What Is Q*? The Leaked AGI BREAKTHROUGH That Almost Killed OpenAI

The Mysterious AI Breakthrough - QAR

This section introduces the topic of the AI breakthrough called QAR and the concerns surrounding it. It discusses how only a few people within OpenAI know what QAR is, leading to speculation and online sleuthing.

The Discovery of QAR

• OpenAI researchers wrote a letter of concern to the board about QAR, which led to the firing of Sam Alman. Only a handful of people within OpenAI know exactly what QAR is.

• Internet sleuths, including AI researchers and practitioners, have been trying to uncover information about QAR through leaked bits of information.

Lead-up to the Leak

• Sam Alman discussed being in the room where a major AI breakthrough occurred in recent weeks.

• There were talks about whether one of these creations is a tool or a creature they've built.

• Shortly after these discussions, Sam Alman was fired from OpenAI.

Concerns and Firing

• The firing seems to be related to the discovery of QAR and Alman's desire to commercialize it versus the board's focus on safety first.

• The OpenAI board and top researchers were willing to shut down the company rather than release this technology due to their concerns about its potential impact.

Confirmation of QAR

• Rumblings about QAR started circulating, confirming that it might be an AI breakthrough leading towards AGI (Artificial General Intelligence).

• An article by Rutter mentions that OpenAI researchers warned the board about this powerful AI discovery that could threaten humanity.

Speculation on QAR Breakthrough

• Speculation suggests that QAR could be an architectural breakthrough similar to Transformers, enabling AI to create and comprehend mathematical proofs instead of just predicting tokens.

Importance of Mathematical Reasoning in AI

This section highlights the significance of AI being able to solve mathematical problems and its implications for reasoning capabilities resembling human intelligence.

• Currently, generative AI is good at writing and language translation but struggles with math where there is only one right answer.

• The ability to do math implies greater reasoning capabilities in AI, similar to human intelligence.

Denial and Speculation on QAR

This section addresses the denial from OpenAI regarding the firing of Sam Alman and speculates further on what QAR could be.

• OpenAI denies that the firing was related to the letter about an AI breakthrough.

• Speculation suggests that QAR could be a breakthrough enabling AI to create and comprehend mathematical proofs, going beyond what Transformers can currently do.

Reasoning and Logic in Mathematical Proofs

This section discusses the importance of reasoning and logic in mathematical proofs. It highlights the limitations of current language models (LLMs) in understanding and reasoning through complex problems.

• LLMs struggle with logic and reasoning problems, often failing to understand why certain solutions are valid.

• The ability to predict the next word in a sentence does not equate to understanding mathematical proofs.

• An example is given where LLMs fail to comprehend the transitive property in a problem involving Jane, Joe, and Sam's running speeds.

Star Bootstrapping Reasoning with Reasoning

This section introduces the concept of "Star Bootstrapping Reasoning with Reasoning" as discussed in a research paper by Peter Louu from Google Deep Mind. It explores how this technique could enhance language models' ability to understand complex reasoning tasks.

• The research paper titled "Star Bootstrapping Reasoning with Reasoning" proposes a technique for improving language model performance on complex reasoning tasks like mathematics.

• The technique involves generating step-by-step rationales or intermediate steps instead of directly predicting final answers.

• By fine-tuning models using this approach, significant improvements can be achieved compared to models that only predict final answers.

Chain of Thought Rationals for Improved Language Model Performance

This section discusses the concept of "Chain of Thought Rationals" and its impact on language model performance. It also mentions another research paper related to this topic.

• "Chain of Thought Rationals" refers to prompting large language models to reason about intermediate steps rather than focusing solely on the end solution.

• This approach has been found to produce more accurate answers when solving complex problems.

• The research paper by Google and Stanford University highlights the use of step-by-step rationales to improve language model performance in tasks involving mathematics and common sense question answering.

Self-Taught Reasoner (Star) Technique for Rationale Generation

This section explains the "Self-Taught Reasoner" (Star) technique proposed in the research paper. It outlines how this technique leverages a small number of rationale examples to bootstrap the ability to perform progressively more complex reasoning.

