remove the inappropriate number, presumably through a forthcoming patch.
Article
GPT-o1: The Best Model I've Ever Tested 🍓 I Need New Tests!
#openai #chatbot #technology #gpto1
OpenAI's 01 Model: A New Benchmark in AI Performance
OpenAi has recently unveiled its latest language model, dubbed "01," which appears to be setting new standards in artificial intelligence capabilities. This article summarizes a detailed test of the 01 model, highlighting its impressive performance across various tasks and comparing it to previous AI models.
Key Features of 01
Improved Thinking Process: The 01 model demonstrates a more sophisticated thinking process, with visible "thoughts" displayed during task completion. This allows users to see a summary of the model's reasoning, although the full chain of thought remains hidden.
Faster Processing: Compared to previous iterations, 01 shows significantly reduced thinking time. For instance, a coding task that previously took 90+ seconds of thinking now only requires about 35 seconds.
Enhanced Code Generation: The model successfully created a fully functional Tetris game in Python on the first attempt, demonstrating superior code generation abilities.
Nuanced Problem-Solving: 01 excels at understanding and addressing nuances in complex problems, often considering aspects that other models overlook.
Improved Accuracy: The model consistently provided accurate answers to a wide range of questions, from mathematical problems to logical reasoning tasks.
Performance Highlights
Coding Task: 01 generated a working Tetris game in Python within 35 seconds of thinking time, improving upon previous attempts both in speed and functionality.
Logical Reasoning: The model correctly solved a problem about envelope dimensions for mailing, considering the possibility of rotation - a nuance often missed by other models.
Self-Referential Tasks: 01 accurately counted the number of words in its own response, demonstrating strong self-awareness and precision.
Complex Scenarios: In a question about "killers in a room," the model showed exceptional reasoning, considering multiple perspectives and nuances that other AIs typically miss.
Scientific Understanding: For the classic "chicken or egg" question, 01 provided a well-reasoned answer based on evolutionary biology.
Areas for Improvement
Despite its impressive performance, 01 still faces challenges with certain types of problems:
Conclusion
OpenAI's 01 model represents a significant leap forward in AI capabilities. Its improved thinking process, faster processing times, and ability to handle nuanced problems set it apart from previous models. While it still faces challenges with certain types of reasoning, its overall performance suggests that AI is moving closer to human-like problem-solving abilities across a wide range of tasks.
As AI continues to evolve, models like 01 are likely to play an increasingly important role in various fields, from coding and data analysis to complex problem-solving and decision-making processes.
Did you notice a huge improvements compared to GPT4o?
Technology is the key
Hi, @taskmaster4450le,
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A fight is brewing as TuSimple tries to move $450M to China and pivot from self-driving trucks to AI animation
TuSimple, once a buzzy startup considered a leader in self-driving trucks, is trying to move its assets to China to fund a new AI-generated animation
TuSimple, once a buzzy startup considered a leader in self-driving trucks, is trying to move its assets to China to fund a new AI-generated animation and video game business. The pivot has not only puzzled and enraged several shareholders, but also threatens to pull the company back into a legal morass mere weeks after reaching a preliminary settlement in a class action lawsuit.
#tusimple #ai #trucking #china #newsonleo
Now, a fight is brewing over roughly $450 million in funds, the bulk of which remains in the United States, TechCrunch has learned. And arguments over the company’s mission lie at the center of it.
Before the company formally disclosed its new business segment in August, a group of shareholders who got wind of the change sent a letter to the company’s board of directors. The letter, viewed by TechCrunch, alleges “potentially fraudulent activities” and asks the board to investigate whether funds were being misappropriated “to facilitate the growth of private ventures” established by Mo Chen, TuSimple’s co-founder and chairman.
Shareholders also complained the company failed to disclose its pursuit of AI animation; the board would eventually publicly announce a new AI animation and gaming business.
The group, which sent the letter anonymously in July, threatened litigation. However, at the time of this writing, no suits have been filed.
TuSimple’s new business segment, which is developing an animated feature film and video game based on the science fiction series “The Three-Body Problem,” is a startling change from its origins.
Google tests desktop windowing for Android tablets
Imagine that — being able to freely rearrange and resize the apps on your screen.
Google is testing a new feature for Android tablets that will let you resize apps freely and arrange them on your screen at will, making it easier to juggle multiple tasks. The “desktop windowing” feature is now available as a developer preview, and for apps that support it, you could even have more than one instance open.
#google #android #technology #windows
Currently, apps on Android tablets open in full-screen by default. When the new mode is enabled, each app will appear in a window with controls that allow you to reposition, maximize, or close the app. You’ll also see a taskbar at the bottom of your screen with your running apps.
It sounds a lot like the iPad’s Stage Manager feature that similarly lets you resize and move windows around your screen or pretty much any desktop operating system. Samsung has also offered its DeX experience for years, bringing desktop-like windows management to Android apps on Galaxy phones and tablets.
Once the feature is rolled out to everyone, you can turn it on by pressing and holding the window handle at the top of an app’s screen. If you have a keyboard attached, you can also use the shortcut meta key (Windows, Command, or Search) + Ctrl + Down to activate desktop mode. (You can exit the mode by closing all your active apps or by dragging a window and dragging it to the top of your screen.)
Google notes that apps locked to portrait orientation are still resizable, which might make things look a bit weird if certain apps aren’t optimized. However, Google plans to address this in a future update by scaling the UI of non-resizable apps while maintaining their aspect ratio.
THE IMPACT OF ARTIFICIAL INTELLIGENCE ON INNOVATION
Iain M. Cockburn
Rebecca Henderson
Scott Stern
Working Paper 24449
http://www.nber.org/papers/w24449
NATIONAL BUREAU OF ECONOMIC RESEARCH
1050 Massachusetts Avenue
Cambridge, MA 02138
March 2018
The authors would like to thank the organizers and participants at the first NBER conference on
the Economics of Artificial Intelligence, and in particular our discussant Matthew Mitchell for
many helpful suggestions and ideas. Michael Kearney provided extraordinary research assistance.
The views expressed herein are those of the authors and do not necessarily reflect the views of the
National Bureau of Economic Research. Funding for this paper was provided by the MIT Sloan
School of Management, by the HBS Division of Research and by the Questrom School of
Management.
At least one co-author has disclosed a financial relationship of potential relevance for this
research. Further information is available online at http://www.nber.org/papers/w24449.ack
NBER working papers are circulated for discussion and comment purposes. They have not been
peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies
official NBER publications.
ABSTRACT
Artificial intelligence may greatly increase the efficiency of the existing economy. But it may
have an even larger impact by serving as a new general-purpose “method of invention” that can
reshape the nature of the innovation process and the organization of R&D. We distinguish
between automation-oriented applications such as robotics and the potential for recent
developments in “deep learning” to serve as a general-purpose method of invention, finding
strong evidence of a “shift” in the importance of application-oriented learning research since
We suggest that this is likely to lead to a significant substitution away from more routinized
labor-intensive research towards research that takes advantage of the interplay between passively
generated large datasets and enhanced prediction algorithms. At the same time, the potential
commercial rewards from mastering this mode of research are likely to usher in a period of
racing, driven by powerful incentives for individual companies to acquire and control critical
large datasets and application-specific algorithms. We suggest that policies which encourage
transparency and sharing of core datasets across both public and private actors may be critical
tools for stimulating research productivity and innovation-oriented competition going forward.
