Machine Learning Street TalkDr. Joscha Bach introduces a surprising idea called "cyber animism" in his AGI-24 talk - the notion that nature might be full of self-organizing software agents, similar to the spirits in ancient belief systems. Bach suggests that consciousness could be a kind of software running on our brains, and wonders if similar "programs" might exist in plants or even entire ecosystems.
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.
Joscha takes us on a tour de force through history, philosophy, and cutting-edge computer science, teasing us to rethink what we know about minds, machines, and the world around us. Joscha believes we should blur the lines between human, artificial, and natural intelligence, and argues that consciousness might be more widespread and interconnected than we ever thought possible.
Dr. Joscha Bach https://x.com/Plinz
This is video 2/9 from our coverage of AGI-24 in Seattle agi-conf.org/2024 Watch the official MLST interview with Joscha which we did right after this talk on our Patreon now on early access - patreon.com/posts/joscha-bach-110199676 (you also get access to our private discord and biweekly calls)
TOC: 00:00:00 Introduction: AGI and Cyberanimism 00:03:57 The Nature of Consciousness 00:08:46 Aristotle's Concepts of Mind and Consciousness 00:13:23 The Hard Problem of Consciousness 00:16:17 Functional Definition of Consciousness 00:20:24 Comparing LLMs and Human Consciousness 00:26:52 Testing for Consciousness in AI Systems 00:30:00 Animism and Software Agents in Nature 00:37:02 Plant Consciousness and Ecosystem Intelligence 00:40:36 The California Institute for Machine Consciousness 00:44:52 Ethics of Conscious AI and Suffering 00:46:29 Philosophical Perspectives on Consciousness 00:49:55 Q&A: Formalisms for Conscious Systems 00:53:27 Coherence, Self-Organization, and Compute Resources
Refs: Aristotle's work on the soul and consciousness Richard Dawkins' work on genes and evolution Gerald Edelman's concept of Neural Darwinism Thomas Metzinger's book "Being No One" Yoshua Bengio's concept of the "consciousness prior" Stuart Hameroff's theories on microtubules and consciousness Christof Koch's work on consciousness Daniel Dennett's "Cartesian Theater" concept Giulio Tononi's Integrated Information Theory Mike Levin's work on organismal intelligence The concept of animism in various cultures Freud's model of the mind Buddhist perspectives on consciousness and meditation The Genesis creation narrative (for its metaphorical interpretation) California Institute for Machine Consciousness
We Are All Software - Joscha BachMachine Learning Street Talk2024-08-21 | Dr. Joscha Bach introduces a surprising idea called "cyber animism" in his AGI-24 talk - the notion that nature might be full of self-organizing software agents, similar to the spirits in ancient belief systems. Bach suggests that consciousness could be a kind of software running on our brains, and wonders if similar "programs" might exist in plants or even entire ecosystems.
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.
Joscha takes us on a tour de force through history, philosophy, and cutting-edge computer science, teasing us to rethink what we know about minds, machines, and the world around us. Joscha believes we should blur the lines between human, artificial, and natural intelligence, and argues that consciousness might be more widespread and interconnected than we ever thought possible.
Dr. Joscha Bach https://x.com/Plinz
This is video 2/9 from our coverage of AGI-24 in Seattle agi-conf.org/2024 Watch the official MLST interview with Joscha which we did right after this talk on our Patreon now on early access - patreon.com/posts/joscha-bach-110199676 (you also get access to our private discord and biweekly calls)
TOC: 00:00:00 Introduction: AGI and Cyberanimism 00:03:57 The Nature of Consciousness 00:08:46 Aristotle's Concepts of Mind and Consciousness 00:13:23 The Hard Problem of Consciousness 00:16:17 Functional Definition of Consciousness 00:20:24 Comparing LLMs and Human Consciousness 00:26:52 Testing for Consciousness in AI Systems 00:30:00 Animism and Software Agents in Nature 00:37:02 Plant Consciousness and Ecosystem Intelligence 00:40:36 The California Institute for Machine Consciousness 00:44:52 Ethics of Conscious AI and Suffering 00:46:29 Philosophical Perspectives on Consciousness 00:49:55 Q&A: Formalisms for Conscious Systems 00:53:27 Coherence, Self-Organization, and Compute Resources
Refs: Aristotle's work on the soul and consciousness Richard Dawkins' work on genes and evolution Gerald Edelman's concept of Neural Darwinism Thomas Metzinger's book "Being No One" Yoshua Bengio's concept of the "consciousness prior" Stuart Hameroff's theories on microtubules and consciousness Christof Koch's work on consciousness Daniel Dennett's "Cartesian Theater" concept Giulio Tononi's Integrated Information Theory Mike Levin's work on organismal intelligence The concept of animism in various cultures Freud's model of the mind Buddhist perspectives on consciousness and meditation The Genesis creation narrative (for its metaphorical interpretation) California Institute for Machine ConsciousnessIts Not About Scale, Its About AbstractionMachine Learning Street Talk2024-10-12 | 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.
MLST is sponsored by Tufa Labs: Are you interested in working on ARC and cutting-edge AI research with the MindsAI team (current ARC winners)? 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. 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
[0:44:50] Ryan Greenblatt's high score on ARC public leaderboard arcprize.orgWhy does the Chinese Room still haunt AI?Machine Learning Street Talk2024-10-11 | Dr. Keith Duggar and Dr. Tim Scarfe in this second edition of the hosts-only "philosophical steakhouse" discuss artificial intelligence, consciousness, and understanding. They explore the computational limits of language models, the challenges of training AI systems to be Turing-complete, and the implications of these limitations for AI capabilities.
The conversation covers philosophical arguments about machine consciousness, including John Searle's famous Chinese Room thought experiment. They discuss different views on what's required for a system to truly understand or be conscious, touching on ideas from various philosophers and scientists.
The hosts also talk about the recent Nobel Prize awarded for work in deep learning, debating its merits and controversies. They touch on the recent Liron Doom Debates show which Duggar was just on, and at the end Ethics vs AI risk.
TOC:
1. Computational Foundations of AI 00:00:00 1.1 Turing Completeness and Computational Limits of Language Models 00:06:37 1.2 Finite State Automata vs. Turing Machines in AI 00:13:16 1.3 Challenges in Training Turing-Complete Systems 00:17:23 1.4 Mapping Turing Machine Programs to Language Models 00:20:41 1.5 Future Directions: Hybrid Systems and Novel Programming Approaches
2. AI Consciousness and Understanding 00:31:30 2.1 Chinese Room Argument and AI Consciousness 00:41:15 2.2 Searle's Views on Physical Realization and Consciousness 00:50:30 2.3 Emergence and Computational Limitations in Cognitive Science 00:59:55 2.4 Friston's Theory on Self-Awareness in Machines 01:04:25 2.5 Causal Structures and Understanding in AI Systems 01:06:50 2.6 Concept Role Semantics and Language Models 01:09:41 2.7 Consciousness and Computational Theories
3. AI Impact and Ethics 01:21:38 3.1 Deep Learning and the Nobel Prize for Hinton 01:29:56 3.2 AI Harm and Existential Risk 01:34:05 3.3 Balancing AI Ethics and Practical Policy
Recorded Friday 11th Oct 2024Bold AI Predictions From Cohere Co-founderMachine Learning Street Talk2024-10-10 | 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.
