Rylan Schaeffer is a PhD student at Stanford studying the engineering, science, and mathematics of intelligence. He authored the paper “Are Emergent Abilities of Large Language Models a Mirage?”, as well as other interesting refutations in the field that we’ll talk about today. He previously interned at Meta on the Llama team, and at Google DeepMin…

Ari Morcos is the CEO of DatologyAI, which makes training deep learning models more performant and efficient by intervening on training data. He was at FAIR and DeepMind before that, where he worked on a variety of topics, including how training data leads to useful representations, lottery ticket hypothesis, and self-supervised learning. His work …

Percy Liang is an associate professor of computer science and statistics at Stanford. These days, he’s interested in understanding how foundation models work, how to make them more efficient, modular, and robust, and how they shift the way people interact with AI—although he’s been working on language models for long before foundation models appear…

Seth Lazar is a professor of philosophy at the Australian National University, where he leads the Machine Intelligence and Normative Theory (MINT) Lab. His unique perspective bridges moral and political philosophy with AI, introducing much-needed rigor to the question of what will make for a good and just AI future. Generally Intelligent is a podca…

Tri Dao is a PhD student at Stanford, co-advised by Stefano Ermon and Chris Re. He’ll be joining Princeton as an assistant professor next year. He works at the intersection of machine learning and systems, currently focused on efficient training and long-range context. About Generally Intelligent We started Generally Intelligent because we believe …

Jamie Simon is a 4th year Ph.D. student at UC Berkeley advised by Mike DeWeese, and also a Research Fellow with us at Generally Intelligent. He uses tools from theoretical physics to build fundamental understanding of deep neural networks so they can be designed from first-principles. In this episode, we discuss reverse engineering kernels, the con…

Bill Thompson is a cognitive scientist and an assistant professor at UC Berkeley. He runs an experimental cognition laboratory where he and his students conduct research on human language and cognition using large-scale behavioral experiments, computational modeling, and machine learning. In this episode, we explore the impact of cultural evolution…

Ben Eysenbach is a PhD student from CMU and a student researcher at Google Brain. He is co-advised by Sergey Levine and Ruslan Salakhutdinov and his research focuses on developing RL algorithms that get state-of-the-art performance while being more simple, scalable, and robust. Recent problems he’s tackled include long horizon reasoning, exploratio…

Jim Fan is a research scientist at NVIDIA and got his PhD at Stanford under Fei-Fei Li. Jim is interested in building generally capable autonomous agents, and he recently published MineDojo, a massively multiscale benchmarking suite built on Minecraft, which was an Outstanding Paper at NeurIPS. In this episode, we discuss the foundation models for …

Sergey Levine, an assistant professor of EECS at UC Berkeley, is one of the pioneers of modern deep reinforcement learning. His research focuses on developing general-purpose algorithms for autonomous agents to learn how to solve any task. In this episode, we talk about the bottlenecks to generalization in reinforcement learning, why simulation is …

Noam Brown is a research scientist at FAIR. During his Ph.D. at CMU, he made the first AI to defeat top humans in No Limit Texas Hold 'Em poker. More recently, he was part of the team that built CICERO which achieved human-level performance in Diplomacy. In this episode, we extensively discuss ideas underlying both projects, the power of spending c…

Sugandha Sharma is a Ph.D. candidate at MIT advised by Prof. Ila Fiete and Prof. Josh Tenenbaum. She explores the computational and theoretical principles underlying higher cognition in the brain by constructing neuro-inspired models and mathematical tools to discover how the brain navigates the world, or how to construct memory mechanisms that don…

Nicklas Hansen is a Ph.D. student at UC San Diego advised by Prof Xiaolong Wang and Prof Hao Su. He is also a student researcher at Meta AI. Nicklas' research interests involve developing machine learning systems, specifically neural agents, that have the ability to learn, generalize, and adapt over their lifetime. In this episode, we talk about lo…

Jack Parker-Holder recently joined DeepMind after his Ph.D. with Stephen Roberts at Oxford. Jack is interested in using reinforcement learning to train generally capable agents, especially via an open-ended learning process where environments can adapt to constantly challenge the agent's capabilities. Before doing his Ph.D., Jack worked for 7 years…

Celeste Kidd is a professor of psychology at UC Berkeley. Her lab studies the processes involved in knowledge acquisition; essentially, how we form our beliefs over time and what allows us to select a subset of all the information we encounter in the world to form those beliefs. In this episode, we chat about attention and curiosity, beliefs and ex…

Archit Sharma is a Ph.D. student at Stanford advised by Chelsea Finn. His recent work is focused on autonomous deep reinforcement learning—that is, getting real world robots to learn to deal with unseen situations without human interventions. Prior to this, he was an AI resident at Google Brain and he interned with Yoshua Bengio at Mila. In this ep…