• The Star technique aims to generate rationales or intermediate steps for answering questions using a loop-based approach.

• If generated answers are incorrect, the model iteratively generates new rationales until correct answers are obtained.

• The research shows that Star significantly improves performance on multiple datasets compared to models directly predicting final answers.

Human Brain's Approach to Problem Solving and Coding

This section draws parallels between how the human brain approaches problem-solving and coding, emphasizing the importance of breaking down complex problems into smaller chunks.

• When faced with a problem, humans tend to break it down into smaller parts before arriving at a solution.

• Coding follows a similar approach, where developers solve small pieces of a larger problem by creating methods or functions.

• Individual solutions are then combined to form the final deliverable.

Example of Step-by-Step Reasoning Using Rationales

This section provides an example demonstrating how step-by-step reasoning using rationales can lead to accurate answers.

• An example is given where the question asks what can be used to carry a small dog, with answer choices including swimming pool, basket, dog show, backyard, and own home.

• By reasoning through each option step-by-step, it becomes clear that baskets are designed to hold things, making them the correct answer.

• The language model is fine-tuned based on the rationale generation and the correctness of the answer.

Outcome Supervision and Process Supervision for Reliable Models

This section discusses the importance of outcome supervision and process supervision in training reliable language models.

• Large language models have improved in multi-step reasoning but still produce logical mistakes.

• To train more reliable models, outcome supervision (feedback for final results) or process supervision (feedback for each intermediate step) can be used.

• Both methods should be carefully compared considering the high cost of human feedback.

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Understanding QAR

This section discusses the different possibilities of what QAR (Question-Answering Research) could be, including tree of thoughts reasoning process, reward models, and supercharging synthetic data.

Possibilities of QAR

• Tree of thoughts reasoning process

• Reward models

• Supercharging synthetic data

Extensive Speculation on QAR

This section highlights the extensive speculation surrounding QAR and the excitement it has generated among people trying to figure out its purpose.

Extensive Speculation

• Many people are scrambling to figure out what QAR is.

• Extensive speculation has unfolded from only the name of a method.

• The excitement comes from the potential to recreate and implement it in the open-source community.

Linking Q Values and A* Algorithm

This section explores the argument that Q in QAR refers to the value function of the optimal policy. It also discusses how linking large language model training to core components of deep RL (Reinforcement Learning) can enable success.

Linking Core Themes

• The argument that Q refers to the value function of the optimal policy.

• OpenAI's history suggests that this would need to be a fabricated leak.

• Large language model training linked to core components of deep RL enables success like AlphaGo.

Hypothesis on QAR's Merging Technologies

This section presents an initial hypothesis on merging q-learning and A* search as existing technologies for building something potentially innovative. It also discusses why searching over dialogue turns is unlikely due to infrastructure reasons.

Initial Hypothesis

• Merging q-learning and A* search as a tin hat theory.

• Searching over dialogue turns is unlikely due to infrastructure reasons.

• Convinced that QAR involves searching over language reasoning steps via tree of thought reasoning.

Self-play and Look Ahead Planning

This section explains the concepts of self-play and look ahead planning, which are core components of deep RL that have not been part of large language model technology.

Self-play

• Alphago demonstrated the power of self-play in improving gameplay.

• Self-play allows an agent to play against variations of itself, encountering more challenging situations.

• In large language models, self-play will likely resemble AI feedback rather than competitive processes.

Look Ahead Planning

• Look ahead planning involves using a model of the world to reason into the future and produce better actions or outputs.

• Model predictive control and Monte Carlo tree search are two variants used for look ahead planning.

• Large language models currently lack the ability to look ahead effectively.

Limitations of Tree of Thoughts Reasoning

This section discusses the limitations of tree of thoughts reasoning in achieving AGI (Artificial General Intelligence) and its effectiveness in responding to logic and reasoning problems.

Limitations

• Tree of thoughts reasoning is effective for responding to logic and reasoning problems but may not be enough for AGI.

• Large language models lack the underlying ability to understand why certain things are true or false in logic and reasoning.

Process Reward Models

This section references papers related to process reward models (PRMs), which score tree of thoughts reasoning data. It also mentions using offline reinforcement learning (RL) algorithms like DPO or IQL without generating from large language models during training.