Disclaimer: This show is part of our Cohere partnership series.Open-Ended AI: The Key to Superhuman Intelligence?Machine Learning Street Talk2024-10-04 | 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.
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
00:49:35 - Self-improving neural networks (Schmidhuber) - Early work on self-referential networks - http://people.idsia.ch/~juergen/selfrefnn.html
00:54:45 - UCL DARK Lab (UCL Computer Science) - RL and Deep Learning research group - ucl.ac.uk/computer-science/news-and-events/inaugural-lecture-series/inaugural-lecture-professor-tim-rocktaschelAGI in 5 Years? Ben Goertzel on SuperintelligenceMachine Learning Street Talk2024-10-01 | 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
Key points discussed: - Using debate between language models to improve truthfulness in AI responses - Scalable oversight for supervising AI models beyond human-level intelligence - The relationship between intelligence and agency in AI systems - Challenges in AI safety and alignment - The potential for a Cambrian explosion in human-like intelligent systems
The discussion also explored broader topics: - The wisdom of crowds vs. expert knowledge in machine learning debates - Deceptive alignment and reward tampering in AI systems - Open-ended AI systems and their implications for development and safety - The space of possible minds and defining superintelligence - Cultural evolution and memetics in understanding intelligence
TOC (*) are best bits 00:00:00 1. Intro: AI alignment and debate techniques for truthful responses * 00:05:00 2. Scalable oversight and hidden information settings 00:10:05 3. AI agency, intelligence, and progress * 00:15:00 4. Base models, RL training, and instrumental goals 00:25:11 5. Deceptive alignment and RL challenges in AI * 00:30:12 6. Open-ended AI systems and future directions 00:35:34 7. Deception, superintelligence, and the space of possible minds * 00:40:00 8. Cultural evolution, memetics, and intelligence measurement
References: 1. [00:00:40] Akbir Khan et al. ICML 2024 Best Paper: "Debating with More Persuasive LLMs Leads to More Truthful Answers" arxiv.org/html/2402.06782v3
7. [00:16:35] Thomas Suddendorf's book "The Gap: The Science of What Separates Us from Other Animals" https://www.amazon.in/GAP-Science-Separates-Other-Animals/dp/0465030149
8. [00:19:10] DeepMind on responsible AI https://deepmind.google/about/responsibility-safety/
21. [00:42:45] Jonathan Cook et al. on Artificial Generational Intelligence arxiv.org/abs/2406.00392
22. [00:45:00] Peng on determinants of cryptocurrency pricing emerald.com/insight/content/doi/10.1108/CAFR-05-2023-0053/full/htmlTaming Silicon Valley - Prof. Gary MarcusMachine Learning Street Talk2024-09-24 | 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.
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 AIRethinking the Mind - Prof. Mark SolmsMachine Learning Street Talk2024-09-18 | 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 cortex-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
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!) amzn.to/3XyETb3
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
12. Jaak Panksepp's SEEKING system [01:25:23] en.wikipedia.org/wiki/Jaak_Panksepp#Affective_neuroscienceThe scientist who coined retrieval augmented generationMachine Learning Street Talk2024-09-16 | 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
Cohere Command Models, check them out - they are amazing for RAG! 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] arxiv.org/abs/2005.11401 2. LAMA (LAnguage Model Analysis) probe / Petroni et al. [00:26:35] arxiv.org/abs/1909.01066 3. KILT (Knowledge Intensive Language Tasks) benchmark / Petroni et al. [00:27:05] arxiv.org/abs/2009.02252 4. Word2Vec algorithm / Tomas Mikolov et al. [01:00:25] 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] arxiv.org/abs/1810.04805 7. 'The Language Game' book / Nick Chater and Morten H. Christiansen [01:11:40] 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 London in June 2024.Is o1-preview reasoning?Machine Learning Street Talk2024-09-15 | Dr. Tim Scarfe and Dr. Keith Duggar discuss OpenAI's new models and their capabilities. They critically analyse claims about AI reasoning, explore the limitations of current language models, and debate the nature of intelligence and computation. Throughout, they emphasize the importance of human oversight in AI applications and discuss potential future developments in the field.
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 1. Introduction and AI hype cycles 00:02:09 2. Computational limits of AI systems 00:03:57 3. Neural Networks vs. Turing Machines 00:11:55 4. Computational models in AI 00:13:03 5. What is Reasoning? 00:21:08 6. Chain-of-thought prompting 00:26:02 7. AI code generation and complexity 00:34:24 8. AI assistance vs. human problem-solving 00:35:04 9. Limitations of AI in reasoning and problem-solving 00:46:27 10. Knowledge acquisition and inference in AI 00:53:36 11. Comparing AI and human reasoning capabilities 00:58:58 12. LLMs as cognitive tools 01:00:32 13. Testing o1-preview on a logic puzzle 01:20:48 14. AI-assisted coding: strengths and limitationsAIs can now imagine video games in real-timeMachine Learning Street Talk2024-09-13 | 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.
10. Gen3 model release by Runway / Runway [23:48] runwayml.com
11. Classifier-free guidance technique / Jonathan Ho and Tim Salimans [24:43] arxiv.org/abs/2207.12598LLMs in enterprise environmentsMachine Learning Street Talk2024-09-12 | 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.
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 Cohere'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 Hansons Vision for Sentient RobotsMachine Learning Street Talk2024-09-10 | 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."
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.
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-24The Fabric of Knowledge - David SpivakMachine Learning Street Talk2024-09-05 | 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.
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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.
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 scholar.google.com/citations?user=luouyakAAAAJ&hl=en Eric Smith's videos on complexity and early life youtube.com/watch?v=SpJZw-68QyE Richard Dawkins' book "The Selfish Gene" amzn.to/3X73X8w Carl Sagan's statement about the cosmos knowing itself amzn.to/3XhPruK Herbert Simon's concept of "satisficing" https://plato.stanford.edu/entries/bounded-rationality/ DeepMind paper on open-ended systems 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) youtube.com/watch?v=UusLtx9fIjsNeural and Non-Neural AI, Reasoning, Transformers, and LSTMsMachine Learning Street Talk2024-08-28 | 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.
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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) arxiv.org/abs/2305.17066 ★ Bremermann's physical limit of computation (1982)
EXTERNAL LINKS CogX 2018 - Professor Juergen Schmidhuber 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 youtube.com/watch?v=I4pQbo5MQOs The Philosophy of Science - Hilary Putnam & Bryan Magee (1977) youtube.com/watch?v=JJB2q8ufAgk Optimal Ordered Problem Solver arxiv.org/abs/cs/0207097 Levin's Universal Search from 1973 rjlipton.com/2011/03/14/levins-great-discoveries https://people.idsia.ch/~juergen/optimalsearch.html On Learning to Think arxiv.org/abs/1511.09249 Mindstorms in Natural Language-Based Societies of Mind
Untersuchungen zu dynamischen neuronalen Netzen https://www.bioinf.jku.at/publications/older/3804.pdf Evolutionary Principles in Self-Referential Learning https://people.idsia.ch/~juergen/diploma1987ocr.pdf Hans-Joachim Bremermann en.wikipedia.org/wiki/Bremermann%27s_limit Highway Networks arxiv.org/abs/1505.00387 https://people.idsia.ch/~juergen/highway-networks.html The principles of Deep Learning Theory amzn.to/3WJtPaj Understanding Deep Learning amzn.to/4doDk63 Discovering Problem Solutions with Low Kolmogorov Complexity and High Generalization Capability (ICML 1995) https://sferics.idsia.ch/pub/juergen/icmlkolmogorov.pdf "History of Modern AI and Deep Learning": https://people.idsia.ch/~juergen/deep-learning-history.htmlAI should NOT be regulated at all! - DomingosMachine Learning Street Talk2024-08-25 | 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.
