The Gradient: Perspectives on AI

Episode 136I spoke with Judy Fan about:* Our use of physical artifacts for sensemaking* Why cognitive tools can be a double-edged sword* Her approach to scientific inquiry and how that approach has developedEnjoy—and let me know what you think!Judy is Assistant Professor of Psychology at Stanford and director of the Cognitive Tools Lab. Her lab employs converging approaches from cognitive science, computational neuroscience, and artificial intelligence to reverse engineer the human cognitive toolkit, especially how people use physical representations of thought — such as sketches and prototypes — to learn, communicate, and solve problems.Find me on Twitter for updates on new episodes, and reach me at editor@thegradient.pub for feedback, ideas, guest suggestions. I spend a lot of time on this podcast—if you like my work, you can support me on Patreon :) You can also support upkeep for the full Gradient team/project through a paid subscription on Substack!Subscribe to The Gradient Podcast: Apple Podcasts | Spotify | Pocket Casts | RSSFollow The Gradient on TwitterOutline:* (00:00) Intro* (00:49) Throughlines and discontinuities in Judy’s research* (06:26) “Meaning” in Judy’s research* (08:05) Production and consumption of artifacts* (13:03) Explanatory questions, why we develop visual artifacts, science as a social enterprise* (15:46) Unifying principles* (17:45) “Hard limits” to knowledge and optimism* (21:47) Tensions in different fields’ forms of sensemaking and establishing truth claims* (30:55) Dichotomies and carving up the space of possible hypotheses, conceptual tools* (33:22) Cognitive tools and projectivism, simplified models vs. nature* (40:28) Scientific training and science as process and habit* (45:51) Developing mental clarity about hypotheses* (51:45) Clarifying and expressing ideas* (1:03:21) Cognitive tools as double-edged* (1:14:21) Historical and social embeddedness of tools* (1:18:34) How cognitive tools impact our imagination* (1:23:30) Normative commitments and the role of cognitive science outside the academy* (1:32:31) OutroLinks:* Judy’s Twitter and lab page* Selected papers (there are lots!)* Overviews* Drawing as a versatile cognitive tool (2023)* Using games to understand the mind (2024)* Socially intelligent machines that learn from humans and help humans learn (2024)* Research papers * Communicating design intent using drawing and text (2024)* Creating ad hoc graphical representations of number (2024)* Visual resemblance and interaction history jointly constrain pictorial meaning (2023)* Explanatory drawings prioritize functional properties at the expense of visual fidelity (2023)* SEVA: Leveraging sketches to evaluate alignment between human and machine visual abstraction (2023)* Parallel developmental changes in children’s production and recognition of line drawings of visual concepts (2023)* Learning to communicate about shared procedural abstractions (2021)* Visual communication of object concepts at different levels of abstraction (2021)* Relating visual production and recognition of objects in the human visual cortex (2020)* Collabdraw: an environment for collaborative sketching with an artificial agent (2019)* Pragmatic inference and visual abstraction enable contextual flexibility in visual communication (2019)* Common object representations for visual production and recognition (2018) Get full access to The Gradient at thegradientpub.substack.com/subscribe

Episode 134I spoke with Pete Wolfendale about:* The flaws in longtermist thinking* Selections from his new book, The Revenge of Reason* Metaphysics* What philosophy has to say about reason and AIEnjoy—and let me know what you think!Pete is an independent philosopher based in Newcastle. Dr. Wolfendale got both his undergraduate degree and his Ph.D in Philosophy at the University of Warwick. His Ph.D thesis offered a re-examination of the Heideggerian Seinsfrage, arguing that Heideggerian scholarship has failed to fully do justice to its philosophical significance, and supplementing the shortcomings in Heidegger’s thought about Being with an alternative formulation of the question. He is the author of