Machine Learning Street Talk (MLST)

Randall Balestriero joins the show to discuss some counterintuitive findings in AI. He shares research showing that huge language models, even when started from scratch (randomly initialized) without massive pre-training, can learn specific tasks like sentiment analysis surprisingly well, train stably, and avoid severe overfitting, sometimes matching the performance of costly pre-trained models. This raises questions about when giant pre-training efforts are truly worth it. He also talks about how self-supervised learning (where models learn from data structure itself) and traditional supervised learning (using labeled data) are fundamentally similar, allowing researchers to apply decades of supervised learning theory to improve newer self-supervised methods. Finally, Randall touches on fairness in AI models used for Earth data (like climate prediction), revealing that these models can be biased, performing poorly in specific locations like islands or coastlines even if they seem accurate overall, which has important implications for policy decisions based on this data. SPONSOR MESSAGES: *** Tufa AI Labs is a brand new research lab in Zurich started by Benjamin Crouzier focussed on o-series style reasoning and AGI. They are hiring a Chief Engineer and ML engineers. Events in Zurich. Goto https://tufalabs.ai/ *** TRANSCRIPT + SHOWNOTES: https://www.dropbox.com/scl/fi/n7yev71nsjso71jyjz1fy/RANDALLNEURIPS.pdf?rlkey=0dn4injp1sc4ts8njwf3wfmxv&dl=0 TOC: 1. Model Training Efficiency and Scale [00:00:00] 1.1 Training Stability of Large Models on Small Datasets [00:04:09] 1.2 Pre-training vs Random Initialization Performance Comparison [00:07:58] 1.3 Task-Specific Models vs General LLMs Efficiency 2. Learning Paradigms and Data Distribution [00:10:35] 2.1 Fair Language Model Paradox and Token Frequency Issues [00:12:02] 2.2 Pre-training vs Single-task Learning Spectrum [00:16:04] 2.3 Theoretical Equivalence of Supervised and Self-supervised Learning [00:19:40] 2.4 Self-Supervised Learning and Supervised Learning Relationships [00:21:25] 2.5 SSL Objectives and Heavy-tailed Data Distribution Challenges 3. Geographic Representation in ML Systems [00:25:20] 3.1 Geographic Bias in Earth Data Models and Neural Representations [00:28:10] 3.2 Mathematical Limitations and Model Improvements [00:30:24] 3.3 Data Quality and Geographic Bias in ML Datasets REFS: [00:01:40] Research on training large language models from scratch on small datasets, Randall Balestriero et al. https://openreview.net/forum?id=wYGBWOjq1Q [00:10:35] The Fair Language Model Paradox (2024), Andrea Pinto, Tomer Galanti, Randall Balestriero https://arxiv.org/abs/2410.11985 [00:12:20] Muppet: Massive Multi-task Representations with Pre-Finetuning (2021), Armen Aghajanyan et al. https://arxiv.org/abs/2101.11038 [00:14:30] Dissociating language and thought in large language models (2023), Kyle Mahowald et al. https://arxiv.org/abs/2301.06627 [00:16:05] The Birth of Self-Supervised Learning: A Supervised Theory, Randall Balestriero et al. https://openreview.net/forum?id=NhYAjAAdQT [00:21:25] VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning, Adrien Bardes, Jean Ponce, Yann LeCun https://arxiv.org/abs/2105.04906 [00:25:20] No Location Left Behind: Measuring and Improving the Fairness of Implicit Representations for Earth Data (2025), Daniel Cai, Randall Balestriero, et al. https://arxiv.org/abs/2502.06831 [00:33:45] Mark Ibrahim et al.'s work on geographic bias in computer vision datasets, Mark Ibrahim https://arxiv.org/pdf/2304.12210

Prof. Kevin Ellis and Dr. Zenna Tavares talk about making AI smarter, like humans. They want AI to learn from just a little bit of information by actively trying things out, not just by looking at tons of data. They discuss two main ways AI can "think": one way is like following specific rules or steps (like a computer program), and the other is more intuitive, like guessing based on patterns (like modern AI often does). They found combining both methods works well for solving complex puzzles like ARC. A key idea is "compositionality" - building big ideas from small ones, like LEGOs. This is powerful but can also be overwhelming. Another important idea is "abstraction" - understanding things simply, without getting lost in details, and knowing there are different levels of understanding. Ultimately, they believe the best AI will need to explore, experiment, and build models of the world, much like humans do when learning something new. SPONSOR MESSAGES: *** Tufa AI Labs is a brand new research lab in Zurich started by Benjamin Crouzier focussed on o-series style reasoning and AGI. They are hiring a Chief Engineer and ML engineers. Events in Zurich. Goto https://tufalabs.ai/ *** TRANSCRIPT: https://www.dropbox.com/scl/fi/3ngggvhb3tnemw879er5y/BASIS.pdf?rlkey=lr2zbj3317mex1q5l0c2rsk0h&dl=0 Zenna Tavares: http://www.zenna.org/ Kevin Ellis: https://www.cs.cornell.edu/~ellisk/ TOC: 1. Compositionality and Learning Foundations [00:00:00] 1.1 Compositional Search and Learning Challenges [00:03:55] 1.2 Bayesian Learning and World Models [00:12:05] 1.3 Programming Languages and Compositionality Trade-offs [00:15:35] 1.4 Inductive vs Transductive Approaches in AI Systems 2. Neural-Symbolic Program Synthesis [00:27:20] 2.1 Integration of LLMs with Traditional Programming and Meta-Programming [00:30:43] 2.2 Wake-Sleep Learning and DreamCoder Architecture [00:38:26] 2.3 Program Synthesis from Interactions and Hidden State Inference [00:41:36] 2.4 Abstraction Mechanisms and Resource Rationality [00:48:38] 2.5 Inductive Biases and Causal Abstraction in AI Systems 3. Abstract Reasoning Systems [00:52:10] 3.1 Abstract Concepts and Grid-Based Transformations in ARC [00:56:08] 3.2 Induction vs Transduction Approaches in Abstract Reasoning [00:59:12] 3.3 ARC Limitations and Interactive Learning Extensions [01:06:30] 3.4 Wake-Sleep Program Learning and Hybrid Approaches [01:11:37] 3.5 Project MARA and Future Research Directions REFS: [00:00:25] DreamCoder, Kevin Ellis et al. https://arxiv.org/abs/2006.08381 [00:01:10] Mind Your Step, Ryan Liu et al. https://arxiv.org/abs/2410.21333 [00:06:05] Bayesian inference, Griffiths, T. L., Kemp, C., & Tenenbaum, J. B. https://psycnet.apa.org/record/2008-06911-003 [00:13:00] Induction and Transduction, Wen-Ding Li, Zenna Tavares, Yewen Pu, Kevin Ellis https://arxiv.org/abs/2411.02272 [00:23:15] Neurosymbolic AI, Garcez, Artur d'Avila et al. https://arxiv.org/abs/2012.05876 [00:33:50] Induction and Transduction (II), Wen-Ding Li, Kevin Ellis et al. https://arxiv.org/abs/2411.02272 [00:38:35] ARC, François Chollet https://arxiv.org/abs/1911.01547 [00:39:20] Causal Reactive Programs, Ria Das, Joshua B. Tenenbaum, Armando Solar-Lezama, Zenna Tavares http://www.zenna.org/publications/autumn2022.pdf [00:42:50] MuZero, Julian Schrittwieser et al. http://arxiv.org/pdf/1911.08265 [00:43:20] VisualPredicator, Yichao Liang https://arxiv.org/abs/2410.23156 [00:48:55] Bayesian models of cognition, Joshua B. Tenenbaum https://mitpress.mit.edu/9780262049412/bayesian-models-of-cognition/ [00:49:30] The Bitter Lesson, Rich Sutton http://www.incompleteideas.net/IncIdeas/BitterLesson.html [01:06:35] Program induction, Kevin Ellis, Wen-Ding Li https://arxiv.org/pdf/2411.02272 [01:06:50] DreamCoder (II), Kevin Ellis et al. https://arxiv.org/abs/2006.08381 [01:11:55] Project MARA, Zenna Tavares, Kevin Ellis https://www.basis.ai/blog/mara/