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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] 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 / 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] amzn.to/3MiWs9B
Rebooting AI: Building Artificial Intelligence We Can Trust [Gary Marcus] amzn.to/3AAywvL
Flash Boys [Michael Lewis] amzn.to/4dUGm1MML Robustness & Engineering - Andrew Ilyas (MIT)Machine Learning Street Talk2024-08-22 | 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.
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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
What Makes A Good Fisherman? Linear Regression under Self-Selection Bias arxiv.org/abs/2205.03246
Towards Tracing Factual Knowledge in Language Models Back to the Training Data [Akyürek] arxiv.org/pdf/2205.11482The AI Bubble: Will It Burst, and What Comes After?Machine Learning Street Talk2024-08-17 | Prof Gary Marcus revisited his keynote from AGI-21, noting that many of the issues he highlighted then are still relevant today despite significant advances in AI.
This is part 1, we will be releasing an in-depth interview with Gary in the coming weeks.
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Gary Marcus criticized current large language models (LLMs) and generative AI for their unreliability, tendency to hallucinate, and inability to truly understand concepts. Marcus argued that the AI field is experiencing diminishing returns with current approaches, particularly the "scaling hypothesis" that simply adding more data and compute will lead to AGI. He advocated for a hybrid approach to AI that combines deep learning with symbolic AI, emphasizing the need for systems with deeper conceptual understanding. Marcus highlighted the importance of developing AI with innate understanding of concepts like space, time, and causality. He expressed concern about the moral decline in Silicon Valley and the rush to deploy potentially harmful AI technologies without adequate safeguards. Marcus predicted a possible upcoming "AI winter" due to inflated valuations, lack of profitability, and overhyped promises in the industry. He stressed the need for better regulation of AI, including transparency in training data, full disclosure of testing, and independent auditing of AI systems. Marcus proposed the creation of national and global AI agencies to oversee the development and deployment of AI technologies. He concluded by emphasizing the importance of interdisciplinary collaboration, focusing on robust AI with deep understanding, and implementing smart, agile governance for AI and AGI.
Pre-order Gary's new book here: Taming Silicon Valley: How We Can Ensure That AI Works for Us amzn.to/4fO46pY
Refs: Closed source vs open-source models slide ~24 mins Fine-tune Llama 3.1 Ultra-Efficiently with Unsloth (Maxime Labonne/Liquid AI) huggingface.co/blog/mlabonne/sft-llama3
TOC: 00:00:00 Introduction 00:02:34 Introduction by Ben G 00:05:17 Gary Marcus begins talk 00:07:38 Critiquing current state of AI 00:12:21 Lack of progress on key AI challenges 00:16:05 Continued reliability issues with AI 00:19:54 Economic challenges for AI industry 00:25:11 Need for hybrid AI approaches 00:29:58 Moral decline in Silicon Valley 00:34:59 Risks of current generative AI 00:40:43 Need for AI regulation and governance 00:49:21 Concluding thoughts 00:54:38 Q&A: Cycles of AI hype and winters 01:00:10 Predicting a potential AI winter 01:02:46 Discussion on interdisciplinary approach 01:05:46 Question on regulating AI 01:07:27 Ben G's perspective on AI winterIs ChatGPT an N-gram model on steroids?Machine Learning Street Talk2024-08-15 | DeepMind machine learning scientist / MIT scholar Dr. Timothy Nguyen discusses his recent paper on understanding transformers through n-gram statistics. Nguyen explains his approach to analyzing transformer behavior using a kind of "template matching" (N-grams), providing insights into how these models process and predict language.
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Key points covered include: A method for describing transformer predictions using n-gram statistics without relying on internal mechanisms. The discovery of a technique to detect overfitting in large language models without using holdout sets. Observations on curriculum learning, showing how transformers progress from simpler to more complex rules during training. Discussion of distance measures used in the analysis, particularly the variational distance. Exploration of model sizes, training dynamics, and their impact on the results.
We also touch on philosophical aspects of describing versus explaining AI behavior, and the challenges in understanding the abstractions formed by neural networks. Nguyen concludes by discussing potential future research directions, including attempts to convert descriptions of transformer behavior into explanations of internal mechanisms.
Timothy Nguyen's earned his B.S. and Ph.D. in mathematics from Caltech and MIT, respectively. He held positions as Research Assistant Professor at the Simons Center for Geometry and Physics (2011-2014) and Visiting Assistant Professor at Michigan State University (2014-2017). During this time, his research expanded into high-energy physics, focusing on mathematical problems in quantum field theory. His work notably provided a simplified and corrected formulation of perturbative path integrals.
Since 2017, Nguyen has been working in industry, applying his expertise to machine learning. He is currently at DeepMind, where he contributes to both fundamental research and practical applications of deep learning to solve real-world problems.
TOC 00:00:00 Timothy Nguyen's background 00:02:50 Paper overview: transformers and n-gram statistics 00:04:55 Template matching and hash table approach 00:08:55 Comparing templates to transformer predictions 00:12:01 Describing vs explaining transformer behavior 00:15:36 Detecting overfitting without holdout sets 00:22:47 Curriculum learning in training 00:26:32 Distance measures in analysis 00:28:58 Model sizes and training dynamics 00:30:39 Future research directions 00:32:06 Conclusion and future topicsJay Alammar on LLMs, RAG, and AI EngineeringMachine Learning Street Talk2024-08-11 | Jay Alammar, renowned AI educator and researcher at Cohere, discusses the latest developments in large language models (LLMs) and their applications in industry. Jay shares his expertise on retrieval augmented generation (RAG), semantic search, and the future of AI architectures.
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Buy Jay's new book here! Hands-On Large Language Models: Language Understanding and Generation amzn.to/4fzOUgh
TOC: 00:00:00 Introduction to Jay Alammar and AI Education 00:01:47 Cohere's Approach to RAG and AI Re-ranking 00:07:15 Implementing AI in Enterprise: Challenges and Solutions 00:09:26 Jay's Role at Cohere and the Importance of Learning in Public 00:15:16 The Evolution of AI in Industry: From Deep Learning to LLMs 00:26:12 Expert Advice for Newcomers in Machine Learning 00:32:39 The Power of Semantic Search and Embeddings in AI Systems 00:37:59 Jay Alammar's Journey as an AI Educator and Visualizer 00:43:36 Visual Learning in AI: Making Complex Concepts Accessible 00:47:38 Strategies for Keeping Up with Rapid AI Advancements 00:49:12 The Future of Transformer Models and AI Architectures 00:51:40 Evolution of the Transformer: From 2017 to Present 00:54:19 Preview of Jay's Upcoming Book on Large Language Models
Disclaimer: This is the fourth video from our Cohere partnership. We were not told what to say in the interview, and didn't edit anything out from the interview. Note also that this combines several previously unpublished interviews from Jay into one, the earlier one at Tim's house was shot in Aug 2023, and the more recent one in Toronto in May 2024.
