Brown University

In recent weeks, OpenAI faced seven lawsuits alleging that ChatGPT contributed to suicides or mental health breakdowns. To spotlight the controversial relationship between AI and mental health, host Bob Safian is joined on stage at Innovation@Brown Showcase by Brown University's Ellie Pavlick, director of a new institute dedicated to exploring AI and mental health, and Soraya Darabi of VC firm TMV, an early investor in mental health AI startups. Pavlick and Darabi weigh the pros and cons of applying AI to emotional well-being, from chatbot therapy to AI friends and romantic partners. Visit the Rapid Response website here: https://www.rapidresponseshow.com/ See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Ellie Pavlick

Director

Brown University

Soraya Darabi

Partner

TMV

Zoom out from continuing tariff turmoil and Trump’s recent attacks on the Fed and the Bureau of Labor Statistics, and you’ll see a broader new economic order is forming. Political economist and Brown University professor Mark Blyth joins Rapid Response to reveal why outdated models still underpin much of our economic understanding, and what we still misunderstand – and underestimate – about China. Blyth also shares why the Democrats struggle to craft an engaging story about the economy, why it's so hard to predict a recession, and what Brown's recent settlement with the Trump Administration tells us about higher education's need to pivot from its reliance on federal funding. Visit the Rapid Response website here: https://www.rapidresponseshow.com/ See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Mark Blyth

Political Economist and Professor

Brown University

Randall Balestriero joins the show to discuss some counterintuitive findings in AI. He shares research showing that huge language models, even when started from scratch (randomly initialized) without massive pre-training, can learn specific tasks like sentiment analysis surprisingly well, train stably, and avoid severe overfitting, sometimes matching the performance of costly pre-trained models. This raises questions about when giant pre-training efforts are truly worth it. He also talks about how self-supervised learning (where models learn from data structure itself) and traditional supervised learning (using labeled data) are fundamentally similar, allowing researchers to apply decades of supervised learning theory to improve newer self-supervised methods. Finally, Randall touches on fairness in AI models used for Earth data (like climate prediction), revealing that these models can be biased, performing poorly in specific locations like islands or coastlines even if they seem accurate overall, which has important implications for policy decisions based on this data. SPONSOR MESSAGES: *** Tufa AI Labs is a brand new research lab in Zurich started by Benjamin Crouzier focussed on o-series style reasoning and AGI. They are hiring a Chief Engineer and ML engineers. Events in Zurich. Goto https://tufalabs.ai/ *** TRANSCRIPT + SHOWNOTES: https://www.dropbox.com/scl/fi/n7yev71nsjso71jyjz1fy/RANDALLNEURIPS.pdf?rlkey=0dn4injp1sc4ts8njwf3wfmxv&dl=0 TOC: 1. Model Training Efficiency and Scale [00:00:00] 1.1 Training Stability of Large Models on Small Datasets [00:04:09] 1.2 Pre-training vs Random Initialization Performance Comparison [00:07:58] 1.3 Task-Specific Models vs General LLMs Efficiency 2. Learning Paradigms and Data Distribution [00:10:35] 2.1 Fair Language Model Paradox and Token Frequency Issues [00:12:02] 2.2 Pre-training vs Single-task Learning Spectrum [00:16:04] 2.3 Theoretical Equivalence of Supervised and Self-supervised Learning [00:19:40] 2.4 Self-Supervised Learning and Supervised Learning Relationships [00:21:25] 2.5 SSL Objectives and Heavy-tailed Data Distribution Challenges 3. Geographic Representation in ML Systems [00:25:20] 3.1 Geographic Bias in Earth Data Models and Neural Representations [00:28:10] 3.2 Mathematical Limitations and Model Improvements [00:30:24] 3.3 Data Quality and Geographic Bias in ML Datasets REFS: [00:01:40] Research on training large language models from scratch on small datasets, Randall Balestriero et al. https://openreview.net/forum?id=wYGBWOjq1Q [00:10:35] The Fair Language Model Paradox (2024), Andrea Pinto, Tomer Galanti, Randall Balestriero https://arxiv.org/abs/2410.11985 [00:12:20] Muppet: Massive Multi-task Representations with Pre-Finetuning (2021), Armen Aghajanyan et al. https://arxiv.org/abs/2101.11038 [00:14:30] Dissociating language and thought in large language models (2023), Kyle Mahowald et al. https://arxiv.org/abs/2301.06627 [00:16:05] The Birth of Self-Supervised Learning: A Supervised Theory, Randall Balestriero et al. https://openreview.net/forum?id=NhYAjAAdQT [00:21:25] VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning, Adrien Bardes, Jean Ponce, Yann LeCun https://arxiv.org/abs/2105.04906 [00:25:20] No Location Left Behind: Measuring and Improving the Fairness of Implicit Representations for Earth Data (2025), Daniel Cai, Randall Balestriero, et al. https://arxiv.org/abs/2502.06831 [00:33:45] Mark Ibrahim et al.'s work on geographic bias in computer vision datasets, Mark Ibrahim https://arxiv.org/pdf/2304.12210

