Anon Leaks NEW Details About Q* | This is AGI
Discover the latest leaked details about Q-Star, believed to be AGI developed at OpenAI, with new insights on its operation and potential to unlock artificial general intelligence. Learn more about the intriguing project Q-Star and its connection to the OpenAI controversy.
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Video Summary & Chapters
1. Introduction 🌟
Overview of Q-Star and its significance in AGI development.
2. Unveiling Q-Star 🚀
Exploring the concept and potential of Q-Star in AI technology.
3. Q-Star Speculations 🤔
Analyzing initial beliefs and speculations surrounding Q-Star.
4. Key Elements of Q-Star 🔑
Discussing crucial components like self-play and look-ahead planning.
5. Debate on AGI 🧠
Insights on the debate regarding the role of scale in achieving AGI.
6. Overview of Recent Leaks
Discussion on recent leaks by Jimmy Apples and Jan LeCoon.
7. Spatial Reasoning Problem
Jan LeCoon's example and the concept of spatial reasoning.
8. Analyzing the Spatial Reasoning Problem
Examining the response from GPT and its implications.
9. New Information Leak
Introduction to the leaked information from AI for success.
10. Understanding Energy-based Models
Exploration of energy-based models and their function.
11. Energy Assignment in Models
Explanation of how energy is assigned based on probability.
12. Learning in Energy-based Models
Process of learning and adjusting parameters in these models.
13. Understanding Energy-Based Models 🧠
Exploring the concept of energy-based models.
14. Q-Star Leak Overview 🕵️♂️
Insights into the leaked details of Q-star.
15. Q-Star's Unique Approach 🌟
Distinguishing Q-star from current token prediction methods.
16. Holistic Decision-Making 🤔
Exploring the deep analysis process of Q-star.
17. Shifting Dialogue Systems 🔄
Fundamental changes in dialogue system operations by Q-star.
18. Core of Q-Star: Energy-Based Model ⚡
Understanding the EBM at the heart of Q-star.
19. Planning and Thinking Process 🤯
Contrasting Q-star's approach with current language models.
20. Innovation in Q-Star 🚀
Optimization process in an abstract representation space.
21. Abstract Thought Representation 💭
Representation of thoughts beyond language in Q-star.
22. Optimizing Abstract Representations 🔍
Gradient descent in refining abstract representations.
23. Transforming Thoughts to Text 📝
Utilizing auto-regressive decoder for textual response.
24. Training the System 🎓
Process of training the EBM within Q-star.
25. Implications of Q-Star 🌐
Significant departure from traditional language modeling techniques.
26. Technical Considerations 🛠️
Factors influencing Q-star's effectiveness and capabilities.
27. Fascination with Q-Star 🤩
Exploring the potential power of Q-star in dialogue systems.
28. Reactions and Speculations 🤔
Community responses to the leaked information about Q-star.
29. Introduction
Overview of Q-Star and its significance.
30. Q-Star Paper Overview
Explanation of the star paper and its focus on reasoning.
31. Teaching Reasoning
Example of teaching a large language model to reason.
32. Iterative Thinking
Techniques for teaching large language models to think.
33. Quiet Star Technique
Introduction to the Quiet Star technique for reasoning.
34. Meta Language for LMs
Teaching language models to think in between predictions.
35. Quiet Star Generalization
Expanding on the star technique for improved predictions.
36. Application and Accessibility
Applicability of techniques to existing language models.
37. Exciting Updates
Quantitative evaluation and open-sourcing of the Quiet Star model.
38. Discussion and Conclusion
Reflecting on the potential impact of Q-Star and GPT-5.
Video Transcript
We may have just gotten another leak about Q-Star.
And for those of you who haven't heard of Q-Star yet,
it's what a lot of people believe is AGI that has been developed internally at OpenAI.
And may even have preceded Ilya Satskaverse starting a mutiny and trying to kick out Sam Altman.
We already know Q-Star is a real thing.
Sam Altman has confirmed that and I'll get into that in a moment.
But now we have new information about what it is.
And it's shaping up to be a continuation of the same things we've been hearing about QStar,
a brand new way for large language models to operate, which truly would unlock artificial general intelligence.
So let's talk about what it is.
Alright, first, a little bit of a backstory, QStar was first heard about a few months ago right around the time that Sam Altman was temporarily fired from open AI.
And in fact, he gave an interview in which he was asked about Q-Star and he said no particular comment on that unfortunate leak
So he basically confirmed they're working on something called Q-Star
There's a project called Q-Star
But he's not giving any information away about what it actually is
But we were able to find bits and pieces by scouring the web and some leaks here and there and the rest of this quote is basically just very
Media-friendly. Hey, I'm not gonna say anything. We're doing a lot of research. We're making a lot of progress
But he did also say in and around that time that he's been in the room only a handful of times in his career
Where major breakthroughs have occurred and he said around that time that that day was one of them
So this could truly be Q star that he's referring to and all of this happened once ago
And we really haven't heard much since then and I also want to point out a lot of this is just rumors and speculation
But it's fun to think about and I enjoy it and I hope you enjoy thinking through what the possibilities are with regards to Q-Star
So I also want to go over what Q-Star originally was thought to be and this is a blog post by Nathan Lambert
And I reviewed it in a previous video about Q-Star, but let's just rehash it quickly
So some at OpenAI believe Q-Star, pronounced Q-Star, could be a breakthrough in the startup search for what's known as artificial general intelligence
AGI, one of the people told Reuters.
OpenAI defines AGI as autonomous systems that can surpass humans and most economically
valuable tasks.
So back when I made the video about what Q-Star could be, there were really two main pieces
to it.
One is that it was really good at solving math, and that may seem really basic, but it turns
out large language models are not really good at math for the same reason as what the
second factor could be of Q-Star.
And that is the ability to have broader planning.
And planning is something that large language models do not do well.
All the large language model is doing is predicting the next token in a sequence of tokens.
It's basically a big matrix math problem and it's trying to figure out what is the most
likely next word in a sentence.
So being able to plan and have higher level thinking and long term planning in general
is really difficult for large language models to do on their own.
There have been a lot of techniques to allow them to basically fake their way into longer term planning with the use of things like tree of thought and think step by step and all of these are good
But it's basically trying to find a fix for a problem that large language models have inherently and in a moment
I'm gonna get to what the new information we just learned is so stick with me for just a minute while I go over what we've already learned
So there are two main things that Nathan Lambert has pointed out that maybe Q star
One is self-play. The idea that an agent can improve its gameplay by playing
against slightly different versions of itself because it'll progressively
encounter more challenging situations. Now this isn't anything new. This has been
done with systems like AlphaGo and a lot of the work that Nvidia is doing right
now with Dr. Jim Fanz team. So self-play has been around for a while and with the
huge increase in transistors and capabilities and processing power of new chips,