DPO (Direct Preference Optimization) arxiv.org/abs/2305.18290Daniel Cahn - Slingshot AI (AI Therapy)Machine Learning Street Talk2024-08-08 | Daniel Cahn, co-founder of Slingshot AI, on the potential of AI in therapy and modelling the mind with AI. Why is anxiety and depression affecting a large population? To what extent are these real categories? Why is the mental health getting worse? How often do you want an AI to agree with you? What are the ethics of persuasive AI? The answers to these questions will surprise you.
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TOC: 00:00:00 Intro 00:01:56 Therapy effectiveness vs drugs and societal implications 00:04:02 Mental health categories: Iatrogenesis and social constructs 00:10:19 Psychiatric treatment models and cognitive perspectives 00:13:30 AI design and human-like interactions: Intentionality debates 00:20:04 AI in therapy: Ethics, anthropomorphism, and loneliness mitigation 00:28:13 Therapy efficacy: Neuroplasticity, suffering, and AI placebos 00:33:29 AI's impact on human agency and cognitive modeling 00:41:17 Social media's effects on brain structure and behavior 00:50:46 AI ethics: Altering values and free will considerations 01:00:00 Work value perception and personal identity formation 01:13:37 Free will, agency, and mutable personal identity in therapy 01:24:27 AI in healthcare: Challenges, ethics, and therapy improvements 01:53:25 AI development: Societal impacts and cultural implications
Refs: Bad Therapy: Why the Kids Aren't Growing Up (Abigail Shrier) amzn.to/3WSLga4
Irreversible Damage: Teenage Girls and the Transgender Craze (Shrier) amzn.to/4d9kpfx (note that this book will be offensive to trans folks, forewarned - Tim hasn't read it/isn't taking a position)
(Note the book cover of "Hillbilly Elegy" was a mistake, I meant to link to "The Minds of Billy Milligan") i.e. a book about multiple personality disorder amzn.to/4dUHZg7
Evidence of Human-Level Bonds Established With a Digital Conversational Agent: Cross-sectional, Retrospective Observational Study (JMIR) formative.jmir.org/2021/5/e27868
The Upside of Stress (Daniel referenced this on a part of the conversation which we edited out) amzn.to/3LYMTwwDo you think that ChatGPT can reason?Machine Learning Street Talk2024-07-29 | Prof. Subbarao Kambhampati argues that while LLMs are impressive and useful tools, especially for creative tasks, they have fundamental limitations in logical reasoning and cannot provide guarantees about the correctness of their outputs. He advocates for hybrid approaches that combine LLMs with external verification systems.
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This is 2/13 of our #ICML2024 series
TOC [00:00:00] Intro [00:02:06] Bio [00:03:02] LLMs are n-gram models on steroids [00:07:26] Is natural language a formal language? [00:08:34] Natural language is formal? [00:11:01] Do LLMs reason? [00:19:13] Definition of reasoning [00:31:40] Creativity in reasoning [00:50:27] Chollet's ARC challenge [01:01:31] Can we reason without verification? [01:10:00] LLMs cant solve some tasks [01:19:07] LLM Modulo framework [01:29:26] Future trends of architecture [01:34:48] Future research directions
Faith and Fate: Limits of Transformers on Compositionality "finetuning multiplication with four digit numbers" (added after pub) arxiv.org/pdf/2305.18654
Embracing negative results openreview.net/forum?id=3RXAiU7sssThe threat of existential risk from AIMachine Learning Street Talk2024-07-28 | How seriously should governments take the threat of existential risk from AI, given the lack of consensus among researchers? On the one hand, existential risks (x-risks) are necessarily somewhat speculative: by the time there is concrete evidence, it may be too late. On the other hand, governments must prioritize — after all, they don’t worry too much about x-risk from alien invasions.
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This is part of our ICML 2024 series (1/13)
Sayash Kapoor is a computer science Ph.D. candidate at Princeton University's Center for Information Technology Policy. His research focuses on the societal impact of AI. Kapoor has previously worked on AI in both industry and academia, with experience at Facebook, Columbia University, and EPFL Switzerland. He is a recipient of a best paper award at ACM FAccT and an impact recognition award at ACM CSCW. Notably, Kapoor was included in TIME's inaugural list of the 100 most influential people in AI.
TOC: 00:00:00 Intro 00:01:57 How seriously should we take Xrisk threat? 00:02:55 Risk too unrealiable to inform policy 00:10:20 Overinflated risks 00:12:05 Perils of utility maximisation 00:13:55 Scaling vs airplane speeds 00:17:31 Shift to smaller models? 00:19:08 Commercial LLM ecosystem 00:22:10 Synthetic data 00:24:09 Is AI complexifying our jobs? 00:25:50 Does ChatGPT make us dumber or smarter? 00:26:55 Are AI Agents overhyped? 00:28:12 Simple vs complex baselines 00:30:00 Cost tradeoff in agent design 00:32:30 Model eval vs downstream perf 00:36:49 Shortcuts in metrics 00:40:09 Standardisation of agent evals 00:41:21 Humans in the loop 00:43:54 Levels of agent generality 00:47:25 ARC challengeScience should be self correcting - Subbarao KambhampatiMachine Learning Street Talk2024-07-24 | ...Sara Hooker on language and reasoning #aiMachine Learning Street Talk2024-07-18 | ...Why US AI Act Compute Thresholds Are Misguided...Machine Learning Street Talk2024-07-18 | Sara Hooker is VP of Research at Cohere and leader of Cohere for AI. We discuss her recent paper critiquing the use of compute thresholds, measured in FLOPs (floating point operations), as an AI governance strategy.
We explore why this approach, recently adopted in both US and EU AI policies, may be problematic and oversimplified. Sara explains the limitations of using raw computational power as a measure of AI capability or risk, and discusses the complex relationship between compute, data, and model architecture.
Equally important, we go into Sara's work on "The AI Language Gap." This research highlights the challenges and inequalities in developing AI systems that work across multiple languages. Sara discusses how current AI models, predominantly trained on English and a handful of high-resource languages, fail to serve the linguistic diversity of our global population. We explore the technical, ethical, and societal implications of this gap, and discuss potential solutions for creating more inclusive and representative AI systems.
We broadly discuss the relationship between language, culture, and AI capabilities, as well as the ethical considerations in AI development and deployment.
TOC: [00:00:00] Intro [00:02:12] FLOPS paper [00:26:42] Hardware lottery [00:30:22] The Language gap [00:33:25] Safety [00:38:31] Emergent [00:41:23] Creativity [00:43:40] Long tail [00:44:26] LLMs and society [00:45:36] Model bias [00:48:51] Language and capabilities [00:52:27] Ethical frameworks and RLHF
Disclaimer: This is the third video from our Cohere partnership. We were not told what to say in the interview, and didn't edit anything out from the interview.There are monsters in your LLM.Machine Learning Street Talk2024-07-14 | Murray Shanahan is a professor of Cognitive Robotics at Imperial College London and a senior research scientist at DeepMind. He challenges our assumptions about AI consciousness and urges us to rethink how we talk about machine intelligence.
