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Machine Learning Street Talk (MLST)

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  1. Eiso Kant (CTO poolside) - Superhuman Coding Is Coming!

    Eiso Kant, CTO of poolside AI, discusses the company's approach to building frontier AI foundation models, particularly focused on software development. Their unique strategy is reinforcement learning from code execution feedback which is a

  2. The Compendium - Connor Leahy and Gabriel Alfour

    Connor Leahy and Gabriel Alfour, AI researchers from Conjecture and authors of "The Compendium," joinus for a critical discussion centered on Artificial Superintelligence (ASI) safety and governance. Drawing from their comprehensive analysi

  3. ARC Prize v2 Launch! (Francois Chollet and Mike Knoop)

    We are joined by Francois Chollet and Mike Knoop, to launch the new version of the ARC prize! In version 2, the challenges have been calibrated with humans such that at least 2 humans could solve each task in a reasonable task, but also adv

  4. Test-Time Adaptation: the key to reasoning with DL (Mohamed Osman)

    Mohamed Osman joins to discuss MindsAI's highest scoring entry to the ARC challenge 2024 and the paradigm of test-time fine-tuning. They explore how the team, now part of Tufa Labs in Zurich, achieved state-of-the-art results using a combin

  5. GSMSymbolic paper - Iman Mirzadeh (Apple)

    Iman Mirzadeh from Apple, who recently published the GSM-Symbolic paper discusses the crucial distinction between intelligence and achievement in AI systems. He critiques current AI research methodologies, highlighting the limitations of La

  6. Reasoning, Robustness, and Human Feedback in AI - Max Bartolo (Cohere)

    Dr. Max Bartolo from Cohere discusses machine learning model development, evaluation, and robustness. Key topics include model reasoning, the DynaBench platform for dynamic benchmarking, data-centric AI development, model training challenge

  7. Tau Language: The Software Synthesis Future (sponsored)

    This sponsored episode features mathematician Ohad Asor discussing logical approaches to AI, focusing on the limitations of machine learning and introducing the Tau language for software development and blockchain tech. Asor argues that mac

  8. John Palazza - Vice President of Global Sales @ CentML ( sponsored)

    John Palazza from CentML joins us in this sponsored interview to discuss the critical importance of infrastructure optimization in the age of Large Language Models and Generative AI. We explore how enterprises can transition from the innova

  9. Transformers Need Glasses! - Federico Barbero

    Federico Barbero (DeepMind/Oxford) is the lead author of "Transformers Need Glasses!". Have you ever wondered why LLMs struggle with seemingly simple tasks like counting or copying long strings of text? We break down the theoretical reasons

  10. Sakana AI - Chris Lu, Robert Tjarko Lange, Cong Lu

    We speak with Sakana AI, who are building nature-inspired methods that could fundamentally transform how we develop AI systems. The guests include Chris Lu, a researcher who recently completed his DPhil at Oxford University under Prof. Jako

  11. Clement Bonnet - Can Latent Program Networks Solve Abstract Reasoning?

    Clement Bonnet discusses his novel approach to the ARC (Abstraction and Reasoning Corpus) challenge. Unlike approaches that rely on fine-tuning LLMs or generating samples at inference time, Clement's method encodes input-output pairs into a

  12. Prof. Jakob Foerster - ImageNet Moment for Reinforcement Learning?

    Prof. Jakob Foerster, a leading AI researcher at Oxford University and Meta, and Chris Lu, a researcher at OpenAI -- they explain how AI is moving beyond just mimicking human behaviour to creating truly intelligent agents that can learn and

  13. Daniel Franzen & Jan Disselhoff - ARC Prize 2024 winners

    Daniel Franzen and Jan Disselhoff, the "ARChitects" are the official winners of the ARC Prize 2024. Filmed at Tufa Labs in Zurich - they revealed how they achieved a remarkable 53.5% accuracy by creatively utilising large language models (L

  14. Sepp Hochreiter - LSTM: The Comeback Story?

    Sepp Hochreiter, the inventor of LSTM (Long Short-Term Memory) networks – a foundational technology in AI. Sepp discusses his journey, the origins of LSTM, and why he believes his latest work, XLSTM, could be the next big thing in AI, parti

  15. Want to Understand Neural Networks? Think Elastic Origami! - Prof. Randall Balestriero

    Professor Randall Balestriero joins us to discuss neural network geometry, spline theory, and emerging phenomena in deep learning, based on research presented at ICML. Topics include the delayed emergence of adversarial robustness in neural