Introduction
Rapid advances in the field of artificial intelligence have profound implications for the
economy as well as society at large. These innovations have the potential to directly influence
both the production and the characteristics of a wide range of products and services, with
important implications for productivity, employment, and competition. But, as important as
these effects are likely to be, artificial intelligence also has the potential to change the innovation
process itself, with consequences that may be equally profound, and which may, over time, come
to dominate the direct effect.
Consider the case of Atomwise, a startup firm which is developing novel technology for
identifying potential drug candidates (and insecticides) by using neural networks to predict the
bioactivity of candidate molecules. The company reports that its deep convolutional neural
networks “far surpass” the performance of conventional “docking” algorithms. After appropriate
training on vast quantities of data, the company’s AtomNet product is described as being able to
“recognize” foundational building blocks of organic chemistry, and is capable of generating
highly accurate predictions of the outcomes of real-world physical experiments (Wallach et al.,
2015).
Such breakthroughs hold out the prospect of substantial improvements in the productivity
of early stage drug screening. Of course, Atomwise’s technology (and that of other companies
leveraging artificial intelligence to advance drug discovery or medical diagnosis) is still at an
early stage: though their initial results seem to be promising, no new drugs have actually come
to market using these new approaches. But whether or not Atomwise delivers fully on its
promise, its technology is representative of the ongoing attempt to develop a new innovation
“playbook”, one that leverages large datasets and learning algorithms to engage in precise
prediction of biological phenomena in order to guide design effective interventions. Atomwise,
for example, is now deploying this approach to the discovery and development of new pesticides
and agents for controlling crop diseases.
Atomwise’s example illustrates two of the ways in which advances in artificial intelligence
have the potential to impact innovation. First, though the origins of artificial intelligence are
broadly in the field of computer science, and its early commercial applications have been in
relatively narrow domains such as robotics, the learning algorithms that are now being developed
suggest that artificial intelligence may ultimately have applications across a very wide range.
From the perspective of the economics of innovation (among others, Bresnahan and Trajtenberg
(1995)), there is an important distinction between the problem of providing innovation incentives
to develop technologies with a relatively narrow domain of application, such robots purposebuilt for narrow tasks, versus technologies with a wide—advocates might say almost limitless—
domain of application, as may be true of the advances in neural networks and machine learning
often referred to as “deep learning.” As such, a first question to be asked is the degree to which
developments in artificial intelligence are not simply examples of new technologies, but rather
may be the kinds of “general purpose technologies” (hereafter GPTs) that have historically been
such influential drivers of long-term technological progress.
Second, while some applications of artificial intelligence will surely constitute lower-cost or
higher-quality inputs into many existing production processes (spurring concerns about the
potential for large job displacements), others, such as deep learning, hold out the prospect of not
only productivity gains across a wide variety of sectors but also changes in the very nature of the
innovation process within those domains. As articulated famously by Griliches (1957), by
enabling innovation across many applications, the “invention of a method of invention” has the
potential to have much larger economic impact than development of any single new product.
Here we argue that recent advances in machine learning and neural networks, through their
ability to improve both the performance of end use technologies and the nature of the innovation
process, are likely to have a particularly large impact on innovation and growth. Thus the
incentives and obstacles that may shape the development and diffusion of these technologies are
an important topic for economic research, and building an understanding of the conditions under
which different potential innovators are able to gain access to these tools and to use them in a
pro-competitive way is a central concern for policy.
This essay begins to unpack the potential impact of advances in artificial intelligence on
innovation, and to identify the role that policy and institutions might play in providing effective
incentives for innovation, diffusion, and competition in this area. We begin in Section II by
highlighting the distinctive economics of research tools, of which deep learning applied to R&D
problems is such an intriguing example. We focus on the interplay between the degree of
generality of application of a new research tool and the role of research tools not simply in
enhancing the efficiency of research activity but in creating a new “playbook” for innovation
itself. We then turn in Section III to briefly contrasting three key technological trajectories
within AI—robotics, symbolic systems, and deep learning. We propose that these often
conflated fields will likely play very different roles in the future of innovation and technical
change. Work in symbolic systems appears to have stalled and is likely to have relatively little
impact going forwards. And while developments in robotics have the potential to further displace
human labor in the production of many goods and services, innovation in robotics technologies
per se has relatively low potential to change the nature of innovation itself. By contrast, deep
learning seems to be an area of research that is highly general-purpose and that has the potential
to change the innovation process itself.
We explore whether this might indeed be the case through an examination of some
quantitative empirical evidence on the evolution of different areas artificial intelligence in terms
of scientific and technical outputs of AI researchers as measured (imperfectly) by the publication
of papers and patents from 1990 through 2015. In particular, we develop what we believe is the
first systematic database that captures the corpus of scientific paper and patenting activity in
artificial intelligence, broadly defined, and divides these outputs into those associated with
robotics, symbolic systems, and deep learning. Though preliminary in nature (and inherently
imperfect given that key elements of research activity in artificial intelligence may not be
observable using these traditional innovation metrics), we find striking evidence for a rapid and
meaningful shift in the application orientation of learning-oriented publications, particularly after
The timing of this shift is informative, since it accords with qualitative evidence about the
surprisingly strong performance of so-called “deep learning” multi-layered neural networks in a
range of tasks including computer vision and other prediction tasks. Supplementary evidence
(not reported here) based on the citation patterns to authors such as Geoffrey Hinton who are
leading figures in deep learning suggests a striking acceleration of work in just the last few years
that builds on a small number of algorithmic breakthroughs related to multi-layered neural
networks.
Developments in
neural networks and machine learning thus raise the question of, even if the underlying scientific
approaches (i.e., the basic multi-layered neural networks algorithms) are open, prospects for
continued progress in this field—and commercial applications thereof—are likely to be
significantly impacted by terms of access to complementary data. Specifically, if there are
increasing returns to scale or scope in data acquisition (there is more learning to be had from the
“larger” dataset), it is possible that early or aggressive entrants into a particular application area
may be able to create a substantial and long-lasting competitive advantage over potential rivals
merely through the control over data rather than through formal intellectual property or demandside network effects.
Strong incentives to maintain data privately has the additional potential
downside that data is not being shared across researchers, thus reducing the ability of all
researchers to access an even larger set of data that would arise from public aggregation. As the
competitive advantage of incumbents is reinforced, the power of new entrants to drive
technological change may be weakened. Though this is an important possibility, it is also the
case that, at least so far, there seems to be a significant amount of entry and experimentation
across most key application sectors.
In contrast to technological
progress in relatively narrow domains, such as traditional automation and industrial robots, we
argue that those areas of artificial intelligence evolving most rapidly—such as deep learning—
are likely to raise serious challenges in both dimensions.
First, consider the challenge in providing appropriate innovation incentives when an
innovation has potential to drive technological and organizational change across a wide number
of distinct applications. Such “general purpose technologies” (David, 1990; Bresnahan and
Trajtenberg, 1995) often take the form of core inventions that have the potential to significantly
enhance productivity or quality across a wide number of fields or sectors. David’s (1990)
foundational study of the electric motor showed that this invention brought about enormous
technological and organizational change across sectors as diverse as manufacturing, agriculture,
retail, and residential construction.
