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Machine Learning Street Talk (MLST)

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/).

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  • 257 episodes
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Episodes257

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  • May 21, 2023 · 2 hr 1 min

    ROBERT MILES - "There is a good chance this kills everyone"

    Please check out Numerai - our sponsor @ https://numerai.com/mlst Numerai is a groundbreaking platform which is taking the data science world by storm. Tim has been using Numerai to build state-of-the-art models which predict the stock market, all while being a part of an inspiring community of data scientists from around the globe. They host the Numerai Data Science Tournament, where data scientists like us use their financial dataset to predict future stock market performance. Support us! https://www.patreon.com/mlst MLST Discord: https://discord.gg/aNPkGUQtc5 Twitter: https://twitter.com/MLStreetTalk Welcome to an exciting episode featuring an outstanding guest, Robert Miles! Renowned for his extraordinary contributions to understanding AI and its potential impacts on our lives, Robert is an artificial intelligence advocate, researcher, and YouTube sensation. He combines engaging discussions with entertaining content, captivating millions of viewers from around the world. With a strong computer science background, Robert has been actively involved in AI safety projects, focusing on raising awareness about potential risks and benefits of advanced AI systems. His YouTube channel is celebrated for making AI safety discussions accessible to a diverse audience through breaking down complex topics into easy-to-understand nuggets of knowledge, and you might also recognise him from his appearances on Computerphile. In this episode, join us as we dive deep into Robert's journey in the world of AI, exploring his insights on AI alignment, superintelligence, and the role of AI shaping our society and future. We'll discuss topics such as the limits of AI capabilities and physics, AI progress and timelines, human-machine hybrid intelligence, AI in conflict and cooperation with humans, and the convergence of AI communities. Robert Miles: @RobertMilesAI https://twitter.com/robertskmiles https://aisafety.info/ YT version: https://www.youtube.com/watch?v=kMLKbhY0ji0 Panel: Dr. Tim Scarfe Dr. Keith Duggar Joint CTOs - https://xrai.glass/ Refs: Are Emergent Abilities of Large Language Models a Mirage? (Rylan Schaeffer) https://arxiv.org/abs/2304.15004 TOC: Intro [00:00:00] Numerai Sponsor Messsage [00:02:17] AI Alignment [00:04:27] Limits of AI Capabilities and Physics [00:18:00] AI Progress and Timelines [00:23:52] AI Arms Race and Innovation [00:31:11] Human-Machine Hybrid Intelligence [00:38:30] Understanding and Defining Intelligence [00:42:48] AI in Conflict and Cooperation with Humans [00:50:13] Interpretability and Mind Reading in AI [01:03:46] Mechanistic Interpretability and Deconfusion Research [01:05:53] Understanding the core concepts of AI [01:07:40] Moon landing analogy and AI alignment [01:09:42] Cognitive horizon and limits of human intelligence [01:11:42] Funding and focus on AI alignment [01:16:18] Regulating AI technology and potential risks [01:19:17] Aligning AI with human values and its dynamic nature [01:27:04] Cooperation and Allyship [01:29:33] Orthogonality Thesis and Goal Preservation [01:33:15] Anthropomorphic Language and Intelligent Agents [01:35:31] Maintaining Variety and Open-ended Existence [01:36:27] Emergent Abilities of Large Language Models [01:39:22] Convergence vs Emergence [01:44:04] Criticism of X-risk and Alignment Communities [01:49:40] Fusion of AI communities and addressing biases [01:52:51] AI systems integration into society and understanding them [01:53:29] Changing opinions on AI topics and learning from past videos [01:54:23] Utility functions and von Neumann-Morgenstern theorems [01:54:47] AI Safety FAQ project [01:58:06] Building a conversation agent using AI safety dataset [02:00:36]

  • May 16, 2023 · 49 min

    AI Senate Hearing - Executive Summary (Sam Altman, Gary Marcus)

    Support us! https://www.patreon.com/mlst MLST Discord: https://discord.gg/aNPkGUQtc5 Twitter: https://twitter.com/MLStreetTalk In a historic and candid Senate hearing, OpenAI CEO Sam Altman, Professor Gary Marcus, and IBM's Christina Montgomery discussed the regulatory landscape of AI in the US. The discussion was particularly interesting due to its timing, as it followed the recent release of the EU's proposed AI Act, which could potentially ban American companies like OpenAI and Google from providing API access to generative AI models and impose massive fines for non-compliance. The speakers openly addressed potential risks of AI technology and emphasized the need for precision regulation. This was a unique approach, as historically, US companies have tried their hardest to avoid regulation. The hearing not only showcased the willingness of industry leaders to engage in discussions on regulation but also demonstrated the need for a balanced approach to avoid stifling innovation. The EU AI Act, scheduled to come into power in 2026, is still just a proposal, but it has already raised concerns about its impact on the American tech ecosystem and potential conflicts between US and EU laws. With extraterritorial jurisdiction and provisions targeting open-source developers and software distributors like GitHub, the Act could create more problems than it solves by encouraging unsafe AI practices and limiting access to advanced AI technologies. One core issue with the Act is the designation of foundation models in the highest risk category, primarily due to their open-ended nature. A significant risk theme revolves around users creating harmful content and determining who should be held accountable – the users or the platforms. The Senate hearing served as an essential platform to discuss these pressing concerns and work towards a regulatory framework that promotes both safety and innovation in AI. 00:00 Show 01:35 Legals 03:44 Intro 10:33 Altman intro 14:16 Christina Montgomery 18:20 Gary Marcus 23:15 Jobs 26:01 Scorecards 28:08 Harmful content 29:47 Startups 31:35 What meets the definition of harmful? 32:08 Moratorium 36:11 Social Media 46:17 Gary's take on BingGPT and pivot into policy 48:05 Democratisation

  • May 11, 2023 · 2 hr 31 min

    Future of Generative AI [David Foster]