We explore the dangers of anthropomorphizing AI, the limitations of current language in describing AI capabilities, and the fascinating intersection of philosophy and artificial intelligence.
Refs (links in the Google doc linked above): Role play with large language models Waluigi effect "Conscious Exotica" - Paper by Murray Shanahan (2016) "Simulators" - Article by Janis from LessWrong "Embodiment and the Inner Life" - Book by Murray Shanahan (2010) "The Technological Singularity" - Book by Murray Shanahan (2015) "Simulacra as Conscious Exotica" - Paper by Murray Shanahan (newer paper of the original focussed on LLMs) A recent paper by Anthropic on using autoencoders to find features in language models (referring to the "Scaling Monosemanticity" paper) Work by Peter Godfrey-Smith on octopus consciousness "Metaphors We Live By" - Book by George Lakoff (1980s) Work by Aaron Sloman on the concept of "space of possible minds" (1984 article mentioned) Wittgenstein's "Philosophical Investigations" (posthumously published) Daniel Dennett's work on the "intentional stance" Alan Turing's original paper on the Turing Test (1950) Thomas Nagel's paper "What is it like to be a bat?" (1974) John Searle's Chinese Room Argument (mentioned but not detailed) Work by Richard Evans on tackling reasoning problems Claude Shannon's quote on knowledge and control "Are We Bodies or Souls?" - Book by Richard Swinburne Reference to work by Ethan Perez and others at Anthropic on potential deceptive behavior in language models Reference to a paper by Murray Shanahan and Antonia Creswell on the "selection inference framework" Mention of work by Francois Chollet, particularly the ARC (Abstraction and Reasoning Corpus) challenge Reference to Elizabeth Spelke's work on core knowledge in infants Mention of Karl Friston's work on planning as inference (active inference) The film "Ex Machina" - Murray Shanahan was the scientific advisor "The Waluigi Effect" Anthropic's constitutional AI approach Loom system by Lara Reynolds and Kyle McDonald for visualizing conversation trees DeepMind's AlphaGo (mentioned multiple times as an example) Mention of the "Golden Gate Claude" experiment Reference to an interview Tim Scarfe conducted with University of Toronto students about self-attention controllability theorem Mention of an interview with Irina Rish Reference to an interview Tim Scarfe conducted with Daniel Dennett Reference to an interview with Maria Santa Caterina Mention of an interview with Philip Goff Nick Chater and Martin Christianson's book ("The Language Game: How Improvisation Created Language and Changed the World") Peter Singer's work from 1975 on ascribing moral status to conscious beings Demis Hassabis' discussion on the "ladder of creativity" Reference to B.F. Skinner and behaviorism
TOC: 00:00:00 Intro 00:05:49 Simulators 00:11:04 The 20 questions game and simulacra stickiness 00:18:50 Murray's experience with Claude 3 00:30:04 RLHF 00:32:41 Anthropic Golden Gate Bridge 00:37:05 Agency 00:41:05 Embodiment and knowledge acquisition 00:57:51 ARC 01:03:31 The conscious stance 01:13:58 Space of possible minds 01:11:05 part 2: Wittgenstein private language argument / subjectivity 01:29:58 Conscious exotica 01:33:23 Dennett and intentional stance 01:40:58 Anthropomorphisation 01:46:47 Reasoning 01:53:56 Turing test 02:04:41 Nagel's bat 02:08:08 Mark Bishop and Idealism/CRA 02:09:32 PanpsychismSolving Chollets ARC-AGI with GPT4oMachine Learning Street Talk2024-07-06 | Ryan Greenblatt from Redwood Research recently published "Getting 50% on ARC-AGI with GPT-4.0," where he used GPT4o to reach a state-of-the-art accuracy on Francois Chollet's ARC Challenge by generating many Python programs.
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We discuss: - Ryan's unique approach to solving the ARC Challenge and achieving impressive results. - The strengths and weaknesses of current AI models. - How AI and humans differ in learning and reasoning. - Combining various techniques to create smarter AI systems. - The potential risks and future advancements in AI, including the idea of agentic AI.
TOC 00:00:00 Intro 00:01:38 Prelude on goals in LLMs 00:02:42 Ryan intro 00:03:11 Ryan's ARC Challenge Approach 00:38:15 Language models, reasoning and agency 01:14:14 Timelines on superintelligence 01:27:05 Growth of superintelligence 02:06:41 Reflections on ARC 02:11:49 Why wouldn't AI knowledge be subjective
Connectionism and Cognitive Architecture: A Critical Analysis [Jerry A. Fodor and Zenon W. Pylyshyn] https://ruccs.rutgers.edu/images/personal-zenon-pylyshyn/proseminars/Proseminar13/ConnectionistArchitecture.pdf
Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model arxiv.org/pdf/2201.11990
Algorithmic Progress in Language Models epochai.org/blog/algorithmic-progress-in-language-modelsAidan Gomez lessons building CohereMachine Learning Street Talk2024-06-29 | ...How Cohere will improve AI Reasoning this yearMachine Learning Street Talk2024-06-29 | Aidan Gomez, CEO of Cohere, reveals how they're tackling AI hallucinations and improving reasoning abilities. He also explains why Cohere doesn't use any output from GPT-4 for training their models.
Aidan shares his personal insights into the world of AI and LLMs and Cohere's unique approach to solving real-world business problems, and how their models are set apart from the competition. Aidan reveals how they are making major strides in AI technology, discussing everything from last mile customer engineering to the robustness of prompts and future architectures.
He also touches on the broader implications of AI for society, including potential risks and the role of regulation. He discusses Cohere's guiding principles and the health the of startup scene. With a particular focus on enterprise applications. Aidan provides a rare look into the internal workings of Cohere and their vision for driving productivity and innovation.
TOC: 00:00:00 Intro 00:01:48 Guiding principles of Cohere 00:02:31 Last mile / customer engineering 00:04:25 Prompt brittleness 00:06:14 Robustness and "delving" 00:10:12 Command R models and catch up 00:12:32 Are LLMs saturating / specialisation 00:16:11 Intelligence 00:21:28 Predictive architectures, data vs inductive priors 00:25:55 Agentic systems 00:28:11 Differentiation 00:33:35 X-Risk / Bostrom 00:39:30 Changing relationship with technology 00:45:08 Policy 00:49:01 Startup scene 00:52:44 Biggest mistake? 00:53:50 Management style 00:56:38 Culture in different Cohere offices?
Disclaimer: This is the second video from our Cohere partnership. We were not told what to say in the interview, and didn't edit anything out from the interview.Schmidhuber feels the AGIMachine Learning Street Talk2024-06-29 | ...Could the universe be conscious?Machine Learning Street Talk2024-06-21 | Philip Goff believes that everything, even tiny particles like electrons, has a little bit of consciousness. This idea is called panpsychism. He explains that this might help us understand why we have feelings and thoughts.
