Prof. Gennady Pekhimenko (CEO of CentML, UofT) joins us in this *sponsored episode* to dive deep into AI system optimization and enterprise implementation. From NVIDIA's technical leadership model to the rise of open-source AI, Pekhimenko shares insights on bridging the gap between academic research and industrial applications. Learn about "dark silicon," GPU utilization challenges in ML workloads, and how modern enterprises can optimize their AI infrastructure. The conversation explores why some companies achieve only 10% GPU efficiency and practical solutions for improving AI system performance. A must-watch for anyone interested in the technical foundations of enterprise AI and hardware optimization.

CentML offers competitive pricing for GenAI model deployment, with flexible options to suit a wide range of models, from small to large-scale deployments. Cheaper, faster, no commitments, pay as you go, scale massively, simple to setup. Check it out!

https://centml.ai/pricing/

SPONSOR MESSAGES:

MLST is also sponsored by Tufa AI Labs - https://tufalabs.ai/

They are hiring cracked ML engineers/researchers to work on ARC and build AGI!

SHOWNOTES (diarised transcript, TOC, references, summary, best quotes etc)

https://www.dropbox.com/scl/fi/w9kbpso7fawtm286kkp6j/Gennady.pdf?rlkey=aqjqmncx3kjnatk2il1gbgknk&st=2a9mccj8&dl=0

TOC:

1. AI Strategy and Leadership

[00:00:00] 1.1 Technical Leadership and Corporate Structure

[00:09:55] 1.2 Open Source vs Proprietary AI Models

[00:16:04] 1.3 Hardware and System Architecture Challenges

[00:23:37] 1.4 Enterprise AI Implementation and Optimization

[00:35:30] 1.5 AI Reasoning Capabilities and Limitations

2. AI System Development

[00:38:45] 2.1 Computational and Cognitive Limitations of AI Systems

[00:42:40] 2.2 Human-LLM Communication Adaptation and Patterns

[00:46:18] 2.3 AI-Assisted Software Development Challenges

[00:47:55] 2.4 Future of Software Engineering Careers in AI Era

[00:49:49] 2.5 Enterprise AI Adoption Challenges and Implementation

3. ML Infrastructure Optimization

[00:54:41] 3.1 MLOps Evolution and Platform Centralization

[00:55:43] 3.2 Hardware Optimization and Performance Constraints

[01:05:24] 3.3 ML Compiler Optimization and Python Performance

[01:15:57] 3.4 Enterprise ML Deployment and Cloud Provider Partnerships

4. Distributed AI Architecture

[01:27:05] 4.1 Multi-Cloud ML Infrastructure and Optimization

[01:29:45] 4.2 AI Agent Systems and Production Readiness

[01:32:00] 4.3 RAG Implementation and Fine-Tuning Considerations

[01:33:45] 4.4 Distributed AI Systems Architecture and Ray Framework

5. AI Industry Standards and Research

[01:37:55] 5.1 Origins and Evolution of MLPerf Benchmarking

[01:43:15] 5.2 MLPerf Methodology and Industry Impact

[01:50:17] 5.3 Academic Research vs Industry Implementation in AI

[01:58:59] 5.4 AI Research History and Safety Concerns

Eliezer Yudkowsky and Stephen Wolfram discuss artificial intelligence and its potential existen‑

tial risks. They traversed fundamental questions about AI safety, consciousness, computational irreducibility, and the nature of intelligence.

The discourse centered on Yudkowsky’s argument that advanced AI systems pose an existential threat to humanity, primarily due to the challenge of alignment and the potential for emergent goals that diverge from human values. Wolfram, while acknowledging potential risks, approached the topic from a his signature measured perspective, emphasizing the importance of understanding computational systems’ fundamental nature and questioning whether AI systems would necessarily develop the kind of goal‑directed behavior Yudkowsky fears.

***

MLST IS SPONSORED BY TUFA AI LABS!

The current winners of the ARC challenge, MindsAI are part of Tufa AI Labs. They are hiring ML engineers. Are you interested?! Please goto https://tufalabs.ai/

***

TOC:

1. Foundational AI Concepts and Risks

[00:00:01] 1.1 AI Optimization and System Capabilities Debate

[00:06:46] 1.2 Computational Irreducibility and Intelligence Limitations

[00:20:09] 1.3 Existential Risk and Species Succession

[00:23:28] 1.4 Consciousness and Value Preservation in AI Systems

2. Ethics and Philosophy in AI

[00:33:24] 2.1 Moral Value of Human Consciousness vs. Computation

[00:36:30] 2.2 Ethics and Moral Philosophy Debate

[00:39:58] 2.3 Existential Risks and Digital Immortality

[00:43:30] 2.4 Consciousness and Personal Identity in Brain Emulation

3. Truth and Logic in AI Systems

[00:54:39] 3.1 AI Persuasion Ethics and Truth

[01:01:48] 3.2 Mathematical Truth and Logic in AI Systems

[01:11:29] 3.3 Universal Truth vs Personal Interpretation in Ethics and Mathematics