Eiso Kant, CTO of poolside AI, discusses the company's approach to building frontier AI foundation models, particularly focused on software development. Their unique strategy is reinforcement learning from code execution feedback which is an important axis for scaling AI capabilities beyond just increasing model size or data volume. Kant predicts human-level AI in knowledge work could be achieved within 18-36 months, outlining poolside's vision to dramatically increase software development productivity and accessibility. SPONSOR MESSAGES: *** Tufa AI Labs is a brand new research lab in Zurich started by Benjamin Crouzier focussed on o-series style reasoning and AGI. They are hiring a Chief Engineer and ML engineers. Events in Zurich. Goto https://tufalabs.ai/ *** Eiso Kant: https://x.com/eisokant https://poolside.ai/ TRANSCRIPT: https://www.dropbox.com/scl/fi/szepl6taqziyqie9wgmk9/poolside.pdf?rlkey=iqar7dcwshyrpeoz0xa76k422&dl=0 TOC: 1. Foundation Models and AI Strategy [00:00:00] 1.1 Foundation Models and Timeline Predictions for AI Development [00:02:55] 1.2 Poolside AI's Corporate History and Strategic Vision [00:06:48] 1.3 Foundation Models vs Enterprise Customization Trade-offs 2. Reinforcement Learning and Model Economics [00:15:42] 2.1 Reinforcement Learning and Code Execution Feedback Approaches [00:22:06] 2.2 Model Economics and Experimental Optimization 3. Enterprise AI Implementation [00:25:20] 3.1 Poolside's Enterprise Deployment Strategy and Infrastructure [00:26:00] 3.2 Enterprise-First Business Model and Market Focus [00:27:05] 3.3 Foundation Models and AGI Development Approach [00:29:24] 3.4 DeepSeek Case Study and Infrastructure Requirements 4. LLM Architecture and Performance [00:30:15] 4.1 Distributed Training and Hardware Architecture Optimization [00:33:01] 4.2 Model Scaling Strategies and Chinchilla Optimality Trade-offs [00:36:04] 4.3 Emergent Reasoning and Model Architecture Comparisons [00:43:26] 4.4 Balancing Creativity and Determinism in AI Models [00:50:01] 4.5 AI-Assisted Software Development Evolution 5. AI Systems Engineering and Scalability [00:58:31] 5.1 Enterprise AI Productivity and Implementation Challenges [00:58:40] 5.2 Low-Code Solutions and Enterprise Hiring Trends [01:01:25] 5.3 Distributed Systems and Engineering Complexity [01:01:50] 5.4 GenAI Architecture and Scalability Patterns [01:01:55] 5.5 Scaling Limitations and Architectural Patterns in AI Code Generation 6. AI Safety and Future Capabilities [01:06:23] 6.1 Semantic Understanding and Language Model Reasoning Approaches [01:12:42] 6.2 Model Interpretability and Safety Considerations in AI Systems [01:16:27] 6.3 AI vs Human Capabilities in Software Development [01:33:45] 6.4 Enterprise Deployment and Security Architecture CORE REFS (see shownotes for URLs/more refs): [00:15:45] Research demonstrating how training on model-generated content leads to distribution collapse in AI models, Ilia Shumailov et al. (Key finding on synthetic data risk) [00:20:05] Foundational paper introducing Word2Vec for computing word vector representations, Tomas Mikolov et al. (Seminal NLP technique) [00:22:15] OpenAI O3 model's breakthrough performance on ARC Prize Challenge, OpenAI (Significant AI reasoning benchmark achievement) [00:22:40] Seminal paper proposing a formal definition of intelligence as skill-acquisition efficiency, François Chollet (Influential AI definition/philosophy) [00:30:30] Technical documentation of DeepSeek's V3 model architecture and capabilities, DeepSeek AI (Details on a major new model) [00:34:30] Foundational paper establishing optimal scaling laws for LLM training, Jordan Hoffmann et al. (Key paper on LLM scaling) [00:45:45] Seminal essay arguing that scaling computation consistently trumps human-engineered solutions in AI, Richard S. Sutton (Influential "Bitter Lesson" perspective)

Connor Leahy and Gabriel Alfour, AI researchers from Conjecture and authors of "The Compendium," joinus for a critical discussion centered on Artificial Superintelligence (ASI) safety and governance. Drawing from their comprehensive analysis in "The Compendium," they articulate a stark warning about the existential risks inherent in uncontrolled AI development, framing it through the lens of "intelligence domination"—where a sufficiently advanced AI could subordinate humanity, much like humans dominate less intelligent species. SPONSOR MESSAGES: *** Tufa AI Labs is a brand new research lab in Zurich started by Benjamin Crouzier focussed on o-series style reasoning and AGI. They are hiring a Chief Engineer and ML engineers. Events in Zurich. Goto https://tufalabs.ai/ *** TRANSCRIPT + REFS + NOTES: https://www.dropbox.com/scl/fi/p86l75y4o2ii40df5t7no/Compendium.pdf?rlkey=tukczgf3flw133sr9rgss0pnj&dl=0 https://www.thecompendium.ai/ https://en.wikipedia.org/wiki/Connor_Leahy https://www.conjecture.dev/about https://substack.com/@gabecc​ TOC: 1. AI Intelligence and Safety Fundamentals [00:00:00] 1.1 Understanding Intelligence and AI Capabilities [00:06:20] 1.2 Emergence of Intelligence and Regulatory Challenges [00:10:18] 1.3 Human vs Animal Intelligence Debate [00:18:00] 1.4 AI Regulation and Risk Assessment Approaches [00:26:14] 1.5 Competing AI Development Ideologies 2. Economic and Social Impact [00:29:10] 2.1 Labor Market Disruption and Post-Scarcity Scenarios [00:32:40] 2.2 Institutional Frameworks and Tech Power Dynamics [00:37:40] 2.3 Ethical Frameworks and AI Governance Debates [00:40:52] 2.4 AI Alignment Evolution and Technical Challenges 3. Technical Governance Framework [00:55:07] 3.1 Three Levels of AI Safety: Alignment, Corrigibility, and Boundedness [00:55:30] 3.2 Challenges of AI System Corrigibility and Constitutional Models [00:57:35] 3.3 Limitations of Current Boundedness Approaches [00:59:11] 3.4 Abstract Governance Concepts and Policy Solutions 4. Democratic Implementation and Coordination [00:59:20] 4.1 Governance Design and Measurement Challenges [01:00:10] 4.2 Democratic Institutions and Experimental Governance [01:14:10] 4.3 Political Engagement and AI Safety Advocacy [01:25:30] 4.4 Practical AI Safety Measures and International Coordination CORE REFS: [00:01:45] The Compendium (2023), Leahy et al. https://pdf.thecompendium.ai/the_compendium.pdf [00:06:50] Geoffrey Hinton Leaves Google, BBC News https://www.bbc.com/news/world-us-canada-65452940 [00:10:00] ARC-AGI, Chollet https://arcprize.org/arc-agi [00:13:25] A Brief History of Intelligence, Bennett https://www.amazon.com/Brief-History-Intelligence-Humans-Breakthroughs/dp/0063286343 [00:25:35] Statement on AI Risk, Center for AI Safety https://www.safe.ai/work/statement-on-ai-risk [00:26:15] Machines of Love and Grace, Amodei https://darioamodei.com/machines-of-loving-grace [00:26:35] The Techno-Optimist Manifesto, Andreessen https://a16z.com/the-techno-optimist-manifesto/ [00:31:55] Techno-Feudalism, Varoufakis https://www.amazon.co.uk/Technofeudalism-Killed-Capitalism-Yanis-Varoufakis/dp/1847927270 [00:42:40] Introducing Superalignment, OpenAI https://openai.com/index/introducing-superalignment/ [00:47:20] Three Laws of Robotics, Asimov https://www.britannica.com/topic/Three-Laws-of-Robotics [00:50:00] Symbolic AI (GOFAI), Haugeland https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence [00:52:30] Intent Alignment, Christiano https://www.alignmentforum.org/posts/HEZgGBZTpT4Bov7nH/mapping-the-conceptual-territory-in-ai-existential-safety [00:55:10] Large Language Model Alignment: A Survey, Jiang et al. http://arxiv.org/pdf/2309.15025 [00:55:40] Constitutional Checks and Balances, Bok https://plato.stanford.edu/entries/montesquieu/

We are joined by Francois Chollet and Mike Knoop, to launch the new version of the ARC prize! In version 2, the challenges have been calibrated with humans such that at least 2 humans could solve each task in a reasonable task, but also adversarially selected so that frontier reasoning models can't solve them. The best LLMs today get negligible performance on this challenge. https://arcprize.org/ SPONSOR MESSAGES: *** Tufa AI Labs is a brand new research lab in Zurich started by Benjamin Crouzier focussed on o-series style reasoning and AGI. They are hiring a Chief Engineer and ML engineers. Events in Zurich. Goto https://tufalabs.ai/ *** TRANSCRIPT: https://www.dropbox.com/scl/fi/0v9o8xcpppdwnkntj59oi/ARCv2.pdf?rlkey=luqb6f141976vra6zdtptv5uj&dl=0 TOC: 1. ARC v2 Core Design & Objectives [00:00:00] 1.1 ARC v2 Launch and Benchmark Architecture [00:03:16] 1.2 Test-Time Optimization and AGI Assessment [00:06:24] 1.3 Human-AI Capability Analysis [00:13:02] 1.4 OpenAI o3 Initial Performance Results 2. ARC Technical Evolution [00:17:20] 2.1 ARC-v1 to ARC-v2 Design Improvements [00:21:12] 2.2 Human Validation Methodology [00:26:05] 2.3 Task Design and Gaming Prevention [00:29:11] 2.4 Intelligence Measurement Framework 3. O3 Performance & Future Challenges [00:38:50] 3.1 O3 Comprehensive Performance Analysis [00:43:40] 3.2 System Limitations and Failure Modes [00:49:30] 3.3 Program Synthesis Applications [00:53:00] 3.4 Future Development Roadmap REFS: [00:00:15] On the Measure of Intelligence, François Chollet https://arxiv.org/abs/1911.01547 [00:06:45] ARC Prize Foundation, François Chollet, Mike Knoop https://arcprize.org/ [00:12:50] OpenAI o3 model performance on ARC v1, ARC Prize Team https://arcprize.org/blog/oai-o3-pub-breakthrough [00:18:30] Chain-of-Thought Prompting Elicits Reasoning in Large Language Models, Jason Wei et al. https://arxiv.org/abs/2201.11903 [00:21:45] ARC-v2 benchmark tasks, Mike Knoop https://arcprize.org/blog/introducing-arc-agi-public-leaderboard [00:26:05] ARC Prize 2024: Technical Report, Francois Chollet et al. https://arxiv.org/html/2412.04604v2 [00:32:45] ARC Prize 2024 Technical Report, Francois Chollet, Mike Knoop, Gregory Kamradt https://arxiv.org/abs/2412.04604 [00:48:55] The Bitter Lesson, Rich Sutton http://www.incompleteideas.net/IncIdeas/BitterLesson.html [00:53:30] Decoding strategies in neural text generation, Sina Zarrieß https://www.mdpi.com/2078-2489/12/9/355/pdf