As emphasized by Bresnahan and Trajtenberg (1995), the presence of a general-purpose
technology gives rise to both vertical and horizontal externalities in the innovation process that
can lead not just to underinvestment but also to distortions in the direction of investment,
depending on the degree to which private and social returns diverge across different application
sectors. Most notably, if there are “innovation complementarities” between the general purpose
technology and each of the application sectors, lack of incentives in one sector can create an
indirect externality that results in a system-wide reduction in innovative investment itself. While
the private incentives for innovative investment in each application sector depend on its the
market structure and appropriability conditions, that sector’s innovation enhances innovation in
the GPT itself, which then induces subsequent demand (and further innovation) in other
downstream application sectors.
Various aspects of artificial intelligence can certainly be understood as a GPT, and learning from
examples such as the microprocessor are likely to be a useful foundation for thinking about both
the magnitude of their impact on the economy, and associated policy challenges.
A second conceptual framework for thinking about AI is the economics of research tools.
Within the research sectors, some innovations open up new avenues of inquiry, or simply
improve productivity “within the lab”. Some of these advances appear to have great potential
across a broad set of domains, beyond their initial application: as highlighted by Griliches (1957)
in his classic studies of hybrid corn, some new research tools are inventions that do not just
create or improve a specific product—instead they constitute a new way of creating new
products, with much broader application. In Griliches’ famous construction, the discovery of
double-cross hybridization “was the invention of a method of inventing.” (Hereinafter, “IMI”.)
Rather than being a means of creating a single a new corn variety, hybrid corn represented a
widely applicable method for breeding many different new varieties. When applied to the
challenge of creating new varieties optimized for many different localities (and even more
broadly, to other crops) the invention of double-cross hybridization had a huge impact on
agricultural productivity.
Advances in machine learning and neural networks appear to have great potential as a
research tool in problems of classification and prediction. These are both important limiting
factors in a variety of research tasks, and, as exemplified by the Atomwise example, application
of “learning” approaches to AI hold out the prospect of dramatically lower costs and improved
performance in R&D projects where these are significant challenges. But as with hybrid corn,
AI based learning may be more usefully understood as an IMI than as a narrowly limited solution
to a specific problem. One the one hand, AI based learning may be able to substantially
“automate discovery” across many domains where classification and prediction tasks play an
important role.
On the other, they may also “expand the playbook” is the sense of opening up
the set of problems that can be feasibly addressed, and radically altering scientific and technical
communities’ conceptual approaches and framing of problems. The invention of optical lenses
in the 17th century had important direct economic impact in applications such as spectacles. But
optical lenses in the form of microscopes and telescopes also had enormous and long-lasting
indirect effects on the progress of science, technological change, growth, and welfare: by making
very small or very distant objects visible for the first time, lenses opened up entirely new
domains of inquiry and technological opportunity. Leung et al. (2016), for example, evocatively
characterize machine learning as an opportunity to “learn to read the genome” in ways that
human cognition and perception cannot.
Of course, many research tools are neither IMIs nor GPTs, and their primary impact is to
reduce the cost or enhance the quality of an existing innovation process. For example, in the
pharmaceutical industry, new kinds of materials promise to enhance the efficiency of specific
research processes. Other research tools can indeed be thought of as IMIs but are nonetheless
relatively limited in application. For example, the development of genetically engineered
research mice (such as the Oncomouse) is an IMI that has had a profound impact on the conduct
and “playbook” of biomedical research, but has no obvious relevance to innovation in areas such
as information technology, energy, or aerospace. The challenge presented by advances in AI is
that they appear to be research tools that not only have the potential to change the method of
innovation itself but also have implications across an extraordinarily wide range of fields.
From a policy perspective, a further important feature of research tools is that it may be
particularly difficult to appropriate their benefits. As emphasized by Scotchmer (1990),
providing appropriate incentives for an upstream innovator that develops only the first “stage” of
an innovation (such as a research tool) can be particularly problematic when contracting is
imperfect and the ultimate application of the new products whose development is enabled by the
upstream innovation is uncertain. Scotchmer and her co-authors emphasized a key point about a
multi-stage research process:
when the ultimate innovation that creates value requires multiple
steps, providing appropriate innovation incentives are not only a question of whether and how to
provide property rights in general, but also of how best to distribute property rights and
incentives across the multiple stages of the innovation process. Lack of incentives for earlystage innovation can therefore mean that the tools required for subsequent innovation do not
even get invented; strong early-stage property rights without adequate contracting opportunities
may result in “hold-up” for later-stage innovators and so reduce the ultimate impact of the tool in
terms of commercial application.
The vertical research spillovers created by new research tools (or IMIs) are not just a
challenge for designing appropriate intellectual property policy.1 They are also exemplars of the
core innovation externality highlighted by endogenous growth theory (Romer, 1990; Aghion and
Howitt, 1992); a central source of underinvestment in innovation is the fact that the intertemporal
spillovers from innovators today to innovators tomorrow cannot be easily captured. While
tomorrow’s innovators benefit from “standing on the shoulders of giants,” their gains are not
easily shared with their predecessors. This is not simply a theoretical idea: an increasing body of
evidence suggests that research tools and the institutions that support their development and
Challenges presented by AI-enabled invention for legal doctrine and the patent process are beyond the scope of
this essay.
diffusion play an important role in generating intertemporal spillovers (among others, Furman
and Stern, 2011; Williams, 2014). A central insight of this work is that control—both in the
form of physical exclusivity as well as in the form of formal intellectual property rights—over
tools and data can shape both the level and direction of innovative activity, and that rules and
institutions governing control over these areas has a powerful influence on the realized amount
and nature of innovation.
Of course, these frameworks cover only a subset of the key informational and
competitive distortions that might arise when considering whether and how to provide optimal
incentives for the type of technological change represented by some areas of AI. But these two
areas in particular seem likely to be important for understanding the implications of the current
dramatic advances in AI supported learning. We therefore turn in the next section to a brief
outline of the ways in which AI is changing, with an eye towards bringing the framework here to
bear on how we might outline a research agenda exploring the innovation policy challenges that
they create.
III.
The Evolution of Artificial Intelligence: Robotics, Symbolic Systems, and Neural
Networks
In his omnibus historical account of AI research, Nilsson (2010) defines AI as “that
activity devoted to making machines intelligent, and intelligence is that quality that enables an
entity to function appropriately and with foresight in its environment.” His account details the
contributions of multiple fields to achievements in AI, including but not limited to biology,
linguistics, psychology and cognitive sciences, neuroscience, mathematics, philosophy and logic,
engineering and computer science. And, of course, regardless of their particular approach,
artificial intelligence research has been united by from the beginning by its engagement with
Turing (1950), and his discussion of the possibility of mechanizing intelligence.
Although early pioneers such as Turing had emphasized the importance of teaching a
machine as one might a child (i.e., emphasizing AI as a learning process), the “symbol
processing hypothesis” (Newell, Shaw, and Simon, 1958; Newell and Simon, 1976) was
premised on the attempt to replicate the logical flow of human decision making through
processing symbols. Early attempts to instantiate this approach yielded striking success in
demonstration projects, such as the ability of a computer to navigate elements of a chess game
(or other board games) or engage in relatively simple conversations with humans by following
specific heuristics and rules embedded into a program. However, while research based on the
concept of a “general problem solver” has continued to be an area of significant academic
interest, and there have been periodic explosions of interest in the use of such approaches to
assist human decision-making (e.g., in the context of early-stage expert systems to guide medical
diagnosis), the symbolic systems approach has been heavily criticized for its inability to
meaningfully impact real-world processes in a scalable way. It is of course possible that this
field will see breakthroughs in the future, but it is fair to say that, while symbolic systems
continues to be an area of academic research, it has not been central to the commercial
application of AI. Nor is it at the heart of the recent reported advances in AI that are associated
with the area of machine learning and prediction.
