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Will AI Take Our Jobs? With Alex Imas (Google/University of Chicago)
Will AI take our jobs? We put the question to Alex Imas, the new Director of AGI Economics at Google DeepMind and a professor at Chicago Booth, whose entire job now is studying how frontier AI reshapes the economy. His short answer: probabl
Why AI Benchmarks Are Lying to You - with Wenhu Chen (Meta/University of Waterloo)
In this episode, we sit down with Wenhu Chen, research scientist at Meta MSL, assistant professor at the University of Waterloo, and the person behind MMLU-Pro and MMMU. If you've read a frontier model release in the last two years, you've
Jürgen Schmidhuber - Part 2: JEPA, the Road to AGI, and Who Really Invented Modern AI
In the second half of our conversation with Jürgen Schmidhuber, we focus on the key ideas he's pursued since the early 1990s and discuss why he believes these concepts are only now being rediscovered. We start with JEPA. Jürgen argues that
Jürgen Schmidhuber - World Models, RL, and the Year that changed AI (Part 1)
In this episode, we host Jürgen Schmidhuber - the man, the legend, one of the godfathers of modern AI. His lab worked out many ideas behind today’s systems (LSTM, world models, artificial curiosity, Transformer variants, and even GAN-style
AI for Science and the Thermodynamics of Generative AI - with Max Welling (UvA, CuspAI)
In this episode, we sit with Max Welling, Professor of Machine Learning at the University of Amsterdam, co-founder and CTO of CuspAI, and a foundational figure behind variational autoencoders (VAEs), equivariant networks, and Bayesian deep
After Math Falls, What's Next? with Julia Kempe (NYU/Meta)
Julia Kempe on Why Math Will Fall Next, Superhuman Provers, and the Return of the Renaissance Researcher In this episode, we sit down with Julia Kempe, a Professor at NYU's Center for Data Science and researcher at Meta FAIR's Foundations o
Intelligence in an Open World - with Mengye Ren (NYU)
We talk with Mengye Ren, Assistant Professor at NYU's Center for Data Science, about what intelligence actually means once you step outside a benchmark, and why scaling a single centralized model isn't the whole story. We get into why intel
Language, Cognition, and the Limits of LLMs - with Tal Linzen (NYU/Google)
We host Tal Linzen, Associate Professor at NYU and Research Scientist at Google, for a conversation on the intersection of cognitive science and large language models. We discussed why children can learn language from around 100 million wor
The Principles of Diffusion Models - with Jesse Lai (Sony AI)
We host Chieh-Hsin (Jesse) Lai, Staff Research Scientist at Sony AI and visiting professor at National Yang Ming Chiao Tung University, Taiwan, for a conversation about diffusion models, the technology behind tools like Stable Diffusion, an
Inside xAI, and the Bet on AI Math - with Christian Szegedy (Math Inc)
We talked with Christian Szegedy, co-inventor of Inception and Batch Normalization, founding scientist at xAI, now at Math Inc, about what it takes to build a frontier lab, and why he left xAI to work on formal mathematics. Christian thinks
Reasoning Models and Planning - with Rao Kambhampati (Arizona State)
We sat down with Rao Kambhampati, a Professor of CS at Arizona State University and former President of AAAI, to talk about reasoning models: what they are, when they work, and when they break. Rao has been working on planning and decision-
What Actually Matters in AI? - with Zhuang Liu (Princeton)
In this episode, we hosted Zhuang Liu, Assistant Professor at Princeton and former researcher at Meta, for a conversation about what actually matters in modern AI and what turns out to be a historical accident. Zhuang is behind some of the
The Future of Coding Agents with Sasha Rush (Cursor/Cornell)
We talked with Sasha Rush, researcher at Cursor and professor at Cornell, about what it actually feels like to we in the heart of the AI revolution and build coding agents right now. Sasha shared how these systems are changing day-to-day wo
The Hidden Engine of Vision with Peyman Milanfar (Google)
How Denoising Secretly Powers Everything in AI Peyman Milanfar is a Distinguished Scientist at Google, leading its Computational Imaging team. He's a member of the National Academy of Engineering, an IEEE Fellow, and one of the key people b
Reinventing AI From Scratch with Yaroslav Bulatov
Yaroslav Bulatov helped build the AI era from the inside, as one of the earliest researchers at both OpenAI and Google Brain. Now he wants to tear it all down and start over. Modern deep learning, he argues, is up to 100x more wasteful than
