Episode 138

I spoke with Meredith Morris about:

* The intersection of AI and HCI and why we need more cross-pollination between AI and adjacent fields

* Disability studies and AI

* Generative ghosts and technological determinism

* Developing a useful definition of AGI

I didn’t get to record an intro for this episode since I’ve been sick.

Enjoy!

Meredith is Director for Human-AI Interaction Research for Google DeepMind and an Affiliate Professor in The Paul G. Allen School of Computer Science & Engineering and in The Information School at the University of Washington, where she participates in the dub research consortium. Her work spans the areas of human-computer interaction (HCI), human-centered AI, human-AI interaction, computer-supported cooperative work (CSCW), social computing, and accessibility. She has been recognized as an ACM Fellow and ACM SIGCHI Academy member for her contributions to HCI.

Find me on Twitter for updates on new episodes, and reach me at editor@thegradient.pub for feedback, ideas, guest suggestions.

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Outline:

* (00:00) Meredith’s influences and earlier work

* (03:00) Distinctions between AI and HCI

* (05:56) Maturity of fields and cross-disciplinary work

* (09:03) Technology and ends

* (10:37) Unique aspects of Meredith’s research direction

* (12:55) Forms of knowledge production in interdisciplinary work

* (14:08) Disability, Bias, and AI

* (18:32) LaMPost and using LMs for writing

* (20:12) Accessibility approaches for dyslexia

* (22:15) Awareness of AI and perceptions of autonomy

* (24:43) The software model of personhood

* (28:07) Notions of intelligence, normative visions and disability studies

* (32:41) Disability categories and learning systems

* (37:24) Bringing more perspectives into CS research and re-defining what counts as CS research

* (39:36) Training interdisciplinary researchers, blurring boundaries in academia and industry

* (43:25) Generative Agents and public imagination

* (45:13) The state of ML conferences, the need for more cross-pollination

* (46:42) Prestige in conferences, the move towards more cross-disciplinary work

* (48:52) Joon Park Appreciation

* (49:51) Training interdisciplinary researchers

* (53:20) Generative Ghosts and technological determinism

* (57:06) Examples of generative ghosts and clones, relationships to agentic systems

* (1:00:39) Reasons for wanting generative ghosts

* (1:02:25) Questions of consent for generative clones and ghosts

* (1:05:01) Labor involved in maintaining generative ghosts, psychological tolls

* (1:06:25) Potential religious and spiritual significance of generative systems

* (1:10:19) Anthropomorphization

* (1:12:14) User experience and cognitive biases

* (1:15:24) Levels of AGI

* (1:16:13) Defining AGI

* (1:23:20) World models and AGI

* (1:26:16) Metacognitive abilities in AGI

* (1:30:06) Towards Bidirectional Human-AI Alignment

* (1:30:55) Pluralistic value alignment

* (1:32:43) Meredith’s perspective on deploying AI systems

* (1:36:09) Meredith’s advice for younger interdisciplinary researchers

Links:

* Meredith’s homepage, Twitter, and Google Scholar

* Papers

* Mediating Group Dynamics through Tabletop Interface Design

* SearchTogether: An Interface for Collaborative Web Search

* AI and Accessibility: A Discussion of Ethical Considerations

* Disability, Bias, and AI

* LaMPost: Design and Evaluation of an AI-assisted Email Writing Prototype for Adults with Dyslexia

* Generative Ghosts

* Levels of AGI

Episode 137

I spoke with Davidad Dalrymple about:

* His perspectives on AI risk

* ARIA (the UK’s Advanced Research and Invention Agency) and its Safeguarded AI Programme

Enjoy—and let me know what you think!

Davidad is a Programme Director at ARIA. He was most recently a Research Fellow in technical AI safety at Oxford. He co-invented the top-40 cryptocurrency Filecoin, led an international neuroscience collaboration, and was a senior software engineer at Twitter and multiple startups.

Find me on Twitter for updates on new episodes, and reach me at editor@thegradient.pub for feedback, ideas, guest suggestions.

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Outline:

* (00:00) Intro

* (00:36) Calibration and optimism about breakthroughs

* (03:35) Calibration and AGI timelines, effects of AGI on humanity

* (07:10) Davidad’s thoughts on the Orthogonality Thesis

* (10:30) Understanding how our current direction relates to AGI and breakthroughs

* (13:33) What Davidad thinks is needed for AGI

* (17:00) Extracting knowledge

* (19:01) Cyber-physical systems and modeling frameworks

* (20:00) Continuities between Davidad’s earlier work and ARIA

* (22:56) Path dependence in technology, race dynamics

* (26:40) More on Davidad’s perspective on what might go wrong with AGI

* (28:57) Vulnerable world, interconnectedness of computers and control

* (34:52) Formal verification and world modeling, Open Agency Architecture

* (35:25) The Semantic Sufficiency Hypothesis

* (39:31) Challenges for modeling

* (43:44) The Deontic Sufficiency Hypothesis and mathematical formalization

* (49:25) Oversimplification and quantitative knowledge

* (53:42) Collective deliberation in expressing values for AI

* (55:56) ARIA’s Safeguarded AI Programme

* (59:40) Anthropic’s ASL levels

* (1:03:12) Guaranteed Safe AI —

* (1:03:38) AI risk and (in)accurate world models

* (1:09:59) Levels of safety specifications for world models and verifiers — steps to achieve high safety

* (1:12:00) Davidad’s portfolio research approach and funding at ARIA

* (1:15:46) Earlier concerns about ARIA — Davidad’s perspective

* (1:19:26) Where to find more information on ARIA and the Safeguarded AI Programme

* (1:20:44) Outro

Links:

* Davidad’s Twitter

* ARIA homepage

* Safeguarded AI Programme

* Papers

* Guaranteed Safe AI

* Davidad’s Open Agency Architecture for Safe Transformative AI

* Dioptics: a Common Generalization of Open Games and Gradient-Based Learners (2019)

* Asynchronous Logic Automata (2008)

Episode 129

I spoke with Dan Hart and Michelle Michael about:

* Developing NSWEduChat, an AI-powered chatbot designed and delivered by the NSW Department of Education for students and teachers.

* The challenges in effectively teaching students as technology develops

* Understanding and defining the importance of the classroom

Enjoy—and let me know what you think!

Dan Hart is Head of AI, and Michelle Michael is Director of Educational Support and Rural Initiatives at the New South Wales (NSW) Department of Education.

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!

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Outline:

* (00:00) Intro

* (00:48) How NSWEduChat came to be, educational principles for AI use

* (02:37) Educational environment in New South Wales

* (04:41) How educators have adapted to new challenges for teaching and assessment

* (07:47) Considering technology advancement while teaching and assessing students

* (12:14) Educating teachers and students about how to use AI tools

* (15:03) AI in the classroom and enabling teachers

* (19:44) Product-first thinking for educational AI

* (22:15) Red teaming and testing

* (24:02) Benchmarking, chatbots as an assistant

* (26:35) The importance of the classroom

* (28:10) Media coverage and hype

* (30:35) Measurement and the benchmarking process/methodology

* (34:50) Principles for how chatbots should interact with students

* (44:29) Producing good educational outcomes at scale

* (46:41) Operating with speed and effectiveness while implementing governance

* (49:03) How the experience of building technologies evolves

* (51:45) Identifying good technologists and educators for development and use

* (55:07) Teaching standards and how AI impacts teachers

* (57:01) How technologists incorporate teaching standards and expertise in their work

* (1:00:03) NSWEduChat model details

* (1:02:55) Value alignment for NSWEduChat

* (1:05:40) Practicing caution in filtering chatbot responses

* (1:07:35) Equity and personalized instruction — how NSWEduChat can help

* (1:10:19) Helping students become “the students they could be”

* (1:13:39) Outro

Links:

* NSWEduChat

* Guardian article on NSWEduChat

Have suggestions for future podcast guests (or other feedback)? Let us know here!

