Machine Learning Street Talk (MLST)

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. YT Version: https://youtu.be/dBZp47999Ko 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 Sara Hooker https://www.sarahooker.me/ https://www.linkedin.com/in/sararosehooker/ https://scholar.google.com/citations?user=2xy6h3sAAAAJ&hl=en https://x.com/sarahookr Interviewer: Tim Scarfe Refs The AI Language gap https://cohere.com/research/papers/the-AI-language-gap.pdf On the Limitations of Compute Thresholds as a Governance Strategy. https://arxiv.org/pdf/2407.05694v1 The Multilingual Alignment Prism: Aligning Global and Local Preferences to Reduce Harm https://arxiv.org/pdf/2406.18682 Cohere Aya https://cohere.com/research/aya RLHF Can Speak Many Languages: Unlocking Multilingual Preference Optimization for LLMs https://arxiv.org/pdf/2407.02552 Back to Basics: Revisiting REINFORCE Style Optimization for Learning from Human Feedback in LLMs https://arxiv.org/pdf/2402.14740 Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence https://www.whitehouse.gov/briefing-room/presidential-actions/2023/10/30/executive-order-on-the-safe-secure-and-trustworthy-development-and-use-of-artificial-intelligence/ EU AI Act https://www.europarl.europa.eu/doceo/document/TA-9-2024-0138_EN.pdf The bitter lesson http://www.incompleteideas.net/IncIdeas/BitterLesson.html Neel Nanda interview https://www.youtube.com/watch?v=_Ygf0GnlwmY Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet https://transformer-circuits.pub/2024/scaling-monosemanticity/ Chollet's ARC challenge https://github.com/fchollet/ARC-AGI Ryan Greenblatt on ARC https://www.youtube.com/watch?v=z9j3wB1RRGA 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.

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. Show notes and full references: https://docs.google.com/document/d/1ICtBI574W-xGi8Z2ZtUNeKWiOiGZ_DRsp9EnyYAISws/edit?usp=sharing Prof Murray Shanahan: https://www.doc.ic.ac.uk/~mpsha/ (look at his selected publications) https://scholar.google.co.uk/citations?user=00bnGpAAAAAJ&hl=en https://en.wikipedia.org/wiki/Murray_Shanahan https://x.com/mpshanahan Interviewer: Dr. Tim Scarfe 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

In the coming decades, the technology that enables virtual and augmented reality will improve beyond recognition. Within a century, world-renowned philosopher David J. Chalmers predicts, we will have virtual worlds that are impossible to distinguish from non-virtual worlds. But is virtual reality just escapism? In a highly original work of 'technophilosophy', Chalmers argues categorically, no: virtual reality is genuine reality. Virtual worlds are not second-class worlds. We can live a meaningful life in virtual reality - and increasingly, we will. What is reality, anyway? How can we lead a good life? Is there a god? How do we know there's an external world - and how do we know we're not living in a computer simulation? In Reality+, Chalmers conducts a grand tour of philosophy, using cutting-edge technology to provide invigorating new answers to age-old questions. David J. Chalmers is an Australian philosopher and cognitive scientist specializing in the areas of philosophy of mind and philosophy of language. He is Professor of Philosophy and Neural Science at New York University, as well as co-director of NYU's Center for Mind, Brain, and Consciousness. Chalmers is best known for his work on consciousness, including his formulation of the "hard problem of consciousness." Reality+: Virtual Worlds and the Problems of Philosophy https://amzn.to/3RYyGD2 https://consc.net/ https://x.com/davidchalmers42 00:00:00 Reality+ Intro 00:12:02 GPT conscious? 10/10 00:14:19 The consciousness processor thought experiment (11/10) 00:20:34 Intelligence and Consciousness entangled? 10/10 00:22:44 Karl Friston / Meta Problem 10/10 00:29:05 Knowledge argument / subjective experience (6/10) 00:32:34 Emergence 11/10 (best chapter) 00:42:45 Working with Douglas Hofstadter 10/10 00:46:14 Intelligence is analogy making? 10/10 00:50:47 Intelligence explosion 8/10 00:58:44 Hypercomputation 10/10 01:09:44 Who designed the designer? (7/10) 01:13:57 Experience machine (7/10)