[01:14:43] 3.4 Quantum Mechanics and Fundamental Reality Debate

4. AI Capabilities and Constraints

[01:21:21] 4.1 AI Perception and Physical Laws

[01:28:33] 4.2 AI Capabilities and Computational Constraints

[01:34:59] 4.3 AI Motivation and Anthropomorphization Debate

[01:38:09] 4.4 Prediction vs Agency in AI Systems

5. AI System Architecture and Behavior

[01:44:47] 5.1 Computational Irreducibility and Probabilistic Prediction

[01:48:10] 5.2 Teleological vs Mechanistic Explanations of AI Behavior

[02:09:41] 5.3 Machine Learning as Assembly of Computational Components

[02:29:52] 5.4 AI Safety and Predictability in Complex Systems

6. Goal Optimization and Alignment

[02:50:30] 6.1 Goal Specification and Optimization Challenges in AI Systems

[02:58:31] 6.2 Intelligence, Computation, and Goal-Directed Behavior

[03:02:18] 6.3 Optimization Goals and Human Existential Risk

[03:08:49] 6.4 Emergent Goals and AI Alignment Challenges

7. AI Evolution and Risk Assessment

[03:19:44] 7.1 Inner Optimization and Mesa-Optimization Theory

[03:34:00] 7.2 Dynamic AI Goals and Extinction Risk Debate

[03:56:05] 7.3 AI Risk and Biological System Analogies

[04:09:37] 7.4 Expert Risk Assessments and Optimism vs Reality

8. Future Implications and Economics

[04:13:01] 8.1 Economic and Proliferation Considerations

SHOWNOTES (transcription, references, summary, best quotes etc):

https://www.dropbox.com/scl/fi/3st8dts2ba7yob161dchd/EliezerWolfram.pdf?rlkey=b6va5j8upgqwl9s2muc924vtt&st=vemwqx7a&dl=0

Francois Chollet, a prominent AI expert and creator of ARC-AGI, discusses intelligence, consciousness, and artificial intelligence.

Chollet explains that real intelligence isn't about memorizing information or having lots of knowledge - it's about being able to handle new situations effectively. This is why he believes current large language models (LLMs) have "near-zero intelligence" despite their impressive abilities. They're more like sophisticated memory and pattern-matching systems than truly intelligent beings.

***

MLST IS SPONSORED BY TUFA AI LABS!

The current winners of the ARC challenge, MindsAI are part of Tufa AI Labs. They are hiring ML engineers. Are you interested?! Please goto https://tufalabs.ai/

***

He introduced his "Kaleidoscope Hypothesis," which suggests that while the world seems infinitely complex, it's actually made up of simpler patterns that repeat and combine in different ways. True intelligence, he argues, involves identifying these basic patterns and using them to understand new situations.

Chollet also talked about consciousness, suggesting it develops gradually in children rather than appearing all at once. He believes consciousness exists in degrees - animals have it to some extent, and even human consciousness varies with age and circumstances (like being more conscious when learning something new versus doing routine tasks).

On AI safety, Chollet takes a notably different stance from many in Silicon Valley. He views AGI development as a scientific challenge rather than a religious quest, and doesn't share the apocalyptic concerns of some AI researchers. He argues that intelligence itself isn't dangerous - it's just a tool for turning information into useful models. What matters is how we choose to use it.

ARC-AGI Prize:

https://arcprize.org/

Francois Chollet:

https://x.com/fchollet

Shownotes:

https://www.dropbox.com/scl/fi/j2068j3hlj8br96pfa7bi/CHOLLET_FINAL.pdf?rlkey=xkbr7tbnrjdl66m246w26uc8k&st=0a4ec4na&dl=0

TOC:

1. Intelligence and Model Building

[00:00:00] 1.1 Intelligence Definition and ARC Benchmark

[00:05:40] 1.2 LLMs as Program Memorization Systems

[00:09:36] 1.3 Kaleidoscope Hypothesis and Abstract Building Blocks

[00:13:39] 1.4 Deep Learning Limitations and System 2 Reasoning

[00:29:38] 1.5 Intelligence vs. Skill in LLMs and Model Building

2. ARC Benchmark and Program Synthesis

[00:37:36] 2.1 Intelligence Definition and LLM Limitations

[00:41:33] 2.2 Meta-Learning System Architecture

[00:56:21] 2.3 Program Search and Occam's Razor

[00:59:42] 2.4 Developer-Aware Generalization

[01:06:49] 2.5 Task Generation and Benchmark Design

3. Cognitive Systems and Program Generation

[01:14:38] 3.1 System 1/2 Thinking Fundamentals

[01:22:17] 3.2 Program Synthesis and Combinatorial Challenges

[01:31:18] 3.3 Test-Time Fine-Tuning Strategies

[01:36:10] 3.4 Evaluation and Leakage Problems

[01:43:22] 3.5 ARC Implementation Approaches

4. Intelligence and Language Systems

[01:50:06] 4.1 Intelligence as Tool vs Agent

[01:53:53] 4.2 Cultural Knowledge Integration

[01:58:42] 4.3 Language and Abstraction Generation

[02:02:41] 4.4 Embodiment in Cognitive Systems

[02:09:02] 4.5 Language as Cognitive Operating System

5. Consciousness and AI Safety

[02:14:05] 5.1 Consciousness and Intelligence Relationship

[02:20:25] 5.2 Development of Machine Consciousness

[02:28:40] 5.3 Consciousness Prerequisites and Indicators

[02:36:36] 5.4 AGI Safety Considerations

[02:40:29] 5.5 AI Regulation Framework

Anil Ananthaswamy is an award-winning science writer and former staff writer and deputy news editor for the London-based New Scientist magazine.

Machine learning systems are making life-altering decisions for us: approving mortgage loans, determining whether a tumor is cancerous, or deciding if someone gets bail. They now influence developments and discoveries in chemistry, biology, and physics—the study of genomes, extrasolar planets, even the intricacies of quantum systems. And all this before large language models such as ChatGPT came on the scene.

We are living through a revolution in machine learning-powered AI that shows no signs of slowing down. This technology is based on relatively simple mathematical ideas, some of which go back centuries, including linear algebra and calculus, the stuff of seventeenth- and eighteenth-century mathematics. It took the birth and advancement of computer science and the kindling of 1990s computer chips designed for video games to ignite the explosion of AI that we see today. In this enlightening book, Anil Ananthaswamy explains the fundamental math behind machine learning, while suggesting intriguing links between artificial and natural intelligence. Might the same math underpin them both?

As Ananthaswamy resonantly concludes, to make safe and effective use of artificial intelligence, we need to understand its profound capabilities and limitations, the clues to which lie in the math that makes machine learning possible.

Why Machines Learn: The Elegant Math Behind Modern AI:

https://amzn.to/3UAWX3D

https://anilananthaswamy.com/

Sponsor message:

DO YOU WANT WORK ON ARC with the MindsAI team (current ARC winners)?

Interested? Apply for an ML research position: [email protected]

Shownotes:

https://www.dropbox.com/scl/fi/wpv22m5jxyiqr6pqfkzwz/anil.pdf?rlkey=9c233jo5armr548ctwo419n6p&st=xzhahtje&dl=0

Chapters:

1. ML Fundamentals and Prerequisites

[00:00:00] 1.1 Differences Between Human and Machine Learning

[00:00:35] 1.2 Mathematical Prerequisites and Societal Impact of ML

[00:02:20] 1.3 Author's Journey and Book Background

[00:11:30] 1.4 Mathematical Foundations and Core ML Concepts

[00:21:45] 1.5 Bias-Variance Tradeoff and Modern Deep Learning

2. Deep Learning Architecture

[00:29:05] 2.1 Double Descent and Overparameterization in Deep Learning

[00:32:40] 2.2 Mathematical Foundations and Self-Supervised Learning

[00:40:05] 2.3 High-Dimensional Spaces and Model Architecture

[00:52:55] 2.4 Historical Development of Backpropagation

3. AI Understanding and Limitations

[00:59:13] 3.1 Pattern Matching vs Human Reasoning in ML Models

[01:00:20] 3.2 Mathematical Foundations and Pattern Recognition in AI

[01:04:08] 3.3 LLM Reliability and Machine Understanding Debate

[01:12:50] 3.4 Historical Development of Deep Learning Technologies

[01:15:21] 3.5 Alternative AI Approaches and Bio-inspired Methods

4. Ethical and Neurological Perspectives

[01:24:32] 4.1 Neural Network Scaling and Mathematical Limitations

[01:31:12] 4.2 AI Ethics and Societal Impact

[01:38:30] 4.3 Consciousness and Neurological Conditions

[01:46:17] 4.4 Body Ownership and Agency in Neuroscience

Professor Michael Levin explores the revolutionary concept of diverse intelligence, demonstrating how cognitive capabilities extend far beyond traditional brain-based intelligence. Drawing from his groundbreaking research, he explains how even simple biological systems like gene regulatory networks exhibit learning, memory, and problem-solving abilities. Levin introduces key concepts like "cognitive light cones" - the scope of goals a system can pursue - and shows how these ideas are transforming our approach to cancer treatment and biological engineering. His insights challenge conventional views of intelligence and agency, with profound implications for both medicine and artificial intelligence development. This deep discussion reveals how understanding intelligence as a spectrum, from molecular networks to human minds, could be crucial for humanity's future technological development. Contains technical discussion of biological systems, cybernetics, and theoretical frameworks for understanding emergent cognition.