Object-Oriented Philosophy: The Noumenon's New Clothes and The Revenge of Reason. His blog is Deontologistics.Find me on Twitter for updates on new episodes, and reach me at editor@thegradient.pub for feedback, ideas, guest suggestions. I spend a lot of time on this podcast—if you like my work, you can support me on Patreon :) You can also support upkeep for the full Gradient team/project through a paid subscription on Substack!Subscribe to The Gradient Podcast: Apple Podcasts | Spotify | Pocket Casts | RSSFollow The Gradient on TwitterOutline:* (00:00) Intro* (01:30) Pete’s experience with (para-)academia, incentive structures* (10:00) Progress in philosophy and the analytic tradition* (17:57) Thinking through metaphysical questions* (26:46) Philosophy of science, uncovering categorical properties vs. dispositions* (31:55) Structure of thought and the world, epistemological excess* (49:31) What reason is, relation to language models, semantic fragmentation of AGI* (1:00:55) Neural net interpretability and intervention* (1:08:16) World models, architecture and behavior of AI systems* (1:12:35) Language acquisition in humans and LMs* (1:15:30) Pretraining vs. evolution* (1:16:50) Technological determinism* (1:18:19) Pete’s thinking on e/acc* (1:27:45) Prometheanism vs. e/acc* (1:29:39) The Weight of Forever — Pete’s critique of What We Owe the Future* (1:30:15) Our rich deontological language and longtermism’s limits* (1:43:33) Longtermism and the opacity of desire* (1:44:41) Longtermism’s historical narrative and technological determinism, theories of power* (1:48:10) The “posthuman” condition, language and techno-linguistic infrastructure* (2:00:15) Type-checking and universal infrastructure* (2:09:23) Multitudes and selfhood* (2:21:12) Definitions of the self and (non-)circularity* (2:32:55) Freedom and aesthetics, aesthetic exploration and selfhood* (2:52:46) OutroLinks:* Pete’s blog and Twitter* Book: The Revenge of Reason* Writings / References* The Weight of Forever* On Neorationalism* So, Accelerationism, what’s that all about? Get full access to The Gradient at thegradientpub.substack.com/subscribe

Episode 132I spoke with Manuel and Lenore Blum about:* Their early influences and mentors* The Conscious Turing Machine and what theoretical computer science can tell us about consciousnessEnjoy—and let me know what you think!Manuel is a pioneer in the field of theoretical computer science and the winner of the 1995 Turing Award in recognition of his contributions to the foundations of computational complexity theory and its applications to cryptography and program checking, a mathematical approach to writing programs that check their work. He worked as a professor of computer science at the University of California, Berkeley until 2001. From 2001 to 2018, he was the Bruce Nelson Professor of Computer Science at Carnegie Mellon University.Lenore is a Distinguished Career Professor of Computer Science, Emeritus at Carnegie Mellon University and former Professor-in-Residence in EECS at UC Berkeley. She is president of the Association for Mathematical Consciousness Science and newly elected member of the American Academy of Arts and Sciences. Lenore is internationally recognized for her work in increasing the participation of girls and women in Science, Technology, Engineering, and Math (STEM) fields. She was a founder of the Association for Women in Mathematics, and founding Co-Director (with Nancy Kreinberg) of the Math/Science Network and its Expanding Your Horizons conferences for middle- and high-school girls.Find me on Twitter for updates on new episodes, and reach me at editor@thegradient.pub for feedback, ideas, guest suggestions. I spend a lot of time on this podcast—if you like my work, you can support me on Patreon :) You can also support upkeep for the full Gradient team/project through a paid subscription on Substack!Subscribe to The Gradient Podcast: Apple Podcasts | Spotify | Pocket Casts | RSSFollow The Gradient on TwitterOutline:* (00:00) Intro* (03:09) Manuel’s interest in consciousness* (05:55) More of the story — from memorization to derivation* (11:15) Warren McCulloch’s mentorship* (14:00) McCulloch’s