Machine Learning Street Talk (MLST)

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: benjamin@tufa.ai Chapters: 00:00:00 Intro 00:02:20 Mathematical Foundations and Future Implications 00:05:14 Background and Journey in ML Mathematics 00:08:27 Historical Mathematical Foundations in ML 00:11:25 Core Mathematical Components of Modern ML 00:14:09 Evolution from Classical ML to Deep Learning 00:21:42 Bias-Variance Trade-off and Double Descent 00:30:39 Self-Supervised vs Supervised Learning Fundamentals 00:32:08 Addressing Spurious Correlations 00:34:25 Language Models and Training Approaches 00:35:48 Future Direction and Unsupervised Learning 00:38:35 Optimization and Dimensionality Challenges 00:43:19 Emergence and Scaling in Large Language Models 01:53:52 Outro

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: benjamin@tufa.ai 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...

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: benjamin@tufa.ai 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: benjamin@tufa.ai 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

Prof. Tim Rocktäschel, AI researcher at UCL and Google DeepMind, talks about open-ended AI systems. These systems aim to keep learning and improving on their own, like evolution does in nature. Ad: Are you a hardcore ML engineer who wants to work for Daniel Cahn at SlingshotAI building AI for mental health? Give him an email! - danielc@slingshot.xyz TOC: 00:00:00 Introduction to Open-Ended AI and Key Concepts 00:01:37 Tim Rocktäschel's Background and Research Focus 00:06:25 Defining Open-Endedness in AI Systems 00:10:39 Subjective Nature of Interestingness and Learnability 00:16:22 Open-Endedness in Practice: Examples and Limitations 00:17:50 Assessing Novelty in Open-ended AI Systems 00:20:05 Adversarial Attacks and AI Robustness 00:24:05 Rainbow Teaming and LLM Safety 00:25:48 Open-ended Research Approaches in AI 00:29:05 Balancing Long-term Vision and Exploration in AI Research 00:37:25 LLMs in Program Synthesis and Open-Ended Learning 00:37:55 Transition from Human-Based to Novel AI Strategies 00:39:00 Expanding Context Windows and Prompt Evolution 00:40:17 AI Intelligibility and Human-AI Interfaces 00:46:04 Self-Improvement and Evolution in AI Systems Show notes (New!) https://www.dropbox.com/scl/fi/5avpsyz8jbn4j1az7kevs/TimR.pdf?rlkey=pqjlcqbtm3undp4udtgfmie8n&st=x50u1d1m&dl=0 REFS: 00:01:47 - UCL DARK Lab (Rocktäschel) - AI research lab focusing on RL and open-ended learning - https://ucldark.com/ 00:02:31 - GENIE (Bruce) - Generative interactive environment from unlabelled videos - https://arxiv.org/abs/2402.15391 00:02:42 - Promptbreeder (Fernando) - Self-referential LLM prompt evolution - https://arxiv.org/abs/2309.16797 00:03:05 - Picbreeder (Secretan) - Collaborative online image evolution - https://dl.acm.org/doi/10.1145/1357054.1357328 00:03:14 - Why Greatness Cannot Be Planned (Stanley) - Book on open-ended exploration - https://www.amazon.com/Why-Greatness-Cannot-Planned-Objective/dp/3319155237 00:04:36 - NetHack Learning Environment (Küttler) - RL research in procedurally generated game - https://arxiv.org/abs/2006.13760 00:07:35 - Open-ended learning (Clune) - AI systems for continual learning and adaptation - https://arxiv.org/abs/1905.10985 00:07:35 - OMNI (Zhang) - LLMs modeling human interestingness for exploration - https://arxiv.org/abs/2306.01711 00:10:42 - Observer theory (Wolfram) - Computationally bounded observers in complex systems - https://writings.stephenwolfram.com/2023/12/observer-theory/ 00:15:25 - Human-Timescale Adaptation (Rocktäschel) - RL agent adapting to novel 3D tasks - https://arxiv.org/abs/2301.07608 00:16:15 - Open-Endedness for AGI (Hughes) - Importance of open-ended learning for AGI - https://arxiv.org/abs/2406.04268 00:16:35 - POET algorithm (Wang) - Open-ended approach to generate and solve challenges - https://arxiv.org/abs/1901.01753 00:17:20 - AlphaGo (Silver) - AI mastering the game of Go - https://deepmind.google/technologies/alphago/ 00:20:35 - Adversarial Go attacks (Dennis) - Exploiting weaknesses in Go AI systems - https://www.ifaamas.org/Proceedings/aamas2024/pdfs/p1630.pdf 00:22:00 - Levels of AGI (Morris) - Framework for categorizing AGI progress - https://arxiv.org/abs/2311.02462 00:24:30 - Rainbow Teaming (Samvelyan) - LLM-based adversarial prompt generation - https://arxiv.org/abs/2402.16822 00:25:50 - Why Greatness Cannot Be Planned (Stanley) - 'False compass' and 'stepping stone collection' concepts - https://www.amazon.com/Why-Greatness-Cannot-Planned-Objective/dp/3319155237 00:27:45 - AI Debate (Khan) - Improving LLM truthfulness through debate - https://proceedings.mlr.press/v235/khan24a.html 00:29:40 - Gemini (Google DeepMind) - Advanced multimodal AI model - https://deepmind.google/technologies/gemini/ 00:30:15 - How to Take Smart Notes (Ahrens) - Effective note-taking methodology - https://www.amazon.com/How-Take-Smart-Notes-Nonfiction/dp/1542866502 (truncated, see...