A second influential trajectory in AI has been broadly in the area of robotics. While the
concepts of “robots” as machines that can perform human tasks dates back at least to the 1940s,
the field of robotics began to meaningfully flourish from the 1980s onwards through a
combination of the advances in numerically controlled machine tools and the development of
more adaptive but still rules-based robotics that rely on the active sensing of a known
environment. Perhaps the most economically consequential application of AI to date has been in
this area, with large scale deployment of “industrial robots” in manufacturing applications.
These machines are precisely programmed to undertake a given task in a highly controlled
environment.
Often located in “cages” within highly specialized industrial processes (most
notably automobile manufacturing), these purpose-built tools are perhaps more aptly described
as highly sophisticated numerically controlled machines rather than as robots with significant AI
content. Over the past twenty years, innovation in robotics has had an important impact on
manufacturing and automation, most notably through the introduction of more responsive robots
that rely on programmed response algorithms that can respond to a variety of stimuli. This
approach, famously pioneered by Rod Brooks (1990), focused the commercial and innovation
orientation of AI away from the modeling of human-like intelligence towards providing feedback
mechanisms that would allow for practical and effective robotics for specified applications. This
insight led, among other applications, to the Roomba and to other adaptable industrial robots that
could interact with humans such as Rethink Robotics’ Baxter). Continued innovation in robotics
technologies (particularly in the ability of robotic devices to sense and interact with their
environment) may lead to wider application and adoption outside industrial automation.
These advances are important, and the most advanced robots continue to capture public
imagination when the term AI is invoked. But innovations in robotics are not, generally
speaking, IMIs.
The increasing automation of laboratory equipment certainly improves research
productivity, but advances in robotics are not (yet) centrally connected to the underlying ways in
which researchers themselves might develop approaches to undertake innovation itself across
multiple domains. There are of course counterexamples to this proposition: robotic space
probes have been a very important research tool in planetary science, and the ability of
automated remote sensing devices to collect data at very large scale or in challenging
environments may transform some fields of research. But robots continue to be used principally
in specialized end-use “production” applications.
Finally, a third stream of research that has been a central element of AI since its founding
can be broadly characterized as a “learning” approach. Rather than being focused on symbolic
logic, or precise sense-and-react systems, the learning approach attempts to create reliable and
accurate methods for the prediction of particular events (either physical or logical) in the
presence of particular inputs. The concept of a neural network has been particularly important
in this area. A neural network is a program that uses a combination of weights and thresholds to
translate a set of inputs into a set of outputs, measures the “closeness” of these outputs to reality,
and then adjusts the weights it uses to narrow the distance between outputs and reality. In this
way, neural networks can learn as they are fed more inputs (Rosenblatt, 1958; 1963).
Over the
course of the 1980s, Hinton and his co-authors further advanced the conceptual framework on
which neural networks are based through the development of “back-propagating multi-layer”
techniques that further enhance their potential for supervised learning.
After being initially heralded as having significant promise, the field of neural networks
has come in and out of fashion, particularly within the United States. From the 1980s through
the mid-2000s, their challenge seemed to be that there were significant limitations to the
technology that could not be easily fixed by using larger training datasets or through the
introduction of additional layers of “neurons.” However, in the mid-2000s, a small number of
new algorithmic approaches demonstrated the potential to enhance prediction through back
propagation through multiple layers. These neural networks increased their predictive power as
they were applied to larger and larger datasets, and were able to scale to an arbitrary level
(among others, a key reference here is Hinton and Salakhutdinov (2006)).
These advances
exhibited a “surprising” level of performance improvement, notably in the context of the
ImageNet visual recognition project competition pioneered by Fei-Fei Li at Stanford
(Krizhevsky, Sutskever and Hinton, 2012).
How Might Different Fields within Artificial Intelligence Impact Innovation?
Distinguishing between these three streams of AI is a critical first step towards
developing a better understanding of how AI is likely to influence the innovation process going
forward, since the three differ significantly in their potential to be either GPTs or IMIs—or both.
First, though a significant amount of public discussion of AI focuses on the potential for
AI to achieve super-human performance over a wide range of human cognitive capabilities, it is
important to note that, at least so far, the significant advances in AI have not been in the form of
the “general problem solver” approaches that were at the core of early work in symbolic systems
(and that were the motivation for considerations of human reasoning such as the Turing test).
Instead, recent advances in both robotics and in deep learning are by and large innovations that
require a significant level of human planning and that apply to a relatively narrow domain of
problem-solving (e.g., face recognition, playing Go, picking up a particular object, etc.) While it
is of course possible that further breakthroughs will lead to a technology that can meaningfully
mimic the nature of human subjective intelligence and emotion, the recent advances that have
attracted scientific and commercial attention are well removed from these domains.
Second, though most economic and policy analysis of AI draws out consequences from
the last two decades of automation to consider the future economic impact of AI (e.g., in job
displacement for an ever-increasing number of tasks), it is important to emphasize that there is a
sharp difference between the advances in robotics that were a primary focus of applications of AI
research during the 2000s and the potential applications of deep learning which have come to the
fore over the last few years.
As we suggested above, current advances in robotics are by and large associated with
applications that are highly specialized and that are focused on end-user applications rather than
on the innovation process itself and these advances do not seem as of yet to have translated to a
more generally applicable IMI. Robotics is therefore an area where we might focus on the
impact of innovation (improved performance) and diffusion (more widespread application) in
terms of job displacement versus job enhancement. We see limited evidence as yet of
widespread applications of robotics outside industrial automation, or of the scale of
improvements in the ability to sense, react to, and manipulate the physically environment that the
use of robotics outside manufacturing probably requires.
But there are exceptions: developments
in the capabilities of “pick and place” robots and rapid progress in autonomous vehicles point to
the possibility for robotics to escape manufacturing and become much more broadly used.
Advances in robotics may well reveal this area of AI be a GPT, as defined by the classic criteria.
Some research tools/IMIs based on algorithms have transformed the nature of research in
some fields, but have lacked generality. These types of algorithmic research tools, based on a
static set of program instructions, are a valuable IMI, but do not appear to have wide
applicability outside a specific domain and do not qualify as GPTs. For example, while far from
perfect, powerful algorithms to scan brain images (so-called functional MRI imaging) have
transformed our understanding of the human brain, not only through the knowledge they have
generated but also by establishing an entirely new paradigm and protocol for brain research.
However, despite its role as a powerful IMI, fMRI lacks the type of general-purpose applicability
that has been associated with the most important GPTs. In contrast, the latest advances in deep
learning have the potential to be both a general-purpose IMI and a classic GPT.
Rather than focusing on small well-characterized datasets or testing settings, it is now
possible to proceed by identifying large pools of unstructured data which can be used to
dynamically develop highly accurate predictions of technical and behavioral phenomena. In
pioneering an unstructured approach to predictive drug candidate selection that brings together a
vast array of previously disparate clinical and biophysical data, for example, Atomwise may
fundamentally reshape the “ideas production function” in drug discovery.
If advances in deep learning do represent the arrival of a general-purpose IMI, it is clear
that there are likely to be very significant long-run economic, social, and technological
consequence.