anti-Freudianism* (15:57) More on McCulloch’s influence* (27:10) On McCulloch and telling stories* (32:35) The Conscious Turing Machine (CTM)* (33:55) A last word on McCulloch* (35:20) Components of the CTM* (39:55) Advantages of the CTM model* (50:20) The problem of free will* (52:20) On pain* (1:01:10) Brainish / CTM’s multimodal inner language, language and thinking* (1:13:55) The CTM’s lack of a “central executive”* (1:18:10) Empiricism and a self, tournaments in the CTM* (1:26:30) Mental causation* (1:36:20) Expertise and the CTM model, role of TCS* (1:46:30) Dreams and dream experience* (1:50:15) Disentangling components of experience from multimodal language* (1:56:10) CTM Robot, meaning and symbols, embodiment and consciousness* (2:00:35) AGI, CTM and AI processors, capabilities* (2:09:30) CTM implications, potential worries* (2:17:15) Advice for younger (computer) scientists* (2:22:57) OutroLinks:* Manuel’s homepage* Lenore’s homepage; find Lenore on Twitter (https://x.com/blumlenore) and Linkedin (https://www.linkedin.com/in/lenore-blum-1a47224)* Articles* “The ‘Accidental Activist’ Who Changed the Face of Mathematics” — Ben Brubaker’s Q&A with Lenore* “How this Turing-Award-winning researcher became a legendary academic advisor” — Sheon Han’s profile of Manuel* Papers (Manuel and Lenore)* AI Consciousness is Inevitable: A Theoretical Computer Science Perspective* A Theory of Consciousness from a Theoretical Computer Science Perspective: Insights from the Conscious Turing Machine* A Theoretical Computer Science Perspective on Consciousness and Artificial General Intelligence* References (McCulloch)* Embodiments of Mind* Rebel Genius Get full access to The Gradient at thegradientpub.substack.com/subscribe

Episode 130I spoke with David Pfau about:* Spectral learning and ML* Learning to disentangle manifolds and (projective) representation theory* Deep learning for computational quantum mechanics* Picking and pursuing research problems and directionsDavid’s work is really (times k for some very large value of k) interesting—I’ve been inspired to descend a number of rabbit holes because of it. (if you listen to this episode, you might become as cool as this guy)While I’m at it — I’m still hovering around 40 ratings on Apple Podcasts. It’d mean a lot if you’d consider helping me bump that up!Enjoy—and let me know what you think!David is a staff research scientist at Google DeepMind. He is also a visiting professor at Imperial College London in the Department of Physics, where he supervises work on applications of deep learning to computational quantum mechanics. His research interests span artificial intelligence, machine learning and scientific computing.Find me on Twitter for updates on new episodes, and reach me at editor@thegradient.pub for feedback, ideas, guest suggestions. I spend a lot of time on this podcast—if you like my work, you can support me on Patreon :) You can also support upkeep for the full Gradient team/project through a paid subscription on Substack!Subscribe to The Gradient Podcast: Apple Podcasts | Spotify | Pocket Casts | RSSFollow The Gradient on TwitterOutline:* (00:00) Intro* (00:52) David Pfau the “critic”* (02:05) Scientific applications of deep learning — David’s interests* (04:57) Brain / neural network analogies* (09:40) Modern ML systems and theories of the brain* (14:19) Desirable properties of theories* (18:07) Spectral Inference Networks* (19:15) Connections to FermiNet / computational physics, a series of papers* (33:52) Deep slow feature analysis — interpretability and findings on eigenfunctions* (39:07) Following up on eigenfunctions (there are indeed only so many hours in a day; I have been asking the Substack people if they can ship 40-hour days, but I don’t think they’ve gotten to it yet)* (42:17) Power iteration and intuitions* (45:23) Projective representation theory* (46:00) ???