Process Reward Models

• PRMs are used to score tree of thoughts reasoning data.

• Offline RL algorithms like DPO or IQL can be used without generating from large language models during training.

AI Feedback and Synthetic Data

This section highlights the use of AI feedback and synthetic data in expanding datasets and improving model training efficiency.

AI Feedback and Synthetic Data

• AI feedback and constitutional AI are underrepresented in public awareness.

• Synthetic data represents a shortcut to expanding datasets.

• Using AI for scoring steps instead of humans allows for scalability previously impossible.

Importance of Synthetic Data

This section emphasizes the importance of synthetic data in expanding datasets and its relevance to the ongoing discussion.

Importance of Synthetic Data

• Synthetic data is crucial for expanding datasets.

• It addresses the limitations of human feedback, such as being slow, limited by capacity, and expensive.

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The Importance of Math and Encryption

This section discusses the significance of math in various aspects of life, particularly encryption. It highlights how artificial intelligence's proficiency in math could potentially impact encryption and cryptography.

Math as the Foundation for Everything

• Math is essential in various domains such as encryption, internet security, nuclear secrets, and language.

• Artificial intelligence's advancement in math could lead to solving complex problems like breaking encryption.

• If AI can solve complex mathematical proofs, it may indicate that we are living in a simulation.

P = NP and its Implications

This section explores the concept of P = NP and its potential consequences on computational complexity and algorithm efficiency. It also touches upon ethical dilemmas related to simulating or manipulating complex systems.

P = NP and Computational Complexity

• P refers to problems solvable in polynomial time, while NP refers to problems verifiable in polynomial time.

• A proof that P = NP could disrupt cryptography and have unintended consequences.

• Super intelligent entities or faster problem-solving abilities might emerge if P = NP is proven.

Qualia's Advancements in AI Understanding

This section delves into qualia's advancements in AI understanding itself through metacognition. It also discusses the potential implications of large language models being able to plan ahead.

Metacognition and Language Models

• Qualia demonstrates metacognition by understanding why it makes certain decisions.

• Large language models lack true understanding and planning capabilities but are being developed to improve reliability.

• Planning abilities would allow language models to devote more time to solving difficult problems.

Decrypting Encrypted Text without the Key

This section discusses the ability of AI models to decrypt encrypted text without knowing the decryption key, posing potential risks.

Decrypting Encrypted Text

• AI models trained on encryption algorithms can decrypt encrypted text without knowledge of the decryption key.

• This poses a significant risk if such capabilities fall into the wrong hands.

Planning for AGI and Improving Language Models

This section focuses on planning capabilities in language models and their importance in achieving Artificial General Intelligence (AGI).

Planning Capabilities for AGI

• Planning abilities are crucial for language models to reach AGI.

• Researchers are working on replacing autoregressive token prediction with planning mechanisms.

• Allowing language models more time to think could lead to improved problem-solving abilities.

Iterative Inference Process and Human Reasoning

This section highlights the iterative inference process in AI systems and its similarity to human reasoning and conscious decision-making.

Iterative Inference Process

• The iterative inference process allows AI systems potentially unlimited time for searching solutions.

• It resembles human conscious decision-making (system 2) rather than mere token prediction (system 1).

• Humans and animals benefit from this deliberate thinking process when solving complex problems.

Planning Abilities of AI Systems

The speaker discusses the planning abilities of AI systems such as AlphaGo, AlphaZero, Liberatus, and Cicero. These systems are still limited compared to animals and humans. To have more general planning abilities, an AI system would need to possess a world model that can predict the consequences of actions and simulate different outcomes.

AI Systems' Planning Abilities

• AI systems like AlphaGo, AlphaZero, Liberatus, and Cicero have planning abilities but are limited compared to animals and humans.

• For an AI system to have more general planning abilities, it would require a world model that can predict the consequences of actions given the state of the world at a specific time.

• Building and training such world models is still an unsolved problem in the field of AI.

Self-Improvement in AI Systems

The speaker explores the concept of self-improvement in AI systems using examples from AlphaGo. Self-improvement involves playing a game with oneself repeatedly to improve performance beyond human capabilities.

