In episode 74 of The Gradient Podcast, Daniel Bashir speaks to Professor Talia Ringer.Professor Ringer is an Assistant Professor with the Programming Languages, Formal Methods, and Software Engineering group at the University of Illinois at Urbana Champaign. Their research leverages proof engineering to allow programmers to more easily build formally verified software systems.Have suggestions for future podcast guests (or other feedback)? Let us know here or reach us at editor@thegradient.pubSubscribe to The Gradient Podcast:  Apple Podcasts  | Spotify | Pocket Casts | RSSFollow The Gradient on TwitterOutline:* (00:00) Daniel’s long annoying intro* (02:15) Origin Story* (04:30) Why / when formal verification is important* (06:40) Concerns about ChatGPT/AutoGPT et al failures, systems for accountability* (08:20) Difficulties in making formal verification accessible* (11:45) Tactics and interactive theorem provers, interface issues* (13:25) How Prof Ringer’s research first crossed paths with ML* (16:00) Concrete problems in proof automation* (16:15) How ML can help people verifying software systems* (20:05) Using LLMs for understanding / reasoning about code* (23:05) Going from tests / formal properties to code* (31:30) Is deep learning the right paradigm for dealing with relations for theorem proving? * (36:50) Architectural innovations, neuro-symbolic systems* (40:00) Hazy definitions in ML* (41:50) Baldur: Proof Generation & Repair with LLMs* (45:55) In-context learning’s effectiveness for LLM-based theorem proving* (47:12) LLMs without fine-tuning for proofs* (48:45) Something ~ surprising ~ about Baldur results (maybe clickbait or maybe not)* (49:32) Asking models to construct proofs with restrictions, translating proofs to formal proofs* (52:07) Methods of proofs and relative difficulties* (57:45) Verifying / providing formal guarantees on ML systems* (1:01:15) Verifying input-output behavior and basic considerations, nature of guarantees* (1:05:20) Certified/verifies systems vs certifying/verifying systems—getting LLMs to spit out proofs along with code* (1:07:15) Interpretability and how much model internals matter, RLHF, mechanistic interpretability* (1:13:50) Levels of verification for deploying ML systems, HCI problems* (1:17:30) People (Talia) actually use Bard* (1:20:00) Dual-use and “correct behavior”* (1:24:30) Good uses of jailbreaking* (1:26:30) Talia’s views on evil AI / AI safety concerns* (1:32:00) Issues with talking about “intelligence,” assumptions about what “general intelligence” means* (1:34:20) Difficulty in having grounded conversations about capabilities, transparency* (1:39:20) Great quotation to steal for your next thinkpiece + intelligence as socially defined* (1:42:45) Exciting research directions* (1:44:48) OutroLinks:* Talia’s Twitter and homepage* Research* Concrete Problems in Proof Automation* Baldur: Whole-Proof Generation and Repair with LLMs* Research ideas Get full access to The Gradient at thegradientpub.substack.com/subscribe

In episode 72 of The Gradient Podcast, Daniel Bashir speaks to Brigham Hyde.Brigham is Co-Founder and CEO of Atropos Health. Prior to Atropos, he served as President of Data and Analytics at Eversana, a life sciences commercialization service provider. He led the investment in Concert AI in the oncology real-world data space at Symphony AI. Brigham has also held research faculty positions at Tufts University and the MIT Media Lab.Have suggestions for future podcast guests (or other feedback)? Let us know here or reach us at editor@thegradient.pubSubscribe to The Gradient Podcast:  Apple Podcasts  | Spotify | Pocket Casts | RSSFollow The Gradient on TwitterOutline:* (00:00) Intro* (01:55) Brigham’s background* (06:00) Current challenges in healthcare* (12:33) Interpretablity and delivering positive patient outcomes* (17:10) How Atropos surfaces relevant data for patient interventions, on personalized observational research studies* (22:10) Quality and quantity of data for patient interventions* (27:25) Challenges and opportunities for generative AI in healthcare* (35:17) Database augmentation for generative models* (36:25) Future work for Atropos* (39:15) Future directions for AI + healthcare* (40:56) OutroLinks:* Atropos Health homepage* Brigham’s Twitter and LinkedIn Get full access to The Gradient at thegradientpub.substack.com/subscribe