In episode 42 of The Gradient Podcast, Daniel Bashir speaks to Andrew Feldman.

Andrew is the co-founder and CEO of Cerebras Systems, an AI accelerator company that has built the largest processor in the industry. Before Cerebras, Andrew co-founded and served as CEO of SeaMicro, which was acquired by AMD in 2012. He has also served in executive positions at Force10 Networks and RiverStone Networks.

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Outline:

* (00:00) Intro

* (02:05) Andrew’s trajectory, from business school to Cerebras

* (10:00) The large model problem and Cerebras’ approach

* (19:50) Cerebras’s GPT-J announcement

* (22:20) Andrew explains weight streaming to Daniel

* (32:30) Andrew’s thoughts on the MLPerf benchmark

* (38:20) The venture landscape for AI accelerator companies

* (42:50) The hardware lottery, hardware support for sparsity

* (45:40) The CHIPS Act, NVIDIA China ban and the accelerator industry

* (48:00) Politics and Chips, US and China

* (52:20) Andrew’s perspective on tackling difficult problems

* (56:42) Outro

Links:

* Cerebras’ Homepage

* GPT-J Announcement

* TotalEnergies

* GlaxoSmithKline (GSK)

* Sources mentioned

* “Political Chips” by Ben Thompson (because Daniel’s a fanboy)

* Daniel’s conversation with Sara Hooker

* The Hardware Lottery

Have suggestions for future podcast guests (or other feedback)? Let us know here!

In episode 41 of The Gradient Podcast, Daniel Bashir speaks to Christopher Manning.

Chris is the Director of the Stanford AI Lab and an Associate Director of the Stanford Human-Centered Artificial Intelligence Institute. He is an ACM Fellow, an AAAI Fellow, and past President of ACL. His work currently focuses on applying deep learning to natural language processing; it has included tree recursive neural networks, GloVe, neural machine translation, and computational linguistic approaches to parsing, among other topics. 

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Outline:

* (00:00) Intro

* (02:40) Chris’s path to AI through computational linguistics

* (06:10) Human language acquisition vs. ML systems

* (09:20) Grounding language in the physical world, multimodality and DALL-E 2 vs. Imagen

* (26:15) Chris’s Linguistics PhD, splitting time between Stanford and Xerox PARC, corpus-based empirical NLP

* (34:45) Rationalist and Empiricist schools in linguistics, Chris’s work in 1990s

* (45:30) GloVe and Attention-based Neural Machine Translation, global and local context in language

* (50:30) Different Neural Architectures for Language, Chris’s work in the 2010s

* (58:00) Large-scale Pretraining, learning to predict the next word helps you learn about the world

* (1:00:00) mBERT’s Internal Representations vs. Universal Dependencies Taxonomy

* (1:01:30) The Need for Inductive Priors for Language Systems

* (1:05:55) Courage in Chris’s Research Career

* (1:10:50) Outro (yes Daniel does have a new outro with ~ music ~)

Links:

* Chris’s webpage

* Papers (1990s-2000s)

* Distributional Phrase Structure Induction

* Fast exact inference with a factored model for Natural Language Parsing

* Accurate Unlexicalized Parsing

* Corpus-based induction of syntactic structure

* Foundations of Statistical Natural Language Processing

* Papers (2010s):

* Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank

* GloVe

* Effective Approaches to Attention-based Neural Machine Translation

* Stanford’s Graph-based Neural dependency parser

* Papers (2020s)

* Electra: Pre-training text encoders as discriminators rather than generators

* Finding Universal Grammatical Relations in Multilingual BERT

* Emergent linguistic structure in artificial neural networks trained by self-supervision

In episode 41 of The Gradient Podcast, Andrey Kurenkov speaks to Professor Jeff Clune.

Jeff is an Associate Professor of Computer Science at the University of British Columbia and a Faculty Member of the Vector Institute. Previously, he was a Research Team Leader at OpenAI and before that a Senior Research Manager and founding member of Uber AI Labs, and prior to that he was an Associate Professor in Computer Science at the University of Wyoming.

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The Gradient is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

Outline:

(00:00) Intro
(01:05) Path into AI
(08:05) Studying biology with simulations
(10:30) Overview of genetic algorithms
(14:00) Evolving gaits with genetic algorithms
(20:00) Quality-Diversity Algorithms
(27:00) Evolving Soft Robots
(32:15) Genetic algorithms for studying Evolution
(39:30) Modularity for Catastrophic Forgetting
(45:15) Curiosity for Learning Diverse Skills
(51:15) Evolving Environments 
(58:3) The Surprising Creativity of Digital Evolution
(1:04:28) Hobbies Outside of Research
(1:07:25) Outro

In episode 40 of The Gradient Podcast, Andrey Kurenkov speaks to Catherine Olsson and Nelson Elhage.

Catherine and Nelson are both members of technical staff at Anthropic, which is an AI safety and research company that’s working to build reliable, interpretable, and steerable AI systems. Catherine and Nelson’s focus is on interpretability, and we will discuss several of their recent works in this interview. 
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Outline:

(00:00) Intro
(01:10) Catherine’s Path into AI
(03:25) Nelson’s Path into AI
(05:23) Overview of Anthropic
(08:21) Mechanistic Interpretability
(15:15) Transformer Circuits 
(21:30) Toy Transformer
(27:25) Induction Heads
(31:00) In-Context Learning
(35:10) Evidence for Induction Heads Enabling In-Context Learning
(39:30) What’s Next
(43:10) Replicating Results
(46:00) Outro

Links:

Anthropic

Zoom In: An Introduction to Circuits

Mechanistic Interpretability, Variables, and the Importance of Interpretable Bases

A Mathematical Framework for Transformer Circuits

In-context Learning and Induction Heads 

PySvelte

In episode 38 of The Gradient Podcast, Daniel Bashir speaks to Been Kim.

Been is a staff research scientist at Google Brain focused on interpretability–helping humans communicate with complex machine learning models by not only building tools but also studying how humans interact with these systems. She has served with a number of conferences including ICLR, NeurIPS, ICML, and AISTATS. She gave the keynotes at ICLR 2022, ECML 2020, and the G20 meeting in Argentina in 2018. Her work TCAV received the UNESCO Netexplo award, was featured at Google I/O 2019 and in Brian Christian’s book The Alignment Problem.

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Outline:

(00:00) Intro(02:20) Path to AI/interpretability(06:10) The Progression of Been’s thinking / PhD thesis(11:30) Towards a Rigorous Science of Interpretable Machine Learning(24:52) Interpretability and Software Testing(27:00) Been’s ICLR Keynote and Human-Machine “Language”(37:30) TCAV(43:30) Mood Board Search and CAV Camera(48:00) TCAV’s Limitations and Follow-up Work(56:00) Acquisition of Chess Knowledge in AlphaZero(1:07:00) Daniel spends a very long time asking “what does it mean to you to be a researcher?”(1:09:00) The everyday drudgery, more lessons from Been(1:11:32) Outro

Links:

* Been’s website

* CAVcamera app

In episode 37 of The Gradient Podcast, Andrey Kurenkov speaks to Laura Weidinger

Laura is a senior research scientist at DeepMind, with her focus being AI ethics. Laura is also a PhD candidate at the University of Cambridge, studying philosophy of science and specifically approaches to measuring the ethics of AI systems. Previously Laura worked in technology policy at UK and EU levels, as a Policy executive at techUK. She then pivoted to cognitive science research and studied human learning at the Max Planck Institute for Human Development in Berlin, and was a Guest Lecturer at the Ada National College for Digital Skills. She received her Master's degree at the Humboldt University of Berlin, from the School of Mind and Brain, with her focus being Neuroscience/ Philosophy/ Cognitive science.