Ben Goertzel discusses AGI development, transhumanism, and the potential societal impacts of superintelligent AI. He predicts human-level AGI by 2029 and argues that the transition to superintelligence could happen within a few years after. Goertzel explores the challenges of AI regulation, the limitations of current language models, and the need for neuro-symbolic approaches in AGI research. He also addresses concerns about resource allocation and cultural perspectives on transhumanism. TOC: [00:00:00] AGI Timeline Predictions and Development Speed [00:00:45] Limitations of Language Models in AGI Development [00:02:18] Current State and Trends in AI Research and Development [00:09:02] Emergent Reasoning Capabilities and Limitations of LLMs [00:18:15] Neuro-Symbolic Approaches and the Future of AI Systems [00:20:00] Evolutionary Algorithms and LLMs in Creative Tasks [00:21:25] Symbolic vs. Sub-Symbolic Approaches in AI [00:28:05] Language as Internal Thought and External Communication [00:30:20] AGI Development and Goal-Directed Behavior [00:35:51] Consciousness and AI: Expanding States of Experience [00:48:50] AI Regulation: Challenges and Approaches [00:55:35] Challenges in AI Regulation [00:59:20] AI Alignment and Ethical Considerations [01:09:15] AGI Development Timeline Predictions [01:12:40] OpenCog Hyperon and AGI Progress [01:17:48] Transhumanism and Resource Allocation Debate [01:20:12] Cultural Perspectives on Transhumanism [01:23:54] AGI and Post-Scarcity Society [01:31:35] Challenges and Implications of AGI Development New! PDF Show notes: https://www.dropbox.com/scl/fi/fyetzwgoaf70gpovyfc4x/BenGoertzel.pdf?rlkey=pze5dt9vgf01tf2wip32p5hk5&st=svbcofm3&dl=0 Refs: 00:00:15 Ray Kurzweil's AGI timeline prediction, Ray Kurzweil, https://en.wikipedia.org/wiki/Technological_singularity 00:01:45 Ben Goertzel: SingularityNET founder, Ben Goertzel, https://singularitynet.io/ 00:02:35 AGI Conference series, AGI Conference Organizers, https://agi-conf.org/2024/ 00:03:55 Ben Goertzel's contributions to AGI, Wikipedia contributors, https://en.wikipedia.org/wiki/Ben_Goertzel 00:11:05 Chain-of-Thought prompting, Subbarao Kambhampati, https://arxiv.org/abs/2405.04776 00:11:35 Algorithmic information content, Pieter Adriaans, https://plato.stanford.edu/entries/information-entropy/ 00:12:10 Turing completeness in neural networks, Various contributors, https://plato.stanford.edu/entries/turing-machine/ 00:16:15 AlphaGeometry: AI for geometry problems, Trieu, Li, et al., https://www.nature.com/articles/s41586-023-06747-5 00:18:25 Shane Legg and Ben Goertzel's collaboration, Shane Legg, https://en.wikipedia.org/wiki/Shane_Legg 00:20:00 Evolutionary algorithms in music generation, Yanxu Chen, https://arxiv.org/html/2409.03715v1 00:22:00 Peirce's theory of semiotics, Charles Sanders Peirce, https://plato.stanford.edu/entries/peirce-semiotics/ 00:28:10 Chomsky's view on language, Noam Chomsky, https://chomsky.info/1983____/ 00:34:05 Greg Egan's 'Diaspora', Greg Egan, https://www.amazon.co.uk/Diaspora-post-apocalyptic-thriller-perfect-MIRROR/dp/0575082097 00:40:35 'The Consciousness Explosion', Ben Goertzel & Gabriel Axel Montes, https://www.amazon.com/Consciousness-Explosion-Technological-Experiential-Singularity/dp/B0D8C7QYZD 00:41:55 Ray Kurzweil's books on singularity, Ray Kurzweil, https://www.amazon.com/Singularity-Near-Humans-Transcend-Biology/dp/0143037889 00:50:50 California AI regulation bills, California State Senate, https://sd18.senate.ca.gov/news/senate-unanimously-approves-senator-padillas-artificial-intelligence-package 00:56:40 Limitations of Compute Thresholds, Sara Hooker, https://arxiv.org/abs/2407.05694 00:56:55 'Taming Silicon Valley', Gary F. Marcus, https://www.penguinrandomhouse.com/books/768076/taming-silicon-valley-by-gary-f-marcus/ 01:09:15 Kurzweil's AGI prediction update, Ray Kurzweil,...

