Dbrain - the blockchain platform to label data and build AI Apps. Ramon’s rating

I. Category & Industry
II. Description & White paper
III. Team, advisers & Road map
IV. Crowdsale
V. Expert and analytical evaluation
VI. Mentions in the media
VII. Author's opinion & Consolidated valuation

Category & Industry.jpg
AI was coined by John McCarthy, an American computer scientist, in 1956 at The Dartmouth Conference where the discipline was born. Today, it is an umbrella term that encompasses everything from robotic process automation to actual robotics. It has gained prominence recently due, in part, to big data, or the increase in speed, size and variety of data businesses are now collecting. AI can perform tasks such as identifying patterns in the data more efficiently than humans, enabling businesses to gain more insight out of their data(https://searchenterpriseai.techtarget.com/definition/AI-Artificial-Intelligence);

Examples of AI technology:
• Automation is the process of making a system or process function automatically. Robotic process automation, for example, can be programmed to perform high-volume, repeatable tasks normally performed by humans. RPA is different from IT automation in that it can adapt to changing circumstances.
• Machine learning is the science of getting a computer to act without programming. Deep learning is a subset of machine learning that, in very simple terms, can be thought of as the automation of predictive analytics. There are three types of machine learning algorithms: supervised learning, in which data sets are labeled so that patterns can be detected and used to label new data sets; unsupervised learning, in which data sets aren't labeled and are sorted according to similarities or differences; and reinforcement learning, in which data sets aren't labeled but, after performing an action or several actions, the AI system is given feedback.
• Machine vision is the science of making computers see. Machine vision captures and analyzes visual information using a camera, analog-to-digital conversion and digital signal processing. It is often compared to human eyesight, but machine vision isn't bound by biology and can be programmed to see through walls, for example. It is used in a range of applications from signature identification to medical image analysis. Computer vision, which is focused on machine-based image processing, is often conflated with machine vision.
• Natural language processing (NLP) is the processing of human -- and not computer -- language by a computer program. One of the older and best known examples of NLP is spam detection, which looks at the subject line and the text of an email and decides if it's junk. Current approaches to NLP are based on machine learning. NLP tasks include text translation, sentiment analysis and speech recognition.
• Pattern recognition is a branch of machine learning that focuses on identifying patterns in data. The term, today, is dated.
• Robotics is a field of engineering focused on the design and manufacturing of robots. Robots are often used to perform tasks that are difficult for humans to perform or perform consistently. They are used in assembly lines for car production or by NASA to move large objects in space. More recently, researchers are using machine learning to build robots that can interact in social settings.

AI applications:
• AI in healthcare. The biggest bets are on improving patient outcomes and reducing costs. Companies are applying machine learning to make better and faster diagnoses than humans. One of the best known healthcare technologies is IBM Watson. It understands natural language and is capable of responding to questions asked of it. The system mines patient data and other available data sources to form a hypothesis, which it then presents with a confidence scoring schema. Other AI applications include chatbots, a computer program used online to answer questions and assist customers, to help schedule follow-up appointments or aiding patients through the billing process, and virtual health assistants that provide basic medical feedback.
• AI in business. Robotic process automation is being applied to highly repetitive tasks normally performed by humans. Machine learning algorithms are being integrated into analytics and CRM platforms to uncover information on how to better serve customers. Chatbots have been incorporated into websites to provide immediate service to customers. Automation of job positions has also become a talking point among academics and IT consultancies such as Gartner and Forrester.
• AI in education. AI can automate grading, giving educators more time. AI can assess students and adapt to their needs, helping them work at their own pace. AI tutors can provide additional support to students, ensuring they stay on track. AI could change where and how students learn, perhaps even replacing some teachers.
• AI in finance. AI applied to personal finance applications, such as Mint or Turbo Tax, is upending financial institutions. Applications such as these could collect personal data and provide financial advice. Other programs, IBM Watson being one, have been applied to the process of buying a home. Today, software performs much of the trading on Wall Street.
• AI in law. The discovery process, sifting through of documents, in law is often overwhelming for humans. Automating this process is a better use of time and a more efficient process. Startups are also building question-and-answer computer assistants that can sift programmed-to-answer questions by examining the taxonomy and ontology associated with a database.
• AI in manufacturing. This is an area that has been at the forefront of incorporating robots into the workflow. Industrial robots used to perform single tasks and were separated from human workers, but as the technology advanced that changed.