Randall Balestriero

Professor

Brown UniversityMeta AI

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 networks ("grokking"), geometric interpretations of neural networks via spline theory, and challenges in reconstruction learning. We also cover geometric analysis of Large Language Models (LLMs) for toxicity detection and the relationship between intrinsic dimensionality and model control in RLHF. SPONSOR MESSAGES: *** CentML offers competitive pricing for GenAI model deployment, with flexible options to suit a wide range of models, from small to large-scale deployments. https://centml.ai/pricing/ Tufa AI Labs is a brand new research lab in Zurich started by Benjamin Crouzier focussed on o-series style reasoning and AGI. Are you interested in working on reasoning, or getting involved in their events? Goto https://tufalabs.ai/ *** Randall Balestriero https://x.com/randall_balestr https://randallbalestriero.github.io/ Show notes and transcript: https://www.dropbox.com/scl/fi/3lufge4upq5gy0ug75j4a/RANDALLSHOW.pdf?rlkey=nbemgpa0jhawt1e86rx7372e4&dl=0 TOC: - Introduction - 00:00:00: Introduction - Neural Network Geometry and Spline Theory - 00:01:41: Neural Network Geometry and Spline Theory - 00:07:41: Deep Networks Always Grok - 00:11:39: Grokking and Adversarial Robustness - 00:16:09: Double Descent and Catastrophic Forgetting - Reconstruction Learning - 00:18:49: Reconstruction Learning - 00:24:15: Frequency Bias in Neural Networks - Geometric Analysis of Neural Networks - 00:29:02: Geometric Analysis of Neural Networks - 00:34:41: Adversarial Examples and Region Concentration - LLM Safety and Geometric Analysis - 00:40:05: LLM Safety and Geometric Analysis - 00:46:11: Toxicity Detection in LLMs - 00:52:24: Intrinsic Dimensionality and Model Control - 00:58:07: RLHF and High-Dimensional Spaces - Conclusion - 01:02:13: Neural Tangent Kernel - 01:08:07: Conclusion REFS: [00:01:35] Humayun – Deep network geometry & input space partitioning https://arxiv.org/html/2408.04809v1 [00:03:55] Balestriero & Paris – Linking deep networks to adaptive spline operators https://proceedings.mlr.press/v80/balestriero18b/balestriero18b.pdf [00:13:55] Song et al. – Gradient-based white-box adversarial attacks https://arxiv.org/abs/2012.14965 [00:16:05] Humayun, Balestriero & Baraniuk – Grokking phenomenon & emergent robustness https://arxiv.org/abs/2402.15555 [00:18:25] Humayun – Training dynamics & double descent via linear region evolution https://arxiv.org/abs/2310.12977 [00:20:15] Balestriero – Power diagram partitions in DNN decision boundaries https://arxiv.org/abs/1905.08443 [00:23:00] Frankle & Carbin – Lottery Ticket Hypothesis for network pruning https://arxiv.org/abs/1803.03635 [00:24:00] Belkin et al. – Double descent phenomenon in modern ML https://arxiv.org/abs/1812.11118 [00:25:55] Balestriero et al. – Batch normalization’s regularization effects https://arxiv.org/pdf/2209.14778 [00:29:35] EU – EU AI Act 2024 with compute restrictions https://www.lw.com/admin/upload/SiteAttachments/EU-AI-Act-Navigating-a-Brave-New-World.pdf [00:39:30] Humayun, Balestriero & Baraniuk – SplineCam: Visualizing deep network geometry https://openaccess.thecvf.com/content/CVPR2023/papers/Humayun_SplineCam_Exact_Visualization_and_Characterization_of_Deep_Network_Geometry_and_CVPR_2023_paper.pdf [00:40:40] Carlini – Trade-offs between adversarial robustness and accuracy https://arxiv.org/pdf/2407.20099 [00:44:55] Balestriero & LeCun – Limitations of reconstruction-based learning methods https://openreview.net/forum?id=ez7w0Ss4g9 (truncated, see shownotes PDF)