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Outline:

(00:00) Intro(01:20) Path to AI(04:25) Research in Cognitive Science(06:40) Interest in AI Ethics(14:30) Ethics Considerations for Researchers(17:38) Ethical and social risks of harm from language models (25:30) Taxonomy of Risks posed by Language Models(27:33) Characteristics of Harmful Text: Towards Rigorous Benchmarking of Language Models(33:25) Main Insight for Measuring Harm(35:40) The EU AI Act(39:10) Alignment of language agents(46:10) GPT-4Chan(53:40) Interests outside of AI(55:30) Outro

Links:

Ethical and social risks of harm from language models 

Taxonomy of Risks posed by Language Models

Characteristics of Harmful Text: Towards Rigorous Benchmarking of Language Models

Alignment of language agents

In episode 36 of The Gradient Podcast, Daniel Bashir speaks to Sebastian Raschka.

Sebastian is an Assistant Professor of Statistics at the University of Wisconsin-Madison and Lead AI Educator at Lightning AI. He has written two bestselling books: Python Machine Learning and Machine Learning with PyTorch and Scikit-Learn.

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Sections:

(00:00) Intro

(01:10) Sebastian’s intro to AI

(05:15) Sebastian’s process for learning new things

(12:15) Learning style varies with purpose

(16:10) Ordinal Regression

(31:00) Solving rank inconsistency with conditional probability

(35:00) Semi-Adversarial Networks

(44:15) Why Sebastian got into education

(52:45) Lightning AI

(1:00:00) Sebastian’s advice for educators

(1:03:30) Be cool like Sebastian and follow the Gradient

(1:03:40) Outro

Episode Links:

* Sebastian’s Homepage

* Sebastian’s Twitter

* Sebastian’s Books

In episode 35 of The Gradient Podcast, guest host Sharon Zhou speaks to Jack Shanahan.

John (Jack) Shanahan was a Lieutenant General in the United States Air Force, retired after a 36-year military career. He was the inaugural Director of the Joint Artificial Intelligence Center (JAIC) in the U.S. Department of Defense (DoD). He was also the Director of the Algorithmic Warfare Cross-Functional Team (Project Maven). Currently, he is a Special Government Employee supporting the National Security Commission on Artificial Intelligence; serves on the Board of Advisors for the Common Mission Project; is an advisor to The Changing Character of War Centre (Oxford University); is a member of the CACI Strategic Advisory Group; and serves as an Advisor to the Military Cyber Professionals Association.

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Outline:

(00:00) Intro(01:20) Introduction to Jack and Sharon(07:30) Project Maven(09:45) Relationship of Tech Sector and DoD(16:40) Need for AI in DoD(20:10) Bringing the tech-DoD divide(30:00) Conclusion

Episode Links:

* John N.T. Shanahan Wikipedia

* AI To Revolutionize U.S. Intelligence Community With General Shanahan

* Email: aidodconversations@gmail.com

In episode 34 of The Gradient Podcast, Daniel Bashir speaks to Sara Hooker.

Sara (@sarahookr) leads Cohere for AI and is a former Research Scientist at Google. Sara founded a Bay Area non-profit called Delta Analytics, which works with non-profits and communities to build technical capacity. She is also one of the co-founders of the Trustworthy ML Initiative, an active participant of the ML Collective research group, and a host of the underrated ML podcast.

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Sections:

(00:00) Intro(02:20) Podcasting gripe-fest(06:00) Sara’s journey: from economics to AI(09:15) Economics vs. AI research(12:45) The Hardware Lottery(19:15) Towards better hardware benchmarks(26:00) Getting away from the hardware lottery(32:30) The myth of compact, interpretable, robust, performant DNNs(35:15) Top-line metrics vs. disaggregated metrics(39:20) Solving memorization in the data pipeline, noisy examples(45:35) Cohere For AI

Episode Links:

* Cohere for AI

* Sara’s Homepage

In episode 33 of The Gradient Podcast, Andrey Kurenkov speaks to Lukas Biewald.

Lukas Biewald is a co-founder of Weights and Biases, a company that creates developer tools for machine learning. Prior to that he was a co-founder and CEO of Figure Eight Inc. (formerly CrowdFlower) — an Internet company that collects training data for machine learning, which was sold for 300 million dollars.

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Outline:

* (00:00) Intro

* (01:18) Start in AI

* (06:17) CrowdFlower / Crowdsourcing

* (21:06) Discovering Deep Learning 

* (25:10) Learning Deep Learning

* (32:50) Weights and Biases

* (37:30) State of Tooling for ML 

* (41:20) Exciting AI Trends

* (44:42) Interests outside of AI

* (45:40) Outro

Links:

* Lukas’s website

* Lukas’s GitHub

* Starting a Second Machine Learning Tools Company, Ten Years Later

* Confession of a so-called AI expert

* What I learned from looking at 200 machine learning tools

* CS 329S: Machine Learning Systems Design

* Designing Machine Learning Systems

Opportunity at Weights & Biases:

Episode 129

I spoke with Kristin Lauter about:

* Elliptic curve cryptography and homomorphic encryption

* Standardizing cryptographic protocols

* Machine Learning on encrypted data

* Attacking post-quantum cryptography with AI

Enjoy—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 Twitter

Outline:

* (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 technologists

Links:

* 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)

In episode 32 of The Gradient Podcast, Andrey Kurenkov speaks to Chip Huyen.

Chip Huyen is a co-founder of Claypot AI, a platform for real-time machine learning. Previously, she was with Snorkel AI and NVIDIA. She teaches CS 329S: Machine Learning Systems Design at Stanford. She has also written four bestselling Vietnamese books, and more recently her new O’Reilly book Designing Machine Learning Systems has just come out! 

Subscribe to The Gradient Podcast:  Apple Podcasts  | Spotify | Pocket Casts | RSSFollow The Gradient on Twitter

She also maintains a Discord server with a focus on Machine Learning Systems.

Outline:

* (00:00) Intro

* (01:30) 3-year trip through Asia, Africa, and South America

* (04:00) Getting into AI at Stanford

* (11:30) Confession of a so-called AI expert

* (16:40) Academia vs Industry

* (17:40) Focus on ML Systems

* (20:00) ML in Academia vs Industry

* (28:15) Maturity of AI in Industry

* (31:45) ML Tools

* (37:20) Real Time ML

* (43:00) ML Systems Class and Book

Links:

* Chip’s website

* MLOps Discord server

* Confession of a so-called AI expert

* What I learned from looking at 200 machine learning tools

* CS 329S: Machine Learning Systems Design

* Designing Machine Learning Systems

In episode 31 of The Gradient Podcast, Daniel Bashir speaks to Preetum Nakkiran.

Preetum is a Research Scientist at Apple, a Visiting Researcher at UCSD, and part of the NSF/Simons Collaboration on the Theoretical Foundations of Deep Learning. He completed his PhD at Harvard, where he co-founded the ML Foundations Group. Preetum’s research focuses on building conceptual tools for understanding learning systems.