Prof. Michael Levin

https://as.tufts.edu/biology/people/faculty/michael-levin

https://x.com/drmichaellevin

Sponsor message:

DO YOU WANT WORK ON ARC with the MindsAI team (current ARC winners)?

Interested? Apply for an ML research position: [email protected]

TOC

1. Intelligence Fundamentals and Evolution

[00:00:00] 1.1 Future Evolution of Human Intelligence and Consciousness

[00:03:00] 1.2 Science Fiction's Role in Exploring Intelligence Possibilities

[00:08:15] 1.3 Essential Characteristics of Human-Level Intelligence and Relationships

[00:14:20] 1.4 Biological Systems Architecture and Intelligence

2. Biological Computing and Cognition

[00:24:00] 2.1 Agency and Intelligence in Biological Systems

[00:30:30] 2.2 Learning Capabilities in Gene Regulatory Networks

[00:35:37] 2.3 Biological Control Systems and Competency Architecture

[00:39:58] 2.4 Scientific Metaphors and Polycomputing Paradigm

3. Systems and Collective Intelligence

[00:43:26] 3.1 Embodiment and Problem-Solving Spaces

[00:44:50] 3.2 Perception-Action Loops and Biological Intelligence

[00:46:55] 3.3 Intelligence, Wisdom and Collective Systems

[00:53:07] 3.4 Cancer and Cognitive Light Cones

[00:57:09] 3.5 Emergent Intelligence and AI Agency

Shownotes:

https://www.dropbox.com/scl/fi/i2vl1vs009thg54lxx5wc/LEVIN.pdf?rlkey=dtk8okhbsejryiu2vrht19qp6&st=uzi0vo45&dl=0

REFS:

[0:05:30] A Fire Upon the Deep - Vernor Vinge sci-fi novel on AI and consciousness

[0:05:35] Maria Chudnovsky - MacArthur Fellow, Princeton mathematician, graph theory expert

[0:14:20] Bow-tie architecture in biological systems - Network structure research by Csete & Doyle

[0:15:40] Richard Watson - Southampton Professor, evolution and learning systems expert

[0:17:00] Levin paper on human issues in AI and evolution

[0:19:00] Bow-tie architecture in Darwin's agential materialism - Levin

[0:22:55] Philip Goff - Work on panpsychism and consciousness in Galileo's Error

[0:23:30] Strange Loop - Hofstadter's work on self-reference and consciousness

[0:25:00] The Hard Problem of Consciousness - Van Gulick

[0:26:15] Daniel Dennett - Theories on consciousness and intentional systems

[0:29:35] Principle of Least Action - Light path selection in physics

[0:29:50] Free Energy Principle - Friston's unified behavioral framework

[0:30:35] Gene regulatory networks - Learning capabilities in biological systems

[0:36:55] Minimal networks with learning capacity - Levin

[0:38:50] Multi-scale competency in biological systems - Levin

[0:41:40] Polycomputing paradigm - Biological computation by Bongard & Levin

[0:45:40] Collective intelligence in biology - Levin et al.

[0:46:55] Niche construction and stigmergy - Torday

[0:53:50] Tasmanian Devil Facial Tumor Disease - Transmissible cancer research

[0:55:05] Cognitive light cone - Computational boundaries of self - Levin

[0:58:05] Cognitive properties in sorting algorithms - Zhang, Goldstein & Levin

Will Williams is CTO of Speechmatics in Cambridge. In this sponsored episode - he shares deep technical insights into modern speech recognition technology and system architecture. The episode covers several key technical areas:

* Speechmatics' hybrid approach to ASR, which focusses on unsupervised learning methods, achieving comparable results with 100x less data than fully supervised approaches. Williams explains why this is more efficient and generalizable than end-to-end models like Whisper.

* Their production architecture implementing multiple operating points for different latency-accuracy trade-offs, with careful latency padding (up to 1.8 seconds) to ensure consistent user experience. The system uses lattice-based decoding with language model integration for improved accuracy.

* The challenges and solutions in real-time ASR, including their approach to diarization (speaker identification), handling cross-talk, and implicit source separation. Williams explains why these problems remain difficult even with modern deep learning approaches.

* Their testing and deployment infrastructure, including the use of mirrored environments for catching edge cases in production, and their strategy of maintaining global models rather than allowing customer-specific fine-tuning.