First, as this new IMI diffuses across many application sectors, the resulting
explosion in technological opportunities and increased productivity of R&D seem likely to
generate economic growth that can eclipse any near-term impact of AI on jobs, organizations,
and productivity. A more subtle implication of this point is that “past is not prologue”: even if
automation over the recent past has resulted in job displacement (e.g., Acemoglu and Restrepo,
2017a), AI is likely to have at least as important an impact through its ability to enhance the
potential for “new tasks” (as in Acemoglu and Restrepo, 2017b).
Finally, if deep learning does indeed prove to be a general-purpose IMI, it will be
important to develop institutions and a policy environment that is conductive to enhancing
innovation through this approach, and to do so in a way that promotes competition and social
welfare. A central concern here may be the interplay between a key input required for deep
learning—large unstructured databases that provide information about physical or logical
events—and the nature of competition. While the underlying algorithms for deep learning are in
the public domain (and can and are being improved on rapidly), the data pools that are essential
to generate predictions may be public or private, and access to them will depend on
organizational boundaries, policy and institutions.
Because the performance of deep learning
algorithms depends critically on the training data that they are created from, it may be possible,
in a particular application area, for a specific company (either an incumbent or start-up) gain a
significant, persistent innovation advantage through their control over data that is independent of
traditional economies of scale or demand-side network effects. This “competition for the
market” is likely to have several consequences. First, it creates incentives for duplicative racing
to establish a data advantage in particular application sectors (say, search, autonomous driving,
or cytology) followed by the establishment of durable barriers to entry that may be of significant
concern for competition policy.
Perhaps even more importantly, this kind of behavior could
result in a balkanization of data within each sector, not only reducing innovative productivity
within the sector, but also reducing spillovers back to the deep learning GPT sector, and to other
application sectors. This suggests that the proactive development of institutions and policies that
encourage competition, data sharing, and openness is likely to be an important determinant of
economic gains from the development and application of deep learning.
V.
Data
This analysis draws upon two distinct datasets, one that captures a set of AI publications
from Thompson Reuters Web of Science, and another that identifies a set of AI patents issued by
the U.S. Patent and Trademark Office. In this section, we provide detail on the assembly of these
datasets and summary statistics for variables in the sample.
. As previously discussed, peer-reviewed and public-domain literature on AI points to the
existence of three distinct fields within AI: robotics, learning systems and symbol systems, each
comprised of numerous subfields. To track development of each of these using this data, we
began by identifying the publications and patents falling into each of these three fields based on
keywords. Appendix 1 lists the terms we used to define each field and identify the papers and
patents belonging to it. .2 In short, the robotics field includes approaches in which a system
engages with and responds to environmental conditions; the symbolic systems field attempts to
represent complex concepts through logical manipulation of symbolic representations, and the
learning systems field processes data through analytical programs modeled on neurologic
systems.
Publication Sample and Summary Statistics
Our analysis focuses on journal articles and book publications through the Web of
Science from 1955 to 2015. We conducted a keyword search utilizing the keywords described in
Appendix A (we tried several variants of these keywords and alternative algorithmic approaches
but this did not result in a meaningful difference in the publication set). We are able to gather
detailed information about each publication, including publication year, journal information,
topical information, as well as author and institutional affiliations.
VII.
Deep Learning as a General-Purpose Invention in the Method of Invention:
Considerations for Organizations, Institutions and Policy
With these results in mind, we now consider the potential implications for innovation and
innovation policy if deep learning is indeed a general-purpose technology (GPT) and/or a
general-purpose invention in the method of invention (IMI). If deep learning is merely a GPT, it
is likely to generate innovation across a range of applications (with potential for spillovers both
back to the learning GPT and also to other application sectors) but will not itself change the
nature of the innovation production function. If it is also a general purpose IMI, we would
expect it to have an even larger impact on economy-wide innovation, growth, and productivity as
dynamics play out—and to trigger even more severe short run disruptions of labor markets and
the internal structure of organizations.
The Management and Organization of Innovation
Perhaps most immediately, the rise of general-purpose predictive analytics using large
datasets seems likely to result in a substitution towards capital and away from labor in the
research production process. Many types of R&D and innovation more generally are effectively
problems of labor-intensive search with high marginal cost per search (Evenson and Kislev,
1975, among others). The development of deep learning holds out the promise of sharply
reduced marginal search costs, inducing R&D organizations to substitute away from highlyskilled labor towards fixed cost investments in AI. These investments are likely to improve
performance in existing “search intensive” research projects, as well as to open up new
opportunities to investigate social and physical phenomena that have previously been considered
intractable or even as beyond the domain of systematic scientific and empirical research.
It is possible that the ability to substitute away from specialized labor and towards capital
(that in principle could be rented or shared) may lower the “barriers to entry” in certain scientific
or research fields—particularly those in which the necessary data and algorithms are freely
available—while erecting new barriers to entry in other areas (e.g. by restricting access to data
and algorithms). As of yet, there are few if any organized markets for “trained” research tools or
services based on deep learning, and few standards to evaluate alternatives. Our analysis
suggests that the development of markets for shared AI services and the widespread availability
of relevant data may be a necessary precursor to the broad adoption and dissemination of deep
learning.
There is also the possibility that the large scale replacement of
skilled technical labor in the research sector by AI will “break science” in some fields by
disrupting the career ladders and labor markets that support the relatively long periods of training
and education required in many scientific and technical occupations.
Finally, it is possible that deep learning will change the nature of scientific and technical
advance itself. Many fields of science and engineering are driven by a mode of inquiry that
focuses on identifying a relatively small number of causal drivers of underlying phenomena built
upon an underlying theory (the parsimony principle as restated by Einstein states that theory
should be “as simple as possible but no simpler.”) However, deep learning offers an alternative
paradigm based on the ability to predict complex multi-causal phenomena using a “black box”
approach that abstracts away from underlying causes but that does allow for a singular prediction
index that can yield sharp insight.
De-emphasizing the understanding of causal mechanisms and
abstract relationships may come at a cost: many major steps forward in science involve the
ability to leverage an understanding of “big picture” theoretical structure to make sense of, of
recognize the implications of, smaller discoveries. For example, it is easy to imagine a deep
learning system trained on a large amount of x-ray diffraction data quickly “discovering” the
double helix structure of DNA at very low marginal cost, but it would likely require human
judgment and insight about a much broader biological context to notice that the proposed
structure suggests a direct mechanism for heredity.
But it is useful to emphasize that there is likely to be a significant gap between the
private and social incentives to share and aggregate data—even among academic researchers or
private sector research communities. One implication of this divergence may be that to the
degree any single research result depends on the aggregation of data from many sources, it will
be important to develop rules of credit and attribution, as well as to develop mechanisms to
replicate the results.
This implies that it will be particularly important to pay attention to the design and
enforcement of formal intellectual property rights. On the one hand it will be important to think
carefully about the laws that currently surround the ownership of data. Should the data about e.g.
my shopping and travel behavior belong to me or to the search engine or ride sharing company
that I use? Might consumers have a strong collective interest in ensuring that these data (suitably
blinded, of course) are in the public domain, so that many companies can use them in the pursuit
of innovation?
On the other, the advent of deep learning has significant implications for the patent
system. Though there has so far been relatively little patenting of deep learning innovations,
historical episodes such as the discovery and attempted wholesale patenting of express sequence
tags and other kinds of genetic data suggests that breakthroughs in research tools—often
combined with a lack of capacity at patent offices and conflicting court decisions—can result in
long periods of uncertainty that has hampered the issuing of new patents, and this in turn has led
to lower research productivity and less competition. Deep learning also presents difficult
questions of legal doctrine for patent systems that have been built around the idea of creative
authors and inventors. For example, “inventorship” has a specific meaning in patent law, with
very important implications for ownership and control of the claimed invention.