Why Healthcare Is AI's Hardest and Most Important Problem with Kyunghyun Cho (NYU)
We talk with Kyunghyun Cho, who is a Professor of Health Statistics and a Professor of Computer Science and Data Science at New York University, and a former Executive Director (https://www.linkedin.com/company/2276/) at Genentech, about wh
Diffusion LLM & Why the Future of AI Won't Be Autoregressive - Stefano Ermon (Stanford /Inception)
In this episode, we talk with Stefano Ermon, Stanford professor, co-founder & CEO of Inception AI, and co-inventor of DDIM, FlashAttention, DPO, and score-based/diffusion models, about why diffusion-based language models may overtake the au
Training Is Nothing Like Learning with Naomi Saphra (Harvard)
Naomi Saphra, Kempner Research Fellow at Harvard and incoming Assistant Professor at Boston University, joins us to explain why you can't do interpretability without understanding training dynamics, in the same way you can't do biology with
EP28: How to Control a Stochastic Agent with Stefano Soatto (VP AWS/ Pro. UCLA)
Stefano Soatto, VP for AI at AWS and Professor at UCLA, the person responsible for agentic AI at AWS, joins us to explain why building reliable AI agents is fundamentally a control theory problem. Stefano sees LLMs as stochastic dynamical s
EP27: Medical Foundation Models - with Tanishq Abraham (Sophont.AI)
Tanishq Abraham, CEO and co-founder of http://Sophont.ai, joins us to talk about building foundation models specifically for medicine. Sophont is trying to be something like an OpenAI or Anthropic but for healthcare - training models across
EP26: Measuring Intelligence in the Wild - Arena and the Future of AI Evaluation
Anastasios Angelopoulos, Co-Founder and CEO of Arena AI (formerly LMArena), joins us to talk about why static benchmarks are failing, how human preference data actually works under the hood, and what it takes to be the "gold standard" of AI
EP25: Personalization, Data, and the Chaos of Fine-Tuning with Fred Sala (UW-Madison / Snorkel AI)
Fred Sala, Assistant Professor at UW-Madison and Chief Scientist at Snorkel AI, joins us to talk about why personalization might be the next frontier for LLMs, why data still matters more than architecture, and how weak supervision refuses
EP24: Can AI Learn to Think About Money? - with Bayan Bruss (Capital One)
Bayan Bruss, VP of Applied AI at Capital One, joins us to talk about building AI systems that can make autonomous financial decisions, and why money might be the hardest problem in machine learning. Bayan leads Capital One's AI Foundations
EP23: Building Open Source AI Frameworks: David Mezzetti on TxtAI and Local-First AI
David Mezzetti, creator of TxtAI, joins us to talk about building open source AI frameworks as a solo developer - and why local-first AI still matters in the age of API-everything. David's path from running a 50-person IT company through ac
EP22: Data Curation for LLMs with Cody Blakeney (Datology AI)
Cody Blakeney from Datology AI joins us to talk about data curation - the unglamorous but critical work of figuring out what to actually train models on. Cody's path from writing CUDA kernels to spending his days staring at weird internet t
EP21: Privacy in the Age of Agents with Niloofar Mireshghallah
Guest: Niloofar Mireshghallah (Incoming Assistant Professor at CMU, Member of Technical Staff at Humans and AI) In this episode, we dive into AI privacy, frontier model capabilities, and why academia still matters. We kick off by discussing
EP20: Yann LeCun
Yann LeCun – Why LLMs Will Never Get Us to AGI "The path to superintelligence - just train up the LLMs, train on more synthetic data, hire thousands of people to school your system in post-training, invent new tweaks on RL-I think is comple
EP19: AI in Finance and Symbolic AI with Atlas Wang
Atlas Wang (UT Austin faculty, XTX Research Director) joins us to explore two fascinating frontiers: the foundations of symbolic AI and the practical challenges of building AI systems for quantitative finance. On the symbolic AI side, Atlas
EP18: AI Robotics
In this episode, we hosted Judah Goldfeder, a PhD candidate at Columbia University and student researcher at Google, to discuss robotics, reproducibility in ML, and smart buildings. Key topics covered: Robotics challenges: We discussed why
EP17: RL with Will Brown
In this episode, we talk with Will Brown, a research lead at Prime Intellect (https://www.primeintellect.ai/), about his journey into reinforcement learning (RL) and multi-agent systems, exploring their theoretical foundations and practical