AI expert Prof. Gary Marcus doesn't mince words about today's artificial intelligence. He argues that despite the buzz, chatbots like ChatGPT aren't as smart as they seem and could cause real problems if we're not careful. Marcus is worried about tech companies putting profits before people. He thinks AI could make fake news and privacy issues even worse. He's also concerned that a few big tech companies have too much power. Looking ahead, Marcus believes the AI hype will die down as reality sets in. He wants to see AI developed in smarter, more responsible ways. His message to the public? We need to speak up and demand better AI before it's too late. Buy Taming Silicon Valley: https://amzn.to/3XTlC5s Gary Marcus: https://garymarcus.substack.com/ https://x.com/GaryMarcus Interviewer: Dr. Tim Scarfe (Refs in top comment) TOC [00:00:00] AI Flaws, Improvements & Industry Critique [00:16:29] AI Safety Theater & Image Generation Issues [00:23:49] AI's Lack of World Models & Human-like Understanding [00:31:09] LLMs: Superficial Intelligence vs. True Reasoning [00:34:45] AI in Specialized Domains: Chess, Coding & Limitations [00:42:10] AI-Generated Code: Capabilities & Human-AI Interaction [00:48:10] AI Regulation: Industry Resistance & Oversight Challenges [00:54:55] Copyright Issues in AI & Tech Business Models [00:57:26] AI's Societal Impact: Risks, Misinformation & Ethics [01:23:14] AI X-risk, Alignment & Moral Principles Implementation [01:37:10] Persistent AI Flaws: System Limitations & Architecture Challenges [01:44:33] AI Future: Surveillance Concerns, Economic Challenges & Neuro-Symbolic AI YT version with refs: https://youtu.be/o9MfuUoGlSw

Prof. Mark Solms, a neuroscientist and psychoanalyst, discusses his groundbreaking work on consciousness, challenging conventional cortex-centric views and emphasizing the role of brainstem structures in generating consciousness and affect. MLST is sponsored by Brave: The Brave Search API covers over 20 billion webpages, built from scratch without Big Tech biases or the recent extortionate price hikes on search API access. Perfect for AI model training and retrieval augmentated generation. Try it now - get 2,000 free queries monthly at http://brave.com/api. Key points discussed: The limitations of vision-centric approaches to consciousness studies. Evidence from decorticated animals and hydranencephalic children supporting the brainstem's role in consciousness. The relationship between homeostasis, the free energy principle, and consciousness. Critiques of behaviorism and modern theories of consciousness. The importance of subjective experience in understanding brain function. The discussion also explored broader topics: The potential impact of affect-based theories on AI development. The role of the SEEKING system in exploration and learning. Connections between neuroscience, psychoanalysis, and philosophy of mind. Challenges in studying consciousness and the limitations of current theories. Mark Solms: https://neuroscience.uct.ac.za/contacts/mark-solms Show notes and transcript: https://www.dropbox.com/scl/fo/roipwmnlfmwk2e7kivzms/ACjZF-VIGC2-Suo30KcwVV0?rlkey=53y8v2cajfcgrf17p1h7v3suz&st=z8vu81hn&dl=0 TOC (*) are best bits 00:00:00 1. Intro: Challenging vision-centric approaches to consciousness * 00:02:20 2. Evidence from decorticated animals and hydranencephalic children * 00:07:40 3. Emotional responses in hydranencephalic children 00:10:40 4. Brainstem stimulation and affective states 00:15:00 5. Brainstem's role in generating affective consciousness * 00:21:50 6. Dual-aspect monism and the mind-brain relationship 00:29:37 7. Information, affect, and the hard problem of consciousness * 00:37:25 8. Wheeler's participatory universe and Chalmers' theories 00:48:51 9. Homeostasis, free energy principle, and consciousness * 00:59:25 10. Affect, voluntary behavior, and decision-making 01:05:45 11. Psychoactive substances, REM sleep, and consciousness research 01:12:14 12. Critiquing behaviorism and modern consciousness theories * 01:24:25 13. The SEEKING system and exploration in neuroscience Refs: 1. Mark Solms' book "The Hidden Spring" [00:20:34] (MUST READ!) https://amzn.to/3XyETb3 2. Karl Friston's free energy principle [00:03:50] https://www.nature.com/articles/nrn2787 3. Hydranencephaly condition [00:07:10] https://en.wikipedia.org/wiki/Hydranencephaly 4. Periaqueductal gray (PAG) [00:08:57] https://en.wikipedia.org/wiki/Periaqueductal_gray 5. Positron Emission Tomography (PET) [00:13:52] https://en.wikipedia.org/wiki/Positron_emission_tomography 6. Paul MacLean's triune brain theory [00:03:30] https://en.wikipedia.org/wiki/Triune_brain 7. Baruch Spinoza's philosophy of mind [00:23:48] https://plato.stanford.edu/entries/spinoza-epistemology-mind 8. Claude Shannon's "A Mathematical Theory of Communication" [00:32:15] https://people.math.harvard.edu/~ctm/home/text/others/shannon/entropy/entropy.pdf 9. Francis Crick's "The Astonishing Hypothesis" [00:39:57] https://en.wikipedia.org/wiki/The_Astonishing_Hypothesis 10. Frank Jackson's Knowledge Argument [00:40:54] https://plato.stanford.edu/entries/qualia-knowledge/ 11. Mesolimbic dopamine system [01:11:51] https://en.wikipedia.org/wiki/Mesolimbic_pathway 12. Jaak Panksepp's SEEKING system [01:25:23] https://en.wikipedia.org/wiki/Jaak_Panksepp#Affective_neuroscience

