Machine Learning Street Talk (MLST)
Ryan Greenblatt - Solving ARC with GPT4o
Episode guests
Podcast summary created with Snipd AI
Quick takeaways
- Ryan Greenblatt used GPT4o to excel in the ARC Challenge by creating Python programs.
- Discussion on current AI models' strengths and weaknesses especially in reasoning abilities.
- Exploring the potential for smarter AI systems by combining various techniques.
- Delving into the future of agentic AI and its autonomous behavior evolution.
- Emphasizing the importance of diversity in AI models for dynamic exploration.
- Speculative scenarios on exponential AI growth and implications for governance and security.
Deep dives
Analyzing Different Problem-solving Approaches
Different problem-solving approaches such as naive perspectives and alternative analytic methods, revealed in mathematics discussions, highlight the diverse angles for addressing challenges.
Profitability in Artificial Intelligence Expertise
Engagement in ventures like Koushi, a platform tapping into AI expertise for trading on real-world event outcomes, showcases the potential for profiting from artificial intelligence knowledge.
Challenges and Limitations of Current Language Models
Insights on current language models reveal both strengths and weaknesses, with limitations observed in contextual reasoning abilities and the absence of cross-context goals.
Optimizing Error Resolution in AI Systems
Leveraging strategies like majority voting and multiple guess submissions enhance error resolution in AI systems, optimizing decision-making processes and outcomes.
AIs Moving Towards a Genshiel Framework
The discussion delves into the potential evolution of artificial intelligence towards more agentic behaviors. It is suggested that AIs may aim to be more autonomous and situationally aware, akin to agents rather than tools. A concern is raised about the need for AI systems to actively pursue tasks, interact with the world, and improve without explicit instructions, highlighting the potential trajectory towards increased agency.
Open-Endedness Research and Diversity Preservation
The conversation explores the concept of open-endedness in AI systems, emphasizing the importance of diversity preservation and continuous accumulation of information. It's noted that maintaining diversity in AI models prevents convergence and encourages dynamic exploration. The limitations of current large language models (LLMs) are highlighted, pointing out the challenges they face in representing low-probability data.
Implications of Depth in Learning Models
The podcast discusses the limitations and potentials of learning models in relation to depth. It is mentioned that while current AI systems may have depth constraints during runtime, similar to human learning over short durations, the operational mechanisms involve layers manipulating activations and updating knowledge. The comparison between AI learning processes and human cognition within short timeframes is elaborated to emphasize the ongoing predictions and updates in both cases.
Speculation on AI Advancements and Recursive Self-Improvement
The episode delves into speculative scenarios regarding AI advancements and the concept of recursive self-improvement. There is a discussion on how the evolution of AI systems, potentially powered by advances in hardware and software, could lead to accelerated progress and changes in various domains. The possibility of exponential growth due to AI advancements and the implications for governance, security, and potential risks are highlighted.
Addressing Security Concerns and Governance in AI Development
Security measures and governance in AI development are emphasized in the conversation, focusing on the need to address potential risks and secure AI systems from theft or misuse. The discussion highlights the importance of securing algorithmic insights and controlling access to computing resources to prevent unauthorized use of powerful AI models. Concerns about governance, alignment, and ethical decision-making in AI development are also addressed.
Considerations on the Future Evolution of AI Technology
The podcast explores futuristic notions of AI technology advancements, highlighting the potential for compressing computational requirements for achieving intelligence. It speculates on the possibility of revolutionary discoveries in AI that could significantly enhance computing efficiency and lead to groundbreaking developments. The conversation reflects on the transformative impact such advancements could have on the economic, technological, and social landscapes.
AI Model Sizes and Performance Comparison
Smaller AI models may perform competitively with larger models, challenging the notion that bigger models are always superior. An example is the Megatron NLG model, larger than GPT-3 but not necessarily better in performance. The trend suggests that smaller, efficient models could surpass historical larger models, supported by newly released models. Scaling laws indicate that optimal model training may require a balance of data and parameters for efficient compute.
AI Impact on Society and Future Scenarios
AI advancements could lead to significant societal disruptions, such as job market changes and accelerated technological progress. The discussion envisions scenarios where AI accelerates research and development, potentially outperforming human capabilities. This could raise governance and control challenges as AI systems become more autonomous. Concerns about transparency, societal impact, and alignment with human values arise, emphasizing the need for cautious and thoughtful technological advancements.
Ryan Greenblatt from Redwood Research recently published "Getting 50% on ARC-AGI with GPT-4.0," where he used GPT4o to reach a state-of-the-art accuracy on Francois Chollet's ARC Challenge by generating many Python programs.
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We discuss:
- Ryan's unique approach to solving the ARC Challenge and achieving impressive results.
- The strengths and weaknesses of current AI models.
- How AI and humans differ in learning and reasoning.
- Combining various techniques to create smarter AI systems.
- The potential risks and future advancements in AI, including the idea of agentic AI.
https://x.com/RyanPGreenblatt
https://www.redwoodresearch.org/
Refs:
Getting 50% (SoTA) on ARC-AGI with GPT-4o [Ryan Greenblatt]
https://redwoodresearch.substack.com/p/getting-50-sota-on-arc-agi-with-gpt
On the Measure of Intelligence [Chollet]
https://arxiv.org/abs/1911.01547
Connectionism and Cognitive Architecture: A Critical Analysis [Jerry A. Fodor and Zenon W. Pylyshyn]
https://ruccs.rutgers.edu/images/personal-zenon-pylyshyn/proseminars/Proseminar13/ConnectionistArchitecture.pdf
Software 2.0 [Andrej Karpathy]
https://karpathy.medium.com/software-2-0-a64152b37c35
Why Greatness Cannot Be Planned: The Myth of the Objective [Kenneth Stanley]
https://amzn.to/3Wfy2E0
Biographical account of Terence Tao’s mathematical development. [M.A.(KEN) CLEMENTS]
https://gwern.net/doc/iq/high/smpy/1984-clements.pdf
Model Evaluation and Threat Research (METR)
https://metr.org/
Why Tool AIs Want to Be Agent AIs
https://gwern.net/tool-ai
Simulators - Janus
https://www.lesswrong.com/posts/vJFdjigzmcXMhNTsx/simulators
AI Control: Improving Safety Despite Intentional Subversion
https://www.lesswrong.com/posts/d9FJHawgkiMSPjagR/ai-control-improving-safety-despite-intentional-subversion
https://arxiv.org/abs/2312.06942
What a Compute-Centric Framework Says About Takeoff Speeds
https://www.openphilanthropy.org/research/what-a-compute-centric-framework-says-about-takeoff-speeds/
Global GDP over the long run
https://ourworldindata.org/grapher/global-gdp-over-the-long-run?yScale=log
Safety Cases: How to Justify the Safety of Advanced AI Systems
https://arxiv.org/abs/2403.10462
The Danger of a “Safety Case"
http://sunnyday.mit.edu/The-Danger-of-a-Safety-Case.pdf
The Future Of Work Looks Like A UPS Truck (~02:15:50)
https://www.npr.org/sections/money/2014/05/02/308640135/episode-536-the-future-of-work-looks-like-a-ups-truck
SWE-bench
https://www.swebench.com/
Using DeepSpeed and Megatron to Train Megatron-Turing NLG
530B, A Large-Scale Generative Language Model
https://arxiv.org/pdf/2201.11990
Algorithmic Progress in Language Models
https://epochai.org/blog/algorithmic-progress-in-language-models
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