Philip discuss another idea called cosmopsychism, which is a theory that suggests the entire universe is a single conscious entity. Instead of individual minds (like human minds) being separate and independent, they are seen as parts of the universe's larger, unified consciousness. In simpler terms, it means that the universe itself has a mind, and our individual consciousnesses are just small parts of this greater, universal mind.
Philip thinks science can't fully explain what it's like to feel things, like the color red or the taste of chocolate. He says we need to include consciousness in our science to understand everything better.
We edited out discussion of the Twitter argument with Sabine on this video, but you can find it on Twitter here - https://x.com/MLStreetTalk/status/1804825098207052061
https://x.com/Philip_Goff
Why? The Purpose of the Universe (Prof Philip Goff) amzn.to/4cbYHqL
Galileo's Error: Foundations for a New Science of Consciousness amzn.to/4eqA5Mi
TOC 00:00:00 Introduction to Consciousness 00:02:52 Panpsychism Explained 00:04:43 Cosmopsychism and Panagentialism 00:08:44 Exploring Agency and Purpose 00:22:51 Physicalism vs. Panpsychism 00:28:03 Critique of Philosophical Views on Consciousness 00:30:51 Frank Jackson's Knowledge Argument 00:33:50 Galileo and the Foundations of Physical Science 00:36:43 Philosophical and Scientific Integration 00:43:57 MindChat and Constructive Disagreement 00:46:29 Book signing and Final Thoughts
We used a clip of Sam Harris from the Know Thyself podcast - youtube.com/watch?v=gqA-ZRpl1jQChollets ARC Challenge + Current WinnersMachine Learning Street Talk2024-06-18 | The ARC Challenge, created by Francois Chollet, tests how well AI systems can generalize from a few examples in a grid-based intelligence test. We interview the current winners of the ARC Challenge—Jack Cole, Mohammed Osman and their collaborator Michael Hodel. They discuss how they tackled ARC (Abstraction and Reasoning Corpus) using language models. We also discuss the new "50%" public set approach announced today from Redwood Research (Ryan Greenblatt).
Jack and Mohammed explain their winning approach, which involves fine-tuning a language model on a large, specifically-generated dataset and then doing additional fine-tuning at test-time, a technique known in this context as "active inference". They use various strategies to represent the data for the language model and believe that with further improvements, the accuracy could reach above 50%. Michael talks about his work on generating new ARC-like tasks to help train the models.
They also debate whether their methods stay true to the "spirit" of Chollet's measure of intelligence. Despite some concerns, they agree that their solutions are promising and adaptable for other similar problems.
Note: Jack's team is still the current official winner at 33% on the private set. Ryan's entry is not on the private leaderboard or eligible. Chollet invented ARC in 2019 (not 2017 as stated)
"Ryan's entry is not a new state of the art. We don't know exactly how well it does since it was only evaluated on 100 tasks from the evaluation set and does 50% on those, reportedly. Meanwhile Jacks team i.e. MindsAI's solution does 54% on the entire eval set and it is seemingly possible to do 60-70% with an ensemble"
Jack Cole: https://x.com/Jcole75Cole https://lab42.global/community-interview-jack-cole/
TOC (autogenerated): 00:00:00 Introduction 00:03:00 Francois Chollet's Intelligence Concept 00:08:00 Human Collaboration 00:15:00 ARC Tasks and Symbolic AI 00:27:00 Evaluation Techniques 00:35:23 (Main Interview) Competitors and Approaches 00:40:00 Meta Learning Challenges 00:48:00 System 1 vs System 2 01:00:00 Inductive Priors and Symbols 01:18:00 Methodologies Comparison 01:25:00 Training Data Size Impact 01:35:00 Generalization Issues 01:47:00 Techniques for AI Applications 01:56:00 Model Efficiency and Scalability 02:10:00 Task Specificity and Generalization 02:13:00 SummaryARC challenge - where does the reasoning happen? #artificialintelligence Machine Learning Street Talk2024-06-18 | ...Cohere is not an AGI company - Nick Frosst (co-founder)Machine Learning Street Talk2024-06-16 | ...Is AGI Just a Fantasy?Machine Learning Street Talk2024-06-15 | Nick Frosst, the co-founder of Cohere, on the future of LLMs, and AGI. Learn how Cohere is solving real problems for business with their new AI models.
Nick talks about his journey at Google Brain, working with AI legends like Geoff Hinton, and the amazing things his company, Cohere, is doing. From creating the must useful language models for businesses to making tools for developers, Nick shares a lot of interesting insights. He even talks about his band, Good Kid! Nick said that RAG is one of the best features of Cohere's new Command R* models. We are about to release a deep-dive on RAG with Patrick Lewis from Cohere, keep an eye out for that - he explains why their models are specifically optimised for RAG use cases.
00:00:00 Intro 00:01:55 Backstory of Cohere 00:02:31 Hinton 00:02:54 Nick's band 00:03:11 How is Cohere differentiated? 00:03:44 Not an AGI company 00:06:00 Command+R 00:06:41 Standout feature: RAG 00:09:07 How is RAG changing the way we build apps? 00:09:44 Build day 00:10:25 Building robust applications 00:11:59 RAG evolution 00:14:45 Unsupervised RAG 00:16:30 Agents and divergence 00:18:27 Agency 00:22:19 Are LLMs general? 00:24:48 Benchmarks 00:27:07 Would Cohere verticalize? 00:27:43 RAG vs long context 00:29:20 Tech hasn't landed yet? 00:31:36 Are LLMs saturating? 00:35:50 Cohere's data acquisition pipeline 00:36:34 SOTA chasing vs fairness 00:37:21 Fall of the data scientist 00:40:37 Final callouts
Disclaimer: This is the first video from our Cohere partnership. We were not told what to say in the interview, and didn't edit anything out from the interview.Consciousness in LLMsMachine Learning Street Talk2024-06-09 | patreon.com/posts/prof-murray-105678581 watch now on early access!Prof. Mark Solms on philosophical clarity (on Patreon early-access now).Machine Learning Street Talk2024-06-09 | ...Prior knowledge in ChatGPTMachine Learning Street Talk2024-05-27 | patreon.com/posts/elliot-murphy-105019438 watch early here (Elliot Murphy show)Mapping GPT revealed something strange...Machine Learning Street Talk2024-05-24 | These two scientists have mapped out the insides or “reachable space” of a language model using control theory, what they discovered was extremely surprising.
Please support us on Patreon to get access to the private Discord server, bi-weekly calls, early access and ad-free listening. patreon.com/mlst
Aman Bhargava from Caltech and Cameron Witkowski from the University of Toronto to discuss their groundbreaking paper, “What’s the Magic Word? A Control Theory of LLM Prompting.” (the main theorem on self-attention controllability was developed in collaboration with Dr. Shi-Zhuo Looi from Caltech).
They frame LLM systems as discrete stochastic dynamical systems. This means they look at LLMs in a structured way, similar to how we analyze control systems in engineering. They explore the “reachable set” of outputs for an LLM. Essentially, this is the range of possible outputs the model can generate from a given starting point when influenced by different prompts. The research highlights that prompt engineering, or optimizing the input tokens, can significantly influence LLM outputs. They show that even short prompts can drastically alter the likelihood of specific outputs. Aman and Cameron’s work might be a boon for understanding and improving LLMs. They suggest that a deeper exploration of control theory concepts could lead to more reliable and capable language models.