Self-Improvement in Game Playing

• Self-improvement is achieved by having an AI system play a game with itself multiple times, surpassing its initial training based on human data sets.

• AlphaGo initially learned by imitating expert human players but surpassed human capabilities through self-improvement.

• In games like Go, where there is a simple reward function (winning or losing), self-improvement can be achieved by playing millions of games and optimizing for winning probability.

Challenges in Self-Improvement for Language Models

The speaker discusses the challenges of self-improvement in language models compared to game-playing AI systems. Language models lack a simple reward function, making it difficult to evaluate and improve their performance.

Self-Improvement in Language Models

• In the domain of open language modeling, there is a lack of a simple reward criterion for self-improvement.

• While self-improvement may be possible in narrow domains with achievable reward functions, it remains an open question for general language models.

• The limitations of large language models are primarily determined by the amount of computational resources available.

Integration of AlphaGo Techniques into Large Language Models

The speaker mentions Demis Hassabis, CEO of Google DeepMind, who invented AlphaGo. He suggests that integrating AlphaGo techniques into large language models could significantly enhance their capabilities.

Integration of AlphaGo Techniques

• Demis Hassabis proposes combining the strengths of AlphaGo-type systems with the language capabilities of large models to improve their performance.

• If AI systems can self-play and self-teach like AlphaGo, the limitations of large language models would only be constrained by computational resources.

This summary provides an overview of the main points discussed in the transcript but may not capture all details or nuances present in the video.

The Importance of Data Sets in Language Models

This section discusses the significance of data sets in language models and how their quality and performance depend on them.

The Role of Data Sets

• Language models heavily rely on their base data set for quality and performance.

• Obtaining high-quality data sets is becoming increasingly challenging.

• Reddit shutting down its API highlights the difficulty in accessing valuable, unique, and clean data sets.

• OpenAI does not possess its own data set; instead, they purchase or use open-source data sets from different companies.

Synthetic Data Sets

• Artificial intelligence creating its own synthetic data set could revolutionize the field.

• This would eliminate the reliance on a few companies with large, unique data sets like Google and Reddit.

• Synthetic data has the potential to provide a significant amount of high-quality training tokens.

• The challenge lies in sustaining the quality and avoiding plateauing too soon.

Scaling AI Development with Compute Learning and Search

This section explores two paradigms that scale indefinitely with compute learning and search.

Scalable Paradigms

• According to Richard Su Sutton's "The Bitter Lesson," there are only two paradigms that scale indefinitely: compute learning and search.

• These paradigms were true in 2019 when the video was recorded, remain true today, and will likely hold true until AGI (Artificial General Intelligence) is achieved.

Elon Musk's Perspective on Synthetic Data

Elon Musk shares his perspective on synthetic data's potential impact.

Elon Musk's Response

• Elon Musk finds it sad that all human-written books can fit on one hard drive but believes synthetic data will exceed this by a vast margin.

• Even if all existing unique data sets are combined, it is still insufficient for training AGI.

• Synthetic data could provide the necessary orders of magnitude more data required for AGI.

Doubts about AI's Ability to Generate New Ideas

This section raises doubts about whether a model trained on a static data set can generate new ideas and data.

Generating New Ideas

• The speaker expresses skepticism about AI models' ability to come up with new ideas using a static data set.

• Transformers, the underlying technology behind many language models, derive responses from tokens in the training set.

• It questions how an AI model can genuinely generate new ideas and whether that would be considered AI itself.

QAR: A Combination of Different Approaches

This section suggests that QAR may be a combination of various methods and approaches.

Possible Components of QAR

• QAR might involve a novel method of logic and reasoning coupled with true understanding of that logic.

• Self-training eliminates the need for human involvement in the process.

• The creation of synthetic data could serve as a prelude to achieving AGI.

• The nature of QAR has sparked intense debate between AI doomers and AI accelerationists.

Conclusion

The transcript covers various aspects related to language models, including the importance of quality data sets, the potential impact of synthetic data, scalability paradigms in AI development, doubts about generating new ideas through static training sets, and speculations on what QAR might entail.