    Generative Deep Learning, 2nd Edition [David Foster] https://www.oreilly.com/library/view/generative-deep-learning/9781098134174/ Support us! https://www.patreon.com/mlst MLST Discord: https://discord.gg/aNPkGUQtc5 Twitter: https://twitter.com/MLStreetTalk In this conversation, Tim Scarfe and David Foster, the author of 'Generative Deep Learning,' dive deep into the world of generative AI, discussing topics ranging from model families and auto regressive models to the democratization of AI technology and its potential impact on various industries. They explore the connection between language and true intelligence, as well as the limitations of GPT and other large language models. The discussion also covers the importance of task-independent world models, the concept of active inference, and the potential of combining these ideas with transformer and GPT-style models. Ethics and regulation in AI development are also discussed, including the need for transparency in data used to train AI models and the responsibility of developers to ensure their creations are not destructive. The conversation touches on the challenges posed by AI-generated content on copyright laws and the diminishing role of effort and skill in copyright due to generative models. The impact of AI on education and creativity is another key area of discussion, with Tim and David exploring the potential benefits and drawbacks of using AI in the classroom, the need for a balance between traditional learning methods and AI-assisted learning, and the importance of teaching students to use AI tools critically and responsibly. Generative AI in music is also explored, with David and Tim discussing the potential for AI-generated music to change the way we create and consume art, as well as the challenges in training AI models to generate music that captures human emotions and experiences. Throughout the conversation, Tim and David touch on the potential risks and consequences of AI becoming too powerful, the importance of maintaining control over the technology, and the possibility of government intervention and regulation. The discussion concludes with a thought experiment about AI predicting human actions and creating transient capabilities that could lead to doom. TOC: Introducing Generative Deep Learning [00:00:00] Model Families in Generative Modeling [00:02:25] Auto Regressive Models and Recurrence [00:06:26] Language and True Intelligence [00:15:07] Language, Reality, and World Models [00:19:10] AI, Human Experience, and Understanding [00:23:09] GPTs Limitations and World Modeling [00:27:52] Task-Independent Modeling and Cybernetic Loop [00:33:55] Collective Intelligence and Emergence [00:36:01] Active Inference vs. Reinforcement Learning [00:38:02] Combining Active Inference with Transformers [00:41:55] Decentralized AI and Collective Intelligence [00:47:46] Regulation and Ethics in AI Development [00:53:59] AI-Generated Content and Copyright Laws [00:57:06] Effort, Skill, and AI Models in Copyright [00:57:59] AI Alignment and Scale of AI Models [00:59:51] Democratization of AI: GPT-3 and GPT-4 [01:03:20] Context Window Size and Vector Databases [01:10:31] Attention Mechanisms and Hierarchies [01:15:04] Benefits and Limitations of Language Models [01:16:04] AI in Education: Risks and Benefits [01:19:41] AI Tools and Critical Thinking in the Classroom [01:29:26] Impact of Language Models on Assessment and Creativity [01:35:09] Generative AI in Music and Creative Arts [01:47:55] Challenges and Opportunities in Generative Music [01:52:11] AI-Generated Music and Human Emotions [01:54:31] Language Modeling vs. Music Modeling [02:01:58] Democratization of AI and Industry Impact [02:07:38] Recursive Self-Improving Superintelligence [02:12:48] AI Technologies: Positive and Negative Impacts [02:14:44] Runaway AGI and Control Over AI [02:20:35] AI Dangers, Cybercrime, and Ethics [02:23:42]

  • May 8, 2023 · 59 min

    PERPLEXITY AI - The future of search.

    https://www.perplexity.ai/ https://www.perplexity.ai/iphone https://www.perplexity.ai/android Interview with Aravind Srinivas, CEO and Co-Founder of Perplexity AI – Revolutionizing Learning with Conversational Search Engines Dr. Tim Scarfe talks with Dr. Aravind Srinivas, CEO and Co-Founder of Perplexity AI, about his journey from studying AI and reinforcement learning at UC Berkeley to launching Perplexity – a startup that aims to revolutionize learning through the power of conversational search engines. By combining the strengths of large language models like GPT-* with search engines, Perplexity provides users with direct answers to their questions in a decluttered user interface, making the learning process not only more efficient but also enjoyable. Aravind shares his insights on how advertising can be made more relevant and less intrusive with the help of large language models, emphasizing the importance of transparency in relevance ranking to improve user experience. He also discusses the challenge of balancing the interests of users and advertisers for long-term success. The interview delves into the challenges of maintaining truthfulness and balancing opinions and facts in a world where algorithmic truth is difficult to achieve. Aravind believes that opinionated models can be useful as long as they don't spread misinformation and are transparent about being opinions. He also emphasizes the importance of allowing users to correct or update information, making the platform more adaptable and dynamic. Lastly, Aravind shares his thoughts on embracing a digital society with large language models, stressing the need for frequent and iterative deployments of these models to reduce fear of AI and misinformation. He envisions a future where using AI tools effectively requires clear thinking and first-principle reasoning, ultimately benefiting society as a whole. Education and transparency are crucial to counter potential misuse of AI for political or malicious purposes. YT version: https://youtu.be/_vMOWw3uYvk Aravind Srinivas: https://www.linkedin.com/in/aravind-srinivas-16051987/ https://scholar.google.com/citations?user=GhrKC1gAAAAJ&hl=en https://twitter.com/aravsrinivas?lang=en Interviewer: Dr. Tim Scarfe (CTO XRAI Glass) Patreon: https://www.patreon.com/mlst Discord: https://discord.gg/ESrGqhf5CB TOC: Introduction and Background of Perplexity AI [00:00:00] The Importance of a Decluttered UI and User Experience [00:04:19] Advertising in Search Engines and Potential Improvements [00:09:02] Challenges and Opportunities in this new Search Modality [00:18:17] Benefits of Perplexity and Personalized Learning [00:21:27] Objective Truth and Personalized Wikipedia [00:26:34] Opinions and Truth in Answer Engines [00:30:53] Embracing the Digital Society with Language Models [00:37:30] Impact on Jobs and Future of Learning [00:40:13] Educating users on when perplexity works and doesn't work [00:43:13] Improving user experience and the possibilities of voice-to-voice interaction [00:45:04] The future of language models and auto-regressive models [00:49:51] Performance of GPT-4 and potential improvements [00:52:31] Building the ultimate research and knowledge assistant [00:55:33] Revolutionizing note-taking and personal knowledge stores [00:58:16] References: Evaluating Verifiability in Generative Search Engines (Nelson F. Liu et al, Stanford University) https://arxiv.org/pdf/2304.09848.pdf Note: this was a sponsored interview.

  • Apr 16, 2023 · 2 hr 47 min

    #114 - Secrets of Deep Reinforcement Learning (Minqi Jiang)