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Sections:

(00:00) Intro

(01:25) Getting into AI through Theoretical Computer Science (TCS)

(09:08) Lack of Motivation in TCS and Learning What Research Is

(12:12) Foundational vs Problem-Solving Research, Antipatterns in TCS

(16:30) Theory and Empirics in Deep Learning

(18:30) What is an Empirical Theory of Deep Learning

(28:21) Deep Double Descent

(40:00) Inductive Biases in SGD, epoch-wise double descent

(45:25) Inductive Biases Stick Around

(47:12) Deep Bootstrap

(59:40) Distributional Generalization - Paper Rejections

(1:02:30) Classical Generalization and Distributional Generalization

(1:16:46) Future Work: Studying Structure in Data

(1:20:51) The Tweets^TM

(1:37:00) Outro

Episode Links:

* Preetum’s Homepage

* Preetum’s PhD Thesis

In episode 30 of The Gradient Podcast, Daniel Bashir speaks to Max Woolf.

Max Woolf (@minimaxir) is currently a Data Scientist at BuzzFeed in San Francisco. Some work he’s done for BuzzFeed includes using StyleGAN to create AI-generated fake boyfriends and AI-generated art quizzes. In his free time, Max creates open source Python and R software on his GitHub. More recently, Max has been developing tooling for AI content generation, such as aitextgen for easy AI text generation.

Max’s projects are funded by his Patreon. If you have found anything on his website helpful, please help contribute!

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Sections:

(00:00) Intro

(01:20) Max’s Intro to Data Science and AI

(07:00) Software Engineering in Data Science, Max’s Perspectives

(09:00) Max’s Work at BuzzFeed

(23:10) Scaling, Inference, Large Models

(27:00) AI Content Generation

(30:45) Discourse About GPT-3

(34:30) AI Inventors

(38:35) Fun Projects and One-Offs: AI-generated Pokémon

(43:35) GPT-3-generated Discussion Topics

(46:30) Advice for Data Scientists

(48:10) BuzzFeed is Hiring :)

(48:20) Outro

Episode Links:

* Max’s Homepage

* Real-World Data Science

In episode 29 of The Gradient Podcast, we chat with Rosanne Liu. Rosanne is a research scientist in Google Brain, and co-founder and executive director of ML Collective, a nonprofit organization for open collaboration and accessible mentorship. Before that she was a founding member of Uber AI. Outside of research, she supports underrepresented communities, and organizes symposiums, workshops, and a weekly reading group “Deep Learning: Classics and Trends” since 2018. She is currently thinking deeply how to democratize AI research even further, and improve the diversity and fairness of the field, while working on multiple fronts of machine learning research including understanding training dynamics, rethinking model capacity and scaling. 

Subscribe to The Gradient Podcast:  Apple Podcasts  | Spotify | Pocket Casts | RSSFollow The Gradient on Twitter

Outline:

* (01:30) How did you go into AI / research

* (6:45) AI research: the unreasonably narrow path and how not to be miserable

* (16:30) ML Collective Overview

* (21:45) Deep Learning: Classics and Trends Reading Group

* (26:25) More details about ML Collective

* (39:35) ICLR 2022 Diversity, Equity & Inclusion

* (48:00) Narrowness vs Variety in research

* (57:20) Favorite Papers 

* (58:50) Measuring the Intrinsic Dimension of Objective Landscapes 

* (01:01:40) Natural Adversarial Objects 

* (01:03:00) Interests outside of AI - Writing

* (01:08:05) Interests outside of AI - Narrating Travels with Charley

* (01:13:22) Outro

In episode 28 of The Gradient Podcast, Daniel Bashir speaks to Ben Green, postdoctoral scholar in the Michigan Society of Fellows and Assistant Professor at the Gerald R. Ford School of Public Policy. Ben’s work focuses on the social and political impacts of government algorithms.

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Sections:

(00:00) Intro

(02:00) Getting Started

(06:15) Soul Searching

(11:55) Decentering Algorithms

(19:50) The Future of the City

(27:25) Ethical Lip Service

(32:30) Ethics Research and Industry Incentives

(36:30) Broadening our Vision of Tech Ethics

(47:35) What Types of Research are Valued?

(52:40) Outro

Episode Links:

* Ben’s Homepage

* Algorithmic Realism

* Special Issue of the Journal of Social Computing

In episode 27 of The Gradient Podcast, Andrey Kurenkov speaks to Max Braun, who leads the AI and robotics software engineering team at Everyday Robots, a moonshot to create robots that can learn to help people in their everyday lives. Previously, he worked on building frontier technology products as an entrepreneur and later at Google and X. Max enjoys exploring the intersection of art, technology, and philosophy as a writer and designer. 

Subscribe to The Gradient Podcast:  Apple Podcasts  | Spotify | Pocket Casts | RSSFollow The Gradient on Twitter

Outline:

* (00:00) Intro

* (01:00) Start in AI

* (5:45) Humanoid Research in Osaka

* (8:45) Joining Google X

* (12:15) Visual Search and Google Glass

* (15:58) Academia Industry Connection

* (18:45) Overview of Robotics Vision

* (26:00) Machine Learning for Robotics

* (32:00) Robot Platform

* (38:00) Development Process and History

* (43:35) QT-Opt

* (49:05) Imitation Learning

* (55:00) Simulation Platform

* (59:45) Sim2Real

* (1:07:00) SayCan

* (1:14:30) Current Objectives

* (1:17:00) Other Projects

* (1:21:40) Outro

Episode Links:

* Max Braun’s Website

* Everyday Robots

* Simulating Artificial Muscles for Controlling a Robotic Arm with Fluctuation

* Introducing the Everyday Robot Project

* Scalable Deep Reinforcement Learning from Robotic Manipulation (QT-Opt)

* Alphabet is putting its prototype robots to work cleaning up around Google’s offices

* Everyday robots are (slowly) leaving the lab

* Can Robots Follow Instructions for New Tasks?

* Efficiently Initializing Reinforcement Learning With Prior Policies

* Combining RL + IL at Scale

* Shortening the Sim to Real Gap

* Action-Image: Teaching Grasping in Sim

* SayCan

* I Made an AI Read Wittgenstein, Then Told It to Play Philosopher

In episode 26 of The Gradient Podcast, Daniel Bashir speaks to Yejin Choi, professor of Computer Science at the University of Washington, and senior research manager at the Allen Institute for Artificial Intelligence.

Subscribe to The Gradient Podcast:  Apple Podcasts  | Spotify | Pocket Casts | RSSFollow The Gradient on Twitter

Sections:

(00:00) Intro

(01:42) Getting Started in the Winter

(09:17) Has NLP lost its way?

(12:57) The Mosaic Project, Commonsense Intelligence

(18:20) A Priori Intuitions and Common Sense in Machines

(21:35) Abductive Reasoning

(24:49) Benchmarking Common Sense

(33:00) DeLorean and COMET - Algorithms for Commonsense Reasoning

(43:30) Positive and Negative uses of Commonsense Models

(49:40) Moral Reasoning

(57:00) Descriptive Morality, Meta-Ethical Concerns

(1:04:30) Potential Misuse

(1:12:15) Future Work

(1:16:23) Outro

Episode Links:

* Yejin’s Homepage

* The Curious Case of Commonsense Intelligence in Daedalus

* Common Sense Comes Close to Computers in Quanta

* Can Computers Learn Common Sense? in The New Yorker

In episode 25 of The Gradient Podcast, Daniel Bashir speaks to David Chalmers, professor of philosophy and Philosophy and Neural Science at New York University, and co-director of NYU’s center for Mind, Brain, and Consciousness.

