Machine Learning Street Talk (MLST)

Machine Learning Street Talk (MLST)
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Machine Learning Street Talk (MLST)

Welcome! We engage in fascinating discussions with pre-eminent figures in the AI field. Our flagship show covers current affairs in AI, cognitive science, neuroscience and philosophy of mind with in-depth analysis. Our approach is unrivalled in terms of scope and rigour – we believe in intellectual diversity in AI, and we touch on all of the main ideas in the field with the hype surgically removed. MLST is run by Tim Scarfe, Ph.D (https://www.linkedin.com/in/ecsquizor/) and features regular appearances from MIT Doctor of Philosophy Keith Duggar (https://www.linkedin.com/in/dr-keith-duggar/).

  1. Michael Levin - Why Intelligence Isn't Limited To Brains.

    2 NGÀY TRƯỚC

    Michael Levin - Why Intelligence Isn't Limited To Brains.

    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] Cogniti

    1 giờ 4 phút
  2. Speechmatics CTO - Next-Generation Speech Recognition

    3 NGÀY TRƯỚC

    Speechmatics CTO - Next-Generation Speech Recognition

    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

    1 giờ 46 phút
  3. Dr. Sanjeev Namjoshi - Active Inference

    4 NGÀY TRƯỚC

    Dr. Sanjeev Namjoshi - Active Inference

    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

    2 giờ 46 phút
  4. Joscha Bach - Why Your Thoughts Aren't Yours.

    6 NGÀY TRƯỚC

    Joscha Bach - Why Your Thoughts Aren't Yours.

    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

    1 giờ 53 phút
  5. Decompiling Dreams: A New Approach to ARC? - Alessandro Palmarini

    19 THG 10

    Decompiling Dreams: A New Approach to ARC? - Alessandro Palmarini

    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

    52 phút
  6. It's Not About Scale, It's About Abstraction - Francois Chollet

    12 THG 10

    It's Not About Scale, It's About Abstraction - Francois Chollet

    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/

    46 phút
  7. Bold AI Predictions From Cohere Co-founder

    10 THG 10

    Bold AI Predictions From Cohere Co-founder

    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

    47 phút
  8. Open-Ended AI: The Key to Superhuman Intelligence? - Prof. Tim Rocktäschel

    4 THG 10

    Open-Ended AI: The Key to Superhuman Intelligence? - Prof. Tim Rocktäschel

    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/

    55 phút
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Giới Thiệu

Welcome! We engage in fascinating discussions with pre-eminent figures in the AI field. Our flagship show covers current affairs in AI, cognitive science, neuroscience and philosophy of mind with in-depth analysis. Our approach is unrivalled in terms of scope and rigour – we believe in intellectual diversity in AI, and we touch on all of the main ideas in the field with the hype surgically removed. MLST is run by Tim Scarfe, Ph.D (https://www.linkedin.com/in/ecsquizor/) and features regular appearances from MIT Doctor of Philosophy Keith Duggar (https://www.linkedin.com/in/dr-keith-duggar/).

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    Machine Learning Street Talk (MLST)
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    2020 - 2024
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    © Machine Learning Street Talk (MLST)
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    Machine Learning Street Talk (MLST)

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