* Technical evolution in ASR, from early days of custom CUDA kernels and manual memory management to modern frameworks, with Williams offering interesting critiques of current PyTorch memory management approaches and arguing for more efficient direct memory allocation in production systems.

Get coding with their API! This is their URL:

https://www.speechmatics.com/

DO YOU WANT WORK ON ARC with the MindsAI team (current ARC winners)?

MLST is sponsored by Tufa Labs:

Focus: ARC, LLMs, test-time-compute, active inference, system2 reasoning, and more.

Interested? Apply for an ML research position: [email protected]

TOC

1. ASR Core Technology & Real-time Architecture

[00:00:00] 1.1 ASR and Diarization Fundamentals

[00:05:25] 1.2 Real-time Conversational AI Architecture

[00:09:21] 1.3 Neural Network Streaming Implementation

[00:12:49] 1.4 Multi-modal System Integration

2. Production System Optimization

[00:29:38] 2.1 Production Deployment and Testing Infrastructure

[00:35:40] 2.2 Model Architecture and Deployment Strategy

[00:37:12] 2.3 Latency-Accuracy Trade-offs

[00:39:15] 2.4 Language Model Integration

[00:40:32] 2.5 Lattice-based Decoding Architecture

3. Performance Evaluation & Ethical Considerations

[00:44:00] 3.1 ASR Performance Metrics and Capabilities

[00:46:35] 3.2 AI Regulation and Evaluation Methods

[00:51:09] 3.3 Benchmark and Testing Challenges

[00:54:30] 3.4 Real-world Implementation Metrics

[01:00:51] 3.5 Ethics and Privacy Considerations

4. ASR Technical Evolution

[01:09:00] 4.1 WER Calculation and Evaluation Methodologies

[01:10:21] 4.2 Supervised vs Self-Supervised Learning Approaches

[01:21:02] 4.3 Temporal Learning and Feature Processing

[01:24:45] 4.4 Feature Engineering to Automated ML

5. Enterprise Implementation & Scale

[01:27:55] 5.1 Future AI Systems and Adaptation

[01:31:52] 5.2 Technical Foundations and History

[01:34:53] 5.3 Infrastructure and Team Scaling

[01:38:05] 5.4 Research and Talent Strategy

[01:41:11] 5.5 Engineering Practice Evolution

Shownotes:

https://www.dropbox.com/scl/fi/d94b1jcgph9o8au8shdym/Speechmatics.pdf?rlkey=bi55wvktzomzx0y5sic6jz99y&st=6qwofv8t&dl=0

Dr. Sanjeev Namjoshi, a machine learning engineer who recently submitted a book on Active Inference to MIT Press, discusses the theoretical foundations and practical applications of Active Inference, the Free Energy Principle (FEP), and Bayesian mechanics. He explains how these frameworks describe how biological and artificial systems maintain stability by minimizing uncertainty about their environment.

DO YOU WANT WORK ON ARC with the MindsAI team (current ARC winners)?

MLST is sponsored by Tufa Labs:

Focus: ARC, LLMs, test-time-compute, active inference, system2 reasoning, and more.

Future plans: Expanding to complex environments like Warcraft 2 and Starcraft 2.

Interested? Apply for an ML research position: [email protected]

Namjoshi traces the evolution of these fields from early 2000s neuroscience research to current developments, highlighting how Active Inference provides a unified framework for perception and action through variational free energy minimization. He contrasts this with traditional machine learning approaches, emphasizing Active Inference's natural capacity for exploration and curiosity through epistemic value.

He sees Active Inference as being at a similar stage to deep learning in the early 2000s - poised for significant breakthroughs but requiring better tools and wider adoption. While acknowledging current computational challenges, he emphasizes Active Inference's potential advantages over reinforcement learning, particularly its principled approach to exploration and planning.

Dr. Sanjeev Namjoshi

https://snamjoshi.github.io/

TOC:

1. Theoretical Foundations: AI Agency and Sentience

[00:00:00] 1.1 Intro

[00:02:45] 1.2 Free Energy Principle and Active Inference Theory

[00:11:16] 1.3 Emergence and Self-Organization in Complex Systems

[00:19:11] 1.4 Agency and Representation in AI Systems

[00:29:59] 1.5 Bayesian Mechanics and Systems Modeling

2. Technical Framework: Active Inference and Free Energy

[00:38:37] 2.1 Generative Processes and Agent-Environment Modeling

[00:42:27] 2.2 Markov Blankets and System Boundaries

[00:44:30] 2.3 Bayesian Inference and Prior Distributions

[00:52:41] 2.4 Variational Free Energy Minimization Framework

[00:55:07] 2.5 VFE Optimization Techniques: Generalized Filtering vs DEM

3. Implementation and Optimization Methods

[00:58:25] 3.1 Information Theory and Free Energy Concepts

[01:05:25] 3.2 Surprise Minimization and Action in Active Inference

[01:15:58] 3.3 Evolution of Active Inference Models: Continuous to Discrete Approaches