Dr. Patrick Lewis, who coined the term RAG (Retrieval Augmented Generation) and now works at Cohere, discusses the evolution of language models, RAG systems, and challenges in AI evaluation. MLST is sponsored by Brave: The Brave Search API covers over 20 billion webpages, built from scratch without Big Tech biases or the recent extortionate price hikes on search API access. Perfect for AI model training and retrieval augmented generation. Try it now - get 2,000 free queries monthly at http://brave.com/api. Key topics covered: - Origins and evolution of Retrieval Augmented Generation (RAG) - Challenges in evaluating RAG systems and language models - Human-AI collaboration in research and knowledge work - Word embeddings and the progression to modern language models - Dense vs sparse retrieval methods in information retrieval The discussion also explored broader implications and applications: - Balancing faithfulness and fluency in RAG systems - User interface design for AI-augmented research tools - The journey from chemistry to AI research - Challenges in enterprise search compared to web search - The importance of data quality in training AI models Patrick Lewis: https://www.patricklewis.io/ Cohere Command Models, check them out - they are amazing for RAG! https://cohere.com/command TOC 00:00:00 1. Intro to RAG 00:05:30 2. RAG Evaluation: Poll framework & model performance 00:12:55 3. Data Quality: Cleanliness vs scale in AI training 00:15:13 4. Human-AI Collaboration: Research agents & UI design 00:22:57 5. RAG Origins: Open-domain QA to generative models 00:30:18 6. RAG Challenges: Info retrieval, tool use, faithfulness 00:42:01 7. Dense vs Sparse Retrieval: Techniques & trade-offs 00:47:02 8. RAG Applications: Grounding, attribution, hallucination prevention 00:54:04 9. UI for RAG: Human-computer interaction & model optimization 00:59:01 10. Word Embeddings: Word2Vec, GloVe, and semantic spaces 01:06:43 11. Language Model Evolution: BERT, GPT, and beyond 01:11:38 12. AI & Human Cognition: Sequential processing & chain-of-thought Refs: 1. Retrieval Augmented Generation (RAG) paper / Patrick Lewis et al. [00:27:45] https://arxiv.org/abs/2005.11401 2. LAMA (LAnguage Model Analysis) probe / Petroni et al. [00:26:35] https://arxiv.org/abs/1909.01066 3. KILT (Knowledge Intensive Language Tasks) benchmark / Petroni et al. [00:27:05] https://arxiv.org/abs/2009.02252 4. Word2Vec algorithm / Tomas Mikolov et al. [01:00:25] https://arxiv.org/abs/1301.3781 5. GloVe (Global Vectors for Word Representation) / Pennington et al. [01:04:35] https://nlp.stanford.edu/projects/glove/ 6. BERT (Bidirectional Encoder Representations from Transformers) / Devlin et al. [01:08:00] https://arxiv.org/abs/1810.04805 7. 'The Language Game' book / Nick Chater and Morten H. Christiansen [01:11:40] https://amzn.to/4grEUpG Disclaimer: This is the sixth video from our Cohere partnership. We were not told what to say in the interview. Filmed in Seattle in June 2024.