Mohamed Osman joins to discuss MindsAI's highest scoring entry to the ARC challenge 2024 and the paradigm of test-time fine-tuning. They explore how the team, now part of Tufa Labs in Zurich, achieved state-of-the-art results using a combination of pre-training techniques, a unique meta-learning strategy, and an ensemble voting mechanism. Mohamed emphasizes the importance of raw data input and flexibility of the network. SPONSOR MESSAGES: *** Tufa AI Labs is a brand new research lab in Zurich started by Benjamin Crouzier focussed on o-series style reasoning and AGI. They are hiring a Chief Engineer and ML engineers. Events in Zurich. Goto https://tufalabs.ai/ *** TRANSCRIPT + REFS: https://www.dropbox.com/scl/fi/jeavyqidsjzjgjgd7ns7h/MoFInal.pdf?rlkey=cjjmo7rgtenxrr3b46nk6yq2e&dl=0 Mohamed Osman (Tufa Labs) https://x.com/MohamedOsmanML Jack Cole (Tufa Labs) https://x.com/MindsAI_Jack How and why deep learning for ARC paper: https://github.com/MohamedOsman1998/deep-learning-for-arc/blob/main/deep_learning_for_arc.pdf TOC: 1. Abstract Reasoning Foundations [00:00:00] 1.1 Test-Time Fine-Tuning and ARC Challenge Overview [00:10:20] 1.2 Neural Networks vs Programmatic Approaches to Reasoning [00:13:23] 1.3 Code-Based Learning and Meta-Model Architecture [00:20:26] 1.4 Technical Implementation with Long T5 Model 2. ARC Solution Architectures [00:24:10] 2.1 Test-Time Tuning and Voting Methods for ARC Solutions [00:27:54] 2.2 Model Generalization and Function Generation Challenges [00:32:53] 2.3 Input Representation and VLM Limitations [00:36:21] 2.4 Architecture Innovation and Cross-Modal Integration [00:40:05] 2.5 Future of ARC Challenge and Program Synthesis Approaches 3. Advanced Systems Integration [00:43:00] 3.1 DreamCoder Evolution and LLM Integration [00:50:07] 3.2 MindsAI Team Progress and Acquisition by Tufa Labs [00:54:15] 3.3 ARC v2 Development and Performance Scaling [00:58:22] 3.4 Intelligence Benchmarks and Transformer Limitations [01:01:50] 3.5 Neural Architecture Optimization and Processing Distribution REFS: [00:01:32] Original ARC challenge paper, François Chollet https://arxiv.org/abs/1911.01547 [00:06:55] DreamCoder, Kevin Ellis et al. https://arxiv.org/abs/2006.08381 [00:12:50] Deep Learning with Python, François Chollet https://www.amazon.com/Deep-Learning-Python-Francois-Chollet/dp/1617294438 [00:13:35] Deep Learning with Python, François Chollet https://www.amazon.com/Deep-Learning-Python-Francois-Chollet/dp/1617294438 [00:13:35] Influence of pretraining data for reasoning, Laura Ruis https://arxiv.org/abs/2411.12580 [00:17:50] Latent Program Networks, Clement Bonnet https://arxiv.org/html/2411.08706v1 [00:20:50] T5, Colin Raffel et al. https://arxiv.org/abs/1910.10683 [00:30:30] Combining Induction and Transduction for Abstract Reasoning, Wen-Ding Li, Kevin Ellis et al. https://arxiv.org/abs/2411.02272 [00:34:15] Six finger problem, Chen et al. https://openaccess.thecvf.com/content/CVPR2024/papers/Chen_SpatialVLM_Endowing_Vision-Language_Models_with_Spatial_Reasoning_Capabilities_CVPR_2024_paper.pdf [00:38:15] DeepSeek-R1-Distill-Llama, DeepSeek AI https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-70B [00:40:10] ARC Prize 2024 Technical Report, François Chollet et al. https://arxiv.org/html/2412.04604v2 [00:45:20] LLM-Guided Compositional Program Synthesis, Wen-Ding Li and Kevin Ellis https://arxiv.org/html/2503.15540 [00:54:25] Abstraction and Reasoning Corpus, François Chollet https://github.com/fchollet/ARC-AGI [00:57:10] O3 breakthrough on ARC-AGI, OpenAI https://arcprize.org/ [00:59:35] ConceptARC Benchmark, Arseny Moskvichev, Melanie Mitchell https://arxiv.org/abs/2305.07141 [01:02:05] Mixtape: Breaking the Softmax Bottleneck Efficiently, Yang, Zhilin and Dai, Zihang and Salakhutdinov, Ruslan and Cohen, William W. http://papers.neurips.cc/paper/9723-mixtape-breaking-the-softmax-bottleneck-efficiently.pdf

Iman Mirzadeh from Apple, who recently published the GSM-Symbolic paper discusses the crucial distinction between intelligence and achievement in AI systems. He critiques current AI research methodologies, highlighting the limitations of Large Language Models (LLMs) in reasoning and knowledge representation. SPONSOR MESSAGES: *** Tufa AI Labs is a brand new research lab in Zurich started by Benjamin Crouzier focussed on o-series style reasoning and AGI. They are hiring a Chief Engineer and ML engineers. Events in Zurich. Goto https://tufalabs.ai/ *** TRANSCRIPT + RESEARCH: https://www.dropbox.com/scl/fi/mlcjl9cd5p1kem4l0vqd3/IMAN.pdf?rlkey=dqfqb74zr81a5gqr8r6c8isg3&dl=0 TOC: 1. Intelligence vs Achievement in AI Systems [00:00:00] 1.1 Intelligence vs Achievement Metrics in AI Systems [00:03:27] 1.2 AlphaZero and Abstract Understanding in Chess [00:10:10] 1.3 Language Models and Distribution Learning Limitations [00:14:47] 1.4 Research Methodology and Theoretical Frameworks 2. Intelligence Measurement and Learning [00:24:24] 2.1 LLM Capabilities: Interpolation vs True Reasoning [00:29:00] 2.2 Intelligence Definition and Measurement Approaches [00:34:35] 2.3 Learning Capabilities and Agency in AI Systems [00:39:26] 2.4 Abstract Reasoning and Symbol Understanding 3. LLM Performance and Evaluation [00:47:15] 3.1 Scaling Laws and Fundamental Limitations [00:54:33] 3.2 Connectionism vs Symbolism Debate in Neural Networks [00:58:09] 3.3 GSM-Symbolic: Testing Mathematical Reasoning in LLMs [01:08:38] 3.4 Benchmark Evaluation and Model Performance Assessment REFS: [00:01:00] AlphaZero chess AI system, Silver et al. https://arxiv.org/abs/1712.01815 [00:07:10] Game Changer: AlphaZero's Groundbreaking Chess Strategies, Sadler & Regan https://www.amazon.com/Game-Changer-AlphaZeros-Groundbreaking-Strategies/dp/9056918184 [00:11:35] Cross-entropy loss in language modeling, Voita http://lena-voita.github.io/nlp_course/language_modeling.html [00:17:20] GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in LLMs, Mirzadeh et al. https://arxiv.org/abs/2410.05229 [00:21:25] Connectionism and Cognitive Architecture: A Critical Analysis, Fodor & Pylyshyn https://www.sciencedirect.com/science/article/pii/001002779090014B [00:28:55] Brain-to-body mass ratio scaling laws, Sutskever https://www.theverge.com/2024/12/13/24320811/what-ilya-sutskever-sees-openai-model-data-training [00:29:40] On the Measure of Intelligence, Chollet https://arxiv.org/abs/1911.01547 [00:33:30] On definition of intelligence, Gignac et al. https://www.sciencedirect.com/science/article/pii/S0160289624000266 [00:35:30] Defining intelligence, Wang https://cis.temple.edu/~wangp/papers.html [00:37:40] How We Learn: Why Brains Learn Better Than Any Machine... for Now, Dehaene https://www.amazon.com/How-We-Learn-Brains-Machine/dp/0525559884 [00:39:35] Surfaces and Essences: Analogy as the Fuel and Fire of Thinking, Hofstadter and Sander https://www.amazon.com/Surfaces-Essences-Analogy-Fuel-Thinking/dp/0465018475 [00:43:15] Chain-of-thought prompting, Wei et al. https://arxiv.org/abs/2201.11903 [00:47:20] Test-time scaling laws in machine learning, Brown https://podcasts.apple.com/mv/podcast/openais-noam-brown-ilge-akkaya-and-hunter-lightman-on/id1750736528?i=1000671532058 [00:47:50] Scaling Laws for Neural Language Models, Kaplan et al. https://arxiv.org/abs/2001.08361 [00:55:15] Tensor product variable binding, Smolensky https://www.sciencedirect.com/science/article/abs/pii/000437029090007M [01:08:45] GSM-8K dataset, OpenAI https://huggingface.co/datasets/openai/gsm8k

