This is the first edition of the @ai-summaries weekly report. Here you will get the latest updates about the AI-summaries project and various statistics and information about the activities of the past week. My goal is to put up a report with updated numbers every Sunday starting today.
What is AI-Summaries?
It is a project was started in January of 2024, and its original mission was to make the content and discussions taking place in various HIVE and LEO-related livestreams available to a wider audience. By providing summaries of the episodes in chunked-up text format, non-english speaking Hivers was suddenly able to translate them to any languages to get an idea of the discussions taking place despite misc. language barriers. It also aimed to cater to the time-constraint many people find themselves in, and simply doesn't have time to listen to hours of podcasts and livestreams each week.
But perhaps the more powerful and underlying part of it, is the addition of the actual data. By posting the summaries to the blockchain, they're by default recorded permanently to the decentralized database which is Hive, for anyone to utilize – including the training of AI agents like LeoAI.
During the course of the last 11 months, hundreds of livestreams has been summarized as their own separate blog posts, in addition to several thousand 3Speak videos from channels like @cttpodcast, @taskmaster4450 and @theycallmedan, where the summaries were posted in the comment's sections of the videos.
And last Sunday (November 17, 2024) the scope of the project was widened significantly:
Introducing the Youtube Summarizer
If data is the new oil, then why give it all away to big tech?
https://inleo.io/threads/view/mightpossibly/re-leothreads-cjhbc6ka?referral=mightpossibly
In short, the idea is to provide an easy to use and effective way to democratize data by putting it on the blockchain. If this is the first you're hearing about the democratization of data and the decentralization of AI, I recommend giving this excellent article by @taskmaster4450le a read, where he also discusses the significance of this tool in that context.
But enough history and background, now that you know a bit about the project's history and the recent developments, let's get on with this week's numbers!
Weekly Stats
Here is an overview of this week's numbers/activity.
Hive/LEO Livestream Summaries
- Summary: Vaultec Interview about VSC on Crypto Lush – November 15, 2024
- Summary: Inleo AMA – November 19, 2024
- Summary: Chain Chatter – November 20, 2024
- Summary: Lion's Den – November 22, 2024
Youtube Summarizer Stats
- Total number of yt-videos processed: 4,100
- Total number of comments posted: 20,545
- Total Output Tokens posted to chain: 3,186,763
Learn More
https://inleo.io/threads/view/mightpossibly/re-leothreads-34kfsyw63?referral=mightpossibly
https://inleo.io/threads/view/mightpossibly/re-leothreads-2uy1hgpek?referral=mightpossibly
Want to contribute? The best way to support it is to subscribe to me and to use the Youtube Summarizer every day to contribute to the adding of the data. There is a near infinite source of information on youtube, and now there is an easy way to tap into it to benefit the value of the network as a whole.
Join us, subscribe today!
Posted Using InLeo Alpha
What an awesome tool!!! I wonder now if we could use the summeries to reconstruct the videos in that summerized format, or at least make it an audible file?
I'm glad you think so! I mean sure, you're very welcome to do something like that. I've used elevenlabs' free plan (ai voice generation) for something similar and it's pretty great. There are also full-on script-to-video tools that allow you to generate entire videos based off of a script such as an article, but those are typically pretty expensive. If you have a bit of video editing skills you can get far with just a narration track from a voice generator and stock video
Oh I don't even know where I would start with making such tool.... I use eleven labs and other ai generation tools but I don't know how to build anything like that at all!
Ah. I think we may have spoken past each other there. I thought you were asking whether it was possible and okay to use these summaries to create new videos.
If you were thinking about automating the creation of such videos, this would not be economically viable for me and also out of scope for this project. In addition, my experience is that ai-generated videos like this still require some post editing by people to make somewhat sense - at least for now. But I do like the idea! Would be cool to see someone give it a go, whether automated or manually.
Haha 😅 yes. I can see how that could confused. Well thanks for the green light anyway!
This tool is a great tool for summarizing videos, I have use it on free trial and now I have subscribed to it... This is a great way of feeding Data..