Ashley Edwards, who was working at DeepMind when she co-authored the Genie paper and is now at Runway, covered several key aspects of the Genie AI system and its applications in video generation, robotics, and game creation. MLST is sponsored by Brave: The Brave Search API covers over 20 billion webpages, built from scratch without Big Tech biases or the recent extortionate price hikes on search API access. Perfect for AI model training and retrieval augmentated generation. Try it now - get 2,000 free queries monthly at http://brave.com/api. Genie's approach to learning interactive environments, balancing compression and fidelity. The use of latent action models and VQE models for video processing and tokenization. Challenges in maintaining action consistency across frames and integrating text-to-image models. Evaluation metrics for AI-generated content, such as FID and PS&R diff metrics. The discussion also explored broader implications and applications: The potential impact of AI video generation on content creation jobs. Applications of Genie in game generation and robotics. The use of foundation models in robotics and the differences between internet video data and specialized robotics data. Challenges in mapping AI-generated actions to real-world robotic actions. Ashley Edwards: https://ashedwards.github.io/ TOC (*) are best bits 00:00:00 1. Intro to Genie & Brave Search API: Trade-offs & limitations * 00:02:26 2. Genie's Architecture: Latent action, VQE, video processing * 00:05:06 3. Genie's Constraints: Frame consistency & image model integration 00:07:26 4. Evaluation: FID, PS&R diff metrics & latent induction methods 00:09:44 5. AI Video Gen: Content creation impact, depth & parallax effects 00:11:39 6. Model Scaling: Training data impact & computational trade-offs 00:13:50 7. Game & Robotics Apps: Gamification & action mapping challenges * 00:16:16 8. Robotics Foundation Models: Action space & data considerations * 00:19:18 9. Mask-GPT & Video Frames: Real-time optimization, RL from videos 00:20:34 10. Research Challenges: AI value, efficiency vs. quality, safety 00:24:20 11. Future Dev: Efficiency improvements & fine-tuning strategies Refs: 1. Genie (learning interactive environments from videos) / Ashley and DM collegues [00:01] https://arxiv.org/abs/2402.15391 2. VQ-VAE (Vector Quantized Variational Autoencoder) / Aaron van den Oord, Oriol Vinyals, Koray Kavukcuoglu [02:43] https://arxiv.org/abs/1711.00937 3. FID (Fréchet Inception Distance) metric / Martin Heusel et al. [07:37] https://arxiv.org/abs/1706.08500 4. PS&R (Precision and Recall) metric / Mehdi S. M. Sajjadi et al. [08:02] https://arxiv.org/abs/1806.00035 5. Vision Transformer (ViT) architecture / Alexey Dosovitskiy et al. [12:14] https://arxiv.org/abs/2010.11929 6. Genie (robotics foundation models) / Google DeepMind [17:34] https://deepmind.google/research/publications/60474/ 7. Chelsea Finn's lab work on robotics datasets / Chelsea Finn [17:38] https://ai.stanford.edu/~cbfinn/ 8. Imitation from observation in reinforcement learning / YuXuan Liu [20:58] https://arxiv.org/abs/1707.03374 9. Waymo's autonomous driving technology / Waymo [22:38] https://waymo.com/ 10. Gen3 model release by Runway / Runway [23:48] https://runwayml.com/ 11. Classifier-free guidance technique / Jonathan Ho and Tim Salimans [24:43] https://arxiv.org/abs/2207.12598

Saurabh Baji discusses Cohere's approach to developing and deploying large language models (LLMs) for enterprise use. * Cohere focuses on pragmatic, efficient models tailored for business applications rather than pursuing the largest possible models. * They offer flexible deployment options, from cloud services to on-premises installations, to meet diverse enterprise needs. * Retrieval-augmented generation (RAG) is highlighted as a critical capability, allowing models to leverage enterprise data securely. * Cohere emphasizes model customization, fine-tuning, and tools like reranking to optimize performance for specific use cases. * The company has seen significant growth, transitioning from developer-focused to enterprise-oriented services. * Major customers like Oracle, Fujitsu, and TD Bank are using Cohere's models across various applications, from HR to finance. * Baji predicts a surge in enterprise AI adoption over the next 12-18 months as more companies move from experimentation to production. * He emphasizes the importance of trust, security, and verifiability in enterprise AI applications. The interview provides insights into Cohere's strategy, technology, and vision for the future of enterprise AI adoption. https://www.linkedin.com/in/saurabhbaji/ https://x.com/sbaji https://cohere.com/ https://cohere.com/business MLST is sponsored by Brave: The Brave Search API covers over 20 billion webpages, built from scratch without Big Tech biases or the recent extortionate price hikes on search API access. Perfect for AI model training and retrieval augmentated generation. Try it now - get 2,000 free queries monthly at http://brave.com/api. TOC (*) are best bits 00:00:00 1. Introduction and Background 00:04:24 2. Cloud Infrastructure and LLM Optimization 00:06:43 2.1 Model deployment and fine-tuning strategies * 00:09:37 3. Enterprise AI Deployment Strategies 00:11:10 3.1 Retrieval-augmented generation in enterprise environments * 00:13:40 3.2 Standardization vs. customization in cloud services * 00:18:20 4. AI Model Evaluation and Deployment 00:18:20 4.1 Comprehensive evaluation frameworks * 00:21:20 4.2 Key components of AI model stacks * 00:25:50 5. Retrieval Augmented Generation (RAG) in Enterprise 00:32:10 5.1 Pragmatic approach to RAG implementation * 00:33:45 6. AI Agents and Tool Integration 00:33:45 6.1 Leveraging tools for AI insights * 00:35:30 6.2 Agent-based AI systems and diagnostics * 00:42:55 7. AI Transparency and Reasoning Capabilities 00:49:10 8. AI Model Training and Customization 00:57:10 9. Enterprise AI Model Management 01:02:10 9.1 Managing AI model versions for enterprise customers * 01:04:30 9.2 Future of language model programming * 01:06:10 10. AI-Driven Software Development 01:06:10 10.1 AI bridging human expression and task achievement * 01:08:00 10.2 AI-driven virtual app fabrics in enterprise * 01:13:33 11. Future of AI and Enterprise Applications 01:21:55 12. Cohere's Customers and Use Cases 01:21:55 12.1 Anthropic's growth and enterprise partnerships * 01:27:14 12.2 Diverse customers using generative AI * 01:27:50 12.3 Industry adaptation to generative AI * 01:29:00 13. Technical Advantages of Cohere Models 01:29:00 13.1 Handling large context windows * 01:29:40 13.2 Low latency impact on developer productivity * Disclaimer: This is the fifth video from our Cohere partnership. We were not told what to say in the interview, and didn't edit anything out from the interview. Filmed in Seattle in Aug 2024.