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. Sponsor: Sign up to Kalshi here https://kalshi.onelink.me/1r91/mlst -- the first 500 traders who deposit $100 will get a free $20 credit! Important disclaimer - In case it's not obvious - this is basically gambling and a *high risk* activity - only trade what you can afford to lose. 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. https://x.com/RyanPGreenblatt https://www.redwoodresearch.org/ Refs: Getting 50% (SoTA) on ARC-AGI with GPT-4o [Ryan Greenblatt] https://redwoodresearch.substack.com/p/getting-50-sota-on-arc-agi-with-gpt On the Measure of Intelligence [Chollet] https://arxiv.org/abs/1911.01547 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 Software 2.0 [Andrej Karpathy] https://karpathy.medium.com/software-2-0-a64152b37c35 Why Greatness Cannot Be Planned: The Myth of the Objective [Kenneth Stanley] https://amzn.to/3Wfy2E0 Biographical account of Terence Tao’s mathematical development. [M.A.(KEN) CLEMENTS] https://gwern.net/doc/iq/high/smpy/1984-clements.pdf Model Evaluation and Threat Research (METR) https://metr.org/ Why Tool AIs Want to Be Agent AIs https://gwern.net/tool-ai Simulators - Janus https://www.lesswrong.com/posts/vJFdjigzmcXMhNTsx/simulators AI Control: Improving Safety Despite Intentional Subversion https://www.lesswrong.com/posts/d9FJHawgkiMSPjagR/ai-control-improving-safety-despite-intentional-subversion https://arxiv.org/abs/2312.06942 What a Compute-Centric Framework Says About Takeoff Speeds https://www.openphilanthropy.org/research/what-a-compute-centric-framework-says-about-takeoff-speeds/ Global GDP over the long run https://ourworldindata.org/grapher/global-gdp-over-the-long-run?yScale=log Safety Cases: How to Justify the Safety of Advanced AI Systems https://arxiv.org/abs/2403.10462 The Danger of a “Safety Case" http://sunnyday.mit.edu/The-Danger-of-a-Safety-Case.pdf The Future Of Work Looks Like A UPS Truck (~02:15:50) https://www.npr.org/sections/money/2014/05/02/308640135/episode-536-the-future-of-work-looks-like-a-ups-truck SWE-bench https://www.swebench.com/ Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model https://arxiv.org/pdf/2201.11990 Algorithmic Progress in Language Models https://epochai.org/blog/algorithmic-progress-in-language-models

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. https://cohere.com/ https://x.com/aidangomez Check out Cohere's amazing new Command R* models here https://cohere.com/command 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.

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/ Mohamed Osman: Mohamed is looking to do a PhD in AI/ML, can you help him? Email: mothman198@outlook.com https://www.linkedin.com/in/mohamedosman1905/ Michael Hodel: https://arxiv.org/pdf/2404.07353v1 https://www.linkedin.com/in/michael-hodel/ https://x.com/bayesilicon https://github.com/michaelhodel Getting 50% (SoTA) on ARC-AGI with GPT-4o - Ryan Greenblatt https://redwoodresearch.substack.com/p/getting-50-sota-on-arc-agi-with-gpt Neural networks for abstraction and reasoning: Towards broad generalization in machines [Mikel Bober-Irizar, Soumya Banerjee] https://arxiv.org/pdf/2402.03507 Measure of intelligence: https://arxiv.org/abs/1911.01547 YT version: https://youtu.be/jSAT_RuJ_Cg

Nick Frosst, 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. This is the first podcast from our new Cohere partnership! 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. Learn more about Cohere Command R* models here: https://cohere.com/commandhttps://github.com/cohere-ai/cohere-toolkit Nick's band Good Kid: https://goodkidofficial.com/ Nick on Twitter: https://x.com/nickfrosst Disclaimer: We are in a partnership with Cohere to release content for them. We were not told what to say in the interview, and didn't edit anything out from the interview. We are currently planning to release 2 shows per month under the partnership about their AI platform, research and strategy.