Description & White paper.jpg
Dbrain(https://dbrain.io) is an award-winning open blockchain platform to collectively build AI apps. It links crowdworkers and data scientists to transform raw data into real-world AI solutions for businesses, creating a win-win situation for all participants in the AI market. With Dbrain, anyone with a connected device can do simple tasks of data labeling and get paid instantly in cryptocurrency. Data scientists use the resulting datasets to train neural networks and build AI apps. Businesses use existing AI solutions or specify new ones to serve their particular needs. After securing $2.5M in seed funding, Dbrain has reached a significant milestone by releasing the Alpha version of the platform, including a web app and a Telegram bot, and is main award winner at the biggest ICO Pitch Competition organized by d10e in Silicon Valley in 2018. The Dbrain team is made up of industry-renowned professionals and has already launched several AI products that we will be expanding on the Dbrain platform.
Artificial Intelligence (AI) happens to be the next big thing. AI is all about data. Datasets used for machine learning are still labeled by hand, which requires a lot of effort. This creates a lot of friction: labeling quality is not guaranteed, and the initial source data is not secured. Dbrain allows people to work together in secure, seamless, integrated processes for buying, designing, and building AI apps, from start to finish.
Dbrain is an open blockchain platform that links crowdworkers and data scientists enabling them to transform raw data into real-world AI solutions. Crowdworkers do simple tasks of data labelling and validation, and are paid instantly in cryptocurrency for their work. Data scientists use the resulting datasets to train Neural Networks (NN) and build AI apps. Businesses use existing AI solutions or specify new ones to serve their particular needs. Dbrain automates AI production and data workflow by providing efficient tools to all parties, including a web application, a Telegram bot, and a mobile app.
Blockchain technology helps us meet many of AI’s current challenges. Using the blockchain, we can confidently manage high-quality data labelling, security concerns, intellectual property rights, and international micropayments. Using existing commercial computation infrastructure allows us to build an affordable, scalable toolkit for developing and deploying AI apps.
Dbrain use blockchain protocols and an in-house cryptocurrency to power the AI production cycle. The SPOCK protocol validates data label quality, ensuring the most accurate datasets possible. The PICARD protocol ensures the security of confidential data and automatically manages relations and fair revenue distribution between stakeholders. Dbraincoin (DBR) is an ERC-20 cryptocurrency to exchange for work, datasets, and AI app usage.
Anyone with a connected device can join Dbrain and get a role in building Industry 4.0. Dbrain platform connects exploding demand for hand-labeled AI data with the abundant supply of global crowdworkers. In particular, reach 2 billion unbanked people in low-wage countries, offering them cryptocurrency income in exchange for data labeling and validation. Integrating this global workforce into its platform, Dbrain provides a secure, unified infrastructure to supercharge businesses through accessible, high-quality AI products.

The Dbrain Whitepaper ( https://dbrain.io/DbrainWhitepaper.pdf), v 0.1, March 27, 2018, 33 sheets, is poorly prepared, especially in “Crowdasale” and “Vision” parts. It has some sort detailed information. But there is no necessary information for the buyer of tokens.

Team, advisers & Road map.jpg
Dmitry Matskevich (https://www.linkedin.com/in/mdima/), Dbrain CEO. Serial entrepreneur, data geek. Founded
2 leading Big Data companies. Sold Flocktory, a B2B AI startup, for $20M in 2017.

The core team consists of talented IT-professionals from developments to design and customer service.

A very strong team of advisors, among them:

Yobie Benjamin (https://www.linkedin.com/in/yobie/);
Matthew Graham (https://www.linkedin.com/in/mg0314a/);
Eleanor ‘Nell’ Watson (https://www.linkedin.com/in/nellwatson/);
Mikhail Larionov (https://www.linkedin.com/in/mikhail-larionov-7601a510/);
and others.

The road map of the project is well detailed. Everything is painted quarterly from 2017 to 2018 years.
A noteworthy moment is the declared partnership with Nvidia, Chronobank, Microsoft BizSpark and also SONM, Hoff, etc.

Dbraincoin
SYMBOL: DBR
Platform: Ethereum
Token Sale Period: April 1, 2018 - April 28, 2018
Total supply: (up to) 40,000,000 DBR
INVESTOR SUPPLY: 23,500,000 DBR
Token Price: 1 DBR = 2 USD
Softcap: 5,000,000 USD
Hardcap: 40,000,000 USD
PAYMENT METHOD: ETH, BTC, XRP
Country: Russia, Belarus, USA
Whitelist/KYC: KYC & Whitelist
Working product: Yes (Telegram bot and WebApp)
Restricted areas: -

Expert and analytical evaluation.jpg

ICObench (https://icobench.com/ico/dbrain) - 3.2, lowest estimate
ICOHolder (https://icoholder.com/en/dbrain-19099) - 3.69
Foundico (https://foundico.com/ico/dbrain.html) - 8.7 (4.35)
ICOmarks (https://icomarks.com/ico/dbrain) - 8.8 (4.4)
Trackico (https://www.trackico.io/ico/dbrain/) - 5, highest estimate
Foxico (https://foxico.io/project/dbrain) - 6.5 (3.25)
ICOguru (https://icoguru.io/ico-list/dbrain/) - 4.1

Mentions in the media.jpg

Author's opinion & Consolidated valuation.jpg

My consolidated valuation is consisted of:

v1 - weighted average, except for the highest and lowest estimates “Expert and analytical evaluation”:
3.69+4.35+4.4+3.25+4.1=19.79/5 = 3.96

v2 - my weighted average evaluation: 1) WEBSITE-4; 2) WHITE PAPER-3; 3) TEAM-4; 4) ADVISORS-4.5; 5) VISION-3; 6) PRODUCT-3; 7) CROWDSALE-3.5; 8) MEDIA-4.5; 9) PARTNERS-4; 10) The current position of the global crypto-currency market-3;
4+3+4+4.5+3+3+3.5+4.5+4+3=36.5/10 = 3.65

Final “Ramon’s rating” for Dbrain ICO = (v1 + v2) / 2; 3.96+3.65= 7.61/2 = 3.8

3.8 of 5.0 points - good score, obtained on strict criteria and impartiality and also taking into account the current state of the global crypto-currency market.

AI happens and grows rapidly, 80% of AI is data. Dbrain provides a simple tool for crowd workers and data scientists to turn data into real-world AI solutions. Crowd workers are instantly rewarded for doing simple tasks on data labeling & validation. Data scientists can use the resulted datasets to train AI apps. Businesses may want to use the existing solutions or request new ones. This direction today has high prospects, no doubt the project is very interesting. I advise all fans of modern technology to carefully study the project and monitor the dynamics of passage of the ICO. Perhaps the project will become a good investing opportunity!

Bountyhive Username: Ramon

Dbrain - the blockchain platform to label data and build AI Apps. Ramon’s rating

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