David Hanson, CEO of Hanson Robotics and creator of the humanoid robot Sofia, explores the intersection of artificial intelligence, ethics, and human potential. In this thought-provoking interview, Hanson discusses his vision for developing AI systems that embody the best aspects of humanity while pushing beyond our current limitations, aiming to achieve what he calls "super wisdom." YT version: https://youtu.be/LFCIEhlsozU MLST is sponsored by Brave: The Brave Search API covers over 20 billion webpages, built from scratch without Big Tech biases or the recent extortionate price hikes on search API access. Perfect for AI model training and retrieval augmentated generation. Try it now - get 2,000 free queries monthly at http://brave.com/api. The interview with David Hanson covers: The importance of incorporating biological drives and compassion into AI systems Hanson's concept of "existential pattern ethics" as a basis for AI morality The potential for AI to enhance human intelligence and wisdom Challenges in developing artificial general intelligence (AGI) The need to democratize AI technologies globally Potential future advancements in human-AI integration and their societal impacts Concerns about technological augmentation exacerbating inequality The role of ethics in guiding AI development and deployment Hanson advocates for creating AI systems that embody the best aspects of humanity while surpassing current human limitations, aiming for "super wisdom" rather than just artificial super intelligence. David Hanson: https://www.hansonrobotics.com/david-hanson/ https://www.youtube.com/watch?v=9u1O954cMmE TOC 1. Introduction and Background [00:00:00] 1.1. David Hanson's interdisciplinary background [0:01:49] 1.2. Introduction to Sofia, the realistic robot [0:03:27] 2. Human Cognition and AI [0:03:50] 2.1. Importance of social interaction in cognition [0:03:50] 2.2. Compassion as distinguishing factor [0:05:55] 2.3. AI augmenting human intelligence [0:09:54] 3. Developing Human-like AI [0:13:17] 3.1. Incorporating biological drives in AI [0:13:17] 3.2. Creating AI with agency [0:20:34] 3.3. Implementing flexible desires in AI [0:23:23] 4. Ethics and Morality in AI [0:27:53] 4.1. Enhancing humanity through AI [0:27:53] 4.2. Existential pattern ethics [0:30:14] 4.3. Expanding morality beyond restrictions [0:35:35] 5. Societal Impact of AI [0:38:07] 5.1. AI adoption and integration [0:38:07] 5.2. Democratizing AI technologies [0:38:32] 5.3. Human-AI integration and identity [0:43:37] 6. Future Considerations [0:50:03] 6.1. Technological augmentation and inequality [0:50:03] 6.2. Emerging technologies for mental health [0:50:32] 6.3. Corporate ethics in AI development [0:52:26] This was filmed at AGI-24

David Spivak, a mathematician known for his work in category theory, discusses a wide range of topics related to intelligence, creativity, and the nature of knowledge. He explains category theory in simple terms and explores how it relates to understanding complex systems and relationships. MLST is sponsored by Brave: The Brave Search API covers over 20 billion webpages, built from scratch without Big Tech biases or the recent extortionate price hikes on search API access. Perfect for AI model training and retrieval augmentated generation. Try it now - get 2,000 free queries monthly at http://brave.com/api. We discuss abstract concepts like collective intelligence, the importance of embodiment in understanding the world, and how we acquire and process knowledge. Spivak shares his thoughts on creativity, discussing where it comes from and how it might be modeled mathematically. A significant portion of the discussion focuses on the impact of artificial intelligence on human thinking and its potential role in the evolution of intelligence. Spivak also touches on the importance of language, particularly written language, in transmitting knowledge and shaping our understanding of the world. David Spivak http://www.dspivak.net/ TOC: 00:00:00 Introduction to category theory and functors 00:04:40 Collective intelligence and sense-making 00:09:54 Embodiment and physical concepts in knowledge acquisition 00:16:23 Creativity, open-endedness, and AI's impact on thinking 00:25:46 Modeling creativity and the evolution of intelligence 00:36:04 Evolution, optimization, and the significance of AI 00:44:14 Written language and its impact on knowledge transmission REFS: Mike Levin's work https://scholar.google.com/citations?user=luouyakAAAAJ&hl=en Eric Smith's videos on complexity and early life https://www.youtube.com/watch?v=SpJZw-68QyE Richard Dawkins' book "The Selfish Gene" https://amzn.to/3X73X8w Carl Sagan's statement about the cosmos knowing itself https://amzn.to/3XhPruK Herbert Simon's concept of "satisficing" https://plato.stanford.edu/entries/bounded-rationality/ DeepMind paper on open-ended systems https://arxiv.org/abs/2406.04268 Karl Friston's work on active inference https://direct.mit.edu/books/oa-monograph/5299/Active-InferenceThe-Free-Energy-Principle-in-Mind MIT category theory lectures by David Spivak (available on the Topos Institute channel) https://www.youtube.com/watch?v=UusLtx9fIjs

