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.

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TOC
00:00:00 Intro
00:01:38 Prelude on goals in LLMs
00:02:42 Ryan intro
00:03:11 Ryan's ARC Challenge Approach
00:38:15 Language models, reasoning and agency
01:14:14 Timelines on superintelligence
01:27:05 Growth of superintelligence
02:06:41 Reflections on ARC
02:11:49 Why wouldn't AI knowledge be subjective

Host: Dr. Tim Scarfe

Pod: [ Ссылка ]

Refs:
Getting 50% (SoTA) on ARC-AGI with GPT-4o [Ryan Greenblatt]
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On the Measure of Intelligence [Chollet]
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Connectionism and Cognitive Architecture: A Critical Analysis [Jerry A. Fodor and Zenon W. Pylyshyn]
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Software 2.0 [Andrej Karpathy]
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Why Greatness Cannot Be Planned: The Myth of the Objective [Kenneth Stanley]
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Biographical account of Terence Tao’s mathematical development. [M.A.(KEN) CLEMENTS]
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Model Evaluation and Threat Research (METR)
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Why Tool AIs Want to Be Agent AIs
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Simulators - Janus
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AI Control: Improving Safety Despite Intentional Subversion
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What a Compute-Centric Framework Says About Takeoff Speeds
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Global GDP over the long run
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Safety Cases: How to Justify the Safety of Advanced AI Systems
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The Danger of a “Safety Case"
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The Future Of Work Looks Like A UPS Truck (~02:15:50)
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SWE-bench
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Using DeepSpeed and Megatron to Train Megatron-Turing NLG
530B, A Large-Scale Generative Language Model
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Algorithmic Progress in Language Models
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