* (46:54) Geomancer and learning to decompose a manifold from data* (47:45) we consider the question of whether you will spend 90 more minutes of this podcast episode (there are not 90 more minutes left in this podcast episode, but there could have been)* (1:08:47) Learning embeddings* (1:11:12) The “unexpected emergent property” of Geomancer* (1:14:43) Learned embeddings and disentangling and preservation of topology* n/b I still haven’t managed to do this in colab because I keep crashing my instance when I use s3o4d :(* (1:21:07) What’s missing from the ~ current (deep learning) paradigm ~* (1:29:04) LLMs as swiss-army knives* (1:32:05) RL and human learning — TD learning in the brain* (1:37:43) Models that cover the Pareto Front (image below)* (1:46:54) AI accelerators and doubling down on transformers* (1:48:27) On Slow Research — chasing big questions and what makes problems attractive* (1:53:50) Future work on Geomancer* (1:55:35) Finding balance in pursuing interesting and lucrative work* (2:00:40) OutroLinks:* Papers* Natural Quantum Monte Carlo Computation of Excited States (2023)* Making sense of raw input (2021)* Integrable Nonparametric Flows (2020)* Disentangling by Subspace Diffusion (2020)* Ab initio solution of the many-electron Schrödinger equation with deep neural networks (2020)* Spectral Inference Networks (2018)* Connecting GANs and Actor-Critic Methods (2016)* Learning Structure in Time Series for Neuroscience and Beyond (2015, dissertation)* Robust learning of low-dimensional dynamics from large neural ensembles (2013)* Probabilistic Deterministic Infinite Automata (2010)* Other* On Slow Research* “I just want to put this out here so that no one ever says ‘we can just get around the data limitations of LLMs with self-play’ ever again.” Get full access to The Gradient at thegradientpub.substack.com/subscribe

Episode 129I spoke with Kristin Lauter about:* Elliptic curve cryptography and homomorphic encryption* Standardizing cryptographic protocols* Machine Learning on encrypted data* Attacking post-quantum cryptography with AIEnjoy—and let me know what you think!Kristin is Senior Director of FAIR Labs North America (2022—present), based in Seattle. Her current research areas are AI4Crypto and Private AI. She joined FAIR (Facebook AI Research) in 2021, after 22 years at Microsoft Research (MSR). At MSR she was Partner Research Manager on the senior leadership team of MSR Redmond. Before joining Microsoft in 1999, she was Hildebrandt Assistant Professor of Mathematics at the University of Michigan (1996-1999). She is an Affiliate Professor of Mathematics at the University of Washington (2008—present). She received all her advanced degrees from the University of Chicago, BA (1990), MS (1991), PhD (1996) in Mathematics. She is best known for her work on Elliptic Curve Cryptography, Supersingular Isogeny Graphs in Cryptography, Homomorphic Encryption (SEALcrypto.org), Private AI, and AI4Crypto. She served as President of the Association for Women in Mathematics from 2015-2017 and on the Council of the American Mathematical Society from 2014-2017.Find me on Twitter for updates on new episodes, and reach me at editor@thegradient.pub for feedback, ideas, guest suggestions. I spend a lot of time on this podcast—if you like my work, you can support me on Patreon :) You can also support upkeep for the full Gradient team/project through a paid subscription on Substack!Subscribe to The Gradient Podcast: Apple Podcasts | Spotify | Pocket Casts | RSSFollow The Gradient on TwitterOutline:* (00:00) Intro* (01:10) Llama 3 and encrypted data — where do we want to be?* (04:20) Tradeoffs: individual privacy vs. aggregated value in e.g. social media forums* (07:48) Kristin’s shift in views on privacy* (09:40) Earlier work on elliptic curve cryptography — applications and theory* (10:50) Inspirations from algebra, number theory, and algebraic geometry* (15:40) On algebra vs. analysis and on clear thinking* (18:38) Elliptic curve cryptography and security, algorithms and concrete running time* (21:31) Cryptographic protocols and setting standards* (26:36) Supersingular isogeny graphs (and higher-dimensional supersingular isogeny graphs)* (32:26) Hard problems for cryptography and finding new problems* (36:42) Guaranteeing security for cryptographic protocols and mathematical foundations* (40:15) Private AI: Crypto-Nets / running neural nets on homomorphically encrypted data* (42:10) Polynomial approximations, activation functions, and