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. https://patreon.com/mlst YT version: https://youtu.be/Bpgloy1dDn0 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 Additional 20 minutes of unreleased footage on our Patreon here: https://www.patreon.com/posts/whats-magic-word-104922629 What's the Magic Word? A Control Theory of LLM Prompting (Aman Bhargava, Cameron Witkowski, Manav Shah, Matt Thomson) https://arxiv.org/abs/2310.04444 LLM Control Theory Seminar (April 2024) https://www.youtube.com/watch?v=9QtS9sVBFM0 Society for the pursuit of AGI (Cameron founded it) https://agisociety.mydurable.com/ Roger Federer demo http://conway.languagegame.io/inference Neural Cellular Automata, Active Inference, and the Mystery of Biological Computation (Aman) https://aman-bhargava.com/ai/neuro/neuromorphic/2024/03/25/nca-do-active-inference.html 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) https://x.com/ABhargava2000 https://x.com/witkowski_cam

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. Buy Maria's book here: https://amzn.to/4avF6kq https://www.linkedin.com/in/mariasantacaterina 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

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 https://amzn.to/4dj0iMj YT version: https://youtu.be/lbb-Si5wa_o Please support us on Patreon to get access to the private Discord server, bi-weekly calls, early access and ad-free listening. https://patreon.com/mlst Chapters should be embedded in the mp3, let me me know if issues

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. At the end there is an unreleased additional interview with Beff. 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. https://patreon.com/mlst Donate: https://www.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 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. 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

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. Buy Chris' textbook here: https://amzn.to/3vvLcCh More about Prof. Chris Bishop: https://en.wikipedia.org/wiki/Christopher_Bishop https://www.microsoft.com/en-us/research/people/cmbishop/ 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. https://patreon.com/mlst Donate: https://www.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 - Control

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. YT: https://www.youtube.com/watch?v=n6nxUiqiz9I Transcript link on YT description 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! Buy Philip's book "How Life Works" here: https://amzn.to/3vSmNqp 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. https://patreon.com/mlst Donate: https://www.paypal.com/donate/?hosted... If you would like to sponsor us, so we can tell your story - reach out on mlstreettalk at gmail

Dr. Paul Lessard and his collaborators have written a paper on "Categorical Deep Learning and Algebraic Theory of Architectures". They aim to make neural networks more interpretable, composable and amenable to formal reasoning. The key is mathematical abstraction, as exemplified by category theory - using monads to develop a more principled, algebraic approach to structuring neural networks. We also discussed the limitations of current neural network architectures in terms of their ability to generalise and reason in a human-like way. In particular, the inability of neural networks to do unbounded computation equivalent to a Turing machine. Paul expressed optimism that this is not a fundamental limitation, but an artefact of current architectures and training procedures. The power of abstraction - allowing us to focus on the essential structure while ignoring extraneous details. This can make certain problems more tractable to reason about. Paul sees category theory as providing a powerful "Lego set" for productively thinking about many practical problems. Towards the end, Paul gave an accessible introduction to some core concepts in category theory like categories, morphisms, functors, monads etc. We explained how these abstract constructs can capture essential patterns that arise across different domains of mathematics. Paul is optimistic about the potential of category theory and related mathematical abstractions to put AI and neural networks on a more robust conceptual foundation to enable interpretability and reasoning. However, significant theoretical and engineering challenges remain in realising this vision. Please support us on Patreon. We are entirely funded from Patreon donations right now. https://patreon.com/mlst If you would like to sponsor us, so we can tell your story - reach out on mlstreettalk at gmail Links: Categorical Deep Learning: An Algebraic Theory of Architectures Bruno Gavranović, Paul Lessard, Andrew Dudzik, Tamara von Glehn, João G. M. Araújo, Petar Veličković Paper: https://categoricaldeeplearning.com/ Symbolica: https://twitter.com/symbolica https://www.symbolica.ai/ Dr. Paul Lessard (Principal Scientist - Symbolica) https://www.linkedin.com/in/paul-roy-lessard/ Interviewer: Dr. Tim Scarfe TOC: 00:00:00 - Intro 00:05:07 - What is the category paper all about 00:07:19 - Composition 00:10:42 - Abstract Algebra 00:23:01 - DSLs for machine learning 00:24:10 - Inscrutibility 00:29:04 - Limitations with current NNs 00:30:41 - Generative code / NNs don't recurse 00:34:34 - NNs are not Turing machines (special edition) 00:53:09 - Abstraction 00:55:11 - Category theory objects 00:58:06 - Cat theory vs number theory 00:59:43 - Data and Code are one in the same 01:08:05 - Syntax and semantics 01:14:32 - Category DL elevator pitch 01:17:05 - Abstraction again 01:20:25 - Lego set for the universe 01:23:04 - Reasoning 01:28:05 - Category theory 101 01:37:42 - Monads 01:45:59 - Where to learn more cat theory