    Patreon: https://www.patreon.com/mlst Discord: https://discord.gg/ESrGqhf5CB Twitter: https://twitter.com/MLStreetTalk In this exclusive interview, Dr. Tim Scarfe sits down with Minqi Jiang, a leading PhD student at University College London and Meta AI, as they delve into the fascinating world of deep reinforcement learning (RL) and its impact on technology, startups, and research. Discover how Minqi made the crucial decision to pursue a PhD in this exciting field, and learn from his valuable startup experiences and lessons. Minqi shares his insights into balancing serendipity and planning in life and research, and explains the role of objectives and Goodhart's Law in decision-making. Get ready to explore the depths of robustness in RL, two-player zero-sum games, and the differences between RL and supervised learning. As they discuss the role of environment in intelligence, emergence, and abstraction, prepare to be blown away by the possibilities of open-endedness and the intelligence explosion. Learn how language models generate their own training data, the limitations of RL, and the future of software 2.0 with interpretability concerns. From robotics and open-ended learning applications to learning potential metrics and MDPs, this interview is a goldmine of information for anyone interested in AI, RL, and the cutting edge of technology. Don't miss out on this incredible opportunity to learn from a rising star in the AI world! TOC Tech & Startup Background [00:00:00] Pursuing PhD in Deep RL [00:03:59] Startup Lessons [00:11:33] Serendipity vs Planning [00:12:30] Objectives & Decision Making [00:19:19] Minimax Regret & Uncertainty [00:22:57] Robustness in RL & Zero-Sum Games [00:26:14] RL vs Supervised Learning [00:34:04] Exploration & Intelligence [00:41:27] Environment, Emergence, Abstraction [00:46:31] Open-endedness & Intelligence Explosion [00:54:28] Language Models & Training Data [01:04:59] RLHF & Language Models [01:16:37] Creativity in Language Models [01:27:25] Limitations of RL [01:40:58] Software 2.0 & Interpretability [01:45:11] Language Models & Code Reliability [01:48:23] Robust Prioritized Level Replay [01:51:42] Open-ended Learning [01:55:57] Auto-curriculum & Deep RL [02:08:48] Robotics & Open-ended Learning [02:31:05] Learning Potential & MDPs [02:36:20] Universal Function Space [02:42:02] Goal-Directed Learning & Auto-Curricula [02:42:48] Advice & Closing Thoughts [02:44:47] References: - Why Greatness Cannot Be Planned: The Myth of the Objective by Kenneth O. Stanley and Joel Lehman https://www.springer.com/gp/book/9783319155234 - Rethinking Exploration: General Intelligence Requires Rethinking Exploration https://arxiv.org/abs/2106.06860 - The Case for Strong Emergence (Sabine Hossenfelder) https://arxiv.org/abs/2102.07740 - The Game of Life (Conway) https://www.conwaylife.com/ - Toolformer: Teaching Language Models to Generate APIs (Meta AI) https://arxiv.org/abs/2302.04761 - OpenAI's POET: Paired Open-Ended Trailblazer https://arxiv.org/abs/1901.01753 - Schmidhuber's Artificial Curiosity https://people.idsia.ch/~juergen/interest.html - Gödel Machines https://people.idsia.ch/~juergen/goedelmachine.html - PowerPlay https://arxiv.org/abs/1112.5309 - Robust Prioritized Level Replay: https://openreview.net/forum?id=NfZ6g2OmXEk - Unsupervised Environment Design: https://arxiv.org/abs/2012.02096 - Excel: Evolving Curriculum Learning for Deep Reinforcement Learning https://arxiv.org/abs/1901.05431 - Go-Explore: A New Approach for Hard-Exploration Problems https://arxiv.org/abs/1901.10995 - Learning with AMIGo: Adversarially Motivated Intrinsic Goals https://www.researchgate.net/publication/342377312_Learning_with_AMIGo_Adversarially_Motivated_Intrinsic_Goals PRML https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf Sutton and Barto https://web.stanford.edu/class/psych209/Readings/SuttonBartoIPRLBook2ndEd.pdf

  • Apr 10, 2023 · 1 hr 49 min

    Unlocking the Brain's Mysteries: Chris Eliasmith on Spiking Neural Networks and the Future of Human-Machine Interaction

    Patreon: https://www.patreon.com/mlst Discord: https://discord.gg/ESrGqhf5CB Twitter: https://twitter.com/MLStreetTalk Chris Eliasmith is a renowned interdisciplinary researcher, author, and professor at the University of Waterloo, where he holds the prestigious Canada Research Chair in Theoretical Neuroscience. As the Founding Director of the Centre for Theoretical Neuroscience, Eliasmith leads the Computational Neuroscience Research Group in exploring the mysteries of the brain and its complex functions. His groundbreaking work, including the Neural Engineering Framework, Neural Engineering Objects software environment, and the Semantic Pointer Architecture, has led to the development of Spaun, the most advanced functional brain simulation to date. Among his numerous achievements, Eliasmith has received the 2015 NSERC "Polany-ee" Award and authored two influential books, "How to Build a Brain" and "Neural Engineering." Chris' homepage: http://arts.uwaterloo.ca/~celiasmi/ Interviewers: Dr. Tim Scarfe and Dr. Keith Duggar TOC: Intro to Chris [00:00:00] Continuous Representation in Biologically Plausible Neural Networks [00:06:49] Legendre Memory Unit and Spatial Semantic Pointer [00:14:36] Large Contexts and Data in Language Models [00:20:30] Spatial Semantic Pointers and Continuous Representations [00:24:38] Auto Convolution [00:30:12] Abstractions and the Continuity [00:36:33] Compression, Sparsity, and Brain Representations [00:42:52] Continual Learning and Real-World Interactions [00:48:05] Robust Generalization in LLMs and Priors [00:56:11] Chip design [01:00:41] Chomsky + Computational Power of NNs and Recursion [01:04:02] Spiking Neural Networks and Applications [01:13:07] Limits of Empirical Learning [01:22:43] Philosophy of Mind, Consciousness etc [01:25:35] Future of human machine interaction [01:41:28] Future research and advice to young researchers [01:45:06] Refs: http://compneuro.uwaterloo.ca/publications/dumont2023.html http://compneuro.uwaterloo.ca/publications/voelker2019lmu.html http://compneuro.uwaterloo.ca/publications/voelker2018.html http://compneuro.uwaterloo.ca/publications/lu2019.html https://www.youtube.com/watch?v=I5h-xjddzlY

  • Apr 2, 2023 · 2 hr 40 min

    #112 AVOIDING AGI APOCALYPSE - CONNOR LEAHY

    Support us! https://www.patreon.com/mlst MLST Discord: https://discord.gg/aNPkGUQtc5 In this podcast with the legendary Connor Leahy (CEO Conjecture) recorded in Dec 2022, we discuss various topics related to artificial intelligence (AI), including AI alignment, the success of ChatGPT, the potential threats of artificial general intelligence (AGI), and the challenges of balancing research and product development at his company, Conjecture. He emphasizes the importance of empathy, dehumanizing our thinking to avoid anthropomorphic biases, and the value of real-world experiences in learning and personal growth. The conversation also covers the Orthogonality Thesis, AI preferences, the mystery of mode collapse, and the paradox of AI alignment. Connor Leahy expresses concern about the rapid development of AI and the potential dangers it poses, especially as AI systems become more powerful and integrated into society. He argues that we need a better understanding of AI systems to ensure their safe and beneficial development. The discussion also touches on the concept of "futuristic whack-a-mole," where futurists predict potential AGI threats, and others try to come up with solutions for those specific scenarios. However, the problem lies in the fact that there could be many more scenarios that neither party can think of, especially when dealing with a system that's smarter than humans. https://www.linkedin.com/in/connor-j-leahy/https://twitter.com/NPCollapse Interviewer: Dr. Tim Scarfe (Innovation CTO @ XRAI Glass https://xrai.glass/) TOC: The success of ChatGPT and its impact on the AI field [00:00:00] Subjective experience [00:15:12] AI Architectural discussion including RLHF [00:18:04] The paradox of AI alignment and the future of AI in society [00:31:44] The impact of AI on society and politics [00:36:11] Future shock levels and the challenges of predicting the future [00:45:58] Long termism and existential risk [00:48:23] Consequentialism vs. deontology in rationalism [00:53:39] The Rationalist Community and its Challenges [01:07:37] AI Alignment and Conjecture [01:14:15] Orthogonality Thesis and AI Preferences [01:17:01] Challenges in AI Alignment [01:20:28] Mechanistic Interpretability in Neural Networks [01:24:54] Building Cleaner Neural Networks [01:31:36] Cognitive horizons / The problem with rapid AI development [01:34:52] Founding Conjecture and raising funds [01:39:36] Inefficiencies in the market and seizing opportunities [01:45:38] Charisma, authenticity, and leadership in startups [01:52:13] Autistic culture and empathy [01:55:26] Learning from real-world experiences [02:01:57] Technical empathy and transhumanism [02:07:18] Moral status and the limits of empathy [02:15:33] Anthropomorphic Thinking and Consequentialism [02:17:42] Conjecture: Balancing Research and Product Development [02:20:37] Epistemology Team at Conjecture [02:31:07] Interpretability and Deception in AGI [02:36:23] Futuristic whack-a-mole and predicting AGI threats [02:38:27] Refs: 1. OpenAI's ChatGPT: https://chat.openai.com/ 2. The Mystery of Mode Collapse (Article): https://www.lesswrong.com/posts/t9svvNPNmFf5Qa3TA/mysteries-of-mode-collapse 3. The Rationalist Guide to the Galaxy https://www.amazon.co.uk/Does-Not-Hate-You-Superintelligence/dp/1474608795 5. Alfred Korzybski: https://en.wikipedia.org/wiki/Alfred_Korzybski 6. Instrumental Convergence: https://en.wikipedia.org/wiki/Instrumental_convergence 7. Orthogonality Thesis: https://en.wikipedia.org/wiki/Orthogonality_thesis 8. Brian Tomasik's Essays on Reducing Suffering: https://reducing-suffering.org/ 9. Epistemological Framing for AI Alignment Research: https://www.lesswrong.com/posts/Y4YHTBziAscS5WPN7/epistemological-framing-for-ai-alignment-research 10. How to Defeat Mind readers: https://www.alignmentforum.org/posts/EhAbh2pQoAXkm9yor/circumventing-interpretability-how-to-defeat-mind-readers 11. Society of mind: https://www.amazon.co.uk/Society-Mind-Marvin-Minsky/dp/0671607405