Dr. Max Bartolo from Cohere discusses machine learning model development, evaluation, and robustness. Key topics include model reasoning, the DynaBench platform for dynamic benchmarking, data-centric AI development, model training challenges, and the limitations of human feedback mechanisms. The conversation also covers technical aspects like influence functions, model quantization, and the PRISM project. Max Bartolo (Cohere): https://www.maxbartolo.com/ https://cohere.com/command TRANSCRIPT: https://www.dropbox.com/scl/fi/vujxscaffw37pqgb6hpie/MAXB.pdf?rlkey=0oqjxs5u49eqa2m7uaol64lbw&dl=0 TOC: 1. Model Reasoning and Verification [00:00:00] 1.1 Model Consistency and Reasoning Verification [00:03:25] 1.2 Influence Functions and Distributed Knowledge Analysis [00:10:28] 1.3 AI Application Development and Model Deployment [00:14:24] 1.4 AI Alignment and Human Feedback Limitations 2. Evaluation and Bias Assessment [00:20:15] 2.1 Human Evaluation Challenges and Factuality Assessment [00:27:15] 2.2 Cultural and Demographic Influences on Model Behavior [00:32:43] 2.3 Adversarial Examples and Model Robustness 3. Benchmarking Systems and Methods [00:41:54] 3.1 DynaBench and Dynamic Benchmarking Approaches [00:50:02] 3.2 Benchmarking Challenges and Alternative Metrics [00:50:33] 3.3 Evolution of Model Benchmarking Methods [00:51:15] 3.4 Hierarchical Capability Testing Framework [00:52:35] 3.5 Benchmark Platforms and Tools 4. Model Architecture and Performance [00:55:15] 4.1 Cohere's Model Development Process [01:00:26] 4.2 Model Quantization and Performance Evaluation [01:05:18] 4.3 Reasoning Capabilities and Benchmark Standards [01:08:27] 4.4 Training Progression and Technical Challenges 5. Future Directions and Challenges [01:13:48] 5.1 Context Window Evolution and Trade-offs [01:22:47] 5.2 Enterprise Applications and Future Challenges REFS: [00:03:10] Research at Cohere with Laura Ruis et al., Max Bartolo, Laura Ruis et al. https://cohere.com/research/papers/procedural-knowledge-in-pretraining-drives-reasoning-in-large-language-models-2024-11-20 [00:04:15] Influence functions in machine learning, Koh & Liang https://arxiv.org/abs/1703.04730 [00:08:05] Studying Large Language Model Generalization with Influence Functions, Roger Grosse et al. https://storage.prod.researchhub.com/uploads/papers/2023/08/08/2308.03296.pdf [00:11:10] The LLM ARChitect: Solving ARC-AGI Is A Matter of Perspective, Daniel Franzen, Jan Disselhoff, and David Hartmann https://github.com/da-fr/arc-prize-2024/blob/main/the_architects.pdf [00:12:10] Hugging Face model repo for C4AI Command A, Cohere and Cohere For AI https://huggingface.co/CohereForAI/c4ai-command-a-03-2025 [00:13:30] OpenInterpreter https://github.com/KillianLucas/open-interpreter [00:16:15] Human Feedback is not Gold Standard, Tom Hosking, Max Bartolo, Phil Blunsom https://arxiv.org/abs/2309.16349 [00:27:15] The PRISM Alignment Dataset, Hannah Kirk et al. https://arxiv.org/abs/2404.16019 [00:32:50] How adversarial examples arise, Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Logan Engstrom, Brandon Tran, Aleksander Madry https://arxiv.org/abs/1905.02175 [00:43:00] DynaBench platform paper, Douwe Kiela et al. https://aclanthology.org/2021.naacl-main.324.pdf [00:50:15] Sara Hooker's work on compute limitations, Sara Hooker https://arxiv.org/html/2407.05694v1 [00:53:25] DataPerf: Community-led benchmark suite, Mazumder et al. https://arxiv.org/abs/2207.10062 [01:04:35] DROP, Dheeru Dua et al. https://arxiv.org/abs/1903.00161 [01:07:05] GSM8k, Cobbe et al. https://paperswithcode.com/sota/arithmetic-reasoning-on-gsm8k [01:09:30] ARC, François Chollet https://github.com/fchollet/ARC-AGI [01:15:50] Command A, Cohere https://cohere.com/blog/command-a [01:22:55] Enterprise search using LLMs, Cohere https://cohere.com/blog/commonly-asked-questions-about-search-from-coheres-enterprise-customers

This sponsored episode features mathematician Ohad Asor discussing logical approaches to AI, focusing on the limitations of machine learning and introducing the Tau language for software development and blockchain tech. Asor argues that machine learning cannot guarantee correctness. Tau allows logical specification of software requirements, automatically creating provably correct implementations with potential to revolutionize distributed systems. The discussion highlights program synthesis, software updates, and applications in finance and governance.SPONSOR MESSAGES:***Tufa AI Labs is a brand new research lab in Zurich started by Benjamin Crouzier focussed on o-series style reasoning and AGI. They are hiring a Chief Engineer and ML engineers. Events in Zurich. Goto https://tufalabs.ai/***TRANSCRIPT + RESEARCH:https://www.dropbox.com/scl/fi/t849j6v1juk3gc15g4rsy/TAU.pdf?rlkey=hh11h2mhog3ncdbeapbzpzctc&dl=0Tau:https://tau.net/Tau Language:https://tau.ai/tau-language/Research:https://tau.net/Theories-and-Applications-of-Boolean-Algebras-0.29.pdfTOC:1. Machine Learning Foundations and Limitations [00:00:00] 1.1 Fundamental Limitations of Machine Learning and PAC Learning Theory [00:04:50] 1.2 Transductive Learning and the Three Curses of Machine Learning [00:08:57] 1.3 Language, Reality, and AI System Design [00:12:58] 1.4 Program Synthesis and Formal Verification Approaches2. Logical Programming Architecture [00:31:55] 2.1 Safe AI Development Requirements [00:32:05] 2.2 Self-Referential Language Architecture [00:32:50] 2.3 Boolean Algebra and Logical Foundations [00:37:52] 2.4 SAT Solvers and Complexity Challenges [00:44:30] 2.5 Program Synthesis and Specification [00:47:39] 2.6 Overcoming Tarski's Undefinability with Boolean Algebra [00:56:05] 2.7 Tau Language Implementation and User Control3. Blockchain-Based Software Governance [01:09:10] 3.1 User Control and Software Governance Mechanisms [01:18:27] 3.2 Tau's Blockchain Architecture and Meta-Programming Capabilities [01:21:43] 3.3 Development Status and Token Implementation [01:24:52] 3.4 Consensus Building and Opinion Mapping System [01:35:29] 3.5 Automation and Financial ApplicationsCORE REFS (more in pinned comment):[00:03:45] PAC (Probably Approximately Correct) Learning framework, Leslie Valianthttps://en.wikipedia.org/wiki/Probably_approximately_correct_learning[00:06:10] Boolean Satisfiability Problem (SAT), Varioushttps://en.wikipedia.org/wiki/Boolean_satisfiability_problem[00:13:55] Knowledge as Justified True Belief (JTB), Matthias Steuphttps://plato.stanford.edu/entries/epistemology/[00:17:50] Wittgenstein's concept of the limits of language, Ludwig Wittgensteinhttps://plato.stanford.edu/entries/wittgenstein/[00:21:25] Boolean algebras, Ohad Osorhttps://tau.net/tau-language-research/[00:26:10] The Halting Problemhttps://plato.stanford.edu/entries/turing-machine/#HaltProb[00:30:25] Alfred Tarski (1901-1983), Mario Gómez-Torrentehttps://plato.stanford.edu/entries/tarski/[00:41:50] DPLLhttps://www.cs.princeton.edu/~zkincaid/courses/fall18/readings/SATHandbook-CDCL.pdf[00:49:50] Tarski's undefinability theorem (1936), Alfred Tarskihttps://plato.stanford.edu/entries/tarski-truth/[00:51:45] Boolean Algebra mathematical foundations, J. Donald Monkhttps://plato.stanford.edu/entries/boolalg-math/[01:02:35] Belief Revision Theory and AGM Postulates, Sven Ove Hanssonhttps://plato.stanford.edu/entries/logic-belief-revision/[01:05:35] Quantifier elimination in atomless boolean algebra, H. Jerome Keislerhttps://people.math.wisc.edu/~hkeisler/random.pdf[01:08:35] Quantifier elimination in Tau language specification, Ohad Asorhttps://tau.ai/Theories-and-Applications-of-Boolean-Algebras-0.29.pdf[01:11:50] Tau Net blockchain platformhttps://tau.net/[01:19:20] Tau blockchain's innovative approach treating blockchain code itself as a contracthttps://tau.net/Whitepaper.pdf