A big thanks for you @mightpossibly
Thank you for your continued support and engagement, Caleb. It really is, isn't it
Its is, is it.... 😂😂😂😂 Lol
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!summarize
This video has already been summarized: https://inleo.io/threads/view/mightpossibly/re-taskmaster4450le-spb1i4
!summarize
Part 1/8:
Understanding Large Language Models: A Deep Dive
Earlier this year, I had the opportunity to collaborate with the Computer History Museum on an exciting project focused on large language models (LLMs). As a frequent creator of educational content on this subject, it was a delight to contribute to this exhibit for a museum I hold in high regard. Initially, I imagined the project would be a simplified version of my existing detailed explainers, but it evolved into an enriching experience that allowed me to highlight crucial concepts often overlooked in more technical discussions.
The aim of this article is to provide a comprehensive yet digestible overview of large language models, explaining their functionality, training processes, and underlying technologies.
Part 2/8:
Conceptualizing Large Language Models
Consider a scenario where you discover a partial movie script featuring a dialogue between a person and their AI assistant. The script includes the person's queries, but the responses of the AI are missing. Imagine you possess a magical machine capable of predicting the next word based on the provided text. You would feed the script into this machine and, by repeating the process, gradually complete the interactions. This is fundamentally how chatbots operate using large language models.
Part 3/8:
An LLM functions as a mathematical entity that predicts the subsequent word for any text given. Rather than delivering a single definitive word, these models generate probabilistic predictions for all potential next words. Building a chatbot involves inputting a scripted interaction alongside user input while prompting the model to compute the next word iteratively. This method produces outputs that reflect a more natural conversation style, especially when it randomly selects from less likely options.
The Training Process
To create an LLM, massive datasets—most often sourced from the internet—are processed. For instance, the training dataset for GPT-3 would take over 2,600 years for an average human to read continuously. Modern models train on exponentially more data.
Part 4/8:
The training can be imagined as adjusting various dials on an extensive machine, where the model's behavior is shaped entirely by numerous continuous values known as parameters or weights. Each model can possess hundreds of billions of these parameters, which no human explicitly sets. Instead, they start at random and are refined through an extensive learning process involving large sets of text.
The training method employs an algorithm known as backpropagation, which adjusts the parameters to enhance the model's accuracy. After being provided with a training example—irrespective of its length—the model predicts what the next word should be and is adjusted based on its accuracy. This iterative process leads to improved predictions on unseen text.
Scaling Computational Power
Part 5/8:
Training large language models requires staggering computational resources. To understand the scale, consider that performing a billion additions and multiplications per second would still take over 100 million years to complete all computations involved in training the largest models. This extraordinary feat is achievable only with specialized hardware, such as GPUs, optimized for parallel computing.
Historically, language models processed data sequentially—one word at a time—until 2017, when Google introduced the transformer model. This revolutionary architecture allows models to ingest text all at once and in parallel, significantly improving processing efficiency.
The Transformer Revolution
Part 6/8:
Transformers represent a significant leap in the way language models operate. The first step in a transformer involves encoding each word as a list of numbers, essential for processing language mathematically. This encoding allows the model to handle the training process using continuous values.
A key feature of the transformer model is its "attention" mechanism. This process allows the numerical representations of words to communicate and adjust their meanings based on surrounding context. For example, the meaning of the word "bank" could be refined to represent a "riverbank" depending on adjacent words in a sentence. Additionally, transformers typically utilize feed-forward neural networks to enhance the model's ability to store information about language patterns gleaned during training.
Part 7/8:
Within this framework, data flows through iterative interactions of attention and feed-forward operations, enriching the model's knowledge. The final step involves generating a prediction based on the adjusted representation of context and learned information.
The Emergent Nature of Predictions
Despite the framework developers create, the unique behavior of LLMs arises from the emergent outcomes of their vast parameters. This complexity makes it particularly challenging to explain why a model arrives at specific predictions.
Part 8/8:
Nevertheless, the results of using large language models for generating text are often astonishingly fluent, relatable, and practical. Anyone in the Bay Area should consider visiting the Computer History Museum to engage with this fascinating exhibit on large language models.
For those curious to dive deeper into transformers and the mechanics of attention, a variety of resources are available. I urge you to explore my comprehensive series on deep learning that visualizes and elaborates on these intricate concepts, or check out my recent presentation on the topic for TNG in Munich.
By sharing this knowledge, I hope to shed light on the complexities of large language models and inspire curiosity about their future potential.