  16. Nicholas Carlini (Google DeepMind)

    Nicholas Carlini from Google DeepMind offers his view of AI security, emergent LLM capabilities, and his groundbreaking model-stealing research. He reveals how LLMs can unexpectedly excel at tasks like chess and discusses the security pitfa

  17. Subbarao Kambhampati - Do o1 models search?

    Join Prof. Subbarao Kambhampati and host Tim Scarfe for a deep dive into OpenAI's O1 model and the future of AI reasoning systems. * How O1 likely uses reinforcement learning similar to AlphaGo, with hidden reasoning tokens that users pay f

  18. How Do AI Models Actually Think? - Laura Ruis

    Laura Ruis, a PhD student at University College London and researcher at Cohere, explains her groundbreaking research into how large language models (LLMs) perform reasoning tasks, the fundamental mechanisms underlying LLM reasoning capabil

  19. Jurgen Schmidhuber on Humans co-existing with AIs

    Jürgen Schmidhuber, the father of generative AI, challenges current AI narratives, revealing that early deep learning work is in his opinion misattributed, where it actually originated in Ukraine and Japan. He discusses his early work on li

  20. Yoshua Bengio - Designing out Agency for Safe AI

    Professor Yoshua Bengio is a pioneer in deep learning and Turing Award winner. Bengio talks about AI safety, why goal-seeking “agentic” AIs might be dangerous, and his vision for building powerful AI tools without giving them agency. Topics

  21. Francois Chollet - ARC reflections - NeurIPS 2024

    François Chollet discusses the outcomes of the ARC-AGI (Abstraction and Reasoning Corpus) Prize competition in 2024, where accuracy rose from 33% to 55.5% on a private evaluation set. SPONSOR MESSAGES: *** CentML offers competitive pricing

  22. Jeff Clune - Agent AI Needs Darwin

    AI professor Jeff Clune ruminates on open-ended evolutionary algorithms—systems designed to generate novel and interesting outcomes forever. Drawing inspiration from nature’s boundless creativity, Clune and his collaborators aim to build “D

  23. Neel Nanda - Mechanistic Interpretability (Sparse Autoencoders)

    Neel Nanda, a senior research scientist at Google DeepMind, leads their mechanistic interpretability team. In this extensive interview, he discusses his work trying to understand how neural networks function internally. At just 25 years old

  24. Jonas Hübotter (ETH) - Test Time Inference

    Jonas Hübotter, PhD student at ETH Zurich's Institute for Machine Learning, discusses his groundbreaking research on test-time computation and local learning. He demonstrates how smaller models can outperform larger ones by 30x through stra

  25. How AI Could Be A Mathematician's Co-Pilot by 2026 (Prof. Swarat Chaudhuri)

    Professor Swarat Chaudhuri from the University of Texas at Austin and visiting researcher at Google DeepMind discusses breakthroughs in AI reasoning, theorem proving, and mathematical discovery. Chaudhuri explains his groundbreaking work on

  26. Nora Belrose - AI Development, Safety, and Meaning

    Nora Belrose, Head of Interpretability Research at EleutherAI, discusses critical challenges in AI safety and development. The conversation begins with her technical work on concept erasure in neural networks through LEACE (LEAst-squares Co

  27. Why Your GPUs are underutilised for AI - CentML CEO Explains

    Prof. Gennady Pekhimenko (CEO of CentML, UofT) joins us in this *sponsored episode* to dive deep into AI system optimization and enterprise implementation. From NVIDIA's technical leadership model to the rise of open-source AI, Pekhimenko s

  28. Eliezer Yudkowsky and Stephen Wolfram on AI X-risk

    Eliezer Yudkowsky and Stephen Wolfram discuss artificial intelligence and its potential existen‑ tial risks. They traversed fundamental questions about AI safety, consciousness, computational irreducibility, and the nature of intelligence.

  29. Pattern Recognition vs True Intelligence - Francois Chollet

    Francois Chollet, a prominent AI expert and creator of ARC-AGI, discusses intelligence, consciousness, and artificial intelligence. Chollet explains that real intelligence isn't about memorizing information or having lots of knowledge - it'

  30. The Elegant Math Behind Machine Learning - Anil Ananthaswamy

    Anil Ananthaswamy is an award-winning science writer and former staff writer and deputy news editor for the London-based New Scientist magazine. Machine learning systems are making life-altering decisions for us: approving mortgage loans, d

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