John Palazza from CentML joins us in this sponsored interview to discuss the critical importance of infrastructure optimization in the age of Large Language Models and Generative AI. We explore how enterprises can transition from the innovation phase to production and scale, highlighting the significance of efficient GPU utilization and cost management. The conversation covers the open-source versus proprietary model debate, the rise of AI agents, and the need for platform independence to avoid vendor lock-in, as well as emerging trends in AI infrastructure and the pivotal role of strategic partnerships. 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. Check out their super fast DeepSeek R1 hosting! 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. They are hiring a Chief Engineer and ML engineers. Events in Zurich. Goto https://tufalabs.ai/ *** TRANSCRIPT: https://www.dropbox.com/scl/fi/dnjsygrgdgq5ng5fdlfjg/JOHNPALAZZA.pdf?rlkey=hl9wyydi9mj077rbg5acdmo3a&dl=0 John Palazza: Vice President of Global Sales @ CentML https://www.linkedin.com/in/john-p-b34655/ TOC: 1. Enterprise AI Organization and Strategy [00:00:00] 1.1 Organizational Structure and ML Ownership [00:02:59] 1.2 Infrastructure Efficiency and GPU Utilization [00:07:59] 1.3 Platform Centralization vs Team Autonomy [00:11:32] 1.4 Enterprise AI Adoption Strategy and Leadership 2. MLOps Infrastructure and Resource Management [00:15:08] 2.1 Technology Evolution and Enterprise Integration [00:19:10] 2.2 Enterprise MLOps Platform Development [00:22:15] 2.3 AI Interface Evolution and Agent-Based Solutions [00:25:47] 2.4 CentML's Infrastructure Solutions [00:30:00] 2.5 Workload Abstraction and Resource Allocation 3. LLM Infrastructure Optimization and Independence [00:33:10] 3.1 GPU Optimization and Cost Efficiency [00:36:47] 3.2 AI Efficiency and Innovation Challenges [00:41:40] 3.3 Cloud Provider Strategy and Infrastructure Control [00:46:52] 3.4 Platform Independence and Vendor Lock-in [00:50:53] 3.5 Technical Innovation and Growth Strategy REFS: [00:01:25] Apple Acquires GraphLab, Apple Inc. https://techcrunch.com/2016/08/05/apple-acquires-turi-a-machine-learning-company/ [00:03:50] Bain Tech Report 2024, Gartner https://www.bain.com/insights/topics/technology-report/ [00:04:50] PaaS vs IaaS Efficiency, Gartner https://www.gartner.com/en/newsroom/press-releases/2024-11-19-gartner-forecasts-worldwide-public-cloud-end-user-spending-to-total-723-billion-dollars-in-2025 [00:14:55] Fashion Quote, Oscar Wilde https://www.amazon.com/Complete-Works-Oscar-Wilde-Collins/dp/0007144369 [00:15:30] PointCast Network, PointCast Inc. https://en.wikipedia.org/wiki/Push_technology [00:18:05] AI Bain Report, Bain & Company https://www.bain.com/insights/how-generative-ai-changes-the-game-in-tech-services-tech-report-2024/ [00:20:40] Uber Michelangelo, Uber Engineering Team https://www.uber.com/en-SE/blog/michelangelo-machine-learning-platform/ [00:20:50] Algorithmia Acquisition, DataRobot https://www.datarobot.com/newsroom/press/datarobot-is-acquiring-algorithmia-enhancing-leading-mlops-architecture-for-the-enterprise/ [00:22:55] Fine Tuning vs RAG, Heydar Soudani, Evangelos Kanoulas & Faegheh Hasibi. https://arxiv.org/html/2403.01432v2 [00:24:40] LLM Agent Survey, Lei Wang et al. https://arxiv.org/abs/2308.11432 [00:26:30] CentML CServe, CentML https://docs.centml.ai/apps/llm [00:29:15] CentML Snowflake, Snowflake https://www.snowflake.com/en/engineering-blog/optimize-llms-with-llama-snowflake-ai-stack/ [00:30:15] NVIDIA H100 GPU, NVIDIA https://www.nvidia.com/en-us/data-center/h100/ [00:33:25] CentML\'s 60% savings, CentML https://centml.ai/platform/

Federico Barbero (DeepMind/Oxford) is the lead author of "Transformers Need Glasses!". Have you ever wondered why LLMs struggle with seemingly simple tasks like counting or copying long strings of text? We break down the theoretical reasons behind these failures, revealing architectural bottlenecks and the challenges of maintaining information fidelity across extended contexts. Federico explains how these issues are rooted in the transformer's design, drawing parallels to over-squashing in graph neural networks and detailing how the softmax function limits sharp decision-making. But it's not all bad news! Discover practical "glasses" that can help transformers see more clearly, from simple input modifications to architectural tweaks. 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. Check out their super fast DeepSeek R1 hosting! 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. They are hiring a Chief Engineer and ML engineers. Events in Zurich. Goto https://tufalabs.ai/ *** https://federicobarbero.com/ TRANSCRIPT + RESEARCH: https://www.dropbox.com/s/h7ys83ztwktqjje/Federico.pdf?dl=0 TOC: 1. Transformer Limitations: Token Detection & Representation [00:00:00] 1.1 Transformers fail at single token detection [00:02:45] 1.2 Representation collapse in transformers [00:03:21] 1.3 Experiment: LLMs fail at copying last tokens [00:18:00] 1.4 Attention sharpness limitations in transformers 2. Transformer Limitations: Information Flow & Quantization [00:18:50] 2.1 Unidirectional information mixing [00:18:50] 2.2 Unidirectional information flow towards sequence beginning in transformers [00:21:50] 2.3 Diagonal attention heads as expensive no-ops in LAMA/Gemma [00:27:14] 2.4 Sequence entropy affects transformer model distinguishability [00:30:36] 2.5 Quantization limitations lead to information loss & representational collapse [00:38:34] 2.6 LLMs use subitizing as opposed to counting algorithms 3. Transformers and the Nature of Reasoning [00:40:30] 3.1 Turing completeness conditions in transformers [00:43:23] 3.2 Transformers struggle with sequential tasks [00:45:50] 3.3 Windowed attention as solution to information compression [00:51:04] 3.4 Chess engines: mechanical computation vs creative reasoning [01:00:35] 3.5 Epistemic foraging introduced REFS: [00:01:05] Transformers Need Glasses!, Barbero et al. https://proceedings.neurips.cc/paper_files/paper/2024/file/b1d35561c4a4a0e0b6012b2af531e149-Paper-Conference.pdf [00:05:30] Softmax is Not Enough, Veličković et al. https://arxiv.org/abs/2410.01104 [00:11:30] Adv Alg Lecture 15, Chawla https://pages.cs.wisc.edu/~shuchi/courses/787-F09/scribe-notes/lec15.pdf [00:15:05] Graph Attention Networks, Veličković https://arxiv.org/abs/1710.10903 [00:19:15] Extract Training Data, Carlini et al. https://arxiv.org/pdf/2311.17035 [00:31:30] 1-bit LLMs, Ma et al. https://arxiv.org/abs/2402.17764 [00:38:35] LLMs Solve Math, Nikankin et al. https://arxiv.org/html/2410.21272v1 [00:38:45] Subitizing, Railo https://link.springer.com/10.1007/978-1-4419-1428-6_578 [00:43:25] NN & Chomsky Hierarchy, Delétang et al. https://arxiv.org/abs/2207.02098 [00:51:05] Measure of Intelligence, Chollet https://arxiv.org/abs/1911.01547 [00:52:10] AlphaZero, Silver et al. https://pubmed.ncbi.nlm.nih.gov/30523106/ [00:55:10] Golden Gate Claude, Anthropic https://www.anthropic.com/news/golden-gate-claude [00:56:40] Chess Positions, Chase & Simon https://www.sciencedirect.com/science/article/abs/pii/0010028573900042 [01:00:35] Epistemic Foraging, Friston https://www.frontiersin.org/journals/computational-neuroscience/articles/10.3389/fncom.2016.00056/full

We speak with Sakana AI, who are building nature-inspired methods that could fundamentally transform how we develop AI systems. The guests include Chris Lu, a researcher who recently completed his DPhil at Oxford University under Prof. Jakob Foerster's supervision, where he focused on meta-learning and multi-agent systems. Chris is the first author of the DiscoPOP paper, which demonstrates how language models can discover and design better training algorithms. Also joining is Robert Tjarko Lange, a founding member of Sakana AI who specializes in evolutionary algorithms and large language models. Robert leads research at the intersection of evolutionary computation and foundation models, and is completing his PhD at TU Berlin on evolutionary meta-learning. The discussion also features Cong Lu, currently a Research Scientist at Google DeepMind's Open-Endedness team, who previously helped develop The AI Scientist and Intelligent Go-Explore. 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. Check out their super fast DeepSeek R1 hosting! 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. They are hiring a Chief Engineer and ML engineers. Events in Zurich. Goto https://tufalabs.ai/ *** * DiscoPOP - A framework where language models discover their own optimization algorithms * EvoLLM - Using language models as evolution strategies for optimization The AI Scientist - A fully automated system that conducts scientific research end-to-end * Neural Attention Memory Models (NAMMs) - Evolved memory systems that make transformers both faster and more accurate TRANSCRIPT + REFS: https://www.dropbox.com/scl/fi/gflcyvnujp8cl7zlv3v9d/Sakana.pdf?rlkey=woaoo82943170jd4yyi2he71c&dl=0 Robert Tjarko Lange https://roberttlange.com/ Chris Lu https://chrislu.page/ Cong Lu https://www.conglu.co.uk/ Sakana https://sakana.ai/blog/ TOC: 1. LLMs for Algorithm Generation and Optimization [00:00:00] 1.1 LLMs generating algorithms for training other LLMs [00:04:00] 1.2 Evolutionary black-box optim using neural network loss parameterization [00:11:50] 1.3 DiscoPOP: Non-convex loss function for noisy data [00:20:45] 1.4 External entropy Injection for preventing Model collapse [00:26:25] 1.5 LLMs for black-box optimization using abstract numerical sequences 2. Model Learning and Generalization [00:31:05] 2.1 Fine-tuning on teacher algorithm trajectories [00:31:30] 2.2 Transformers learning gradient descent [00:33:00] 2.3 LLM tokenization biases towards specific numbers [00:34:50] 2.4 LLMs as evolution strategies for black box optimization [00:38:05] 2.5 DiscoPOP: LLMs discovering novel optimization algorithms 3. AI Agents and System Architectures [00:51:30] 3.1 ARC challenge: Induction vs. transformer approaches [00:54:35] 3.2 LangChain / modular agent components [00:57:50] 3.3 Debate improves LLM truthfulness [01:00:55] 3.4 Time limits controlling AI agent systems [01:03:00] 3.5 Gemini: Million-token context enables flatter hierarchies [01:04:05] 3.6 Agents follow own interest gradients [01:09:50] 3.7 Go-Explore algorithm: archive-based exploration [01:11:05] 3.8 Foundation models for interesting state discovery [01:13:00] 3.9 LLMs leverage prior game knowledge 4. AI for Scientific Discovery and Human Alignment [01:17:45] 4.1 Encoding Alignment & Aesthetics via Reward Functions [01:20:00] 4.2 AI Scientist: Automated Open-Ended Scientific Discovery [01:24:15] 4.3 DiscoPOP: LLM for Preference Optimization Algorithms [01:28:30] 4.4 Balancing AI Knowledge with Human Understanding [01:33:55] 4.5 AI-Driven Conferences and Paper Review

