119 episodes

Welcome! The team at MLST is inspired by academic research and each week we engage in dynamic discussion with pre-eminent figures in the AI field. Our flagship show covers current affairs in AI 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 without succumbing to hype. MLST is run by Tim Scarfe, Ph.D (https://www.linkedin.com/in/ecsquizor/)

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

    • Technology
    • 4.7 • 52 Ratings

Welcome! The team at MLST is inspired by academic research and each week we engage in dynamic discussion with pre-eminent figures in the AI field. Our flagship show covers current affairs in AI 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 without succumbing to hype. MLST is run by Tim Scarfe, Ph.D (https://www.linkedin.com/in/ecsquizor/)

    Decoding the Genome: Unraveling the Complexities with AI and Creativity [Prof. Jim Hughes, Oxford]

    Decoding the Genome: Unraveling the Complexities with AI and Creativity [Prof. Jim Hughes, Oxford]

    Support us! https://www.patreon.com/mlst
    MLST Discord: https://discord.gg/aNPkGUQtc5
    Twitter: https://twitter.com/MLStreetTalk

    In this eye-opening discussion between Tim Scarfe and Prof. Jim Hughes, a professor of gene regulation at Oxford University, they explore the intersection of creativity, genomics, and artificial intelligence. Prof. Hughes brings his expertise in genomics and insights from his interdisciplinary research group, which includes machine learning experts, mathematicians, and molecular biologists.

    The conversation begins with an overview of Prof. Hughes' background and the importance of creativity in scientific research. They delve into the challenges of unlocking the secrets of the human genome and how machine learning, specifically convolutional neural networks, can assist in decoding genome function.

    As they discuss validation and interpretability concerns in machine learning, they acknowledge the need for experimental tests and ponder the complex nature of understanding the basic code of life. They touch upon the fascinating world of morphogenesis and emergence, considering the potential crossovers into AI and their implications for self-repairing systems in medicine.

    Examining the ethical and regulatory aspects of genomics and AI, the duo explores the implications of having access to someone's genome, the potential to predict traits or diseases, and the role of AI in understanding complex genetic signals. They also consider the challenges of keeping up with the rapidly expanding body of scientific research and the pressures faced by researchers in academia.

    To wrap up the discussion, Tim and Prof. Hughes shed light on the significance of creativity and diversity in scientific research, emphasizing the need for divergent processes and diverse perspectives to foster innovation and avoid consensus-driven convergence.
    Filmed at https://www.creativemachine.io/Prof. Jim Hughes: https://www.rdm.ox.ac.uk/people/jim-hughesDr. Tim Scarfe: https://xrai.glass/

    Table of Contents:

    1. [0:00:00] Introduction and Prof. Jim Hughes' background
    2. [0:02:48] Creativity and its role in science
    3. [0:07:13] Challenges in understanding the human genome
    4. [0:13:20] Using convolutional neural networks to decode genome function
    5. [0:15:32] Validation and interpretability concerns in machine learning
    6. [0:17:56] Challenges in understanding the basic code of life
    7. [0:19:36] Morphogenesis, emergence, and potential crossovers into AI
    8. [0:21:38] Ethics and regulation in genomics and AI
    9. [0:23:30] The role of AI in understanding and managing genetic risks
    10. [0:32:37] Creativity and diversity in scientific research

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

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

    Please check out Numerai - our sponsor @


    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:




    YT version: https://www.youtube.com/watch?v=kMLKbhY0ji0


    Dr. Tim Scarfe

    Dr. Keith Duggar

    Joint CTOs - https://xrai.glass/


    Are Emergent Abilities of Large Language Models a Mirage? (Rylan Schaeffer)



    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]

    • 2 hr 1 min
    AI Senate Hearing - Executive Summary (Sam Altman, Gary Marcus)

    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

    • 49 min
    Future of Generative AI [David Foster]

    Future of Generative AI [David Foster]

    Generative Deep Learning, 2nd Edition [David Foster]


    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.


    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]

    • 2 hr 31 min
    PERPLEXITY AI - The future of search.

    PERPLEXITY AI - The future of search.




    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:



    Interviewer: Dr. Tim Scarfe (CTO XRAI Glass)
    Patreon: https://www.patreon.com/mlst
    Discord: https://discord.gg/ESrGqhf5CB

    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]

    Evaluating Verifiability in Generative Search Engines (Nelson F. Liu et al, Stanford University)

    Note: this was a sponsored interview.

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

    #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!


    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]


    - Why Greatness Cannot Be Planned: The Myth of the Objective by Kenneth O. Stanley and Joel Lehman


    - Rethinking Exploration: General Intelligence Requires Rethinking Exploration


    - The Case for Strong Emergence (Sabine Hossenfelder)


    - The Game of Life (Conway)


    - Toolformer: Teaching Language Models to Generate APIs (Meta AI)


    - OpenAI's POET: Paired Open-Ended Trailblazer


    - Schmidhuber's Artificial Curiosity


    - Gödel Machines


    - PowerPlay


    - 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


    - Go-Explore: A New Approach for Hard-Exploration Problems


    - Learning with AMIGo: Adversarially Motivated Intrinsic Goals




    Sutton and Barto


    • 2 hr 47 min

Customer Reviews

4.7 out of 5
52 Ratings

52 Ratings


Fascinating Discussions on AI & Humanity

Brilliant episode on the philosophy of information. So good I didn’t even notice any music 🫣

Every technology to date has been a reflection of the ever present human ego & desire for power, profit & control of worldly resources & territories.

Can AI systems ultimately learn to model good from evil in spite of their potential design flaws

Rainbow Stalin ,

Please don’t use music while talking

Please don’t use music while people are talking. I just listened to the new episode with David Chalmers, but it was very difficult due to the music.

Ryan7102 ,

Excellent guests and down-to-earth discussion

Naom Chomsky interview was AMAZING. I listened twice and followed up on every thread he mentioned. Props to interviewers for asking leading questions, and fixing up the audio. IF this podcast does nothing else again ever, it will have been worth it to capture this great man's (final?) thoughts on AI, which I've heard him discuss nowhere else (sadly he's gone down a political rabbit hole in his writings). Huge props to you gentlemen!

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