In addition to these traditional innovation policy questions, the prospect for deep learning
raises a wide variety of other issues, including issues relating to privacy, the potential for bias
(deep learning has been found to reinforce stereotypes already present in society), and consumer
protection (related to areas such as search, advertising, and consumer targeting and monitoring).
The key is that, to the extent that deep learning is general-purpose, the issues that arise across
each of these domains (and more) will play out across a wide variety of sectors and contexts and
at a global rather than local level. Little analysis has been conducted that can help design
institutions that will be responsive at the level of application sectors that also internalize the
potential issues that may arise with the fact that deep learning is likely to be a GPT.
Finally, the broad applicability of deep learning (and possibly robotics) across many
sectors is likely to engender a race within each sector to establish a proprietary advantage that
leverages these new approaches. As such, the arrival of deep learning raises issues for
competition policy. In each application sector, there is the possibility that firms that are able to
establish an advantage at an early stage, and in doing so position themselves to be able to
generate more data (about their technology, about customer behavior, about their organizational
processes) will be able to erect a deep-learning-driven barrier to entry that will ensure market
dominance over at least the medium term.
This suggests that rules ensuring data accessibility are
not only a matter of research productivity or aggregation, but also speak to the potential to guard
against lock-in and anticompetitive conduct. At the present moment there seem to be a large
number of individual companies attempting to take advantage of AI across a wide variety of
domains (e.g., there are probably more than 20 firms engaging in significant levels of research in
autonomous vehicles, and no firm has yet to show a decisive advantage), but this high level of
activity likely reflects an expectation for the prospects for significant market power in the future.
Ensuring that deep learning does not enhance monopolization and increase barriers to entry
across a range of sectors will be a key topic going forward.
VIII. Concluding Thoughts
The purpose of this exploratory essay has not been to provide a systematic account or
prediction of the likely impact of AI on innovation, nor clear guidance for policy or the
management of innovation. Instead, our goal has been to raise a specific possibility—that deep
learning represents a new general-purpose invention of a method of invention—and to draw out
some preliminary implications of that hypothesis for management, institutions, and policy.
Our preliminary analysis highlights a few key ideas that have not been central to the
economics and policy discussion so far. First, at least from the perspective of innovation, it is
useful to distinguish between the significant and important advances in fields such as robotics
from the potential of a general-purpose method of invention based on application of multilayered neural networks to large amounts of digital data to be an “invention in the method of
invention”.
Both the existing qualitative evidence and our preliminary empirical analysis
documents a striking shift since 2009 towards deep learning based application-oriented research
that is consistent with this possibility. Second, and relatedly, the prospect of a change in the
innovation process raises key issues for a range of policy and management areas, ranging from
how to evaluate this new type of science to the potential for prediction methods to induce new
barriers to entry across a wide range of industries. Proactive analysis of the appropriate private
and public policy responses towards these breakthroughs seems like an extremely promising area
for future research.
‘Godmother of AI’ launches World Labs with $230M funding at $1B valuation
Fei-Fei Li, former Google Cloud AI director and co-director of the Stanford Institute for Human-Centered Artificial Intelligence, announced the launch of “World Labs” after two successful funding rounds.
Artificial intelligence startup World Labs announced its official launch on Sept. 13. The firm is reportedly valued at over $1 billion and has raised more than $230 million in funding to build “spatial intelligence” systems.
#ai #spatialai #technology #feifeili #newsonleo
World Labs was co-founded by Fei-Fei-Li, the former Google Cloud AI boss. Due to her involvement in much of the foundational research behind modern generative artificial intelligence, Fei-Fei-Li is often referred to as the “Godmother of AI.” She’s also the current co-director of the Stanford Institute for Human-Centered Artificial Intelligence.
Spatial artificial intelligence
World Labs describes its primary product as “Large World Models” (LWMs). In a blog post announcing the company’s launch, it pointed out that current generative AI models can only interact with the world through text, audio, and video.
Humans, on the other hand, experience the world as a three-dimensional space with physics that relates to the passage of time.
“To advance beyond the capabilities of today’s models,” the World Labs team wrote, “we need spatially intelligent AI that can model the world and reason about objects, places, and interactions in 3D space and time.”
World Labs aims to bridge the gap between AI models that interpret the world through a 2D lens and artificial agents capable of perceiving 3D worlds by “creating and editing virtual spaces complete with physics, semantics, and control.”
In addition to #upvote and #reblogged we now can add #threaded to our efforts to promote articles.
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i recently realized that lowering my expectations while trading crypto is a smart way that helps me to manage my capital with lesser risks
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https://img.inleo.io/DQmYSvP68kbKz7WgBpUUMy2TR78aSMCDHAP6EbzyjiiQr3q/images%20(8).jpeg
In other words. , make more effort , try new things , be willing to take risks
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Publishing a post about the pros and cons of autobiographical blogging.
https://peakd.com/hive-173575/@axietrashgame/the-pros-and-cons-of-autobiographical-blogging
#cent
Above is a wrong link. The edited version of the above thread takes time to appear on InLeo. This is the right link: https://peakd.com/hive-173575/@axietrashgame/the-pros-and-cons-of-autobiographical-blogging#@hivebuzz/notify-1726217072
What are your takes on the abortion policy?
#inleo #dailydook #cent
It's quite elusive to grasp how some people can oppose big government, advocating for minimal interference in personal lives, yet when it comes to abortion, they push for extreme measures like proposing an office to track women's pregnancies.
That's the irony of our time. It's more on personal or group interest that works there.
You can't make up those #twists, or can you? 👀
It's a reality we have to accept that when it concerns people's interest they go for big government. They only minimal government when it affects them personally in a negative way. #cent #pob #inleo
Though I cannot do away with it completely for I am also part of an institution that loves big government when it comes to dole out, personally, it's against my principle and policy. #cent #pob #inleo
I am very interested in the gender of the citizens pushing for an office to monitor people's pregnancies. !DOOK
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The best advice I can give is to abstain and you must do, use protection or contraceptive rather than abortion.
Cheers
You are right. Abstinence is the best option. If one can't help it, they should protect themselves
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https://img.inleo.io/DQmaG11c52iV22S28dHuXYEfyUZMre2gQbvbyK1pr8mMiJK/images%20(9).jpeg
Adulthood is not easy , but we keep grinding , we will keep moving
#life #meme
#cent #dailydook
untill we don't muve !DOOK
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Dinner is far. What about deciding what to have for breakfast? No one gave us a manual for adulthood
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Treat your family right, keep your good friends. You will need them both in good time, as well as challenging time.
#cent #freecompliments
I learnt that relationships are maintained. It takes as much effort to keep a friend as it takes to make one. !DOOK
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Do not beg for love , friendship or attention..
#life #motivational #cent
Coinbase tries to grow its new cbBTC token #freecompliments #liotes #hive #cent
Ukraine businesses hire more women and teens in traditionally male dominated roles amid labour shortages.
#dailydook #cent #askonleo
thats bad 😔 !DOOK
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Lol , life is really hard
#meme #cent #funny
#freecompliments #memes
This looks like the perfect shape to me, lol. #freecompliments !DOOK
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Hello everyone, I am new here, learning to use this platform
Welcome to Hive blockchain and INLEO.
Ask questions where you need clarifications and have fun exploring, creating and engaging with other content creators here.