Clement Bonnet discusses his novel approach to the ARC (Abstraction and Reasoning Corpus) challenge. Unlike approaches that rely on fine-tuning LLMs or generating samples at inference time, Clement's method encodes input-output pairs into a latent space, optimizes this representation with a search algorithm, and decodes outputs for new inputs. This end-to-end architecture uses a VAE loss, including reconstruction and prior losses. 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. Check out their super fast DeepSeek R1 hosting! 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. They are hiring a Chief Engineer and ML engineers. Events in Zurich. Goto https://tufalabs.ai/ *** TRANSCRIPT + RESEARCH OVERVIEW: https://www.dropbox.com/scl/fi/j7m0gaz1126y594gswtma/CLEMMLST.pdf?rlkey=y5qvwq2er5nchbcibm07rcfpq&dl=0 Clem and Matthew- https://www.linkedin.com/in/clement-bonnet16/ https://github.com/clement-bonnet https://mvmacfarlane.github.io/ TOC 1. LPN Fundamentals [00:00:00] 1.1 Introduction to ARC Benchmark and LPN Overview [00:05:05] 1.2 Neural Networks' Challenges with ARC and Program Synthesis [00:06:55] 1.3 Induction vs Transduction in Machine Learning 2. LPN Architecture and Latent Space [00:11:50] 2.1 LPN Architecture and Latent Space Implementation [00:16:25] 2.2 LPN Latent Space Encoding and VAE Architecture [00:20:25] 2.3 Gradient-Based Search Training Strategy [00:23:39] 2.4 LPN Model Architecture and Implementation Details 3. Implementation and Scaling [00:27:34] 3.1 Training Data Generation and re-ARC Framework [00:31:28] 3.2 Limitations of Latent Space and Multi-Thread Search [00:34:43] 3.3 Program Composition and Computational Graph Architecture 4. Advanced Concepts and Future Directions [00:45:09] 4.1 AI Creativity and Program Synthesis Approaches [00:49:47] 4.2 Scaling and Interpretability in Latent Space Models REFS [00:00:05] ARC benchmark, Chollet https://arxiv.org/abs/2412.04604 [00:02:10] Latent Program Spaces, Bonnet, Macfarlane https://arxiv.org/abs/2411.08706 [00:07:45] Kevin Ellis work on program generation https://www.cs.cornell.edu/~ellisk/ [00:08:45] Induction vs transduction in abstract reasoning, Li et al. https://arxiv.org/abs/2411.02272 [00:17:40] VAEs, Kingma, Welling https://arxiv.org/abs/1312.6114 [00:27:50] re-ARC, Hodel https://github.com/michaelhodel/re-arc [00:29:40] Grid size in ARC tasks, Chollet https://github.com/fchollet/ARC-AGI [00:33:00] Critique of deep learning, Marcus https://arxiv.org/vc/arxiv/papers/2002/2002.06177v1.pdf

Prof. Jakob Foerster, a leading AI researcher at Oxford University and Meta, and Chris Lu, a researcher at OpenAI -- they explain how AI is moving beyond just mimicking human behaviour to creating truly intelligent agents that can learn and solve problems on their own. Foerster champions open-source AI for responsible, decentralised development. He addresses AI scaling, goal misalignment (Goodhart's Law), and the need for holistic alignment, offering a quick look at the future of AI and how to guide it. 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. Check out their super fast DeepSeek R1 hosting! 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. They are hiring a Chief Engineer and ML engineers. Events in Zurich. Goto https://tufalabs.ai/ *** TRANSCRIPT/REFS: https://www.dropbox.com/scl/fi/yqjszhntfr00bhjh6t565/JAKOB.pdf?rlkey=scvny4bnwj8th42fjv8zsfu2y&dl=0 Prof. Jakob Foerster https://x.com/j_foerst https://www.jakobfoerster.com/ University of Oxford Profile: https://eng.ox.ac.uk/people/jakob-foerster/ Chris Lu: https://chrislu.page/ TOC 1. GPU Acceleration and Training Infrastructure [00:00:00] 1.1 ARC Challenge Criticism and FLAIR Lab Overview [00:01:25] 1.2 GPU Acceleration and Hardware Lottery in RL [00:05:50] 1.3 Data Wall Challenges and Simulation-Based Solutions [00:08:40] 1.4 JAX Implementation and Technical Acceleration 2. Learning Frameworks and Policy Optimization [00:14:18] 2.1 Evolution of RL Algorithms and Mirror Learning Framework [00:15:25] 2.2 Meta-Learning and Policy Optimization Algorithms [00:21:47] 2.3 Language Models and Benchmark Challenges [00:28:15] 2.4 Creativity and Meta-Learning in AI Systems 3. Multi-Agent Systems and Decentralization [00:31:24] 3.1 Multi-Agent Systems and Emergent Intelligence [00:38:35] 3.2 Swarm Intelligence vs Monolithic AGI Systems [00:42:44] 3.3 Democratic Control and Decentralization of AI Development [00:46:14] 3.4 Open Source AI and Alignment Challenges [00:49:31] 3.5 Collaborative Models for AI Development REFS [[00:00:05] ARC Benchmark, Chollet https://github.com/fchollet/ARC-AGI [00:03:05] DRL Doesn't Work, Irpan https://www.alexirpan.com/2018/02/14/rl-hard.html [00:05:55] AI Training Data, Data Provenance Initiative https://www.nytimes.com/2024/07/19/technology/ai-data-restrictions.html [00:06:10] JaxMARL, Foerster et al. https://arxiv.org/html/2311.10090v5 [00:08:50] M-FOS, Lu et al. https://arxiv.org/abs/2205.01447 [00:09:45] JAX Library, Google Research https://github.com/jax-ml/jax [00:12:10] Kinetix, Mike and Michael https://arxiv.org/abs/2410.23208 [00:12:45] Genie 2, DeepMind https://deepmind.google/discover/blog/genie-2-a-large-scale-foundation-world-model/ [00:14:42] Mirror Learning, Grudzien, Kuba et al. https://arxiv.org/abs/2208.01682 [00:16:30] Discovered Policy Optimisation, Lu et al. https://arxiv.org/abs/2210.05639 [00:24:10] Goodhart's Law, Goodhart https://en.wikipedia.org/wiki/Goodhart%27s_law [00:25:15] LLM ARChitect, Franzen et al. https://github.com/da-fr/arc-prize-2024/blob/main/the_architects.pdf [00:28:55] AlphaGo, Silver et al. https://arxiv.org/pdf/1712.01815.pdf [00:30:10] Meta-learning, Lu, Towers, Foerster https://direct.mit.edu/isal/proceedings-pdf/isal2023/35/67/2354943/isal_a_00674.pdf [00:31:30] Emergence of Pragmatics, Yuan et al. https://arxiv.org/abs/2001.07752 [00:34:30] AI Safety, Amodei et al. https://arxiv.org/abs/1606.06565 [00:35:45] Intentional Stance, Dennett https://plato.stanford.edu/entries/ethics-ai/ [00:39:25] Multi-Agent RL, Zhou et al. https://arxiv.org/pdf/2305.10091 [00:41:00] Open Source Generative AI, Foerster et al. https://arxiv.org/abs/2405.08597