Dr. Minqi Jiang and Dr. Marc Rigter explain an innovative new method to make the intelligence of agents more general-purpose by training them to learn many worlds before their usual goal-directed training, which we call "reinforcement learning". Their new paper is called "Reward-free curricula for training robust world models" https://arxiv.org/pdf/2306.09205.pdf https://twitter.com/MinqiJiang https://twitter.com/MarcRigter Interviewer: Dr. Tim Scarfe Please support us on Patreon, Tim is now doing MLST full-time and taking a massive financial hit. If you love MLST and want this to continue, please show your support! In return you get access to shows very early and private discord and networking. https://patreon.com/mlst We are also looking for show sponsors, please get in touch if interested mlstreettalk at gmail. MLST Discord: https://discord.gg/machine-learning-street-talk-mlst-937356144060530778

Nick Chater is Professor of Behavioural Science at Warwick Business School, who works on rationality and language using a range of theoretical and experimental approaches. We discuss his books The Mind is Flat, and the Language Game. Please support me on Patreon (this is now my main job!) - https://patreon.com/mlst - Access the private Discord, networking, and early access to content. MLST Discord: https://discord.gg/machine-learning-street-talk-mlst-937356144060530778 https://twitter.com/MLStreetTalk Buy The Language Game: https://amzn.to/3SRHjPm Buy The Mind is Flat: https://amzn.to/3P3BUUC YT version: https://youtu.be/5cBS6COzLN4 https://www.wbs.ac.uk/about/person/nick-chater/ https://twitter.com/nickjchater?lang=en

See what Sam Altman advised Kenneth when he left OpenAI! Professor Kenneth Stanley has just launched a brand new type of social network, which he calls a "Serendipity network". The idea is that you follow interests, NOT people. It's a social network without the popularity contest. We discuss the phgilosophy and technology behind the venture in great detail. The main ideas of which came from Kenneth's famous book "Why greatness cannot be planned". See what Sam Altman advised Kenneth when he left OpenAI! Professor Kenneth Stanley has just launched a brand new type of social network, which he calls a "Serendipity network".The idea is that you follow interests, NOT people. It's a social network without the popularity contest. YT version: https://www.youtube.com/watch?v=pWIrXN-yy8g Chapters should be baked into the MP3 file now MLST public Discord: https://discord.gg/machine-learning-street-talk-mlst-937356144060530778 Please support our work on Patreon - get access to interviews months early, private Patreon, networking, exclusive content and regular calls with Tim and Keith. https://patreon.com/mlst Get Maven here: https://www.heymaven.com/ Kenneth: https://twitter.com/kenneth0stanley https://www.kenstanley.net/home Host - Tim Scarfe: https://www.linkedin.com/in/ecsquizor/ https://www.mlst.ai/ Original MLST show with Kenneth: https://www.youtube.com/watch?v=lhYGXYeMq_E Tim explains the book more here: https://www.youtube.com/watch?v=wNhaz81OOqw