  • Apr 1, 2023 · 26 min

    #111 - AI moratorium, Eliezer Yudkowsky, AGI risk etc

    Support us! https://www.patreon.com/mlst MLST Discord: https://discord.gg/aNPkGUQtc5 Send us a voice message which you want us to publish: https://podcasters.spotify.com/pod/show/machinelearningstreettalk/message In a recent open letter, over 1500 individuals called for a six-month pause on the development of advanced AI systems, expressing concerns over the potential risks AI poses to society and humanity. However, there are issues with this approach, including global competition, unstoppable progress, potential benefits, and the need to manage risks instead of avoiding them. Decision theorist Eliezer Yudkowsky took it a step further in a Time magazine article, calling for an indefinite and worldwide moratorium on Artificial General Intelligence (AGI) development, warning of potential catastrophe if AGI exceeds human intelligence. Yudkowsky urged for an immediate halt to all large AI training runs and the shutdown of major GPU clusters, calling for international cooperation to enforce these measures. However, several counterarguments question the validity of Yudkowsky's concerns: 1. Hard limits on AGI 2. Dismissing AI extinction risk 3. Collective action problem 4. Misplaced focus on AI threats While the potential risks of AGI cannot be ignored, it is essential to consider various arguments and potential solutions before making drastic decisions. As AI continues to advance, it is crucial for researchers, policymakers, and society as a whole to engage in open and honest discussions about the potential consequences and the best path forward. With a balanced approach to AGI development, we may be able to harness its power for the betterment of humanity while mitigating its risks. Eliezer Yudkowsky: https://en.wikipedia.org/wiki/Eliezer_Yudkowsky Connor Leahy: https://twitter.com/NPCollapse (we will release that interview soon) Gary Marcus: http://garymarcus.com/index.html Tim Scarfe is the innovation CTO of XRAI Glass: https://xrai.glass/ Gary clip filmed at AIUK https://ai-uk.turing.ac.uk/programme/ and our appreciation to them for giving us a press pass. Check out their conference next year! WIRED clip from Gary came from here: https://www.youtube.com/watch?v=Puo3VkPkNZ4 Refs: Statement from the listed authors of Stochastic Parrots on the “AI pause” letterTimnit Gebru, Emily M. Bender, Angelina McMillan-Major, Margaret Mitchell https://www.dair-institute.org/blog/letter-statement-March2023 Eliezer Yudkowsky on Lex: https://www.youtube.com/watch?v=AaTRHFaaPG8 Pause Giant AI Experiments: An Open Letter https://futureoflife.org/open-letter/pause-giant-ai-experiments/ Pausing AI Developments Isn't Enough. We Need to Shut it All Down (Eliezer Yudkowsky) https://time.com/6266923/ai-eliezer-yudkowsky-open-letter-not-enough/

  • Mar 23, 2023 · 57 min

    #110 Dr. STEPHEN WOLFRAM - HUGE ChatGPT+Wolfram announcement!

    HUGE ANNOUNCEMENT, CHATGPT+WOLFRAM! You saw it HERE first! YT version: https://youtu.be/z5WZhCBRDpU Support us! https://www.patreon.com/mlst MLST Discord: https://discord.gg/aNPkGUQtc5 Stephen's announcement post: https://writings.stephenwolfram.com/2023/03/chatgpt-gets-its-wolfram-superpowers/ OpenAI's announcement post: https://openai.com/blog/chatgpt-plugins In an era of technology and innovation, few individuals have left as indelible a mark on the fabric of modern science as our esteemed guest, Dr. Steven Wolfram. Dr. Wolfram is a renowned polymath who has made significant contributions to the fields of physics, computer science, and mathematics. A prodigious young man too, Wolfram earned a Ph.D. in theoretical physics from the California Institute of Technology by the age of 20. He became the youngest recipient of the prestigious MacArthur Fellowship at the age of 21. Wolfram's groundbreaking computational tool, Mathematica, was launched in 1988 and has become a cornerstone for researchers and innovators worldwide. In 2002, he published "A New Kind of Science," a paradigm-shifting work that explores the foundations of science through the lens of computational systems. In 2009, Wolfram created Wolfram Alpha, a computational knowledge engine utilized by millions of users worldwide. His current focus is on the Wolfram Language, a powerful programming language designed to democratize access to cutting-edge technology. Wolfram's numerous accolades include honorary doctorates and fellowships from prestigious institutions. As an influential thinker, Dr. Wolfram has dedicated his life to unraveling the mysteries of the universe and making computation accessible to all. First of all... we have an announcement to make, you heard it FIRST here on MLST! .... Intro [00:00:00] Big announcement! Wolfram + ChatGPT! [00:02:57] What does it mean to understand? [00:05:33] Feeding information back into the model [00:13:48] Semantics and cognitive categories [00:20:09] Navigating the ruliad [00:23:50] Computational irreducibility [00:31:39] Conceivability and interestingness [00:38:43] Human intelligible sciences [00:43:43]