[01:26:00] 3.4 Uncertainty Reduction and Control Systems in Active Inference

4. Safety and Regulatory Frameworks

[01:32:40] 4.1 Historical Evolution of Risk Management and Predictive Systems

[01:36:12] 4.2 Agency and Reality: Philosophical Perspectives on Models

[01:39:20] 4.3 Limitations of Symbolic AI and Current System Design

[01:46:40] 4.4 AI Safety Regulation and Corporate Governance

5. Socioeconomic Integration and Modeling

[01:52:55] 5.1 Economic Policy and Public Sentiment Modeling

[01:55:21] 5.2 Free Energy Principle: Libertarian vs Collectivist Perspectives

[01:58:53] 5.3 Regulation of Complex Socio-Technical Systems

[02:03:04] 5.4 Evolution and Current State of Active Inference Research

6. Future Directions and Applications

[02:14:26] 6.1 Active Inference Applications and Future Development

[02:22:58] 6.2 Cultural Learning and Active Inference

[02:29:19] 6.3 Hierarchical Relationship Between FEP, Active Inference, and Bayesian Mechanics

[02:33:22] 6.4 Historical Evolution of Free Energy Principle

[02:38:52] 6.5 Active Inference vs Traditional Machine Learning Approaches

Transcript and shownotes with refs and URLs:

https://www.dropbox.com/scl/fi/qj22a660cob1795ej0gbw/SanjeevShow.pdf?rlkey=w323r3e8zfsnve22caayzb17k&st=el1fdgfr&dl=0

Dr. Joscha Bach discusses advanced AI, consciousness, and cognitive modeling. He presents consciousness as a virtual property emerging from self-organizing software patterns, challenging panpsychism and materialism. Bach introduces "Cyberanima," reinterpreting animism through information processing, viewing spirits as self-organizing software agents.

He addresses limitations of current large language models and advocates for smaller, more efficient AI models capable of reasoning from first principles. Bach describes his work with Liquid AI on novel neural network architectures for improved expressiveness and efficiency.

The interview covers AI's societal implications, including regulation challenges and impact on innovation. Bach argues for balancing oversight with technological progress, warning against overly restrictive regulations.

Throughout, Bach frames consciousness, intelligence, and agency as emergent properties of complex information processing systems, proposing a computational framework for cognitive phenomena and reality.

SPONSOR MESSAGE:

DO YOU WANT WORK ON ARC with the MindsAI team (current ARC winners)? MLST is sponsored by Tufa Labs: Focus: ARC, LLMs, test-time-compute, active inference, system2 reasoning, and more. Future plans: Expanding to complex environments like Warcraft 2 and Starcraft 2. Interested? Apply for an ML research position: benjamin@tufa.ai

TOC

[00:00:00] 1.1 Consciousness and Intelligence in AI Development

[00:07:44] 1.2 Agency, Intelligence, and Their Relationship to Physical Reality

[00:13:36] 1.3 Virtual Patterns and Causal Structures in Consciousness

[00:25:49] 1.4 Reinterpreting Concepts of God and Animism in Information Processing Terms

[00:32:50] 1.5 Animism and Evolution as Competition Between Software Agents

2. Self-Organizing Systems and Cognitive Models in AI

[00:37:59] 2.1 Consciousness as self-organizing software

[00:45:49] 2.2 Critique of panpsychism and alternative views on consciousness

[00:50:48] 2.3 Emergence of consciousness in complex systems

[00:52:50] 2.4 Neuronal motivation and the origins of consciousness

[00:56:47] 2.5 Coherence and Self-Organization in AI Systems

3. Advanced AI Architectures and Cognitive Processes

[00:57:50] 3.1 Second-Order Software and Complex Mental Processes

[01:01:05] 3.2 Collective Agency and Shared Values in AI

[01:05:40] 3.3 Limitations of Current AI Agents and LLMs

[01:06:40] 3.4 Liquid AI and Novel Neural Network Architectures

[01:10:06] 3.5 AI Model Efficiency and Future Directions

[01:19:00] 3.6 LLM Limitations and Internal State Representation

4. AI Regulation and Societal Impact

[01:31:23] 4.1 AI Regulation and Societal Impact

[01:49:50] 4.2 Open-Source AI and Industry Challenges

Refs in shownotes and MP3 metadata

Shownotes:

https://www.dropbox.com/scl/fi/g28dosz19bzcfs5imrvbu/JoschaInterview.pdf?rlkey=s3y18jy192ktz6ogd7qtvry3d&st=10z7q7w9&dl=0