We dropped an additional, more technical video on the research on our Twitter account here: https://x.com/MLStreetTalk/status/1795093759471890606
Aman and Cameron also want to thank Dr. Shi-Zhuo Looi and Prof. Matt Thomson from from Caltech for help and advice on their research. (https://thomsonlab.caltech.edu/ and https://pma.caltech.edu/people/looi-shi-zhuo)
TOC: 00:00:00 - Main Intro 00:06:25 - Bios 00:07:50 - Control Theory and Governors 00:09:37 - LLM Control Theory 00:17:17 - Federer Game 00:19:49 - Building LLM Controllers 00:20:56 - Priors in LLMs 00:28:44 - Manipulating LLMs 00:34:11 - Adversarial Examples and Robustification 00:36:54 - Model vs Software 00:39:12 - Experiments in the Paper 00:44:36 - Language as an Interstate Freeway 00:46:41 - Collective Intelligence 00:58:54 - Biomimetic Intelligence 01:03:37 - Society for the Pursuit of AGI 01:05:47 - ICLR RejectionAI cannot interpret reality.Machine Learning Street Talk2024-05-06 | Maria SantacaterinaCAN MACHINES REPLACE US? (AI vs Humanity)Machine Learning Street Talk2024-05-06 | Maria Santacaterina, with her background in the humanities, brings a critical perspective on the current state and future implications of AI technology, its impact on society, and the nature of human intelligence and creativity. She emphasizes that despite technological advancements, AI lacks fundamental human traits such as consciousness, empathy, intuition, and the ability to engage in genuine creative processes. Maria argues that AI, at its core, processes data but does not have the capability to understand or generate new, intrinsic meaning or ideas as humans do.
Throughout the conversation, Maria highlights her concern about the overreliance on AI in critical sectors such as healthcare, the justice system, and business. She stresses that while AI can serve as a tool, it should not replace human judgment and decision-making. Maria points out that AI systems often operate on past data, which may lead to outdated or incorrect decisions if not carefully managed.
The discussion also touches upon the concept of "adaptive resilience", which Maria describes in her book. She explains adaptive resilience as the capacity for individuals and enterprises to evolve and thrive amidst challenges by leveraging technology responsibly, without undermining human values and capabilities.
A significant portion of the conversation focussed on ethical considerations surrounding AI. Tim and Maria agree that there's a pressing need for strong governance and ethical frameworks to guide AI development and deployment. They discuss how AI, without proper ethical considerations, risks exacerbating issues like privacy invasion, misinformation, and unintended discrimination.
Maria is skeptical about claims of achieving Artificial General Intelligence (AGI) or a technological singularity where machines surpass human intelligence in all aspects. She argues that such scenarios neglect the complex, dynamic nature of human intelligence and consciousness, which cannot be fully replicated or replaced by machines.
Tim and Maria discuss the importance of keeping human agency and creativity at the forefront of technology development. Maria asserts that efforts to automate or standardize complex human actions and decisions are misguided and could lead to dehumanizing outcomes. They both advocate for using AI as an aid to enhance human capabilities rather than a substitute.
In closing, Maria encourages a balanced approach to AI adoption, urging stakeholders to prioritize human well-being, ethical standards, and societal benefit above mere technological advancement. The conversation ends with Maria pointing people to her book for more in-depth analysis and thoughts on the future interaction between humans and technology.
TOC 00:00:00 - Intro to Book 00:03:23 - What Life Is 00:10:10 - Agency 00:18:04 - Tech and Society 00:21:51 - System 1 and 2 00:22:59 - We Are Being Pigeonholed 00:30:22 - Agency vs Autonomy 00:36:37 - Explanations 00:40:24 - AI Reductionism 00:49:50 - How Are Humans Intelligent 01:00:22 - Semantics 01:01:53 - Emotive AI and Pavlovian Dogs 01:04:05 - Technology, Social Media and Organisation 01:18:34 - Systems Are Not That Automated 01:19:33 - Hiring 01:22:34 - Subjectivity in Orgs 01:32:28 - The AGI Delusion 01:45:37 - GPT-laziness Syndrome 01:54:58 - Diversity Preservation 01:58:24 - Ethics 02:11:43 - Moral Realism 02:16:17 - Utopia 02:18:02 - Reciprocity 02:20:52 - Tyranny of Categorisation
Interviewer: Dr. Tim ScarfeWhat’s the Magic Word? A Control Theory of LLM Prompting #artificialintelligenceMachine Learning Street Talk2024-05-05 | "What's the Magic Word? A Control Theory of LLM Prompting" Aman Bhargava, Cameron Witkowski, Manav Shah, Matt Thomson arxiv.org/abs/2310.04444
Video out on Patreon today!Dr. THOMAS PARR - Active InferenceMachine Learning Street Talk2024-05-01 | Thomas Parr and his collaborators wrote a book titled "Active Inference: The Free Energy Principle in Mind, Brain and Behavior" which introduces Active Inference from both a high-level conceptual perspective and a low-level mechanistic, mathematical perspective.
Active inference, developed by the legendary neuroscientist Prof. Karl Friston - is a unifying mathematical framework which frames living systems as agents which minimize surprise and free energy in order to resist entropy and persist over time. It unifies various perspectives from physics, biology, statistics, and psychology - and allows us to explore deep questions about agency, biology, causality, modelling, and consciousness.
Buy Active Inference: The Free Energy Principle in Mind, Brain, and Behavior Thomas Parr, Giovanni Pezzulo, Karl Friston amzn.to/4dj0iMj
Please support us on Patreon to get access to the private Discord server, bi-weekly calls, early access and ad-free listening. patreon.com/mlst
TOC: 00:00:00 Intro 00:05:10 When Thomas met Friston 00:06:13 ChatGPT comparison 00:08:40 Do NNs learn a world model? 00:11:04 Book intro 00:13:22 High road low road of Active Inference 00:17:16 Resisting entropic forces 00:20:51 Agency vs free will 00:26:01 Are agents real? non-physical agents 00:35:54 Mind is flat / predictive brain 00:44:23 Volition 00:50:26 Externalism 00:51:57 Bridge with Enactivism 00:53:27 Bayesian Surprise 01:01:47 Variational inference 01:05:47 Why Bayesian? 01:12:04 Causality 01:17:35 Hand crafted models 01:26:45 Chapter 10 - bringing it together 01:28:58 Consciousness 01:33:10 Humans are incoherent 01:35:25 Experience writing a book
Interviewer: Dr. Tim ScarfeConnor Leahy - e/acc, AGI and the future.Machine Learning Street Talk2024-04-21 | Connor is the CEO of Conjecture and one of the most famous names in the AI alignment movement. This is the "behind the scenes footage" and bonus Patreon interviews from the day of the Beff Jezos debate, including an interview with Daniel Clothiaux. It's a great insight into Connor's philosophy.
