Join Prof. Subbarao Kambhampati and host Tim Scarfe for a deep dive into OpenAI's O1 model and the future of AI reasoning systems.
How O1 likely uses reinforcement learning similar to AlphaGo, with hidden reasoning tokens that users pay for but never see
The evolution from traditional Large Language Models to more sophisticated reasoning systems
The concept of "fractal intelligence" in AI - where models work brilliantly sometimes but fail unpredictably
Why O1's improved performance comes with substantial computational costs
The ongoing debate between single-model approaches (OpenAI) vs hybrid systems (Google)
The critical distinction between AI as an intelligence amplifier vs autonomous decision-maker
SPONSOR MESSAGES:
CentML offers competitive pricing for GenAI model deployment, with flexible options to suit a wide range of models, from small to large-scale deployments.
https://centml.ai/pricing/
Tufa AI Labs is a brand new research lab in Zurich started by Benjamin Crouzier focussed on o-series style reasoning and AGI. Are you interested in working on reasoning, or getting involved in their events?
Goto https://tufalabs.ai/
TOC:
- O1 Architecture and Reasoning Foundations
[00:00:00] 1.1 Fractal Intelligence and Reasoning Model Limitations
[00:04:28] 1.2 LLM Evolution: From Simple Prompting to Advanced Reasoning
[00:14:28] 1.3 O1's Architecture and AlphaGo-like Reasoning Approach
[00:23:18] 1.4 Empirical Evaluation of O1's Planning Capabilities
- Monte Carlo Methods and Model Deep-Dive
[00:29:30] 2.1 Monte Carlo Methods and MARCO-O1 Implementation
[00:31:30] 2.2 Reasoning vs. Retrieval in LLM Systems
[00:40:40] 2.3 Fractal Intelligence Capabilities and Limitations
[00:45:59] 2.4 Mechanistic Interpretability of Model Behavior
[00:51:41] 2.5 O1 Response Patterns and Performance Analysis
- System Design and Real-World Applications
[00:59:30] 3.1 Evolution from LLMs to Language Reasoning Models
[01:06:48] 3.2 Cost-Efficiency Analysis: LLMs vs O1
[01:11:28] 3.3 Autonomous vs Human-in-the-Loop Systems
[01:16:01] 3.4 Program Generation and Fine-Tuning Approaches
[01:26:08] 3.5 Hybrid Architecture Implementation Strategies
Transcript: https://www.dropbox.com/scl/fi/d0ef4ovnfxi0lknirkvft/Subbarao.pdf?rlkey=l3rp29gs4hkut7he8u04mm1df&dl=0
REFS:
[00:02:00] Monty Python (1975)
Witch trial scene: flawed logical reasoning.
https://www.youtube.com/watch?v=zrzMhU_4m-g
[00:04:00] Cade Metz (2024)
Microsoft–OpenAI partnership evolution and control dynamics.
https://www.nytimes.com/2024/10/17/technology/microsoft-openai-partnership-deal.html
[00:07:25] Kojima et al. (2022)
Zero-shot chain-of-thought prompting ('Let's think step by step').
https://arxiv.org/pdf/2205.11916
[00:12:50] DeepMind Research Team (2023)
Multi-bot game solving with external and internal planning.
https://deepmind.google/research/publications/139455/
[00:15:10] Silver et al. (2016)
AlphaGo's Monte Carlo Tree Search and Q-learning.
https://www.nature.com/articles/nature16961
[00:16:30] Kambhampati, S. et al. (2023)
Evaluates O1's planning in "Strawberry Fields" benchmarks.
https://arxiv.org/pdf/2410.02162
[00:29:30] Alibaba AIDC-AI Team (2023)
MARCO-O1: Chain-of-Thought + MCTS for improved reasoning.
https://arxiv.org/html/2411.14405
[00:31:30] Kambhampati, S. (2024)
Explores LLM "reasoning vs retrieval" debate.
https://arxiv.org/html/2403.04121v2
[00:37:35] Wei, J. et al. (2022)
Chain-of-thought prompting (introduces last-letter concatenation).
https://arxiv.org/pdf/2201.11903
[00:42:35] Barbero, F. et al. (2024)
Transformer attention and "information over-squashing."
https://arxiv.org/html/2406.04267v2
[00:46:05] Ruis, L. et al. (2023)
Influence functions to understand procedural knowledge in LLMs.
https://arxiv.org/html/2411.12580v1
(truncated - continued in shownotes/transcript doc)