Alessandro Palmarini is a post-baccalaureate researcher at the Santa Fe Institute working under the supervision of Melanie Mitchell. He completed his undergraduate degree in Artificial Intelligence and Computer Science at the University of Edinburgh. Palmarini's current research focuses on developing AI systems that can efficiently acquire new skills from limited data, inspired by François Chollet's work on measuring intelligence. His work builds upon the DreamCoder program synthesis system, introducing a novel approach called "dream decompiling" to improve library learning in inductive program synthesis. Palmarini is particularly interested in addressing the Abstraction and Reasoning Corpus (ARC) challenge, aiming to create AI systems that can perform abstract reasoning tasks more efficiently than current approaches. His research explores the balance between computational efficiency and data efficiency in AI learning processes.

DO YOU WANT WORK ON ARC with the MindsAI team (current ARC winners)? MLST is sponsored by Tufa Labs: Focus: ARC, LLMs, test-time-compute, active inference, system2 reasoning, and more. Future plans: Expanding to complex environments like Warcraft 2 and Starcraft 2. Interested? Apply for an ML research position: benjamin@tufa.ai

TOC:

1. Intelligence Measurement in AI Systems

[00:00:00] 1.1 Defining Intelligence in AI Systems

[00:02:00] 1.2 Research at Santa Fe Institute

[00:04:35] 1.3 Impact of Gaming on AI Development

[00:05:10] 1.4 Comparing AI and Human Learning Efficiency

2. Efficient Skill Acquisition in AI

[00:06:40] 2.1 Intelligence as Skill Acquisition Efficiency

[00:08:25] 2.2 Limitations of Current AI Systems in Generalization

[00:09:45] 2.3 Human vs. AI Cognitive Processes

[00:10:40] 2.4 Measuring AI Intelligence: Chollet's ARC Challenge

3. Program Synthesis and ARC Challenge

[00:12:55] 3.1 Philosophical Foundations of Program Synthesis

[00:17:14] 3.2 Introduction to Program Induction and ARC Tasks

[00:18:49] 3.3 DreamCoder: Principles and Techniques

[00:27:55] 3.4 Trade-offs in Program Synthesis Search Strategies

[00:31:52] 3.5 Neural Networks and Bayesian Program Learning

4. Advanced Program Synthesis Techniques

[00:32:30] 4.1 DreamCoder and Dream Decompiling Approach

[00:39:00] 4.2 Beta Distribution and Caching in Program Synthesis

[00:45:10] 4.3 Performance and Limitations of Dream Decompiling

[00:47:45] 4.4 Alessandro's Approach to ARC Challenge

[00:51:12] 4.5 Conclusion and Future Discussions

Refs:

Full reflist on YT VD, Show Notes and MP3 metadata

Show Notes: https://www.dropbox.com/scl/fi/x50201tgqucj5ba2q4typ/Ale.pdf?rlkey=0ubvk7p5gtyx1gpownpdadim8&st=5pniu3nq&dl=0

François Chollet discusses the limitations of Large Language Models (LLMs) and proposes a new approach to advancing artificial intelligence. He argues that current AI systems excel at pattern recognition but struggle with logical reasoning and true generalization.

This was Chollet's keynote talk at AGI-24, filmed in high-quality. We will be releasing a full interview with him shortly. A teaser clip from that is played in the intro!

Chollet introduces the Abstraction and Reasoning Corpus (ARC) as a benchmark for measuring AI progress towards human-like intelligence. He explains the concept of abstraction in AI systems and proposes combining deep learning with program synthesis to overcome current limitations. Chollet suggests that breakthroughs in AI might come from outside major tech labs and encourages researchers to explore new ideas in the pursuit of artificial general intelligence.

TOC

1. LLM Limitations and Intelligence Concepts

[00:00:00] 1.1 LLM Limitations and Composition

[00:12:05] 1.2 Intelligence as Process vs. Skill

[00:17:15] 1.3 Generalization as Key to AI Progress

2. ARC-AGI Benchmark and LLM Performance

[00:19:59] 2.1 Introduction to ARC-AGI Benchmark

[00:20:05] 2.2 Introduction to ARC-AGI and the ARC Prize

[00:23:35] 2.3 Performance of LLMs and Humans on ARC-AGI

3. Abstraction in AI Systems

[00:26:10] 3.1 The Kaleidoscope Hypothesis and Abstraction Spectrum

[00:30:05] 3.2 LLM Capabilities and Limitations in Abstraction

[00:32:10] 3.3 Value-Centric vs Program-Centric Abstraction

[00:33:25] 3.4 Types of Abstraction in AI Systems

4. Advancing AI: Combining Deep Learning and Program Synthesis

[00:34:05] 4.1 Limitations of Transformers and Need for Program Synthesis

[00:36:45] 4.2 Combining Deep Learning and Program Synthesis

[00:39:59] 4.3 Applying Combined Approaches to ARC Tasks

[00:44:20] 4.4 State-of-the-Art Solutions for ARC

Shownotes (new!): https://www.dropbox.com/scl/fi/i7nsyoahuei6np95lbjxw/CholletKeynote.pdf?rlkey=t3502kbov5exsdxhderq70b9i&st=1ca91ewz&dl=0