Thanks Winanda
Welcome to the platform master ji
Have a nice day and wonderful journey
#freecompliments
Welcome to INLEO. How did you find out about it?
Yeah, that's a good question.
Thanks Ken, My friend @guurry123 invited me
welcome!
Thank you Evan
Welcome. You can start threading and asking questions.
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Welcome, I hope you get a hang of it soon but until then keep threading your thoughts.
!DOOK
🧵 1. "12 Years a Slave" revisited: A brutal journey from freedom to captivity, showcasing the harrowing realities of slavery through Solomon Northup's ordeal. #cinema
🧵 2. Northup's tale in "12 Years a Slave" sheds light on the dehumanizing impact of colonialism and the horrors of the American slave trade.
🧵 3. With powerful performances and vivid storytelling, the film portrays the normalization of evil in a profit-driven colonial system.
🧵 4. Steve McQueen's direction in "12 Years a Slave" masterfully captures the inhumane treatment and endurance of Solomon Northup.
🧵 5. As Northup grapples with his newfound reality, the film delves into the darkness of slavery, revealing the resilience and suffering of those affected.
🧵 Read more at: https://medium.com/framerated/12-years-a-slave-an-unflinching-portrait-of-a-system-built-on-suffering-3521d0a43a59
I love artworks
#artlovers #architectures #photographers
This one is nice 🙂
#freecompliments !DOOK
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Thanks #freecompliments
Thanks#freecomplement
really want to catch up on hive fest but I’m not mentally or emotionally available right now. Too much input, I need some output or silence right now
I know that feeling. May you have the silence/time alone you need. Have a nice day!
!PIZZA !LUV
@selfhelp4trolls, @ahmadmanga(1/10) sent you LUV. | tools | discord | community | HiveWiki | <>< daily
Sorry about that. You should take as much rest as you can. Take care 🥰
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I felt the same way yesterday, try taking a nap or a walk. !DOOK
The dollar is weakening ahead of the Fed's highly anticipated interest rate decision. Uncertainty surrounding the size of the cut, with recent economic indicators, has led to increased speculation for a more significant rate reduction.
The uncertainty around the Fed's decision is definitely shaking things up in the market.
#freecompliments !DOOK
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I have been powering up a lot for the last few months and am now pretty much close to making it 75k $hive power. This should be done in Sep month.
Its the best time to grow Hive power.
I agree this one of the best times to accumelate HIVE Power.
yes ..
That's huge. Congrats on this milestone 🥳🥳🥳
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What kind of day will I have mentally tomorrow. It is Karen's Birthday. She would have been 61. :(
Ah that's tough man :(
Won't be easy but I hope you can remember the great moments you had together
Thanks. The great moments we had is what keeps me going :) !BBH !DOOK
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Trying to motivate myself to look at my #splinterlands soulbound cards and decide if I want to level some up before the price goes up. Iziar seems to be a must have. Any others?
Since I have two weeks of vacations I'm thinking about going premium on #inleo...
Might as well since I'll have more free time to post stuff.
Only wish the mobile webpage worked a little better, maybe an actual app would be good.
Hello foodie Lions 🦁! Happy Friday. Welcome to the first session of today's show. 🥗🍲🫕
This is the #threadcast for Day 80 of the #foodtalk on Leo, 13/9/2024 for 12/9/2024. It's time for some meal inspirations and food conversation. Let's get into it, learn and connect. Don't forget to use #foodtalk in your comments.
Discussion
More about food with tips and tricks will be dropped in the threadcast.
Upvote the comments you find interesting, engage and connect with others. Let's have fun. #foodie
This is my favorite breakfast, I'm not eating it today, but it's my favorite in Venezuela, it's called empanadas.
#foodie #threadcast #foodtalk
Hello @lanzjoseg, welcome to the #foodtalk threadcast.
I can see pineapple, pawpaw, avocado and the other fruits, so is it the pastry that is called empanadas or the entire meal?
The threadcast hashtag is used when you want to set up a threadcast about any topic and when you have up to 15 comments underneath, your icon will be at the top of the page and any one can click on it to engage.
Hello @coyotelation, can you share any Mexican dish with us? I would love to know some names of Mexican foods and what they look like. #foodtalk #mexicanfoods #foodie
For sure.
Thanks so much, I have seen them and they look delicious.
You're welcome. ✌🏻
@coyotelation @sabrinah What food do you prefer to have on your birthday? @calebmarvel01 made himself a birthday meal, check it out. #foodtalk #foodie #birthdayfood
https://inleo.io/threads/view/calebmarvel01/re-qbnxatfxvl?referral=calebmarvel01
Just waking up 😴😴😴..
You needed it.
But I over slept my sleep
😂😂😂
Lol, some days are like that.
My dear, it's fine anyway..
Are you ready for some games tonight?
I'll have a good roast pork. I'll have a barbecue tomorrow. 😋
Nice one, please don't forget to snap pictures of your food, I want to feed my eyes and enjoy the food and I'm sure @taskmaster4450le would not want to miss a good roast pork and barbecue from you.
Now that reminds me, I have not posted my birthday cake. I should make a short video about it soon.
It will be cool to see your video.
Yeah, really cool. 😁
I'd like Fried rice, Coleslaw, Chicken and Moi-moi, please 🥺
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Nice, that's a great choice of a delicious meal. Is it your favourite food?
No, it is the one I love buying if I crave food prepared by someone else 😁
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Hmmm...now this your response tells me that you cook...spill the beans, lol.
Of I cook. But not as someone who loves doing it. Out of necessity.
Its one of the reasons I got a fridge so I can only do it once a week and store
#freecompliments !DOOK
And sending some fried frog legs your way ..
COMO FAZER TACOS MEXICANOS | TORTILHA E ACOMPANHAMENTOS
Hello dear friend, thanks for sharing this Tacos recipe?
Do you know what she put in the bowl first before putting the all purpose flour? Do you think it is blended Graham's Crackers?
#foodtalk
She added natural yogurt, cream cheese, salt and lemon.
Oh, okay. That will be yummy. Thanks so much.
You're welcome.
Hi, @coyotelation,
This post has been voted on by @darkcloaks because you are an active member of the Darkcloaks gaming community.
Get started with Darkcloaks today, and follow us on Inleo for the latest updates.
COMO FAZER CHILLI MEXICANO: O SEGREDO DO CHILLI DE CARNE MAIS FAMOSO DA INTERNET
This looks delicious and can also be eaten with rice.
Thanks for sharing.
Great tip.
Yeah, thanks.
Hi, @coyotelation,
This post has been voted on by @darkcloaks because you are an active member of the Darkcloaks gaming community.
Get started with Darkcloaks today, and follow us on Inleo for the latest updates.
COMO FAZER BURRITO DE CARNE | COMIDAS MEXICANAS EP04
This looks good. I use shawarma wraps to wrap up the filling.
It's Friday Foodie Lions. Welcome to the first session of the #foodtalk on Leo.
Share your meals in this threadcast, photos or videos. #foodtalk #foodie
Are there foods you eat on daily or weekly basis? Share some of them. #foodtalk #foodie
How do you store your leftover foods? #foodtalk #foodie #leftovers
In the fridge 🥺
Okay, how long does it last or stay fresh in your fridge?