  • Mar 20, 2023 · 2 hr 51 min

    #109 - Dr. DAN MCQUILLAN - Resisting AI

    YT version: https://youtu.be/P1j3VoKBxbc (references in pinned comment) Support us! https://www.patreon.com/mlst MLST Discord: https://discord.gg/aNPkGUQtc5 Dan McQuillan, a visionary in digital culture and social innovation, emphasizes the importance of understanding technology's complex relationship with society. As an academic at Goldsmiths, University of London, he fosters interdisciplinary collaboration and champions data-driven equity and ethical technology. Dan's career includes roles at Amnesty International and Social Innovation Camp, showcasing technology's potential to empower and bring about positive change. In this conversation, we discuss the challenges and opportunities at the intersection of technology and society, exploring the profound impact of our digital world. Interviewer: Dr. Tim Scarfe [00:00:00] Dan's background and journey to academia [00:03:30] Dan's background and journey to academia [00:04:10] Writing the book "Resisting AI" [00:08:30] Necropolitics and its relation to AI [00:10:06] AI as a new form of colonization [00:12:57] LLMs as a new form of neo-techno-imperialism [00:15:47] Technology for good and AGI's skewed worldview [00:17:49] Transhumanism, eugenics, and intelligence [00:20:45] Valuing differences (disability) and challenging societal norms [00:26:08] Re-ontologizing and the philosophy of information [00:28:19] New materialism and the impact of technology on society [00:30:32] Intelligence, meaning, and materiality [00:31:43] The constraints of physical laws and the importance of science [00:32:44] Exploring possibilities to reduce suffering and increase well-being [00:33:29] The division between meaning and material in our experiences [00:35:36] Machine learning, data science, and neoplatonic approach to understanding reality [00:37:56] Different understandings of cognition, thought, and consciousness [00:39:15] Enactivism and its variants in cognitive science [00:40:58] Jordan Peterson [00:44:47] Relationism, relativism, and finding the correct relational framework [00:47:42] Recognizing privilege and its impact on social interactions [00:49:10] Intersectionality / Feminist thinking and the concept of care in social structures [00:51:46] Intersectionality and its role in understanding social inequalities [00:54:26] The entanglement of history, technology, and politics [00:57:39] ChatGPT article - we come to bury ChatGPT [00:59:41] Statistical pattern learning and convincing patterns in AI [01:01:27] Anthropomorphization and understanding in AI [01:03:26] AI in education and critical thinking [01:06:09] European Union policies and trustable AI [01:07:52] AI reliability and the halo effect [01:09:26] AI as a tool enmeshed in society [01:13:49] Luddites [01:15:16] AI is a scam [01:15:31] AI and Social Relations [01:16:49] Invisible Labor in AI and Machine Learning [01:21:09] Exploititative AI / alignment [01:23:50] Science fiction AI / moral frameworks [01:27:22] Discussing Stochastic Parrots and Nihilism [01:30:36] Human Intelligence vs. Language Models [01:32:22] Image Recognition and Emulation vs. Experience [01:34:32] Thought Experiments and Philosophy in AI Ethics (mimicry) [01:41:23] Abstraction, reduction, and grounding in reality [01:43:13] Process philosophy and the possibility of change [01:49:55] Mental health, AI, and epistemic injustice [01:50:30] Hermeneutic injustice and gendered techniques [01:53:57] AI and politics [01:59:24] Epistemic injustice and testimonial injustice [02:11:46] Fascism and AI discussion [02:13:24] Violence in various systems [02:16:52] Recognizing systemic violence [02:22:35] Fascism in Today's Society [02:33:33] Pace and Scale of Technological Change [02:37:38] Alternative approaches to AI and society [02:44:09] Self-Organization at Successive Scales / cybernetics

  • Mar 16, 2023 · 2 hr 9 min

    #108 - Dr. JOEL LEHMAN - Machine Love [Staff Favourite]

    Support us! https://www.patreon.com/mlst MLST Discord: https://discord.gg/aNPkGUQtc5 We are honoured to welcome Dr. Joel Lehman, an eminent machine learning research scientist, whose work in AI safety, reinforcement learning, creative open-ended search algorithms, and indeed the philosophy of open-endedness and abandoning objectives has paved the way for innovative ideas that challenge our preconceptions and inspire new visions for the future. Dr. Lehman's thought-provoking book, "Why Greatness Cannot Be Planned" penned with with our MLST favourite Professor Kenneth Stanley has left an indelible mark on the field and profoundly impacted the way we view innovation and the serendipitous nature of discovery. Those of you who haven't watched our special edition show on that, should do so at your earliest convenience! Building upon this foundation, Dr. Lehman has ventured into the domain of AI systems that embody principles of love, care, responsibility, respect, and knowledge, drawing from the works of Maslow, Erich Fromm, and positive psychology. YT version: https://youtu.be/23-TXgJEv-Q http://joellehman.com/ https://twitter.com/joelbot3000 Interviewer: Dr. Tim Scarfe TOC: Intro [00:00:00] Model [00:04:26] Intro and Paper Intro [00:08:52] Subjectivity [00:16:07] Reflections on Greatness Book [00:19:30] Representing Subjectivity [00:29:24] Nagal's Bat [00:31:49] Abstraction [00:38:58] Love as Action Rather Than Feeling [00:42:58] Reontologisation [00:57:38] Self Help [01:04:15] Meditation [01:09:02] The Human Reward Function / Effective... [01:16:52] Machine Hate [01:28:32] Societal Harms [01:31:41] Lenses We Use Obscuring Reality [01:56:36] Meta Optimisation and Evolution [02:03:14] Conclusion [02:07:06] References: What Is It Like to Be a Bat? (Thomas Nagel) https://warwick.ac.uk/fac/cross_fac/iatl/study/ugmodules/humananimalstudies/lectures/32/nagel_bat.pdf Why Greatness Cannot Be Planned: The Myth of the Objective (Kenneth O. Stanley and Joel Lehman) https://link.springer.com/book/10.1007/978-3-319-15524-1 Machine Love (Joel Lehman) https://arxiv.org/abs/2302.09248 How effective altruists ignored risk (Carla Cremer) https://www.vox.com/future-perfect/23569519/effective-altrusim-sam-bankman-fried-will-macaskill-ea-risk-decentralization-philanthropy Philosophy tube - The Rich Have Their Own Ethics: Effective Altruism https://www.youtube.com/watch?v=Lm0vHQYKI-Y Abandoning Objectives: Evolution through the Search for Novelty Alone (Joel Lehman and Kenneth O. Stanley) https://www.cs.swarthmore.edu/~meeden/DevelopmentalRobotics/lehman_ecj11.pdf