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Sections:

(00:00) Intro(00:42) “Today’s neural networks may be slightly conscious”(03:55) Openness to Machine Consciousness(09:37) Integrated Information Theory(18:41) Epistemic Gaps, Verbal Reports(25:52) Vision Models and Consciousness(33:37) Reasoning about Consciousness(38:20) Illusionism(41:30) Best Approaches to the Hard Problem(44:21) Panpsychism(46:35) Outro

Episode Links:

* Chalmers’ Homepage

* Facing Up to the Hard Problem of Consciousness (1995)

* Reality+: Virtual Worlds and the Problems of Philosophy

* Amanda Askell on AI Consciousness

In episode 24 of The Gradient Podcast, Daniel Bashir talks to Greg Yang, senior researcher at Microsoft Research. Greg Yang’s Tensor Programs framework recently received attention for its role in the µTransfer paradigm for tuning the hyperparameters of large neural networks.

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Sections:

(00:00) Intro(01:50) Start in AI / Research(05:55) Fear of Math in ML(08:00) Presentation of Research(17:35) Path to MSR(21:20) Origin of Tensor Programs(26:05) Refining TP’s Presentation(39:55) The Sea of Garbage (Initializations) and the Oasis(47:44) Scaling Up Further(55:53) On Theory and Practice in Deep Learning(01:05:28) Outro

Episode Links:

* Greg’s Homepage

* Greg’s Twitter

* µP GitHub

* Visual Intro to Gaussian Processes (Distill)

In the 23rd interview of The Gradient Podcast, we talk to Nick Walton, the CEO and Co-Founder of Latitude, the goal of which is to make AI a tool of freedom and creativity for everyone, and which is currently developing AI Dungeon and Voyage.

Subscribe to The Gradient Podcast:  

* Apple Podcasts

* Spotify

* Pocket Casts

* RSS

Outline:(00:00) Intro(01:38) How did you go into AI / research(3:50) Origin of AI Dungeon(8:15) What is a Dungeon Master(12:!5) Brief history of AI Dungeon(17:30) AI in videogames, past and future(23:35) Early days of AI Dungeon(29:45) AI Dungeon as a Creative Tool(33:50) Technical Aspects of AI Dungeon(39:15) Voyage(48:27) Visuals in AI Dungeon(50:45) How to Control AI in Games(55:38) Future of AI in Games(57:50) Funny stories(59:45) Interests / Hobbies(01:01:45) Outro

Episode 128

I spoke with Sergiy Nesterenko about:

* Developing an automated system for designing PCBs

* Difficulties in human and automated PCB design

* Building a startup at the intersection of different areas of expertise

By the way — I hit 40 ratings on Apple Podcasts (and am at 66 on Spotify). It’d mean a lot (really, a lot) if you’d consider leaving a rating or a review. I read everything, and it’s very heartening and helpful to hear what you think.

Enjoy, and let me know what you think!

Sergiy is founder and CEO of Quilter. Sergiy spent 5 years at SpaceX developing radiation-hardened avionics for SpaceX's Falcon 9 and Falcon Heavy's second stage rockets, before discovering a big problem: designing printed circuit boards for all the electronics in these rockets was tedious, manual and error prone. So in 2019, he founded Quilter to build the next generation of AI-powered tooling for electrical engineers.

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 Twitter

Outline:

* (00:00) Intro

* (00:45) Quilter origins and difficulties in designing PCBs

* (04:12) PCBs and schematic implementations

* (06:40) Iteration cycles and simulations

* (08:35) Octilinear traces and first-principles design for PCBs

* (12:38) The design space of PCBs

* (15:27) Benchmarks for PCB design

* (20:05) RL and PCB design

* (22:48) PCB details, track widths

* (25:09) Board functionality and aesthetics

* (27:53) PCB designers and automation

* (30:24) Quilter as a compiler

* (33:56) Gluing social worlds and bringing together expertise

* (36:00) Process knowledge vs. first-principles thinking

* (42:05) Example boards

* (44:45) Auto-routers for PCBs

* (48:43) Difficulties for scaling to larger boards

* (50:42) Customers and skepticism

* (53:42) On experiencing negative feedback

* (56:42) Maintaining stamina while building Quilter

* (1:00:00) Endgame for Quilter and future directions

* (1:03:24) Outro

Links:

* Quilter homepage

* Other pages/features mentioned:

* Thin-to-thick traces

* Octilinear trace routing

* Comment from Tom Fleet

In episode 22 of The Gradient Podcast, we talk to Connor Leahy, an AI researcher focused on AI alignment and a co-founder of EleutherAI.

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Connor is an AI researcher working on understanding large ML models and aligning them to human values, and a cofounder of EleutherAI, a decentralized grassroots collective of volunteer researchers, engineers, and developers focused on AI alignment, scaling, and open source AI research. The organization's flagship project is the GPT-Neo family of models designed to match those developed by OpenAI as GPT-3.

Sections:

(00:00:00) Intro(00:01:20) Start in AI(00:08:00) Being excited about GPT-2 (00:18:00) Discovering AI safety and alignment(00:21:10) Replicating GPT-2 (00:27:30) Deciding whether to relese GPT-2 weights(00:36:15) Life after GPT-2 (00:40:05) GPT-3 and Start of Eleuther AI(00:44:40) Early days of Eleuther AI(00:47:30) Creating the Pile, GPT-Neo, Hacker Culture(00:55:10) Growth of Eleuther AI, Cultivating Community(01:02:22) Why release a large language model(01:08:50) AI Risk and Alignment(01:21:30) Worrying (or not) about Superhuman AI(01:25:20) AI alignment and releasing powerful models(01:32:08) AI risk and research norms(01:37:10) Work on GPT-3 replication, GPT-NeoX(01:38:48) Joining Eleuther AI(01:43:28) Personal interests / hobbies(01:47:20) Outro

Links to things discussed:

* Replicating GPT2–1.5B , GPT2, Counting Consciousness and the Curious Hacker

* The Hacker Learns to Trust

* The Pile

* GPT-Neo

* GPT-J

* Why Release a Large Language Model?

* What A Long, Strange Trip It's Been: EleutherAI One Year Retrospective

* GPT-NeoX

In interview 21 of The Gradient Podcast, we talk to Percy Liang, an Associate Professor of Computer Science at Stanford University and the director of the Center for Research on Foundation Models.

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Percy Liang’s research spans many topics in machine learning and natural language processing, including robustness, interpretability, semantics, and reasoning.  He is also a strong proponent of reproducibility through the creation of CodaLab Worksheets.  His awards include the Presidential Early Career Award for Scientists and Engineers (2019), IJCAI Computers and Thought Award (2016), an NSF CAREER Award (2016), a Sloan Research Fellowship (2015), a Microsoft Research Faculty Fellowship (2014), and multiple paper awards at ACL, EMNLP, ICML, and COLT.

Sections:

(00:00) Intro(01:21) Start in AI(06:52) Interest in Language(10:17) Start of PhD(12:22) Semantic Parsing(17:49) Focus on ML robustness(22:30) Foundation Models, model robustness(28:55) Foundation Model bias(34:48) Foundation Model research by academia(37:13) Current research interests(39:40) Surprising robustness results(44:24) Reproducibility and CodaLab(50:17) Outro

Papers / Topics discussed:

* On the Opportunities and Risks of Foundation Models

* Reflections on Foundation Models

* Removing spurious features can hurt accuracy and affect groups disproportionately.