What cuisines are you familiar with? #foodtalk #cuisine #foodie
Do you know about Mexican foods? #foodtalk #mexicancuisine #mexicanfood #foodie
https://inleo.io/threads/view/coyotelation/re-winanda-fyuqfzxm?referral=coyotelation
I am familiar with African, British, and American cuisines and tried some others. It will be amazing to have knowledge of most cuisines and recreate them. #foodtalk #cuisines #foodie
https://inleo.io/threads/view/winanda/re-winanda-2uoq2d4lp?referral=winanda
I store mine in the freezer for more shelf life just as I store my freshly cooked foods.
https://inleo.io/threads/view/winanda/re-winanda-2i5av4qia?referral=winanda
What did you have for breakfast? #foodtalk #foodie #breakfast
Have you had coffee today? How many cups have you had? #foodtalk #coffee #foodie
How much water do you drink in a day? #foodtalk #water
I had chocolate bread and jam with malt and milk drink. It was a sweet breakfast. #foodtalk #breakfast #foodie
https://inleo.io/threads/view/winanda/re-winanda-kmliuzld?referral=winanda
I have not had coffee today, it's been a while I had coffee. #foodtalk #coffee #foodie.
https://inleo.io/threads/view/winanda/re-winanda-y6qan1vc?referral=winanda
What is the first thing you eat or drink in the morning? #foodtalk
Liezl Jayne Strydom- 1 Hour Weight Loss Meal Prep - 93g Protein Per Day + Super Easy. #foodtalk #mealprep #weightloss #foodie #healthymeals
Here is a thread for a quick look into fiber-rich foods to eat. #foodtalk #healthyfood
https://inleo.io/threads/view/winanda/re-winanda-xtfr7cxe?referral=winanda
What can you eat this vegetable soup with apart from any swallow options like eba, oat swallow, pounded yam, plantain swallow, etc.? @sabrinah @luchyl @calebmarvel01 @ellacentric @bfrominside
#foodtalk #vegetablesoup #healthyfood #foodie
https://inleo.io/threads/view/bfrominside/re-leothreads-2fjkxwceo?referral=bfrominside
Rice🍚
Nice, same here. I could also use boiled yam, plantain and potatoes.
Ouuu that works too. I hate having vegetable soup with swallos
Oh, really? Let me guess, it won't roll down easily.
Yessss😭😭😭
white rice and white yam even boiled plantain
Friday The 13th
Directed by
Sean S. Cunningham
1980
📽🔪👁🩸🍿
#fridaythe13th #seanscunningham
#betsypalmer #kevinbacon
#movies #moviesonleo
Rate this Movie
It looks like the movie will be interesting 🤔
#freecompliments !DOOK
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will be? you‘re gonna watch it? Perfect day today, Friday 13th, to watch it! 👍🏽
ps: you see that it‘s from 1980, right?! 👀
Lol. I saw it 😂
I wonder if it's on Netflix though. Or recommend a place to find it, will you? Thanks.
#freecompliments !DOOK
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i don‘t know where to watch it, i just saw todays date and thought it‘s a fitting day to put it on chain …
You got me when you said "put it on chain" . You did the right thing
#freecompliments !DOOK .
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btw. there are 3 sequels from back in the 80ies and a remake from 2008 and maybe more … just checked, it’s on Amazon Prime Video …
I Sabrina vow to challenge the status quo, to make the glass ceiling my floor. To put put my magic into the world courageously. To use naysayers as gasoline for my fire. #freecompliments #dailydook #cent
Oh, I love it, perfect timing, Have you read my long form post yet today? It is the same theme of what you just Threaded. !BBH !DOOK
Thanks. No. Not yet. Let me check it out
#freecompliments !DOOK
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Thank you :) !BBH !DOOK
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so help me.......
This is the #selfie & #memory #threadcast. You are your brand, your face is your brand. Post a selfie of yourself, or yourself with friends, pets, food, or just what ever you are doing. Or just a memory. Or a Leo Short.
Winter is leaving and soon my Deborah the neighbor character will be back! #selfie, #memory
:) !BBH !DOOK
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One with my older brother. #selfie #memory
Awesome. Thank you :) !BBH !DOOK
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Selfie while having coffee 😎
#selfie #memory
Thank you :) !BBH !DOOK
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https://inleo.io/threads/view/moretea/re-leothreads-2kw5ltjqz?referral=moretea
This is a cooool photo!!
!LUV
@moretea, @katerinaramm(1/1) sent you LUV. | tools | discord | community | HiveWiki | <>< daily
Just like the garden of eden ;) !BBH !DOOK
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you mean the leaf right there?! 😆👍🏽 didn‘t notice that before …
The one over your private part ;) lol
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Upscaled it!
Not sure what you did?
Uploaded the picture and enhanced it with Ai.
No day 3 live stream for HiveFEst yet today?
technically HF is over (the presentations at least). I may still post one just for pics as today is a free wandering day
That would be awesome :) Look forward to it :)
🚀
It's only 2 conference days so probably won't be
Oh, what do they do for the other 3 days?
Opening drinks, closing dinner, boat ride, other activities, free time etc
https://hivefe.st/ should have the schedule
Thank you sir :)
close. Take a look at app.hivefe.st instead to follow along with the evolving schedule
A #threadcast for todays DIY day at #hivefest.
so due to inclement weather or boating trip has been cancelled (possibly rescheduled, but unconfirmed)
Meteorology is more guess work than science
I’ll be cruising around the city most the day, what do the Hivians not present want to see or know?
Still need to check where the pool will be... are you joining for billiards?
The app with the schedule should be updated now with the address. I’ll be there, it’s near where I’m staying.
Yep, see you soon.
Been making some bad art on a beautiful beach.
Okay, time to eat and go to work. Thread ya from work ;)
Okay, I hope you have a great day. #freecompliments !DOOK
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🧵 1. Celebrating 30 years of The Nightmare Before Christmas - a stop-motion masterpiece that still thrills and chills with its macabre charm. #cinema
🧵 2. Enter Halloween Town with its eerie cello and violin theme, a spectral scarecrow, and a Jack-o’-lantern that sets the stage for a dark adventure.
🧵 3. The film's captivating opening immerses viewers with a medieval fair-like atmosphere, beckoning with the question, "Wouldn't you like to see something strange?"
🧵 4. From devilish chants to whimsical characters, The Nightmare Before Christmas continues to transport audiences to a fantastical and delightfully creepy realm.
🧵 5. Three decades on, this iconic film remains a Halloween classic, enchanting viewers with its unique blend of spooky visuals and memorable music.
🧵 Read more at: https://medium.com/framerated/the-nightmare-before-christmas-a-stop-motion-masterclass-of-the-macabre-d5def6d5bdab
@taskmaster4450le by @leofinance YouTube Now The 4th Largest TV Provider In The US #youtube #television #broadcast #neoxian #proofofbrain #archon #gosh
‘It does not matter how slowly you go as long as you do not stop.’ – Confucius
#motivationalquote #dailydook
That's right. Slow and steady wins the race at all times
#freecompliments !DOOK
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Hello friends! I am at the beach, thinking. What about you?
#photographers
That's really gorgeous. Ocean?
@galenkp by @weekend experiences Weekend-Engagement topics: WEEK 223
#weekend-engagement #topics #gosh
Do you already have thoughts on some important and interesting topics?
https://inleo.io/threads/view/taskmaster4450le/re-gbkktdlucd
I didn't know we could post PDFs. Do we really have a feature for such files? @taskmaster4450le
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A useful feature, it can allow publishing more technical documentation. !VSC
No way to post. Have to extract the data from the pdf into text form.