Support MLST: Please support us on Patreon. We are entirely funded from Patreon donations right now. Patreon supports get private discord access, biweekly calls, very early-access + exclusive content and lots more. patreon.com/mlst Donate: paypal.com/donate/?hosted_button_id=K2TYRVPBGXVNA If you would like to sponsor us, so we can tell your story - reach out on mlstreettalk at gmail
Topics: Externalized cognition and the role of society and culture in human intelligence The potential for AI systems to develop agency and autonomy The future of AGI as a complex mixture of various components The concept of agency and its relationship to power The importance of coherence in AI systems The balance between coherence and variance in exploring potential upsides The role of dynamic, competent, and incorruptible institutions in handling risks and developing technology Concerns about AI widening the gap between the haves and have-nots The concept of equal access to opportunity and maintaining dynamism in the system Leahy's perspective on life as a process that "rides entropy" The importance of distinguishing between epistemological, decision-theoretic, and aesthetic aspects of morality (inc ref to Hume's Guillotine) The concept of continuous agency and the idea that the first AGI will be a messy admixture of various components The potential for AI systems to become more physically embedded in the future The challenges of aligning AI systems and the societal impacts of AI technologies like ChatGPT and Bing The importance of humility in the face of complexity when considering the future of AI and its societal implications
TOC: 00:00:00 Intro 00:00:56 Connor's Philosophy 00:03:53 Office Skit 00:05:08 Connor on e/acc and Beff 00:07:28 Intro to Daniel's Philosophy 00:08:35 Connor on Entropy, Life, and Morality 00:19:10 Connor on London 00:20:21 Connor Office Interview 00:20:46 Friston Patreon Preview 00:21:48 Why Are We So Dumb? 00:23:52 The Voice of the People, the Voice of God / Populism 00:26:35 Mimetics 00:30:03 Governance 00:33:19 Agency 00:40:25 Daniel Interview - Externalised Cognition, Bing GPT, AGI 00:56:29 Beff + Connor Bonus Patreons Interview
Disclaimer: this video is not an endorsement of e/acc or AGI agential existential risk from us - the hosts of MLST consider both of these views to be quite extreme. We seek diverse views on the channel.Microsofts professor Chris Bishop on the Sparks of AGIMachine Learning Street Talk2024-04-10 | ...Prof. Chris Bishops NEW Deep Learning Textbook!Machine Learning Street Talk2024-04-10 | Professor Chris Bishop is a Technical Fellow and Director at Microsoft Research AI4Science, in Cambridge. He is also Honorary Professor of Computer Science at the University of Edinburgh, and a Fellow of Darwin College, Cambridge. In 2004, he was elected Fellow of the Royal Academy of Engineering, in 2007 he was elected Fellow of the Royal Society of Edinburgh, and in 2017 he was elected Fellow of the Royal Society. Chris was a founding member of the UK AI Council, and in 2019 he was appointed to the Prime Minister’s Council for Science and Technology.
At Microsoft Research, Chris oversees a global portfolio of industrial research and development, with a strong focus on machine learning and the natural sciences. Chris obtained a BA in Physics from Oxford, and a PhD in Theoretical Physics from the University of Edinburgh, with a thesis on quantum field theory.
Chris's contributions to the field of machine learning have been truly remarkable. He has authored (what is arguably) the original textbook in the field - 'Pattern Recognition and Machine Learning' (PRML) which has served as an essential reference for countless students and researchers around the world, and that was his second textbook after his highly acclaimed first textbook Neural Networks for Pattern Recognition.
Recently, Chris has co-authored a new book with his son, Hugh, titled 'Deep Learning: Foundations and Concepts.' This book aims to provide a comprehensive understanding of the key ideas and techniques underpinning the rapidly evolving field of deep learning. It covers both the foundational concepts and the latest advances, making it an invaluable resource for newcomers and experienced practitioners alike.
Support MLST: Please support us on Patreon. We are entirely funded from Patreon donations right now. Patreon supports get private discord access, biweekly calls, early-access + exclusive content and lots more. patreon.com/mlst Donate: paypal.com/donate/?hosted_button_id=K2TYRVPBGXVNA If you would like to sponsor us, so we can tell your story - reach out on mlstreettalk at gmail
TOC: 00:00:00 - Intro to Chris 00:06:54 - Changing Landscape of AI 00:08:16 - Symbolism 00:09:32 - PRML 00:11:02 - Bayesian Approach 00:14:49 - Are NNs One Model or Many, Special vs General 00:20:04 - Can Language Models Be Creative 00:22:35 - Sparks of AGI 00:25:52 - Creativity Gap in LLMs 00:35:40 - New Deep Learning Book 00:39:01 - Favourite Chapters 00:44:11 - Probability Theory 00:45:42 - AI4Science 00:48:31 - Inductive Priors 00:58:52 - Drug Discovery 01:05:19 - Foundational Bias Models 01:07:46 - How Fundamental Is Our Physics Knowledge? 01:12:05 - Transformers 01:12:59 - Why Does Deep Learning Work? 01:16:59 - Inscrutability of NNs 01:18:01 - Example of Simulator 01:21:09 - ControlPhilip Ball on agency and how life works.Machine Learning Street Talk2024-04-08 | ...AI AGENCY ISNT HERE YET... (Dr. Philip Ball)Machine Learning Street Talk2024-04-07 | Dr. Philip Ball is a freelance science writer. He just wrote a book called "How Life Works", discussing the how the science of Biology has advanced in the last 20 years. We focus on the concept of Agency in particular.
He trained as a chemist at the University of Oxford, and as a physicist at the University of Bristol. He worked previously at Nature for over 20 years, first as an editor for physical sciences and then as a consultant editor. His writings on science for the popular press have covered topical issues ranging from cosmology to the future of molecular biology.
Philip is the author of many popular books on science, including H2O: A Biography of Water, Bright Earth: The Invention of Colour, The Music Instinct and Curiosity: How Science Became Interested in Everything. His book Critical Mass won the 2005 Aventis Prize for Science Books, while Serving the Reich was shortlisted for the Royal Society Winton Science Book Prize in 2014.
This is one of Tim's personal favourite MLST shows, so we have designated it a special edition. Enjoy!
TOC: 00:00:00 Outside interview 00:09:17 Nativism / capacities 00:11:50 Generative AI 00:17:59 Inscrutability and agency 00:22:06 Agency on creativity 00:26:38 Could we make an agential GPT-4? 00:31:06 If it agential if you tell the agents what to do 00:35:40 What is agency? 00:44:29 Are agents real 00:48:32 Causality and agency 00:54:40 Ghost in the machine / intelligibility 01:00:11 Multi scale / organisation view 01:04:05 Collective intelligence 01:09:00 Canalisation 01:13:36 Intelligence is specialised 01:16:29 No free lunch 01:18:19 Super intelligence 01:22:05 Mind is flat / confabulated goals 01:25:07 Is planning/goals explicit? 01:30:17 Sentience in LLMs 01:34:56 Are LLMs simulators? 01:40:04 Could LLMs feel? 01:49:33 Digital physics view 01:51:46 Agential vs nonagential AI 01:54:02 Bostrom thinks goals and intelligence are separate 02:05:41 Simulation sharing
Support MLST: Please support us on Patreon. We are entirely funded from Patreon donations right now. Patreon supports get private discord access, biweekly calls, early-access + exclusive content and lots more. patreon.com/mlst Donate: paypal.com/donate/?hosted_button_id=K2TYRVPBGXVNA If you would like to sponsor us, so we can tell your story - reach out on mlstreettalk at gmail