expressivity* (44:32) Scaling up, Llama 2 inference on encrypted data* (46:10) Transitioning between MSR and FAIR, industry research* (52:45) An efficient algorithm for integer lattice reduction (AI4Crypto)* (56:23) Local minima, convergence and limit guarantees, scaling* (58:27) SALSA: Attacking Lattice Cryptography with Transformers* (58:38) Learning With Errors (LWE) vs. standard ML assumptions* (1:02:25) Powers of small primes and faster learning* (1:04:35) LWE and linear regression on a torus* (1:07:30) Secret recovery algorithms and transformer accuracy* (1:09:10) Interpretability / encoding information about secrets* (1:09:45) Future work / scaling up* (1:12:08) Reflections on working as a mathematician among technologistsLinks:* Kristin’s Meta, Wikipedia, Google Scholar, and Twitter pages* Papers and sources mentioned/referenced:* The Advantages of Elliptic Curve Cryptography for Wireless Security (2004)* Cryptographic Hash Functions from Expander Graphs (2007, introducing Supersingular Isogeny Graphs)* Families of Ramanujan Graphs and Quaternion Algebras (2008 — the higher-dimensional analogues of Supersingular Isogeny Graphs)* Cryptographic Cloud Storage (2010)* Can homomorphic encryption be practical? (2011)* ML Confidential: Machine Learning on Encrypted Data (2012)* CryptoNets: Applying neural networks to encrypted data with high throughput and accuracy (2016)* A community effort to protect genomic data sharing, collaboration and outsourcing (2017)* The Homomorphic Encryption Standard (2022)* Private AI: Machine Learning on Encrypted Data (2022)* SALSA: Attacking Lattice Cryptography with Transformers (2022)* SalsaPicante: A Machine Learning Attack on LWE with Binary Secrets* SALSA VERDE: a machine learning attack on LWE with sparse small secrets* Salsa Fresca: Angular Embeddings and Pre-Training for ML Attacks on Learning With Errors* The cool and the cruel: separating hard parts of LWE secrets* An efficient algorithm for integer lattice reduction (2023) Get full access to The Gradient at thegradientpub.substack.com/subscribe

Episode 127I spoke with Christopher Thi Nguyen about:* How we lose control of our values* The tradeoffs of legibility, aggregation, and simplification* Gamification and its risksEnjoy—and let me know what you think!C. Thi Nguyen as of July 2020 is Associate Professor of Philosophy at the University of Utah. His research focuses on how social structures and technology can shape our rationality and our agency. He has published on trust, expertise, group agency, community art, cultural appropriation, aesthetic value, echo chambers, moral outrage porn, and games. He received his PhD from UCLA. Once, he was a food writer for the Los Angeles Times.I spend a lot of time on this podcast—if you like my work, you can support me on Patreon :)Reach me at editor@thegradient.pub for feedback, ideas, guest suggestions. Subscribe to The Gradient Podcast: Apple Podcasts | Spotify | Pocket Casts | RSSFollow The Gradient on TwitterOutline:* (00:00) Intro* (01:10) The ubiquity of James C. Scott* (06:03) Legibility and measurement* (12:50) Value capture, classes and measurement* (17:30) Political value choice in ML* (23:30) Why value collapse happens* (33:00) Blackburn, “Hume and Thick Connexions” — projectivism and legibility* (36:20) Heuristics and decision-making* (40:08) Institutional classification systems* (46:55) Back to Hume* (48:27) Epistemic arms races, stepping outside our conceptual architectures* (56:40) The “what to do” question* (1:04:00) Gamification, aesthetic engagement* (1:14:51) Echo chambers and defining utility* (1:22:10) Progress, AGI millenarianism* (disclaimer: I don’t know what’s going to happen with the world, either.)* (1:26:04) Parting visions* (1:30:02) OutroLinks:* Chrisopher’s Twitter and homepage* Games: Agency as Art* Papers referenced* Transparency is Surveillance* Games and the art of agency* Autonomy and Aesthetic Engagement* Art as a Shelter from Science* Value Capture* Hostile Epistemology* Hume and Thick Connexions (Simon Blackburn) Get full access to The Gradient at thegradientpub.substack.com/subscribe