Daniel Franzen and Jan Disselhoff, the "ARChitects" are the official winners of the ARC Prize 2024. Filmed at Tufa Labs in Zurich - they revealed how they achieved a remarkable 53.5% accuracy by creatively utilising large language models (LLMs) in new ways. Discover their innovative techniques, including depth-first search for token selection, test-time training, and a novel augmentation-based validation system. Their results were extremely surprising. 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. Check out their super fast DeepSeek R1 hosting! 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. They are hiring a Chief Engineer and ML engineers. Events in Zurich. Goto https://tufalabs.ai/ *** Jan Disselhoff https://www.linkedin.com/in/jan-disselhoff-1423a2240/ Daniel Franzen https://github.com/da-fr ARC Prize: http://arcprize.org/ TRANSCRIPT AND BACKGROUND READING: https://www.dropbox.com/scl/fi/utkn2i1ma79fn6an4yvjw/ARCHitects.pdf?rlkey=67pe38mtss7oyhjk2ad0d2aza&dl=0 TOC 1. Solution Architecture and Strategy Overview [00:00:00] 1.1 Initial Solution Overview and Model Architecture [00:04:25] 1.2 LLM Capabilities and Dataset Approach [00:10:51] 1.3 Test-Time Training and Data Augmentation Strategies [00:14:08] 1.4 Sampling Methods and Search Implementation [00:17:52] 1.5 ARC vs Language Model Context Comparison 2. LLM Search and Model Implementation [00:21:53] 2.1 LLM-Guided Search Approaches and Solution Validation [00:27:04] 2.2 Symmetry Augmentation and Model Architecture [00:30:11] 2.3 Model Intelligence Characteristics and Performance [00:37:23] 2.4 Tokenization and Numerical Processing Challenges 3. Advanced Training and Optimization [00:45:15] 3.1 DFS Token Selection and Probability Thresholds [00:49:41] 3.2 Model Size and Fine-tuning Performance Trade-offs [00:53:07] 3.3 LoRA Implementation and Catastrophic Forgetting Prevention [00:56:10] 3.4 Training Infrastructure and Optimization Experiments [01:02:34] 3.5 Search Tree Analysis and Entropy Distribution Patterns REFS [00:01:05] Winning ARC 2024 solution using 12B param model, Franzen, Disselhoff, Hartmann https://github.com/da-fr/arc-prize-2024/blob/main/the_architects.pdf [00:03:40] Robustness of analogical reasoning in LLMs, Melanie Mitchell https://arxiv.org/html/2411.14215 [00:07:50] Re-ARC dataset generator for ARC task variations, Michael Hodel https://github.com/michaelhodel/re-arc [00:15:00] Analysis of search methods in LLMs (greedy, beam, DFS), Chen et al. https://arxiv.org/html/2408.00724v2 [00:16:55] Language model reachability space exploration, University of Toronto https://www.youtube.com/watch?v=Bpgloy1dDn0 [00:22:30] GPT-4 guided code solutions for ARC tasks, Ryan Greenblatt https://redwoodresearch.substack.com/p/getting-50-sota-on-arc-agi-with-gpt [00:41:20] GPT tokenization approach for numbers, OpenAI https://platform.openai.com/docs/guides/text-generation/tokenizer-examples [00:46:25] DFS in AI search strategies, Russell & Norvig https://www.amazon.com/Artificial-Intelligence-Modern-Approach-4th/dp/0134610997 [00:53:10] Paper on catastrophic forgetting in neural networks, Kirkpatrick et al. https://www.pnas.org/doi/10.1073/pnas.1611835114 [00:54:00] LoRA for efficient fine-tuning of LLMs, Hu et al. https://arxiv.org/abs/2106.09685 [00:57:20] NVIDIA H100 Tensor Core GPU specs, NVIDIA https://developer.nvidia.com/blog/nvidia-hopper-architecture-in-depth/ [01:04:55] Original MCTS in computer Go, Yifan Jin https://stanford.edu/~rezab/classes/cme323/S15/projects/montecarlo_search_tree_report.pdf

Sepp Hochreiter, the inventor of LSTM (Long Short-Term Memory) networks – a foundational technology in AI. Sepp discusses his journey, the origins of LSTM, and why he believes his latest work, XLSTM, could be the next big thing in AI, particularly for applications like robotics and industrial simulation. He also shares his controversial perspective on Large Language Models (LLMs) and why reasoning is a critical missing piece in current AI systems. 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. Check out their super fast DeepSeek R1 hosting! 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. They are hiring a Chief Engineer and ML engineers. Events in Zurich. Goto https://tufalabs.ai/ *** TRANSCRIPT AND BACKGROUND READING: https://www.dropbox.com/scl/fi/n1vzm79t3uuss8xyinxzo/SEPPH.pdf?rlkey=fp7gwaopjk17uyvgjxekxrh5v&dl=0 Prof. Sepp Hochreiter https://www.nx-ai.com/ https://x.com/hochreitersepp https://scholar.google.at/citations?user=tvUH3WMAAAAJ&hl=en TOC: 1. LLM Evolution and Reasoning Capabilities [00:00:00] 1.1 LLM Capabilities and Limitations Debate [00:03:16] 1.2 Program Generation and Reasoning in AI Systems [00:06:30] 1.3 Human vs AI Reasoning Comparison [00:09:59] 1.4 New Research Initiatives and Hybrid Approaches 2. LSTM Technical Architecture [00:13:18] 2.1 LSTM Development History and Technical Background [00:20:38] 2.2 LSTM vs RNN Architecture and Computational Complexity [00:25:10] 2.3 xLSTM Architecture and Flash Attention Comparison [00:30:51] 2.4 Evolution of Gating Mechanisms from Sigmoid to Exponential 3. Industrial Applications and Neuro-Symbolic AI [00:40:35] 3.1 Industrial Applications and Fixed Memory Advantages [00:42:31] 3.2 Neuro-Symbolic Integration and Pi AI Project [00:46:00] 3.3 Integration of Symbolic and Neural AI Approaches [00:51:29] 3.4 Evolution of AI Paradigms and System Thinking [00:54:55] 3.5 AI Reasoning and Human Intelligence Comparison [00:58:12] 3.6 NXAI Company and Industrial AI Applications REFS: [00:00:15] Seminal LSTM paper establishing Hochreiter's expertise (Hochreiter & Schmidhuber) https://direct.mit.edu/neco/article-abstract/9/8/1735/6109/Long-Short-Term-Memory [00:04:20] Kolmogorov complexity and program composition limitations (Kolmogorov) https://link.springer.com/article/10.1007/BF02478259 [00:07:10] Limitations of LLM mathematical reasoning and symbolic integration (Various Authors) https://www.arxiv.org/pdf/2502.03671 [00:09:05] AlphaGo’s Move 37 demonstrating creative AI (Google DeepMind) https://deepmind.google/research/breakthroughs/alphago/ [00:10:15] New AI research lab in Zurich for fundamental LLM research (Benjamin Crouzier) https://tufalabs.ai [00:19:40] Introduction of xLSTM with exponential gating (Beck, Hochreiter, et al.) https://arxiv.org/abs/2405.04517 [00:22:55] FlashAttention: fast & memory-efficient attention (Tri Dao et al.) https://arxiv.org/abs/2205.14135 [00:31:00] Historical use of sigmoid/tanh activation in 1990s (James A. McCaffrey) https://visualstudiomagazine.com/articles/2015/06/01/alternative-activation-functions.aspx [00:36:10] Mamba 2 state space model architecture (Albert Gu et al.) https://arxiv.org/abs/2312.00752 [00:46:00] Austria’s Pi AI project integrating symbolic & neural AI (Hochreiter et al.) https://www.jku.at/en/institute-of-machine-learning/research/projects/ [00:48:10] Neuro-symbolic integration challenges in language models (Diego Calanzone et al.) https://openreview.net/forum?id=7PGluppo4k [00:49:30] JKU Linz’s historical and neuro-symbolic research (Sepp Hochreiter) https://www.jku.at/en/news-events/news/detail/news/bilaterale-ki-projekt-unter-leitung-der-jku-erhaelt-fwf-cluster-of-excellence/ YT:...

Professor Randall Balestriero joins us to discuss neural network geometry, spline theory, and emerging phenomena in deep learning, based on research presented at ICML. Topics include the delayed emergence of adversarial robustness in neural networks ("grokking"), geometric interpretations of neural networks via spline theory, and challenges in reconstruction learning. We also cover geometric analysis of Large Language Models (LLMs) for toxicity detection and the relationship between intrinsic dimensionality and model control in RLHF. 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/ *** Randall Balestriero https://x.com/randall_balestr https://randallbalestriero.github.io/ Show notes and transcript: https://www.dropbox.com/scl/fi/3lufge4upq5gy0ug75j4a/RANDALLSHOW.pdf?rlkey=nbemgpa0jhawt1e86rx7372e4&dl=0 TOC: - Introduction - 00:00:00: Introduction - Neural Network Geometry and Spline Theory - 00:01:41: Neural Network Geometry and Spline Theory - 00:07:41: Deep Networks Always Grok - 00:11:39: Grokking and Adversarial Robustness - 00:16:09: Double Descent and Catastrophic Forgetting - Reconstruction Learning - 00:18:49: Reconstruction Learning - 00:24:15: Frequency Bias in Neural Networks - Geometric Analysis of Neural Networks - 00:29:02: Geometric Analysis of Neural Networks - 00:34:41: Adversarial Examples and Region Concentration - LLM Safety and Geometric Analysis - 00:40:05: LLM Safety and Geometric Analysis - 00:46:11: Toxicity Detection in LLMs - 00:52:24: Intrinsic Dimensionality and Model Control - 00:58:07: RLHF and High-Dimensional Spaces - Conclusion - 01:02:13: Neural Tangent Kernel - 01:08:07: Conclusion REFS: [00:01:35] Humayun – Deep network geometry & input space partitioning https://arxiv.org/html/2408.04809v1 [00:03:55] Balestriero & Paris – Linking deep networks to adaptive spline operators https://proceedings.mlr.press/v80/balestriero18b/balestriero18b.pdf [00:13:55] Song et al. – Gradient-based white-box adversarial attacks https://arxiv.org/abs/2012.14965 [00:16:05] Humayun, Balestriero & Baraniuk – Grokking phenomenon & emergent robustness https://arxiv.org/abs/2402.15555 [00:18:25] Humayun – Training dynamics & double descent via linear region evolution https://arxiv.org/abs/2310.12977 [00:20:15] Balestriero – Power diagram partitions in DNN decision boundaries https://arxiv.org/abs/1905.08443 [00:23:00] Frankle & Carbin – Lottery Ticket Hypothesis for network pruning https://arxiv.org/abs/1803.03635 [00:24:00] Belkin et al. – Double descent phenomenon in modern ML https://arxiv.org/abs/1812.11118 [00:25:55] Balestriero et al. – Batch normalization’s regularization effects https://arxiv.org/pdf/2209.14778 [00:29:35] EU – EU AI Act 2024 with compute restrictions https://www.lw.com/admin/upload/SiteAttachments/EU-AI-Act-Navigating-a-Brave-New-World.pdf [00:39:30] Humayun, Balestriero & Baraniuk – SplineCam: Visualizing deep network geometry https://openaccess.thecvf.com/content/CVPR2023/papers/Humayun_SplineCam_Exact_Visualization_and_Characterization_of_Deep_Network_Geometry_and_CVPR_2023_paper.pdf [00:40:40] Carlini – Trade-offs between adversarial robustness and accuracy https://arxiv.org/pdf/2407.20099 [00:44:55] Balestriero & LeCun – Limitations of reconstruction-based learning methods https://openreview.net/forum?id=ez7w0Ss4g9 (truncated, see shownotes PDF)