Chelsea Finn is an Assistant Professor at Stanford and part of the Google Brain team. She's interested in the capability of robots and other agents to develop broadly intelligent behavior through learning and interaction at scale. In this episode, we chat about some of the biggest bottlenecks in RL and robotics—including distribution shifts, Sim2Re…

Hattie Zhou is a Ph.D. student at Mila working with Hugo Larochelle and Aaron Courville. Her research focuses on understanding how and why neural networks work, starting with deconstructing why lottery tickets work and most recently exploring how forgetting may be fundamental to learning. Prior to Mila, she was a data scientist at Uber and did rese…

Minqi Jiang is a Ph.D. student at UCL and FAIR, advised by Tim Rocktäschel and Edward Grefenstette. Minqi is interested in how simulators can enable AI agents to learn useful behaviors that generalize to new settings. He is especially focused on problems at the intersection of generalization, human-AI coordination, and open-ended systems. In this e…

Oleh Rybkin is a Ph.D. student at the University of Pennsylvania and a student researcher at Google. He is advised by Kostas Daniilidis and Sergey Levine. Oleh's research focus is on reinforcement learning, particularly unsupervised and model-based RL in the visual domain. In this episode, we discuss agents that explore and plan (and do yoga), how …

Andrew Lampinen is a Research Scientist at DeepMind. He previously completed his Ph.D. in cognitive psychology at Stanford. In this episode, we discuss generalization and transfer learning, how to think about language and symbols, what AI can learn from psychology (and vice versa), mental time travel, and the need for more human-like tasks. [Podcas…

Yilun Du is a graduate student at MIT advised by Professors Leslie Kaelbling, Tomas Lozano-Perez, and Josh Tenenbaum. He's interested in building robots that can understand the world like humans and construct world representations that enable task planning over long horizons.

Martín Arjovsky did his Ph.D. at NYU with Leon Bottou. Some of his well-known works include the Wasserstein GAN and a paradigm called Invariant Risk Minimization. In this episode, we discuss out-of-distribution generalization, geometric information theory, and the importance of good benchmarks.

Yash Sharma is a Ph.D. student at the International Max Planck Research School for Intelligent Systems. He previously studied electrical engineering at Cooper Union and has spent time at Borealis AI and IBM Research. Yash’s early work was on adversarial examples and his current research interests span a variety of topics in representation disentang…

Jonathan Frankle (Google Scholar) (Website) is finishing his PhD at MIT, advised by Michael Carbin. His main research interest is using experimental methods to understand the behavior of neural networks. His current work focuses on finding sparse, trainable neural networks. **Highlights from our conversation:** 🕸 "Why is sparsity everywhere? This i…

Jacob Steinhardt (Google Scholar) (Website) is an assistant professor at UC Berkeley. His main research interest is in designing machine learning systems that are reliable and aligned with human values. Some of his specific research directions include robustness, rewards specification and reward hacking, as well as scalable alignment. Highlights: 📜…

Vincent Sitzmann (Google Scholar) (Website) is a postdoc at MIT. His work is on neural scene representations in computer vision. Ultimately, he wants to make representations that AI agents can use to solve the same visual tasks humans solve regularly, but that are currently impossible for AI. **Highlights from our conversation:** 👁 “Vision is about…

Dylan Hadfield-Menell (Google Scholar) (Website) recently finished his PhD at UC Berkeley and is starting as an assistant professor at MIT. He works on the problem of designing AI algorithms that pursue the intended goal of their users, designers, and society in general. This is known as the value alignment problem. Highlights from our conversation…

Drew Linsley (Google Scholar) is a Paul J. Salem senior research associate at Brown, advised by Thomas Serre. He is working on building computational models of the visual system that serve the dual purpose of (1) explaining biological function and (2) extending artificial vision. Highlights from our conversation: 🧠 Building neural-inspired inductiv…

Giancarlo Kerg (Google Scholar) is a PhD student at Mila, supervised by Yoshua Bengio and Guillaume Lajoie. He is working on out-of-distribution generalization and modularity in memory-augmented neural networks. Highlights from our conversation: 🧮 Pure math foundations as an approach to progress and structural understanding in deep learning researc…

Yujia Huang (Website) is a PhD student at Caltech, working at the intersection of deep learning and neuroscience. She worked on optics and biophotonics before venturing into machine learning. Now, she hopes to design “less artificial” artificial intelligence. Highlights from our conversation: 🏗 How recurrent generative feedback, a neuro-inspired de…

Julian Chibane (Google Scholar) is a PhD student at the Real Virtual Humans group at the Max Planck Institute for Informatics in Germany. His recent work centers around intrinsic functions for 3D reconstruction. Highlights from our conversation: 🖼 How, surprisingly, the IF-Net architecture learned reasonable representations of humans & objects with…