* Selective classification can magnify disparities across groups

* Just train twice: improving group robustness without training group information

* LILA: language-informed latent actions

* CodaLab

In episode 20 of The Gradient Podcast, we talk to Eric Jang, a research scientist on the Robotics team at Google.

Eric is a research scientist on the Robotics team at Google. His research focuses on answering whether big data and small algorithms can yield unprecedented capabilities in the domain of robotics, just like the computer vision, translation, and speech revolutions before it. Specifically, he focuses on robotic manipulation and self-supervised robotic learning.

Sections:

(00:00) Intro(00:50) Start in AI / Research(03:58) Joining Google Robotics(10:08) End to End Learning of Semantic Grasping(19:11) Off Policy RL for Robotic Grasping(29:33) Grasp2Vec(40:50) Watch, Try, Learn Meta-Learning from Demonstrations and Rewards(50:12) BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learning(59:41) Just Ask for Generalization(01:09:02) Data for Robotics(01:22:10) To Understand Language is to Understand Generalization (01:32:38) Outro

Papers discussed:

* Grasp2Vec: Learning Object Representations from Self-Supervised Grasping

* End-to-End Learning of Semantic Grasping

* Deep reinforcement learning for vision-based robotic grasping: A simulated comparative evaluation of off-policy methods

* Watch, Try, Learn Meta-Learning from Demonstrations and Rewards

* BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learning

* Just Ask for Generalization

* To Understand Language is to Understand Generalization

* Robots Must Be Ephemeralized

In episode 19 of The Gradient Podcast, we talk to Rishi Bommasani, a Ph.D student at Stanford focused on Foundation Models.

Rish is a second-year Ph.D. student in the CS Department at Stanford, where he is advised by Percy Liang and Dan Jurafsky. His research focuses on understanding AI systems and their social impact, as well as using NLP to further scientific inquiry. Over the past year, he helped build and organize the Stanford Center for Research on Foundation Models (CRFM).

Sections:

(00:00:00) Intro(00:01:05) How did you get into AI?(00:09:55) Towards Understanding Position Embeddings(00:14:23) Long-Distance Dependencies don’t have to be Long(00:18:55) Interpreting Pretrained Contextualized Representations via Reductions to Static Embeddings(00:30:25) Masters Thesis(00:34:05) Start of PhD and work on foundation models(00:42:14) Why were people intested in foundation models(00:46:45) Formation of CRFM(00:51:25) Writing report on foundation models(00:56:33) Challenges in writing report(01:05:45) Response to reception(01:15:35) Goals of CRFM(01:25:43) Current research focus(01:30:35) Interests outside of research(01:33:10) Outro

Papers discussed:

* Towards Understanding Position Embeddings

* Long-Distance Dependencies don’t have to be Long: Simplifying through Provably (Approximately) Optimal Permutations

* Interpreting Pretrained Contextualized Representations via Reductions to Static Embeddings

* Generalized Optimal Linear Orders

* On the Opportunities and Risks of Foundation Models

* Reflections on Foundation Models

In episode 18 of The Gradient Podcast, we talked to Upol Ehsan, an Explainable AI (XAI) researcher who combines his background in Philosophy and Human-Computer Interaction to address problems in XAI beyond just opening the "black-box" of AI. You can find his Gradient article charting this vision here.

Subscribe to The Gradient Podcast:  Apple Podcasts  | Spotify | Pocket Casts | RSSFollow The Gradient on Twitter

Papers Discussed:

* Rationalization: A Neural Machine Translation Approach to Generating Natural Language Explanations

* Automated Rationale Generation: A Technique for Explainable AI and its Effects on Human Perceptions

* Human-centered Explainable AI: Towards a Reflective Sociotechnical Approach

* Expanding Explainability: Towards Social Transparency in AI systems

* The Who in Explainable AI: How AI Background Shapes Perceptions of AI Explanations

* Explainability Pitfalls: Beyond Dark Patterns in Explainable AI

Exciting update!

In addition to listening to the audio recording, you can now experience the interview over at The Gradient’s main site, with live captions and the ability to jump to certain sections.

In addition, you can experience it as follows: Interactive Transcript | Transcript PDF | Interview on YouTube

About Upol:Upol Ehsan cares about people first, technology second. He is a doctoral candidate in the School of Interactive Computing at Georgia Tech and an affiliate at the Data & Society Research Institute. Combining his expertise in AI and background in Philosophy, his work in Explainable AI (XAI) aims to foster a future where anyone, regardless of their background, can use AI-powered technology with dignity.

Actively publishing in top peer-reviewed venues like CHI, his work has received multiple awards and been covered in major media outlets. Bridging industry and academia, he serves on multiple program committees in HCI and AI conferences (e.g., DIS, IUI, NeurIPS) and actively connects these communities (e.g, the widely attended HCXAI workshop at CHI). By promoting equity and ethics in AI, he wants to ensure stakeholders who aren’t at the table do not end up on the menu. Outside research, he is an advisor for Aalor Asha, an educational institute he started for underprivileged children subjected to child labor.Follow him on Twitter: @upolehsan

In episode 17 of The Gradient Podcast, we talk to Miles Brundage, Head of Policy Research at OpenAI and a researcher passionate about the responsible governance of artificial intelligence.

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Links:

* Will Technology Make Work Better for Everyone?

* Economic Possibilities for Our Children: Artificial Intelligence and the Future of Work, Education, and Leisure

* Taking Superintelligence Seriously

* The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation

* Release Strategies and the Social Impact of Language Models

* All the News that’s Fit to Fabricate: AI-Generated Text as a Tool of Media Misinformation

* Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims

Timeline:

(00:00) Intro(01:05) How did you get started in AI(07:05) Writing about AI on Slate(09:20) Start of PhD(13:00) AI and the End of Scarcity(18:12) Malicious Uses of AI(28:00) GPT-2 and Publication Norms(33:30) AI-Generated Text for Misinformation(37:05) State of AI Misinformation(41:30) Trustworthy AI(48:50) OpenAI Policy Research Team(53:15) Outro

Miles is a researcher and research manager, and is passionate about the responsible governance of artificial intelligence. In 2018, he joined OpenAI, where he began as a Research Scientist and recently became Head of Policy Research. Before that, he was a Research Fellow at the University of Oxford's Future of Humanity Institute, where he is still a Research Affiliate).He also serves as a member of Axon's AI and Policing Technology Ethics Board. He completed a PhD in Human and Social Dimensions of Science and Technology from Arizona State University in 2019.

Podcast Theme: “MusicVAE: Trio 16-bar Sample #2” from "MusicVAE: A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music"

Hosted by Andrey Kurenkov (@andrey_kurenkov), a PhD student with the Stanford Vision and Learning Lab working on learning techniques for robotic manipulation and search.

In episode 16 of The Gradient Podcast, we talk to Jeffrey Ding, a postdoctoral fellow at Stanford's Center for International Security and Cooperation

(01:35) Getting into AI research(04:20) Interest in studying China(06:50) Deciphering China’s AI Dream(23:25) Beyond the AI Arms Race(36:45) China's Current Capabilities in AI(46:45) AI as a General Purpose and Strategic Technology(57:38) ChinaAI Newsletter(01:04:20) Teaching AI to Policy People(01:06:30) Current Focus(01:09:10) Interests Outside of Work + Outro

Subscribe to The Gradient Podcast: Apple Podcasts | Spotify | Pocket Casts | RSS

Jeffrey Ding (@jjding99) is a postdoctoral fellow at Stanford's Center for International Security and Cooperation, sponsored by Stanford's Institute for Human-Centered Artificial Intelligence, as well as a research affiliate with the Centre for the Governance of AI at the University of Oxford. His current research is centered on how technological change affects the rise and fall of great powers, with an eye toward the implications of advances in AI for a possible U.S.-China power transition. He also puts out the excellent ChinaAI newsletter, which has (sometimes) weekly translations of Chinese-language musings on AI and related topics.