Nicholas Carlini from Google DeepMind offers his view of AI security, emergent LLM capabilities, and his groundbreaking model-stealing research. He reveals how LLMs can unexpectedly excel at tasks like chess and discusses the security pitfalls of LLM-generated code. 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/ *** Transcript: https://www.dropbox.com/scl/fi/lat7sfyd4k3g5k9crjpbf/CARLINI.pdf?rlkey=b7kcqbvau17uw6rksbr8ccd8v&dl=0 TOC: 1. ML Security Fundamentals [00:00:00] 1.1 ML Model Reasoning and Security Fundamentals [00:03:04] 1.2 ML Security Vulnerabilities and System Design [00:08:22] 1.3 LLM Chess Capabilities and Emergent Behavior [00:13:20] 1.4 Model Training, RLHF, and Calibration Effects 2. Model Evaluation and Research Methods [00:19:40] 2.1 Model Reasoning and Evaluation Metrics [00:24:37] 2.2 Security Research Philosophy and Methodology [00:27:50] 2.3 Security Disclosure Norms and Community Differences 3. LLM Applications and Best Practices [00:44:29] 3.1 Practical LLM Applications and Productivity Gains [00:49:51] 3.2 Effective LLM Usage and Prompting Strategies [00:53:03] 3.3 Security Vulnerabilities in LLM-Generated Code 4. Advanced LLM Research and Architecture [00:59:13] 4.1 LLM Code Generation Performance and O(1) Labs Experience [01:03:31] 4.2 Adaptation Patterns and Benchmarking Challenges [01:10:10] 4.3 Model Stealing Research and Production LLM Architecture Extraction REFS: [00:01:15] Nicholas Carlini’s personal website & research profile (Google DeepMind, ML security) - https://nicholas.carlini.com/ [00:01:50] CentML AI compute platform for language model workloads - https://centml.ai/ [00:04:30] Seminal paper on neural network robustness against adversarial examples (Carlini & Wagner, 2016) - https://arxiv.org/abs/1608.04644 [00:05:20] Computer Fraud and Abuse Act (CFAA) – primary U.S. federal law on computer hacking liability - https://www.justice.gov/jm/jm-9-48000-computer-fraud [00:08:30] Blog post: Emergent chess capabilities in GPT-3.5-turbo-instruct (Nicholas Carlini, Sept 2023) - https://nicholas.carlini.com/writing/2023/chess-llm.html [00:16:10] Paper: “Self-Play Preference Optimization for Language Model Alignment” (Yue Wu et al., 2024) - https://arxiv.org/abs/2405.00675 [00:18:00] GPT-4 Technical Report: development, capabilities, and calibration analysis - https://arxiv.org/abs/2303.08774 [00:22:40] Historical shift from descriptive to algebraic chess notation (FIDE) - https://en.wikipedia.org/wiki/Descriptive_notation [00:23:55] Analysis of distribution shift in ML (Hendrycks et al.) - https://arxiv.org/abs/2006.16241 [00:27:40] Nicholas Carlini’s essay “Why I Attack” (June 2024) – motivations for security research - https://nicholas.carlini.com/writing/2024/why-i-attack.html [00:34:05] Google Project Zero’s 90-day vulnerability disclosure policy - https://googleprojectzero.blogspot.com/p/vulnerability-disclosure-policy.html [00:51:15] Evolution of Google search syntax & user behavior (Daniel M. Russell) - https://www.amazon.com/Joy-Search-Google-Master-Information/dp/0262042878 [01:04:05] Rust’s ownership & borrowing system for memory safety - https://doc.rust-lang.org/book/ch04-00-understanding-ownership.html [01:10:05] Paper: “Stealing Part of a Production Language Model” (Carlini et al., March 2024) – extraction attacks on ChatGPT, PaLM-2 - https://arxiv.org/abs/2403.06634 [01:10:55] First model stealing paper (Tramèr et al., 2016) – attacking ML APIs via prediction - https://arxiv.org/abs/1609.02943

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: 1. **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 2. **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 3. **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)

Laura Ruis, a PhD student at University College London and researcher at Cohere, explains her groundbreaking research into how large language models (LLMs) perform reasoning tasks, the fundamental mechanisms underlying LLM reasoning capabilities, and whether these models primarily rely on retrieval or develop procedural knowledge. 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 1. LLM Foundations and Learning 1.1 Scale and Learning in Language Models [00:00:00] 1.2 Procedural Knowledge vs Fact Retrieval [00:03:40] 1.3 Influence Functions and Model Analysis [00:07:40] 1.4 Role of Code in LLM Reasoning [00:11:10] 1.5 Semantic Understanding and Physical Grounding [00:19:30] 2. Reasoning Architectures and Measurement 2.1 Measuring Understanding and Reasoning in Language Models [00:23:10] 2.2 Formal vs Approximate Reasoning and Model Creativity [00:26:40] 2.3 Symbolic vs Subsymbolic Computation Debate [00:34:10] 2.4 Neural Network Architectures and Tensor Product Representations [00:40:50] 3. AI Agency and Risk Assessment 3.1 Agency and Goal-Directed Behavior in Language Models [00:45:10] 3.2 Defining and Measuring Agency in AI Systems [00:49:50] 3.3 Core Knowledge Systems and Agency Detection [00:54:40] 3.4 Language Models as Agent Models and Simulator Theory [01:03:20] 3.5 AI Safety and Societal Control Mechanisms [01:07:10] 3.6 Evolution of AI Capabilities and Emergent Risks [01:14:20] REFS: [00:01:10] Procedural Knowledge in Pretraining & LLM Reasoning Ruis et al., 2024 https://arxiv.org/abs/2411.12580 [00:03:50] EK-FAC Influence Functions in Large LMs Grosse et al., 2023 https://arxiv.org/abs/2308.03296 [00:13:05] Surfaces and Essences: Analogy as the Core of Cognition Hofstadter & Sander https://www.amazon.com/Surfaces-Essences-Analogy-Fuel-Thinking/dp/0465018475 [00:13:45] Wittgenstein on Language Games https://plato.stanford.edu/entries/wittgenstein/ [00:14:30] Montague Semantics for Natural Language https://plato.stanford.edu/entries/montague-semantics/ [00:19:35] The Chinese Room Argument David Cole https://plato.stanford.edu/entries/chinese-room/ [00:19:55] ARC: Abstraction and Reasoning Corpus François Chollet https://arxiv.org/abs/1911.01547 [00:24:20] Systematic Generalization in Neural Nets Lake & Baroni, 2023 https://www.nature.com/articles/s41586-023-06668-3 [00:27:40] Open-Endedness & Creativity in AI Tim Rocktäschel https://arxiv.org/html/2406.04268v1 [00:30:50] Fodor & Pylyshyn on Connectionism https://www.sciencedirect.com/science/article/abs/pii/0010027788900315 [00:31:30] Tensor Product Representations Smolensky, 1990 https://www.sciencedirect.com/science/article/abs/pii/000437029090007M [00:35:50] DreamCoder: Wake-Sleep Program Synthesis Kevin Ellis et al. https://courses.cs.washington.edu/courses/cse599j1/22sp/papers/dreamcoder.pdf [00:36:30] Compositional Generalization Benchmarks Ruis, Lake et al., 2022 https://arxiv.org/pdf/2202.10745 [00:40:30] RNNs & Tensor Products McCoy et al., 2018 https://arxiv.org/abs/1812.08718 [00:46:10] Formal Causal Definition of Agency Kenton et al. https://arxiv.org/pdf/2208.08345v2 [00:48:40] Agency in Language Models Sumers et al. https://arxiv.org/abs/2309.02427 [00:55:20] Heider & Simmel’s Moving Shapes Experiment https://www.nature.com/articles/s41598-024-65532-0 [01:00:40] Language Models as Agent Models Jacob Andreas, 2022 https://arxiv.org/abs/2212.01681 [01:13:35] Pragmatic Understanding in LLMs Ruis et al. https://arxiv.org/abs/2210.14986