  • Mar 13, 2023 · 1 hr 43 min

    #107 - Dr. RAPHAËL MILLIÈRE - Linguistics, Theory of Mind, Grounding

    Support us! https://www.patreon.com/mlst MLST Discord: https://discord.gg/aNPkGUQtc5 Dr. Raphaël Millière is the 2020 Robert A. Burt Presidential Scholar in Society and Neuroscience in the Center for Science and Society, and a Lecturer in the Philosophy Department at Columbia University. His research draws from his expertise in philosophy and cognitive science to explore the implications of recent progress in deep learning for models of human cognition, as well as various issues in ethics and aesthetics. He is also investigating what underlies the capacity to represent oneself as oneself at a fundamental level, in humans and non-human animals; as well as the role that self-representation plays in perception, action, and memory. In a world where technology is rapidly advancing, Dr. Millière is striving to gain a better understanding of how artificial neural networks work, and to establish fair and meaningful comparisons between humans and machines in various domains in order to shed light on the implications of artificial intelligence for our lives. https://www.raphaelmilliere.com/ https://twitter.com/raphaelmilliere Here is a version with hesitation sounds like "um" removed if you prefer (I didn't notice them personally): https://share.descript.com/view/aGelyTl2xpN YT: https://www.youtube.com/watch?v=fhn6ZtD6XeE TOC: Intro to Raphael [00:00:00] Intro: Moving Beyond Mimicry in Artificial Intelligence (Raphael Millière) [00:01:18] Show Kick off [00:07:10] LLMs [00:08:37] Semantic Competence/Understanding [00:18:28] Forming Analogies/JPG Compression Article [00:30:17] Compositional Generalisation [00:37:28] Systematicity [00:47:08] Language of Thought [00:51:28] Bigbench (Conceptual Combinations) [00:57:37] Symbol Grounding [01:11:13] World Models [01:26:43] Theory of Mind [01:30:57] Refs (this is truncated, full list on YT video description): Moving Beyond Mimicry in Artificial Intelligence (Raphael Millière) https://nautil.us/moving-beyond-mimicry-in-artificial-intelligence-238504/ On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜 (Bender et al) https://dl.acm.org/doi/10.1145/3442188.3445922 ChatGPT Is a Blurry JPEG of the Web (Ted Chiang) https://www.newyorker.com/tech/annals-of-technology/chatgpt-is-a-blurry-jpeg-of-the-web The Debate Over Understanding in AI's Large Language Models (Melanie Mitchell) https://arxiv.org/abs/2210.13966 Talking About Large Language Models (Murray Shanahan) https://arxiv.org/abs/2212.03551 Climbing towards NLU: On Meaning, Form, and Understanding in the Age of Data (Bender) https://aclanthology.org/2020.acl-main.463/ The symbol grounding problem (Stevan Harnad) https://arxiv.org/html/cs/9906002 Why the Abstraction and Reasoning Corpus is interesting and important for AI (Mitchell) https://aiguide.substack.com/p/why-the-abstraction-and-reasoning Linguistic relativity (Sapir–Whorf hypothesis) https://en.wikipedia.org/wiki/Linguistic_relativity Cooperative principle (Grice's four maxims of conversation - quantity, quality, relation, and manner) https://en.wikipedia.org/wiki/Cooperative_principle

  • Mar 11, 2023 · 2 hr 59 min

    #106 - Prof. KARL FRISTON 3.0 - Collective Intelligence [Special Edition]

    This show is sponsored by Numerai, please visit them here with our sponsor link (we would really appreciate it) http://numer.ai/mlst Prof. Karl Friston recently proposed a vision of artificial intelligence that goes beyond machines and algorithms, and embraces humans and nature as part of a cyber-physical ecosystem of intelligence. This vision is based on the principle of active inference, which states that intelligent systems can learn from their observations and act on their environment to reduce uncertainty and achieve their goals. This leads to a formal account of collective intelligence that rests on shared narratives and goals. To realize this vision, Friston suggests developing a shared hyper-spatial modelling language and transaction protocol, as well as novel methods for measuring and optimizing collective intelligence. This could harness the power of artificial intelligence for the common good, without compromising human dignity or autonomy. It also challenges us to rethink our relationship with technology, nature, and each other, and invites us to join a global community of sense-makers who are curious about the world and eager to improve it. YT version: https://www.youtube.com/watch?v=V_VXOdf1NMw Support us! https://www.patreon.com/mlst MLST Discord: https://discord.gg/aNPkGUQtc5 TOC: Intro [00:00:00] Numerai (Sponsor segment) [00:07:10] Designing Ecosystems of Intelligence from First Principles (Friston et al) [00:09:48] Information / Infosphere and human agency [00:18:30] Intelligence [00:31:38] Reductionism [00:39:36] Universalism [00:44:46] Emergence [00:54:23] Markov blankets [01:02:11] Whole part relationships / structure learning [01:22:33] Enactivism [01:29:23] Knowledge and Language [01:43:53] ChatGPT [01:50:56] Ethics (is-ought) [02:07:55] Can people be evil? [02:35:06] Ethics in Al, subjectiveness [02:39:05] Final thoughts [02:57:00] References: Designing Ecosystems of Intelligence from First Principles (Friston et al) https://arxiv.org/abs/2212.01354 GLOM - How to represent part-whole hierarchies in a neural network (Hinton) https://arxiv.org/pdf/2102.12627.pdf Seven Brief Lessons on Physics (Carlo Rovelli) https://www.amazon.co.uk/Seven-Brief-Lessons-Physics-Rovelli/dp/0141981725 How Emotions Are Made: The Secret Life of the Brain (Lisa Feldman Barrett) https://www.amazon.co.uk/How-Emotions-Are-Made-Secret/dp/B01N3D4OON Am I Self-Conscious? (Or Does Self-Organization Entail Self-Consciousness?) (Karl Friston) https://www.frontiersin.org/articles/10.3389/fpsyg.2018.00579/full Integrated information theory (Giulio Tononi) https://en.wikipedia.org/wiki/Integrated_information_theory

  • Mar 4, 2023 · 1 hr 20 min

    #105 - Dr. MICHAEL OLIVER [CSO - Numerai]

    Access Numerai here: http://numer.ai/mlst Michael Oliver is the Chief Scientist at Numerai, a hedge fund that crowdsources machine learning models from data scientists. He has a PhD in Computational Neuroscience from UC Berkeley and was a postdoctoral researcher at the Allen Institute for Brain Science before joining Numerai in 2020. He is also the host of Numerai Quant Club, a YouTube series where he discusses Numerai’s research, data and challenges. YT version: https://youtu.be/61s8lLU7sFg TOC: [00:00:00] Introduction to Michael and Numerai [00:02:03] Understanding / new Bing [00:22:47] Quant vs Neuroscience [00:36:43] Role of language in cognition and planning, and subjective... [00:45:47] Boundaries in finance modelling [00:48:00] Numerai [00:57:37] Aggregation systems [01:00:52] Getting started on Numeral [01:03:21] What models are people using [01:04:23] Numerai Problem Setup [01:05:49] Regimes in financial data and quant talk [01:11:18] Esoteric approaches used on Numeral? [01:13:59] Curse of dimensionality [01:16:32] Metrics [01:19:10] Outro References: Growing Neural Cellular Automata (Alexander Mordvintsev) https://distill.pub/2020/growing-ca/ A Thousand Brains: A New Theory of Intelligence (Jeff Hawkins) https://www.amazon.fr/Thousand-Brains-New-Theory-Intelligence/dp/1541675819 Perceptual Neuroscience: The Cerebral Cortex (Vernon B. Mountcastle) https://www.amazon.ca/Perceptual-Neuroscience-Cerebral-Vernon-Mountcastle/dp/0674661885 Numerai Quant Club with Michael Oliver https://www.youtube.com/watch?v=eLIxarbDXuQ&list=PLz3D6SeXhT3tTu8rhZmjwDZpkKi-UPO1F Numerai YT channel https://www.youtube.com/@Numerai/featured Support us! https://www.patreon.com/mlst MLST Discord: https://discord.gg/aNPkGUQtc5