Brandon Rohrer who obtained his Ph.D from MIT is driven by understanding algorithms ALL the way down to their nuts and bolts, so he can make them accessible to everyone by first explaining them in the way HE himself would have wanted to learn! Please support us on Patreon for loads of exclusive content and private Discord: https://patreon.com/mlst (public discord) https://discord.gg/aNPkGUQtc5 https://twitter.com/MLStreetTalk Brandon Rohrer is a seasoned data science leader and educator with a rich background in creating robust, efficient machine learning algorithms and tools. With a Ph.D. in Mechanical Engineering from MIT, his expertise encompasses a broad spectrum of AI applications — from computer vision and natural language processing to reinforcement learning and robotics. Brandon's career has seen him in Principle-level roles at Microsoft and Facebook. An educator at heart, he also shares his knowledge through detailed tutorials, courses, and his forthcoming book, "How to Train Your Robot." YT version: https://www.youtube.com/watch?v=4Ps7ahonRCY Brandon's links: https://github.com/brohrer https://www.youtube.com/channel/UCsBKTrp45lTfHa_p49I2AEQ https://www.linkedin.com/in/brohrer/ How transformers work: https://e2eml.school/transformers Brandon's End-to-End Machine Learning school courses, posts, and tutorials https://e2eml.school Free course: https://end-to-end-machine-learning.teachable.com/p/complete-course-library-full-end-to-end-machine-learning-catalog Blog: https://e2eml.school/blog.html Ziptie: Learning Useful Features [Brandon Rohrer] https://www.brandonrohrer.com/ziptie TOC should be baked into the MP3 file now 00:00:00 - Intro to Brandon 00:00:36 - RLHF 00:01:09 - Limitations of transformers 00:07:23 - Agency - we are all GPTs 00:09:07 - BPE / representation bias 00:12:00 - LLM true believers 00:16:42 - Brandon's style of teaching 00:19:50 - ML vs real world = Robotics 00:29:59 - Reward shaping 00:37:08 - No true Scotsman - when do we accept capabilities as real 00:38:50 - Externalism 00:43:03 - Building flexible robots 00:45:37 - Is reward enough 00:54:30 - Optimization curse 00:58:15 - Collective intelligence 01:01:51 - Intelligence + creativity 01:13:35 - ChatGPT + Creativity 01:25:19 - Transformers Tutorial

The world's second-most famous AI doomer Connor Leahy sits down with Beff Jezos, the founder of the e/acc movement debating technology, AI policy, and human values. As the two discuss technology, AI safety, civilization advancement, and the future of institutions, they clash on their opposing perspectives on how we steer humanity towards a more optimal path. Watch behind the scenes, get early access and join the private Discord by supporting us on Patreon. We have some amazing content going up there with Max Bennett and Kenneth Stanley this week! https://patreon.com/mlst (public discord) https://discord.gg/aNPkGUQtc5 https://twitter.com/MLStreetTalk Post-interview with Beff and Connor: https://www.patreon.com/posts/97905213 Pre-interview with Connor and his colleague Dan Clothiaux: https://www.patreon.com/posts/connor-leahy-and-97631416 Leahy, known for his critical perspectives on AI and technology, challenges Jezos on a variety of assertions related to the accelerationist movement, market dynamics, and the need for regulation in the face of rapid technological advancements. Jezos, on the other hand, provides insights into the e/acc movement's core philosophies, emphasizing growth, adaptability, and the dangers of over-legislation and centralized control in current institutions. Throughout the discussion, both speakers explore the concept of entropy, the role of competition in fostering innovation, and the balance needed to mediate order and chaos to ensure the prosperity and survival of civilization. They weigh up the risks and rewards of AI, the importance of maintaining a power equilibrium in society, and the significance of cultural and institutional dynamism. Beff Jezos (Guillaume Verdon): https://twitter.com/BasedBeffJezos https://twitter.com/GillVerd Connor Leahy: https://twitter.com/npcollapse YT: https://www.youtube.com/watch?v=0zxi0xSBOaQ TOC: 00:00:00 - Intro 00:03:05 - Society library reference 00:03:35 - Debate starts 00:05:08 - Should any tech be banned? 00:20:39 - Leaded Gasoline 00:28:57 - False vacuum collapse method? 00:34:56 - What if there are dangerous aliens? 00:36:56 - Risk tolerances 00:39:26 - Optimizing for growth vs value 00:52:38 - Is vs ought 01:02:29 - AI discussion 01:07:38 - War / global competition 01:11:02 - Open source F16 designs 01:20:37 - Offense vs defense 01:28:49 - Morality / value 01:43:34 - What would Conor do 01:50:36 - Institutions/regulation 02:26:41 - Competition vs. Regulation Dilemma 02:32:50 - Existential Risks and Future Planning 02:41:46 - Conclusion and Reflection Note from Tim: I baked the chapter metadata into the mp3 file this time, does that help the chapters show up in your app? Let me know. Also I accidentally exported a few minutes of dead audio at the end of the file - sorry about that just skip on when the episode finishes.