Podcast Theme: “MusicVAE: Trio 16-bar Sample #2” from "MusicVAE: A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music"

Hosted by Andrey Kurenkov (@andrey_kurenkov), a PhD student with the Stanford Vision and Learning Lab working on learning techniques for robotic manipulation and search.

In episode 15 of The Gradient Podcast, we talk to Stanford PhD Candidate Alex Tamkin

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Alex Tamkin is a fourth-year PhD student in Computer Science at Stanford, advised by Noah Goodman and part of the Stanford NLP Group. His research focuses on understanding, building, and controlling pretrained models, especially in domain-general or multimodal settings.

We discuss:

* Viewmaker Networks: Learning Views for Unsupervised Representation Learning

* DABS: A Domain-Agnostic Benchmark for Self-Supervised Learning

* On the Opportunities and Risks of Foundation Models

* Understanding the Capabilities, Limitations, and Societal Impact of Large Language Models

* Mentoring, teaching and fostering a healthy and inclusive research culture

* Scientific communication and breaking down walls between fields

Podcast Theme: “MusicVAE: Trio 16-bar Sample #2” from "MusicVAE: A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music"

In episode 14 of The Gradient Podcast, we interview Stanford PhD Candidate Peter Henderson

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Peter is a joint JD-PhD student at Stanford University advised by Dan Jurafsky. He is also an OpenPhilanthropy AI Fellow and a Graduate Student Fellow at the Regulation, Evaluation, and Governance Lab. His research focuses on creating robust decision-making systems, with three main goals: (1) use AI to make governments more efficient and fair; (2) ensure that AI isn’t deployed in ways that can harm people; (3) create new ML methods for applications that are beneficial to society.

Links:

* Reproducibility and Reusability in Deep Reinforcement Learning. 

* Benchmark Environments for Multitask Learning in Continuous Domains

* Reproducibility of Bench-marked Deep Reinforcement Learning Tasks for Continuous Control.

* Deep Reinforcement Learning that Matters

* Reproducibility and Replicability in Deep Reinforcement Learning (and Other Deep Learning Methods)

* Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning

* How blockers can turn into a paper: A retrospective on 'Towards The Systematic Reporting of the Energy and Carbon Footprints of Machine Learning

* When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset”

* How US law will evaluate artificial intelligence for Covid-19

Podcast Theme: “MusicVAE: Trio 16-bar Sample #2” from "MusicVAE: A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music"

In episode 13 of The Gradient Podcast, we interview Stanford Professor Chelsea Finn

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Chelsea is an Assistant Professor at Stanford University. Her lab, IRIS, studies intelligence through robotic interaction at scale, and is affiliated with SAIL and the Statistical ML Group. I also spend time at Google as a part of the Google Brain team. Her research deals with the capability of robots and other agents to develop broadly intelligent behavior through learning and interaction.

Links:

* Learning to Learn with Gradients

* Visual Model-Based Reinforcement Learning as a Path towards Generalist Robots

* RoboNet: A Dataset for Large-Scale Multi-Robot Learning

* Greedy Hierarchical Variational Autoencoders for Large-Scale Video

* Example-Driven Model-Based Reinforcement Learning for Solving Long-Horizon Visuomotor Tasks   

Podcast Theme: “MusicVAE: Trio 16-bar Sample #2” from "MusicVAE: A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music".

Episode 127

I spoke with Christopher Thi Nguyen about:

* How we lose control of our values

* The tradeoffs of legibility, aggregation, and simplification

* Gamification and its risks

Enjoy—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 Twitter

Outline:

* (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) Outro

Links:

* 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)

In episode 12 of The Gradient Podcast, we interview Devi Parikh, a professor at Georgia Tech whose research focuses on computer vision, natural language processing, embodied AI, human-AI collaboration, and AI for creativity.

Devi Parikh is an Associate Professor in the School of Interactive Computing at Georgia Tech, and a Research Scientist at Facebook AI Research (FAIR). Her research interests are in computer vision, natural language processing, embodied AI, human-AI collaboration, and AI for creativity. In the past, she has also been an Assistant Professor at Virginia Tech and a Research Assistant Professor at Toyota Technological Institute at Chicago (TTIC). She received her M.S. and Ph.D. degrees from the Electrical and Computer Engineering department at Carnegie Mellon University in 2007 and 2009 respectively. 

Links:

* Humans of AI Podcast

* Feel The Music: Automatically Generating A Dance For An Input Song

* Exploring Crowd Co-creation Scenarios for Sketches

* Neuro-Symbolic Generative Art: A Preliminary Study

* Creative Sketch Generation

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Podcast Theme: “MusicVAE: Trio 16-bar Sample #2” from "MusicVAE: A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music".

In episode 11 of The Gradient Podcast, we interview Sergey Levine, a professor at Berkeley whose research focuses on machine learning for decision making and control, with an emphasis on deep learning and reinforcement learning algorithms for robotics.

Sergey Levine received a BS and MS in Computer Science from Stanford University in 2009, and a Ph.D. in Computer Science from Stanford University in 2014. He joined the faculty of the Department of Electrical Engineering and Computer Sciences at UC Berkeley in fall 2016. His work focuses on machine learning for decision making and control, with an emphasis on deep learning and reinforcement learning algorithms, and includes developing algorithms for end-to-end training of deep neural network policies that combine perception and control, scalable algorithms for inverse reinforcement learning, deep reinforcement learning algorithms, and more.

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Podcast Theme: “MusicVAE: Trio 16-bar Sample #2” from "MusicVAE: A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music".

In episode 10 of The Gradient Podcast, we interview data scientist, researcher, developer, educator, and entrepreneur Jeremy Howard.

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Jeremy Howard is a data scientist, researcher, developer, educator, and entrepreneur. Jeremy is a founding researcher at fast.ai, a research institute dedicated to making deep learning more accessible. He is also a Distinguished Research Scientist at the University of San Francisco, the chair of WAMRI, and is Chief Scientist at platform.ai. Previously, Jeremy was the founding CEO Enlitic, which was the first company to apply deep learning to medicine, was the President and Chief Scientist of the data science platform Kaggle, and was the founding CEO of two successful Australian startups.

Podcast Theme: “MusicVAE: Trio 16-bar Sample #2” from "MusicVAE: A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music".

In episode 9 of The Gradient Podcast, we interview Yannic Kilcher, an AI researcher and educator.

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Evan is an AI safety veteran who’s done research at leading AI labs like OpenAI, and whose experience also includes stints at Google, Ripple andYelp. He currently works at the Machine Intelligence Research Institute (MIRI) as a Research Fellow, and joined me to talk about his views on AI safety, the alignment problem, and whether humanity is likely to survive the advent of superintelligent AI.

Podcast Theme: “MusicVAE: Trio 16-bar Sample #2” from "MusicVAE: A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music".

In episode 8 of The Gradient Podcast, we interview Yannic Kilcher, an AI researcher and educator.

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Yannic graduated with his PhD from ETH Zurich’s data analytics lab and is now the Chief Technology Officer of DeepJudge, a company building the next-generation AI-powered context-sensitive legal document processing platform. He famously produces videos on his very popular Youtube channel, which cover machine learning research papers, programming, and issues of the AI community, and the broader impact of AI in society.