  • Feb 22, 2023 · 1 hr 28 min

    #104 - Prof. CHRIS SUMMERFIELD - Natural General Intelligence [SPECIAL EDITION]

    Support us! https://www.patreon.com/mlst MLST Discord: https://discord.gg/aNPkGUQtc5 Christopher Summerfield, Department of Experimental Psychology, University of Oxford is a Professor of Cognitive Neuroscience at the University of Oxford and a Research Scientist at Deepmind UK. His work focusses on the neural and computational mechanisms by which humans make decisions. Chris has just released an incredible new book on AI called "Natural General Intelligence". It's my favourite book on AI I have read so so far. The book explores the algorithms and architectures that are driving progress in AI research, and discusses intelligence in the language of psychology and biology, using examples and analogies to be comprehensible to a wide audience. It also tackles longstanding theoretical questions about the nature of thought and knowledge. With Chris' permission, I read out a summarised version of Chapter 2 from his book on which was on Intelligence during the 30 minute MLST introduction. Buy his book here: https://global.oup.com/academic/product/natural-general-intelligence-9780192843883?cc=gb&lang=en& YT version: https://youtu.be/31VRbxAl3t0 Interviewer: Dr. Tim Scarfe TOC: [00:00:00] Walk and talk with Chris on Knowledge and Abstractions [00:04:08] Intro to Chris and his book [00:05:55] (Intro) Tim reads Chapter 2: Intelligence [00:09:28] Intro continued: Goodhart's law [00:15:37] Intro continued: The "swiss cheese" situation [00:20:23] Intro continued: On Human Knowledge [00:23:37] Intro continued: Neats and Scruffies [00:30:22] Interview kick off [00:31:59] What does it mean to understand? [00:36:18] Aligning our language models [00:40:17] Creativity [00:41:40] "Meta" AI and basins of attraction [00:51:23] What can Neuroscience impart to AI [00:54:43] Sutton, neats and scruffies and human alignment [01:02:05] Reward is enough [01:19:46] Jon Von Neumann and Intelligence [01:23:56] Compositionality References: The Language Game (Morten H. Christiansen, Nick Chater https://www.penguin.co.uk/books/441689/the-language-game-by-morten-h-christiansen-and--nick-chater/9781787633483 Theory of general factor (Spearman) https://www.proquest.com/openview/7c2c7dd23910c89e1fc401e8bb37c3d0/1?pq-origsite=gscholar&cbl=1818401 Intelligence Reframed (Howard Gardner) https://books.google.co.uk/books?hl=en&lr=&id=Qkw4DgAAQBAJ&oi=fnd&pg=PT6&dq=howard+gardner+multiple+intelligences&ots=ERUU0u5Usq&sig=XqiDgNUIkb3K9XBq0vNbFmXWKFs#v=onepage&q=howard%20gardner%20multiple%20intelligences&f=false The master algorithm (Pedro Domingos) https://www.amazon.co.uk/Master-Algorithm-Ultimate-Learning-Machine/dp/0241004543 A Thousand Brains: A New Theory of Intelligence (Jeff Hawkins) https://www.amazon.co.uk/Thousand-Brains-New-Theory-Intelligence/dp/1541675819 The bitter lesson (Rich Sutton) http://www.incompleteideas.net/IncIdeas/BitterLesson.html

  • Feb 11, 2023 · 1 hr 1 min

    #103 - Prof. Edward Grefenstette - Language, Semantics, Philosophy

    Support us! https://www.patreon.com/mlst MLST Discord: https://discord.gg/aNPkGUQtc5 YT: https://youtu.be/i9VPPmQn9HQ Edward Grefenstette is a Franco-American computer scientist who currently serves as Head of Machine Learning at Cohere and Honorary Professor at UCL. He has previously been a research scientist at Facebook AI Research and staff research scientist at DeepMind, and was also the CTO of Dark Blue Labs. Prior to his move to industry, Edward was a Fulford Junior Research Fellow at Somerville College, University of Oxford, and was lecturing at Hertford College. He obtained his BSc in Physics and Philosophy from the University of Sheffield and did graduate work in the philosophy departments at the University of St Andrews. His research draws on topics and methods from Machine Learning, Computational Linguistics and Quantum Information Theory, and has done work implementing and evaluating compositional vector-based models of natural language semantics and empirical semantic knowledge discovery. https://www.egrefen.com/ https://cohere.ai/ TOC: [00:00:00] Introduction [00:02:52] Differential Semantics [00:06:56] Concepts [00:10:20] Ontology [00:14:02] Pragmatics [00:16:55] Code helps with language [00:19:02] Montague [00:22:13] RLHF [00:31:54] Swiss cheese problem / retrieval augmented [00:37:06] Intelligence / Agency [00:43:33] Creativity [00:46:41] Common sense [00:53:46] Thinking vs knowing References: Large language models are not zero-shot communicators (Laura Ruis) https://arxiv.org/abs/2210.14986 Some remarks on Large Language Models (Yoav Goldberg) https://gist.github.com/yoavg/59d174608e92e845c8994ac2e234c8a9 Quantum Natural Language Processing (Bob Coecke) https://www.cs.ox.ac.uk/people/bob.coecke/QNLP-ACT.pdf Constitutional AI: Harmlessness from AI Feedback https://www.anthropic.com/constitutional.pdf Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks (Patrick Lewis) https://www.patricklewis.io/publication/rag/ Natural General Intelligence (Prof. Christopher Summerfield) https://global.oup.com/academic/product/natural-general-intelligence-9780192843883 ChatGPT with Rob Miles - Computerphile https://www.youtube.com/watch?v=viJt_DXTfwA

  • Feb 11, 2023 · 55 min

    #102 - Prof. MICHAEL LEVIN, Prof. IRINA RISH - Emergence, Intelligence, Transhumanism

    Support us! https://www.patreon.com/mlst MLST Discord: https://discord.gg/aNPkGUQtc5 YT: https://youtu.be/Vbi288CKgis Michael Levin is a Distinguished Professor in the Biology department at Tufts University, and the holder of the Vannevar Bush endowed Chair. He is the Director of the Allen Discovery Center at Tufts and the Tufts Center for Regenerative and Developmental Biology. His research focuses on understanding the biophysical mechanisms of pattern regulation and harnessing endogenous bioelectric dynamics for rational control of growth and form. The capacity to generate a complex, behaving organism from the single cell of a fertilized egg is one of the most amazing aspects of biology. Levin' lab integrates approaches from developmental biology, computer science, and cognitive science to investigate the emergence of form and function. Using biophysical and computational modeling approaches, they seek to understand the collective intelligence of cells, as they navigate physiological, transcriptional, morphognetic, and behavioral spaces. They develop conceptual frameworks for basal cognition and diverse intelligence, including synthetic organisms and AI. Also joining us this evening is Irina Rish. Irina is a Full Professor at the Université de Montréal's Computer Science and Operations Research department, a core member of Mila - Quebec AI Institute, as well as the holder of the Canada CIFAR AI Chair and the Canadian Excellence Research Chair in Autonomous AI. She has a PhD in AI from UC Irvine. Her research focuses on machine learning, neural data analysis, neuroscience-inspired AI, continual lifelong learning, optimization algorithms, sparse modelling, probabilistic inference, dialog generation, biologically plausible reinforcement learning, and dynamical systems approaches to brain imaging analysis. Interviewer: Dr. Tim Scarfe TOC: [00:00:00] Introduction [00:02:09] Emergence [00:13:16] Scaling Laws [00:23:12] Intelligence [00:44:36] Transhumanism Prof. Michael Levin https://en.wikipedia.org/wiki/Michael_Levin_(biologist) https://www.drmichaellevin.org/ https://twitter.com/drmichaellevin Prof. Irina Rish https://twitter.com/irinarish https://irina-rish.com/