[0:01:15] Abstraction and Reasoning Corpus (ARC): AI benchmark (François Chollet)

https://arxiv.org/abs/1911.01547

[0:05:30] Monty Hall problem: Probability puzzle (Steve Selvin)

https://www.tandfonline.com/doi/abs/10.1080/00031305.1975.10479121

[0:06:20] LLM training dynamics analysis (Tirumala et al.)

https://arxiv.org/abs/2205.10770

[0:10:20] Transformer limitations on compositionality (Dziri et al.)

https://arxiv.org/abs/2305.18654

[0:10:25] Reversal Curse in LLMs (Berglund et al.)

https://arxiv.org/abs/2309.12288

[0:19:25] Measure of intelligence using algorithmic information theory (François Chollet)

https://arxiv.org/abs/1911.01547

[0:20:10] ARC-AGI: GitHub repository (François Chollet)

https://github.com/fchollet/ARC-AGI

[0:22:15] ARC Prize: $1,000,000+ competition (François Chollet)

https://arcprize.org/

[0:33:30] System 1 and System 2 thinking (Daniel Kahneman)

https://www.amazon.com/Thinking-Fast-Slow-Daniel-Kahneman/dp/0374533555

[0:34:00] Core knowledge in infants (Elizabeth Spelke)

https://www.harvardlds.org/wp-content/uploads/2017/01/SpelkeKinzler07-1.pdf

[0:34:30] Embedding interpretive spaces in ML (Tennenholtz et al.)

https://arxiv.org/abs/2310.04475

[0:44:20] Hypothesis Search with LLMs for ARC (Wang et al.)

https://arxiv.org/abs/2309.05660

[0:44:50] Ryan Greenblatt's high score on ARC public leaderboard

https://arcprize.org/

Ivan Zhang, co-founder of Cohere, discusses the company's enterprise-focused AI solutions. He explains Cohere's early emphasis on embedding technology and training models for secure environments.

Zhang highlights their implementation of Retrieval-Augmented Generation in healthcare, significantly reducing doctor preparation time. He explores the shift from monolithic AI models to heterogeneous systems and the importance of improving various AI system components. Zhang shares insights on using synthetic data to teach models reasoning, the democratization of software development through AI, and how his gaming skills transfer to running an AI company.

He advises young developers to fully embrace AI technologies and offers perspectives on AI reliability, potential risks, and future model architectures.

https://cohere.com/

https://ivanzhang.ca/

https://x.com/1vnzh

TOC:

00:00:00 Intro

00:03:20 AI & Language Model Evolution

00:06:09 Future AI Apps & Development

00:09:29 Impact on Software Dev Practices

00:13:03 Philosophical & Societal Implications

00:16:30 Compute Efficiency & RAG

00:20:39 Adoption Challenges & Solutions

00:22:30 GPU Optimization & Kubernetes Limits

00:24:16 Cohere's Implementation Approach

00:28:13 Gaming's Professional Influence

00:34:45 Transformer Optimizations

00:36:45 Future Models & System-Level Focus

00:39:20 Inference-Time Computation & Reasoning

00:42:05 Capturing Human Thought in AI

00:43:15 Research, Hiring & Developer Advice

REFS:

00:02:31 Cohere, https://cohere.com/

00:02:40 The Transformer architecture, https://arxiv.org/abs/1706.03762

00:03:22 The Innovator's Dilemma, https://www.amazon.com/Innovators-Dilemma-Technologies-Management-Innovation/dp/1633691780

00:09:15 The actor model, https://en.wikipedia.org/wiki/Actor_model

00:14:35 John Searle's Chinese Room Argument, https://plato.stanford.edu/entries/chinese-room/

00:18:00 Retrieval-Augmented Generation, https://arxiv.org/abs/2005.11401

00:18:40 Retrieval-Augmented Generation, https://docs.cohere.com/v2/docs/retrieval-augmented-generation-rag

00:35:39 Let’s Verify Step by Step, https://arxiv.org/pdf/2305.20050

00:39:20 Adaptive Inference-Time Compute, https://arxiv.org/abs/2410.02725

00:43:20 Ryan Greenblatt ARC entry, https://redwoodresearch.substack.com/p/getting-50-sota-on-arc-agi-with-gpt

Disclaimer: This show is part of our Cohere partnership series