Katja Schwartz came to machine learning from physics, and is now working on 3D geometric scene understanding at the Max Planck Institute for Intelligent Systems. Her most recent work, “Generative Radiance Fields for 3D-Aware Image Synthesis,” revealed that radiance fields are a powerful representation for generative image synthesis, leading to 3D c…

Joel Lehman was previously a founding member at Uber AI Labs and assistant professor at the IT University of Copenhagen. He's now a research scientist at OpenAI, where he focuses on open-endedness, reinforcement learning, and AI safety. Joel’s PhD dissertation introduced the novelty search algorithm. That work inspired him to write the popular scie…

Cinjon Resnick was formerly from Google Brain and now is doing his PhD at NYU. We talk about why he believes scene understanding is critical to out of distribution generalization, and how his theses have evolved since he started his PhD. Some topics we over: How Cinjon started his research by trying to grow a baby through language and games, before…

Sarah Jane Hong is the co-founder of Latent Space, a startup building the first fully AI-rendered 3D engine in order to democratize creativity. We touch on what it was like taking classes under Geoff Hinton in 2013, the trouble with using natural language prompts to render a scene, why a model’s ability to scale is more important than getting state…

We interview Kelvin Guu, a researcher at Google AI and the creator of REALM. The conversation is a wide-ranging tour of language models, how computers interact with world knowledge, and much more.

Alex Davis is an Assistant Professor in the Department of Engineering and Public Policy at Carnegie Mellon University. He is a member of the Behavior, Decision, and Policy Group, the Carnegie Electricity Industry Center, and the Center for Climate and Energy Decision Making. He is currently CMU's acting director of a multi-year, multi-institutional…

If you’re still trying to make sense of what exactly everyone means by “artificial intelligence,” you’re not alone. In this episode, we chat with AI expert and CMU Electrical and Computer Engineering professor Radu Marculescu to figure out what all this AI hype means, and how it may affect our future.…

Intelligent devices that we are bringing into our home may call in to question what kind of privacy we are giving up to make life more convenient and personalized. In this episode, Carnegie Mellon University CyLab Associate Professor Lujo Bauer discusses computer security and privacy, and what we can expect in the ever-growing world of artificial i…

As artificial intelligence becomes pervasive, engineers are improving the underlying technology it runs on to make it faster and more efficient. In this podcast, Carnegie Mellon University Electrical & Computer Engineering (ECE) Professor Franz Franchetti discusses what hardware means for the future of AI and what challenges still need to be overco…

This week, Carnegie Mellon's internationally-acclaimed hacking team, the Plaid Parliament of Pwning (PPP), will be traveling to Las Vegas to compete for its fifth "World Series of Hacking" title at the DefCon security conference. In this short piece, PPP's Carolina Zarate talks hacking and other hobbies, and shares how she got into security.Music: …

This week, Carnegie Mellon's internationally-acclaimed hacking team, the Plaid Parliament of Pwning (PPP), will be traveling to Las Vegas to compete for its fifth "World Series of Hacking" title at the DefCon security conference. In this short piece, PPP's Zach Wade shares how he got into security and how competitions like DefCon are more than just…

Buggy, also known as Sweepstakes, is a Carnegie Mellon University tradition, a relay style race held at the annual CMU Spring Carnival where the buggy, a torpedo-like "racecar," serves as the baton. And in true CMU form, two teams are creating autonomous, self-driving buggies.By CMU Engineering

Chris Pistorius, professor of materials science and engineering and director of the Center for Iron & Steelmaking Research, discusses how the American steel industry has changed over the past 30 years and whether it can support new government infrastructure projects. Is the steel industry up to President Trump's "Made in America" challenge?Original…

Hackers are in high demand by companies to help strengthen their security, but there's currently a shortage of talent. CyLab director David Brumley argues that the problem is that society at-large does not fully understand what hacking means. In this episode, we'll hear from four members of CMU's top internationally ranked hacking team, the Plaid P…

When it comes to climate change, we all know that CO2 emissions are a big problem—but they aren’t the only one. In this episode, we’re talking to researchers in CMU’s Smart Infrastructure Institute and the Center for Atmospheric Particle Studies, who have partnered with People’s Gas to examine their natural gas pipelines for leaking methane, one of…

We've all heard of hacking contests where the participants are computer security experts. But a hacking contest where the participants are... computers? That's a new one, and in this episode, we hear from CMU-spinoff ForAllSecure who is heading to the national stage to compete against the nation's best autonomous hacking systems.…

Some studies have shown that the average person has over 50 online accounts-- that's a lot of passwords to recall on a daily basis. In this episode, computer science and engineering professor Lorrie Cranor offers her insight on what makes a good password good and how we all can better protect our online data.…

We're told from a very early age that putting electronic devices in our mouth, let along swallowing them, isn't a great idea. In this episode, engineering professor Chris Bettinger talks about how edible electronics may someday help diagnose and treat human disease.By CMU Engineering