  • Feb 10, 2023 · 26 min

    #100 Dr. PATRICK LEWIS (co:here) - Retrieval Augmented Generation

    Dr. Patrick Lewis is a London-based AI and Natural Language Processing Research Scientist, working at co:here. Prior to this, Patrick worked as a research scientist at the Fundamental AI Research Lab (FAIR) at Meta AI. During his PhD, Patrick split his time between FAIR and University College London, working with Sebastian Riedel and Pontus Stenetorp. Patrick’s research focuses on the intersection of information retrieval techniques (IR) and large language models (LLMs). He has done extensive work on Retrieval-Augmented Language Models. His current focus is on building more powerful, efficient, robust, and update-able models that can perform well on a wide range of NLP tasks, but also excel on knowledge-intensive NLP tasks such as Question Answering and Fact Checking. YT version: https://youtu.be/Dm5sfALoL1Y MLST Discord: https://discord.gg/aNPkGUQtc5 Support us! https://www.patreon.com/mlst References: Patrick Lewis (Natural Language Processing Research Scientist @ co:here) https://www.patricklewis.io/ Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks (Patrick Lewis et al) https://arxiv.org/abs/2005.11401 Atlas: Few-shot Learning with Retrieval Augmented Language Models (Gautier Izacard, Patrick Lewis, et al) https://arxiv.org/abs/2208.03299 Improving language models by retrieving from trillions of tokens (RETRO) (Sebastian Borgeaud et al) https://arxiv.org/abs/2112.04426

  • Feb 5, 2023 · 1 hr 39 min

    #99 - CARLA CREMER & IGOR KRAWCZUK - X-Risk, Governance, Effective Altruism

    YT version (with references): https://www.youtube.com/watch?v=lxaTinmKxs0 Support us! https://www.patreon.com/mlst MLST Discord: https://discord.gg/aNPkGUQtc5 Carla Cremer and Igor Krawczuk argue that AI risk should be understood as an old problem of politics, power and control with known solutions, and that threat models should be driven by empirical work. The interaction between FTX and the Effective Altruism community has sparked a lot of discussion about the dangers of optimization, and Carla's Vox article highlights the need for an institutional turn when taking on a responsibility like risk management for humanity. Carla's “Democratizing Risk” paper found that certain types of risks fall through the cracks if they are just categorized into climate change or biological risks. Deliberative democracy has been found to be a better way to make decisions, and AI tools can be used to scale this type of democracy and be used for good, but the transparency of these algorithms to the citizens using the platform must be taken into consideration. Aggregating people’s diverse ways of thinking about a problem and creating a risk-averse procedure gives a likely, highly probable outcome for having converged on the best policy. There needs to be a good reason to trust one organization with the risk management of humanity and all the different ways of thinking about risk must be taken into account. AI tools can help to scale this type of deliberative democracy, but the transparency of these algorithms must be taken into consideration. The ambition of the EA community and Altruism Inc. is to protect and do risk management for the whole of humanity and this requires an institutional turn in order to do it effectively. The dangers of optimization are real, and it is essential to ensure that the risk management of humanity is done properly and ethically. By understanding the importance of aggregating people’s diverse ways of thinking about a problem, and creating a risk-averse procedure, it is possible to create a likely, highly probable outcome for having converged on the best policy. Carla Zoe Cremer https://carlacremer.github.io/ Igor Krawczuk https://krawczuk.eu/ Interviewer: Dr. Tim Scarfe TOC: [00:00:00] Introduction: Vox article and effective altruism / FTX [00:11:12] Luciano Floridi on Governance and Risk [00:15:50] Connor Leahy on alignment [00:21:08] Ethan Caballero on scaling [00:23:23] Alignment, Values and politics [00:30:50] Singularitarians vs AI-thiests [00:41:56] Consequentialism [00:46:44] Does scale make a difference? [00:51:53] Carla's Democratising risk paper [01:04:03] Vox article - How effective altruists ignored risk [01:20:18] Does diversity breed complexity? [01:29:50] Collective rationality [01:35:16] Closing statements

  • Feb 3, 2023 · 1 hr 6 min

    [NO MUSIC] #98 - Prof. LUCIANO FLORIDI - ChatGPT, Singularitarians, Ethics, Philosophy of Information

    Support us! https://www.patreon.com/mlst MLST Discord: https://discord.gg/aNPkGUQtc5 YT version: https://youtu.be/YLNGvvgq3eg We are living in an age of rapid technological advancement, and with this growth comes a digital divide. Professor Luciano Floridi of the Oxford Internet Institute / Oxford University believes that this divide not only affects our understanding of the implications of this new age, but also the organization of a fair society. The Information Revolution has been transforming the global economy, with the majority of global GDP now relying on intangible goods, such as information-related services. This in turn has led to the generation of immense amounts of data, more than humanity has ever seen in its history. With 95% of this data being generated by the current generation, Professor Floridi believes that we are becoming overwhelmed by this data, and that our agency as humans is being eroded as a result. According to Professor Floridi, the digital divide has caused a lack of balance between technological growth and our understanding of this growth. He believes that the infosphere is becoming polluted and the manifold of the infosphere is increasingly determined by technology and AI. Identifying, anticipating and resolving these problems has become essential, and Professor Floridi has dedicated his research to the Philosophy of Information, Philosophy of Technology and Digital Ethics. We must equip ourselves with a viable philosophy of information to help us better understand and address the risks of this new information age. Professor Floridi is leading the charge, and his research on Digital Ethics, the Philosophy of Information and the Philosophy of Technology is helping us to better anticipate, identify and resolve problems caused by the digital divide. TOC: [00:00:00] Introduction to Luciano and his ideas [00:14:00] Chat GPT / language models [00:28:45] AI risk / "Singularitarians" [00:37:15] Forms of governance [00:43:56] Re-ontologising the world [00:55:56] It from bit and Computationalism and philosophy without purpose [01:03:05] Getting into Digital Ethics Interviewer: Dr. Tim Scarfe References: GPT‐3: Its Nature, Scope, Limits, and Consequences [Floridi] https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3827044 Ultraintelligent Machines, Singularity, and Other Sci-fi Distractions about AI [Floridi] https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4222347 The Philosophy of Information [Floridi] https://www.amazon.co.uk/Philosophy-Information-Luciano-Floridi/dp/0199232393 Information: A Very Short Introduction [Floridi] https://www.amazon.co.uk/Information-Very-Short-Introduction-Introductions/dp/0199551375 https://en.wikipedia.org/wiki/Luciano_Floridi https://www.philosophyofinformation.net/