Episode 125False universalism freaks me out. It doesn’t freak me out as a first principle because of epistemic violence; it freaks me out because it works. I spoke with Professor Thomas Mullaney about:* Telling stories about your work and balancing what feels meaningful with practical realities* Destabilizing our understandings of the technologies we feel familiar with, and the work of researching the history of the Chinese typewriter* The personal nature of researchThe Chinese Typewriter and The Chinese Computer are two of the best books I’ve read in a very long time. And they’re not just good and interesting, but important to read, for the history they tell and the ideas and arguments they present—I can’t recommend them and Professor Mullaney’s other work enough.Tom is Professor of History and Professor of East Asian Languages and Cultures, by courtesy. He is also the Kluge Chair in Technology and Society at the Library of Congress, and a Guggenheim Fellow. He is the author or lead editor of 8 books, including The Chinese Computer, The Chinese Typewriter (winner of the Fairbank prize), Your Computer is on Fire, and Coming to Terms with the Nation: Ethnic Classification in Modern China.Reach me at editor@thegradient.pub for feedback, ideas, guest suggestions. Subscribe to The Gradient Podcast: Apple Podcasts | Spotify | Pocket Casts | RSSFollow The Gradient on TwitterOutline:* (00:00) Intro* (01:00) “In Their Own Words” interview: on telling stories about your work* (07:42) Clashing narratives and authenticity/inauthenticity in pursuing your work* (15:48) Why Professor Mullaney pursued studying the Chinese typewriter* (18:20) Worldmaking, transforming the physical world to fit our descriptive models* (30:07) Internal and illegible continuities/coherence in work* (31:45) The role of a “self”* (43:06) The 2008 Beijing Olympics and false (alphabetical) universalism, projectivism* (1:04:23) “Kicking the ladder” and the personal nature of research* (1:18:07) The “Technolinguistic Chinese Exclusion Act” — the situatedness of historians in their work* (1:33:00) Is the Chinese typewriter project finished? / on the resolution of problems* (1:43:35) OutroLinks:* Professor Mullaney’s homepage and Twitter* In Their Own Words: Thomas Mullaney* Books* The Chinese Computer: A Global History of the Information Age* The Chinese Typewriter: A History* Coming to Terms with the Nation: Ethnic Classification in Modern China Get full access to The Gradient at thegradientpub.substack.com/subscribe

Episode 123I spoke with Suhail Doshi about:* Why benchmarks aren’t prepared for tomorrow’s AI models* How he thinks about artists in a world with advanced AI tools* Building a unified computer vision model that can generate, edit, and understand pixels. Suhail is a software engineer and entrepreneur known for founding Mixpanel, Mighty Computing, and Playground AI (they’re hiring!).Reach me at editor@thegradient.pub for feedback, ideas, guest suggestions. Subscribe to The Gradient Podcast: Apple Podcasts | Spotify | Pocket Casts | RSSFollow The Gradient on TwitterOutline:* (00:00) Intro* (00:54) Ad read — MLOps conference* (01:30) Suhail is *not* in pivot hell but he *is* all-in on 50% AI-generated music* (03:45) AI and music, similarities to Playground* (07:50) Skill vs. creative capacity in art* (12:43) What we look for in music and art* (15:30) Enabling creative expression* (18:22) Building a unified computer vision model, underinvestment in computer vision* (23:14) Enhancing the aesthetic quality of images: color and contrast, benchmarks vs user desires* (29:05) “Benchmarks are not prepared for how powerful these models will become”* (31:56) Personalized models and personalized benchmarks* (36:39) Engaging users and benchmark development* (39:27) What a foundation model for graphics requires* (45:33) Text-to-image is insufficient* (46:38) DALL-E 2 and Imagen comparisons, FID* (49:40) Compositionality* (50:37) Why Playground focuses on images vs. 3d, video, etc.* (54:11) Open source and Playground’s strategy* (57:18) When to stop open-sourcing?* (1:03:38) Suhail’s thoughts on AGI discourse* (1:07:56) OutroLinks:* Playground homepage* Suhail on Twitter Get full access to The Gradient at thegradientpub.substack.com/subscribe