Watch behind the scenes, get early access and join the private Discord by supporting us on Patreon: https://patreon.com/mlst (public discord) https://discord.gg/aNPkGUQtc5 https://twitter.com/MLStreetTalk YT version: https://youtu.be/n8G50ynU0Vg In this interview on MLST, Dr. Tim Scarfe interviews Mahault Albarracin, who is the director of product for R&D at VERSES and also a PhD student in cognitive computing at the University of Quebec in Montreal. They discuss a range of topics related to consciousness, cognition, and machine learning. Throughout the conversation, they touch upon various philosophical and computational concepts such as panpsychism, computationalism, and materiality. They consider the "hard problem" of consciousness, which is the question of how and why we have subjective experiences. Albarracin shares her views on the controversial Integrated Information Theory and the open letter of opposition it received from the scientific community. She reflects on the nature of scientific critique and rivalry, advising caution in declaring entire fields of study as pseudoscientific. A substantial part of the discussion is dedicated to the topic of science itself, where Albarracin talks about thresholds between legitimate science and pseudoscience, the role of evidence, and the importance of validating scientific methods and claims. They touch upon language models, discussing whether they can be considered as having a "theory of mind" and the implications of assigning such properties to AI systems. Albarracin challenges the idea that there is a pure form of intelligence independent of material constraints and emphasizes the role of sociality in the development of our cognitive abilities. Albarracin offers her thoughts on scientific endeavors, the predictability of systems, the nature of intelligence, and the processes of learning and adaptation. She gives insights into the concept of using degeneracy as a way to increase resilience within systems and the role of maintaining a degree of redundancy or extra capacity as a buffer against unforeseen events. The conversation concludes with her discussing the potential benefits of collective intelligence, likening the adaptability and resilience of interconnected agent systems to those found in natural ecosystems. https://www.linkedin.com/in/mahault-albarracin-1742bb153/ 00:00:00 - Intro / IIT scandal 00:05:54 - Gaydar paper / What makes good science 00:10:51 - Language 00:18:16 - Intelligence 00:29:06 - X-risk 00:40:49 - Self modelling 00:43:56 - Anthropomorphisation 00:46:41 - Mediation and subjectivity 00:51:03 - Understanding 00:56:33 - Resiliency Technical topics: 1. Integrated Information Theory (IIT) - Giulio Tononi 2. The "hard problem" of consciousness - David Chalmers 3. Panpsychism and Computationalism in philosophy of mind 4. Active Inference Framework - Karl Friston 5. Theory of Mind and its computation in AI systems 6. Noam Chomsky's views on language models and linguistics 7. Daniel Dennett's Intentional Stance theory 8. Collective intelligence and system resilience 9. Redundancy and degeneracy in complex systems 10. Michael Levin's research on bioelectricity and pattern formation 11. The role of phenomenology in cognitive science