Randall Balestriero

Professor

Brown UniversityMeta AI

Yann LeCun is a French computer scientist known for his pioneering work on convolutional neural networks, optical character recognition and computer vision. He is a Silver Professor at New York University and Vice President, Chief AI Scientist at Meta. Along with Yoshua Bengio and Geoffrey Hinton, he was awarded the 2018 Turing Award for their work on deep learning, earning them the nickname of the "Godfathers of Deep Learning". Dr. Randall Balestriero has been researching learnable signal processing since 2013, with a focus on learnable parametrized wavelets and deep wavelet transforms. His research has been used by NASA, leading to applications such as Marsquake detection. During his PhD at Rice University, Randall explored deep networks from a theoretical perspective and improved state-of-the-art methods such as batch-normalization and generative networks. Later, when joining Meta AI Research (FAIR) as a postdoc with Prof. Yann LeCun, Randall further broadened his research interests to include self-supervised learning and the biases emerging from data-augmentation and regularization, resulting in numerous publications. Episode recorded live at NeurIPS. YT: https://youtu.be/9dLd6n9yT8U (references are there) Support us! https://www.patreon.com/mlst Host: Dr. Tim Scarfe TOC: [00:00:00] LeCun interview [00:18:25] Randall Balestriero interview (mostly on spectral SSL paper, first ref)

Yann LeCun

Chief AI Scientist

MetaNew York University

Randall Balestriero

Professor

Brown UniversityMeta AI

Glenn Loury is a professor of economics and social sciences at Brown University, and a prominent podcaster and social critic who speaks and writes about race, inequality, and social policy. Please support this podcast by checking out our sponsors: – Lambda: https://lambdalabs.com/lex – LMNT: https://drinkLMNT.com/lex to get free sample pack – Coinbase: https://coinbase.com/lex to get $10 in free Bitcoin – ROKA: https://roka.com/ and use code LEX to get 20% off your first order – MasterClass: https://masterclass.com/lex to get 15% off EPISODE LINKS: Glenn’s Twitter: https://twitter.com/GlennLoury Glenn’s Substack: https://glennloury.substack.com Glenn’s YouTube: https://youtube.com/c/GlennLouryShow The Anatomy of Racial Inequality (book): https://amzn.to/3N7jmz0 Race, Incarceration, and American Values (book): https://amzn.to/398ywpk PODCAST INFO: Podcast website: https://lexfridman.com/podcast Apple Podcasts: https://apple.co/2lwqZIr Spotify: https://spoti.fi/2nEwCF8 RSS: https://lexfridman.com/feed/podcast/ YouTube Full Episodes: https://youtube.com/lexfridman YouTube Clips: https://youtube.com/lexclips SUPPORT & CONNECT: – Check out the sponsors above, it’s the best way to support this podcast – Support on Patreon: https://www.patreon.com/lexfridman – Twitter: https://twitter.com/lexfridman – Instagram: https://www.instagram.com/lexfridman – LinkedIn: https://www.linkedin.com/in/lexfridman – Facebook: https://www.facebook.com/lexfridman – Medium: https://medium.com/@lexfridman OUTLINE: Here’s the timestamps for the episode. On some podcast players you should be able to click the timestamp to jump to that time. (00:00) – Introduction (07:12) – Martin Luther King Jr. (16:00) – History of slavery (30:39) – Equality of outcome (47:02) – Math and economics (1:03:17) – Racial groups (1:16:33) – Black patriotism (1:26:26) – MLK and Malcolm X (1:40:07) – Joe Rogan controversy (1:59:23) – Accusation of racism (2:07:08) – Elon Musk and Twitter (2:12:41) – Universities (2:21:19) – Cognitive inequality (2:33:45) – Politics (2:53:10) – Ketanji Brown Jackson (2:59:14) – Thomas Sowell (3:04:28) – Barack Obama (3:23:06) – Mortality (3:35:20) – Meaning of life