Jürgen Schmidhuber, the father of generative AI shares his groundbreaking work in deep learning and artificial intelligence. In this exclusive interview, he discusses the history of AI, some of his contributions to the field, and his vision for the future of intelligent machines. Schmidhuber offers unique insights into the exponential growth of technology and the potential impact of AI on humanity and the universe. YT version: https://youtu.be/DP454c1K_vQ MLST is sponsored by Brave: The Brave Search API covers over 20 billion webpages, built from scratch without Big Tech biases or the recent extortionate price hikes on search API access. Perfect for AI model training and retrieval augmentated generation. Try it now - get 2,000 free queries monthly at http://brave.com/api. TOC 00:00:00 Intro 00:03:38 Reasoning 00:13:09 Potential AI Breakthroughs Reducing Computation Needs 00:20:39 Memorization vs. Generalization in AI 00:25:19 Approach to the ARC Challenge 00:29:10 Perceptions of Chat GPT and AGI 00:58:45 Abstract Principles of Jurgen's Approach 01:04:17 Analogical Reasoning and Compression 01:05:48 Breakthroughs in 1991: the P, the G, and the T in ChatGPT and Generative AI 01:15:50 Use of LSTM in Language Models by Tech Giants 01:21:08 Neural Network Aspect Ratio Theory 01:26:53 Reinforcement Learning Without Explicit Teachers Refs: ★ "Annotated History of Modern AI and Deep Learning" (2022 survey by Schmidhuber): ★ Chain Rule For Backward Credit Assignment (Leibniz, 1676) ★ First Neural Net / Linear Regression / Shallow Learning (Gauss & Legendre, circa 1800) ★ First 20th Century Pioneer of Practical AI (Quevedo, 1914) ★ First Recurrent NN (RNN) Architecture (Lenz, Ising, 1920-1925) ★ AI Theory: Fundamental Limitations of Computation and Computation-Based AI (Gödel, 1931-34) ★ Unpublished ideas about evolving RNNs (Turing, 1948) ★ Multilayer Feedforward NN Without Deep Learning (Rosenblatt, 1958) ★ First Published Learning RNNs (Amari and others, ~1972) ★ First Deep Learning (Ivakhnenko & Lapa, 1965) ★ Deep Learning by Stochastic Gradient Descent (Amari, 1967-68) ★ ReLUs (Fukushima, 1969) ★ Backpropagation (Linnainmaa, 1970); precursor (Kelley, 1960) ★ Backpropagation for NNs (Werbos, 1982) ★ First Deep Convolutional NN (Fukushima, 1979); later combined with Backprop (Waibel 1987, Zhang 1988). ★ Metalearning or Learning to Learn (Schmidhuber, 1987) ★ Generative Adversarial Networks / Artificial Curiosity / NN Online Planners (Schmidhuber, Feb 1990; see the G in Generative AI and ChatGPT) ★ NNs Learn to Generate Subgoals and Work on Command (Schmidhuber, April 1990) ★ NNs Learn to Program NNs: Unnormalized Linear Transformer (Schmidhuber, March 1991; see the T in ChatGPT) ★ Deep Learning by Self-Supervised Pre-Training. Distilling NNs (Schmidhuber, April 1991; see the P in ChatGPT) ★ Experiments with Pre-Training; Analysis of Vanishing/Exploding Gradients, Roots of Long Short-Term Memory / Highway Nets / ResNets (Hochreiter, June 1991, further developed 1999-2015 with other students of Schmidhuber) ★ LSTM journal paper (1997, most cited AI paper of the 20th century) ★ xLSTM (Hochreiter, 2024) ★ Reinforcement Learning Prompt Engineer for Abstract Reasoning and Planning (Schmidhuber 2015) ★ Mindstorms in Natural Language-Based Societies of Mind (2023 paper by Schmidhuber's team) https://arxiv.org/abs/2305.17066 ★ Bremermann's physical limit of computation (1982) EXTERNAL LINKS CogX 2018 - Professor Juergen Schmidhuber https://www.youtube.com/watch?v=17shdT9-wuA Discovering Neural Nets with Low Kolmogorov Complexity and High Generalization Capability (Neural Networks, 1997) https://sferics.idsia.ch/pub/juergen/loconet.pdf The paradox at the heart of mathematics: Gödel's Incompleteness Theorem - Marcus du Sautoy https://www.youtube.com/watch?v=I4pQbo5MQOs (Refs truncated, full version on YT VD)