Check out his Youtube channel here and follow him on Twitter here

Podcast Theme: “MusicVAE: Trio 16-bar Sample #2” from "MusicVAE: A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music".

In episode 7 of The Gradient Podcast, we interview founder and owner of Silero Alexander Veysov. You can find a transcript of our conversation here, and the repositories for Open Speech To Text and Silero Models here.

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Alexander Veysov is the founder / owner of Silero, a small company building Speech / NLP enabled products, and author of Open STT. Silero has recently shipped its own Russian STT engine. Previously he worked in a then Moscow-based VC firm and Ponominalu.ru, a ticketing startup acquired by MTS (major Russian TelCo). He received his BA and MA in Economics in Moscow State University for International Relations (MGIMO). You can follow his channel in telegram (@snakers41).

Theme: “MusicVAE: Trio 16-bar Sample #2” from "MusicVAE: A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music".

In episode 6 of The Gradient Podcast, we interview Deep Learning pioneer Yann LeCun.Subscribe to The Gradient Podcast: Apple Podcasts | Spotify | Pocket Casts | RSSFollow The Gradient on Twitter

Yann LeCun is the VP & Chief AI Scientist at Facebook and Silver Professor at NYU and he was also the founding Director of Facebook AI Research and of the NYU Center for Data Science. He famously pioneered the use of Convolutional Neural Nets for image processing in the 80s and 90s, and is generally regarded as one of the people whose work was pivotal to the Deep Learning revolution in AI. In fact he is the recipient of the 2018 ACM Turing Award (with Geoffrey Hinton and Yoshua Bengio) for "conceptual and engineering breakthroughs that have made deep neural networks a critical component of computing".

Theme: “MusicVAE: Trio 16-bar Sample #2” from "MusicVAE: A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music".

In episode 5 of The Gradient Podcast, we interview NLP researcher Anna Rogers.

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Anna Rogers is a post-doctoral associate at the University of Copenhagen, working with the research groups in the Center for Social Data Science and Machine Learning section. Her main research area is Natural Language Processing, with focus on interpretability and evaluation of deep learning models. She is also known for her work on improving peer review in NLP and as organizer of the workshop on Insights from Negative Results in NLP.Check out her article What Can We Do To Improve Peer Review in NLP and her tutorial Reviewing NLP Research.

In episode 4 of The Gradient Podcast, we interview artist, engineer, and entrepreneur Joel Simon.

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Joel Simon is a multidisciplinary artist, toolmaker, and researcher. He studied computer science and art at Carnegie Mellon University, worked on bioinformatics at Rockefeller University, and most recently is the founder and director of Morphogen, a generative design company developing Artbreeder, a massively collaborative creative tool and network. His interests lie in the intersection of computer science, biology and design as well as furniture-design, collaborative-creativity, sculpture and game-design.

Theme: “MusicVAE: Trio 16-bar Sample #2” from "MusicVAE: A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music".

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In episode 3 of The Gradient Podcast, we interview researcher and entrepreneur Abubakar Abid. Follow him on Twitter and check out the websites of his company Gradio and his side project the Fatima Fellowship.

Abubakar is an entrepreneur and researcher focused on AI and its applications to medicine. He is currently running the company Gradio, which is developing a product to generate an easy-to-use UI for any ML model, function, or API.  He is also running the Fatima Al-Fihri Predoctoral Fellowship, which is a 9-month program for computer science students from around the world who are planning on applying to PhD programs in the United States. 

Theme: “MusicVAE: Trio 16-bar Sample #2” from "MusicVAE: A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music".

Episode 126

I spoke with Vivek Natarajan about:

* Improving access to medical knowledge with AI

* How an LLM for medicine should behave

* Aspects of training Med-PaLM and AMIE

* How to facilitate appropriate amounts of trust in users of medical AI systems

Vivek Natarajan is a Research Scientist at Google Health AI advancing biomedical AI to help scale world class healthcare to everyone. Vivek is particularly interested in building large language models and multimodal foundation models for biomedical applications and leads the Google Brain moonshot behind Med-PaLM, Google's flagship medical large language model. Med-PaLM has been featured in The Scientific American, The Economist, STAT News, CNBC, Forbes, New Scientist among others.

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.

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Outline:

* (00:00) Intro

* (00:35) The concept of an “AI doctor”

* (06:54) Accessibility to medical expertise

* (10:31) Enabling doctors to do better/different work

* (14:35) Med-PaLM

* (15:30) Instruction tuning, desirable traits in LLMs for medicine

* (23:41) Axes for evaluation of medical QA systems

* (30:03) Medical LLMs and scientific consensus

* (35:32) Demographic data and patient interventions

* (40:14) Data contamination in Med-PaLM

* (42:45) Grounded claims about capabilities

* (45:48) Building trust

* (50:54) Genetic Discovery enabled by a LLM

* (51:33) Novel hypotheses in genetic discovery

* (57:10) Levels of abstraction for hypotheses

* (1:01:10) Directions for continued progress

* (1:03:05) Conversational Diagnostic AI

* (1:03:30) Objective Structures Clinical Examination as an evaluative framework

* (1:09:08) Relative importance of different types of data

* (1:13:52) Self-play — conversational dispositions and handling patients

* (1:16:41) Chain of reasoning and information retention

* (1:20:00) Performance in different areas of medical expertise

* (1:22:35) Towards accurate differential diagnosis

* (1:31:40) Feedback mechanisms and expertise, disagreement among clinicians

* (1:35:26) Studying trust, user interfaces

* (1:38:08) Self-trust in using medical AI models

* (1:41:39) UI for medical AI systems

* (1:43:50) Model reasoning in complex scenarios

* (1:46:33) Prompting

* (1:48:41) Future outlooks

* (1:54:53) Outro

Links:

* Vivek’s Twitter and homepage

* Papers

* Towards Expert-Level Medical Question Answering with LLMs (2023)

* LLMs encode clinical knowledge (2023)

* Towards Generalist Biomedical AI (2024)

* AMIE

* Genetic Discovery enabled by a LLM (2023)

In episode 2 of The Gradient Podcast, we interview AI artist Helana Sarin. Check out her work and follow her over at her Twitter @NeuralBricolage.

Helena Sarin is a visual artist and software engineer and is among the most prominent artists utilizing AI for their work. After she discovered GANs (Generative Adversarial Networks) several years ago and then made generative models her primary medium. She is a frequent speaker at ML/AI conferences, for the past year delivering invited talks at MIT, Library of Congress and Capitol One, and her artwork was exhibited at AI Art exhibitions in Zurich, Dubai, Oxford, Shanghai and Miami. Lastly, Helena was among the earliest authors to contribute a piece to The Gradient with 2018’s “Playing a game of GANstruction”, in which she described the process she follows to make her art.

Image credit: Happy Nation - The Waterpark By Helena Sarin

Theme: “MusicVAE: Trio 16-bar Sample #2” from "MusicVAE: A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music".

Hello world! After more than 3 years of publishing overviews and perspectives from the AI community on thegradient.pub, The Gradient now has a podcast. In this first episode our lead editors take a look back on how it all started, as well as a look ahead at where things are heading. Keep an eye out for our next episode, coming soon!

Theme: “MusicVAE: Trio 16-bar Sample #2” from "MusicVAE: A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music".

Episode 125

False 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 research

The 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 Twitter

Outline:

* (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) Outro

Links:

* 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