Glenn Loury

Professor of Economics and Social Sciences

Brown University

Today we’re joined by Karianne Bergen, an assistant professor at Brown University. In our conversation with Karianne, we explore her work at the intersection of earthquake seismology and machine learning, where she’s working on interpretable data classification for seismology. We discuss some of the challenges that present themselves when trying to solve this problem, and the state of applying machine learning to seismological events and earth sciences. Karianne also shares her thoughts on the different relationships that computer scientists and natural scientists have with machine learning, and how to bridge that gap to create tools that work broadly for all scientists. The complete show notes for this episode can be found at twimlai.com/go/554

Karianne Bergen

Assistant Professor

Brown University

We are now sponsored by Weights and Biases! Please visit our sponsor link: http://wandb.me/MLST Patreon: https://www.patreon.com/mlst Yann LeCun thinks that it's specious to say neural network models are interpolating because in high dimensions, everything is extrapolation. Recently Dr. Randall Balestriero, Dr. Jerome Pesente and prof. Yann LeCun released their paper learning in high dimensions always amounts to extrapolation. This discussion has completely changed how we think about neural networks and their behaviour. [00:00:00] Pre-intro [00:11:58] Intro Part 1: On linearisation in NNs [00:28:17] Intro Part 2: On interpolation in NNs [00:47:45] Intro Part 3: On the curse [00:48:19] LeCun [01:40:51] Randall B YouTube version: https://youtu.be/86ib0sfdFtw

Yann LeCun

Chief AI Scientist

MetaNew York University

Randall Balestriero

Professor

Brown UniversityMeta AI

When a robot goes bad, who is responsible? It’s not always clear if the user or the manufacturer is liable when a robot leaves the lot. Human behavior can be complex—and often contradictory. Asking machines to interpret that behavior is quite the task. Will it one day be possible for a robot to have its own sense of right and wrong? And barring robots acting of their own accord, whose job is it to make sure their actions can’t be hijacked?  AJung Moon explains the ethical ramifications of robot AI. Ryan Gariepy talks about the levels of responsibility in robotic manufacturing. Stefanie Tellex highlights security vulnerabilities (and scares us, just a little). Brian Gerkey of Open Robotics discusses reaching the high bar of safety needed to deploy robots. And Brian Christian explores the multi-disciplinary ways humans can impart behavior norms to robots. If you want to read up on some of our research on robots as threats, you can check our all our bonus material over at redhat.com/commandlineheroes. Follow along with the episode transcript.

AJung Moon

Robotics Ethics Researcher

McGill University

Ryan Gariepy

Co-founder / CTO

Clearpath Robotics

Stefanie Tellex

Associate Professor

Brown University

Brian Gerkey

CEO

Open Robotics

Charles Isbell is the Dean of the College of Computing at Georgia Tech. Michael Littman is a computer scientist at Brown University. Please support this podcast by checking out our sponsors: – Athletic Greens: https://athleticgreens.com/lex and use code LEX to get 1 month of fish oil – Eight Sleep: https://www.eightsleep.com/lex and use code LEX to get special savings – MasterClass: https://masterclass.com/lex to get 2 for price of 1 – Cash App: https://cash.app/ and use code LexPodcast to get $10 EPISODE LINKS: Charles’s Twitter: https://twitter.com/isbellHFh Charles’s Website: https://www.cc.gatech.edu/~isbell/ Michael’s Twitter: https://twitter.com/mlittmancs Michael’s Website: https://www.littmania.com/ Michael’s YouTube: https://www.youtube.com/user/mlittman PODCAST INFO: Podcast website: https://lexfridman.com/podcast Apple Podcasts: https://apple.co/2lwqZIr Spotify: https://spoti.fi/2nEwCF8 RSS: https://lexfridman.com/feed/podcast/ YouTube Full Episodes: https://youtube.com/lexfridman YouTube Clips: https://youtube.com/lexclips SUPPORT & CONNECT: – Check out the sponsors above, it’s the best way to support this podcast – Support on Patreon: https://www.patreon.com/lexfridman – Twitter: https://twitter.com/lexfridman – Instagram: https://www.instagram.com/lexfridman – LinkedIn: https://www.linkedin.com/in/lexfridman – Facebook: https://www.facebook.com/LexFridmanPage – Medium: https://medium.com/@lexfridman OUTLINE: Here’s the timestamps for the episode. On some podcast players you should be able to click the timestamp to jump to that time. (00:00) – Introduction (07:51) – Is machine learning just statistics? (12:14) – NeurIPS vs ICML (14:30) – Data is more important than algorithm (20:14) – The role of hardship in education (28:57) – How Charles and Michael met (33:30) – Key to success: never be satisfied (36:47) – Bell Labs (48:15) – Teaching machine learning (58:25) – Westworld and Ex Machina (1:06:24) – Simulation (1:13:14) – The college experience in the times of COVID (1:41:52) – Advice for young people (1:48:44) – How to learn to program (2:00:07) – Friendship