Professor Pedro Domingos, is an AI researcher and professor of computer science. He expresses skepticism about current AI regulation efforts and argues for faster AI development rather than slowing it down. He also discusses the need for new innovations to fulfil the promises of current AI techniques. MLST is sponsored by Brave: The Brave Search API covers over 20 billion webpages, built from scratch without Big Tech biases or the recent extortionate price hikes on search API access. Perfect for AI model training and retrieval augmented generation. Try it now - get 2,000 free queries monthly at http://brave.com/api. Show notes: * Domingos' views on AI regulation and why he believes it's misguided * His thoughts on the current state of AI technology and its limitations * Discussion of his novel "2040", a satirical take on AI and tech culture * Explanation of his work on "tensor logic", which aims to unify neural networks and symbolic AI * Critiques of other approaches in AI, including those of OpenAI and Gary Marcus * Thoughts on the AI "bubble" and potential future developments in the field Prof. Pedro Domingos: https://x.com/pmddomingos 2040: A Silicon Valley Satire [Pedro's new book] https://amzn.to/3T51ISd TOC: 00:00:00 Intro 00:06:31 Bio 00:08:40 Filmmaking skit 00:10:35 AI and the wisdom of crowds 00:19:49 Social Media 00:27:48 Master algorithm 00:30:48 Neurosymbolic AI / abstraction 00:39:01 Language 00:45:38 Chomsky 01:00:49 2040 Book 01:18:03 Satire as a shield for criticism? 01:29:12 AI Regulation 01:35:15 Gary Marcus 01:52:37 Copyright 01:56:11 Stochastic parrots come home to roost 02:00:03 Privacy 02:01:55 LLM ecosystem 02:05:06 Tensor logic Refs: The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World [Pedro Domingos] https://amzn.to/3MiWs9B Rebooting AI: Building Artificial Intelligence We Can Trust [Gary Marcus] https://amzn.to/3AAywvL Flash Boys [Michael Lewis] https://amzn.to/4dUGm1M

Andrew Ilyas, a PhD student at MIT who is about to start as a professor at CMU. We discuss Data modeling and understanding how datasets influence model predictions, Adversarial examples in machine learning and why they occur, Robustness in machine learning models, Black box attacks on machine learning systems, Biases in data collection and dataset creation, particularly in ImageNet and Self-selection bias in data and methods to address it. MLST is sponsored by Brave: The Brave Search API covers over 20 billion webpages, built from scratch without Big Tech biases or the recent extortionate price hikes on search API access. Perfect for AI model training and retrieval augmentated generation. Try it now - get 2,000 free queries monthly at http://brave.com/api Andrew's site: https://andrewilyas.com/ https://x.com/andrew_ilyas TOC: 00:00:00 - Introduction and Andrew's background 00:03:52 - Overview of the machine learning pipeline 00:06:31 - Data modeling paper discussion 00:26:28 - TRAK: Evolution of data modeling work 00:43:58 - Discussion on abstraction, reasoning, and neural networks 00:53:16 - "Adversarial Examples Are Not Bugs, They Are Features" paper 01:03:24 - Types of features learned by neural networks 01:10:51 - Black box attacks paper 01:15:39 - Work on data collection and bias 01:25:48 - Future research plans and closing thoughts References: Adversarial Examples Are Not Bugs, They Are Features https://arxiv.org/pdf/1905.02175 TRAK: Attributing Model Behavior at Scale https://arxiv.org/pdf/2303.14186 Datamodels: Predicting Predictions from Training Data https://arxiv.org/pdf/2202.00622 Adversarial Examples Are Not Bugs, They Are Features https://arxiv.org/pdf/1905.02175 IMAGENET-TRAINED CNNS https://arxiv.org/pdf/1811.12231 ZOO: Zeroth Order Optimization Based Black-box https://arxiv.org/pdf/1708.03999 A Spline Theory of Deep Networks https://proceedings.mlr.press/v80/balestriero18b/balestriero18b.pdf Scaling Monosemanticity https://transformer-circuits.pub/2024/scaling-monosemanticity/ Adversarial Examples Are Not Bugs, They Are Features https://gradientscience.org/adv/ Adversarial Robustness Limits via Scaling-Law and Human-Alignment Studies https://proceedings.mlr.press/v235/bartoldson24a.html Prior Convictions: Black-Box Adversarial Attacks with Bandits and Priors https://arxiv.org/abs/1807.07978 Estimation of Standard Auction Models https://arxiv.org/abs/2205.02060 From ImageNet to Image Classification: Contextualizing Progress on Benchmarks https://arxiv.org/abs/2005.11295 Estimation of Standard Auction Models https://arxiv.org/abs/2205.02060 What Makes A Good Fisherman? Linear Regression under Self-Selection Bias https://arxiv.org/abs/2205.03246 Towards Tracing Factual Knowledge in Language Models Back to the Training Data [Akyürek] https://arxiv.org/pdf/2205.11482