Charles Isbell

Dean of the College of Computing

Georgia Institute of TechnologyGeorgia Tech

Michael Littman

Professor of Computer Science

Brown University

Michael Littman is a computer scientist at Brown University. Please support this podcast by checking out our sponsors: – SimpliSafe: https://simplisafe.com/lex and use code LEX to get a free security camera – ExpressVPN: https://expressvpn.com/lexpod and use code LexPod to get 3 months free – MasterClass: https://masterclass.com/lex to get 2 for price of 1 – BetterHelp: https://betterhelp.com/lex to get 10% off EPISODE LINKS: Michael’s Twitter: https://twitter.com/mlittmancs Michael’s Website: https://www.littmania.com/ Michael’s YouTube: https://www.youtube.com/user/mlittman PODCAST INFO: Podcast website: https://lexfridman.com/podcast Apple Podcasts: https://apple.co/2lwqZIr Spotify: https://spoti.fi/2nEwCF8 RSS: https://lexfridman.com/feed/podcast/ YouTube Full Episodes: https://youtube.com/lexfridman YouTube Clips: https://youtube.com/lexclips SUPPORT & CONNECT: – Check out the sponsors above, it’s the best way to support this podcast – Support on Patreon: https://www.patreon.com/lexfridman – Twitter: https://twitter.com/lexfridman – Instagram: https://www.instagram.com/lexfridman – LinkedIn: https://www.linkedin.com/in/lexfridman – Facebook: https://www.facebook.com/LexFridmanPage – Medium: https://medium.com/@lexfridman OUTLINE: Here’s the timestamps for the episode. On some podcast players you should be able to click the timestamp to jump to that time. (00:00) – Introduction (07:43) – Robot and Frank (10:02) – Music (13:13) – Starring in a TurboTax commercial (23:26) – Existential risks of AI (41:48) – Reinforcement learning (1:07:36) – AlphaGo and David Silver (1:17:15) – Will neural networks achieve AGI? (1:29:42) – Bitter Lesson (1:42:32) – Does driving require a theory of mind? (1:51:58) – Book Recommendations (1:57:20) – Meaning of life

Michael Littman

Professor of Computer Science

Brown University

Jim Gates (S James Gates Jr.) is a theoretical physicist and professor at Brown University working on supersymmetry, supergravity, and superstring theory. He served on former President Obama’s Council of Advisors on Science and Technology. He is the co-author of a new book titled Proving Einstein Right about the scientists who set out to prove Einstein’s theory of relativity. This conversation is part of the Artificial Intelligence podcast. If you would like to get more information about this podcast go to https://lexfridman.com/ai or connect with @lexfridman on Twitter, LinkedIn, Facebook, Medium, or YouTube where you can watch the video versions of these conversations. If you enjoy the podcast, please rate it 5 stars on Apple Podcasts, follow on Spotify, or support it on Patreon. This episode is presented by Cash App. Download it (App Store, Google Play), use code “LexPodcast”.  Episode Links: Proving Einstein Right (book) Here’s the outline of the episode. On some podcast players you should be able to click the timestamp to jump to that time. 00:00 – Introduction 03:13 – Will we ever venture outside our solar system? 05:16 – When will the first human step foot on Mars? 11:14 – Are we alone in the universe? 13:55 – Most beautiful idea in physics 16:29 – Can the mind be digitized? 21:15 – Does the possibility of superintelligence excite you? 22:25 – Role of dreaming in creativity and mathematical thinking 30:51 – Existential threats 31:46 – Basic particles underlying our universe 41:28 – What is supersymmetry? 52:19 – Adinkra symbols 1:00:24 – String theory 1:07:02 – Proving Einstein right and experimental validation of general relativity 1:19:07 – Richard Feynman 1:22:01 – Barack Obama’s Council of Advisors on Science and Technology 1:30:20 – Exciting problems in physics that are just within our reach 1:31:26 – Mortality

Jim Gates

Theoretical Physicist and Professor

Brown University