In episode 72 of The Gradient Podcast, Daniel Bashir speaks to Professor Scott Aaronson. Scott is the Schlumberger Centennial Chair of Computer Science at the University of Texas at Austin and director of its Quantum Information Center. His research interests focus on the capabilities and limits of quantum computers and computational complexity theory more broadly. He has recently been on leave to work at OpenAI, where he is researching theoretical foundations of AI safety. Have suggestions for future podcast guests (or other feedback)? Let us know here or reach us at editor@thegradient.pubSubscribe to The Gradient Podcast:  Apple Podcasts  | Spotify | Pocket Casts | RSSFollow The Gradient on TwitterOutline:* (00:00) Intro* (01:45) Scott’s background* (02:50) Starting grad school in AI, transitioning to quantum computing and the AI / quantum computing intersection* (05:30) Where quantum computers can give us exponential speedups, simulation overhead, Grover’s algorithm* (10:50) Overselling of quantum computing applied to AI, Scott’s analysis on quantum machine learning* (18:45) ML problems that involve quantum mechanics and Scott’s work* (21:50) Scott’s recent work at OpenAI* (22:30) Why Scott was skeptical of AI alignment work early on* (26:30) Unexpected improvements in modern AI and Scott’s belief update* (32:30) Preliminary Analysis of DALL-E 2 (Marcus & Davis)* (34:15) Watermarking GPT outputs* (41:00) Motivations for watermarking and language model detection* (45:00) Ways around watermarking* (46:40) Other aspects of Scott’s experience with OpenAI, theoretical problems* (49:10) Thoughts on definitions for humanistic concepts in AI* (58:45) Scott’s “reform AI alignment stance” and Eliezer Yudkowsky’s recent comments (+ Daniel pronounces Eliezer wrong), orthogonality thesis, cases for stopping scaling* (1:08:45) OutroLinks:* Scott’s blog* AI-related work* Quantum Machine Learning Algorithms: Read the Fine Print* A very preliminary analysis of DALL-E 2 w/ Marcus and Davis* New AI classifier for indicating AI-written text and Watermarking GPT Outputs* Writing* Should GPT exist?* AI Safety Lecture* Why I’m not terrified of AI Get full access to The Gradient at thegradientpub.substack.com/subscribe

In episode 71 of The Gradient Podcast, Daniel Bashir speaks to Ted Underwood.Ted is a professor in the School of Information Sciences with an appointment in the Department of English at the University of Illinois at Urbana Champaign. Trained in English literary history, he turned his research focus to applying machine learning to large digital collections. His work explores literary patterns that become visible across long timelines when we consider many works at once—often, his work involves correcting and enriching digital collections to make them more amenable to interesting literary research.Have suggestions for future podcast guests (or other feedback)? Let us know here or reach us at editor@thegradient.pubSubscribe to The Gradient Podcast:  Apple Podcasts  | Spotify | Pocket Casts | RSSFollow The Gradient on TwitterOutline:* (00:00) Intro* (01:42) Ted’s background / origin story* (04:35) Context in interpreting statistics, “you need a model,” the need for data about human responses to literature and how that manifested in Ted’s work* (07:25) The recognition that we can model literary prestige/genre because of ML* (08:30) Distant reading and the import of statistics over large digital libraries* (12:00) Literary prestige* (12:45) How predictable is fiction? Scales of predictability in texts* (13:55) Degrees of autocorrelation in biography and fiction and the structure of narrative, how LMs might offer more sophisticated analysis* (15:15) Braided suspense / suspense at different scales of a story* (17:05) The Literary Uses of High-Dimensional Space: how “big data” came to impact the humanities, skepticism from humanists and responses, what you can do with word count* (20:50) Why we could use more time to digest statistical ML—how acceleration in AI advances might impact pedagogy* (22:30) The value in explicit models* (23:30) Poetic “revolutions” and literary prestige* (25:53) Distant vs. close reading in poetry—follow-up work for “The Longue Durée”* (28:20) Sophistication of NLP and approaching the human experience* (29:20) What about poetry renders it prestigious?* (32:20) Individualism/liberalism and evolution of poetic taste* (33:20) Why there is resistance to quantitative approaches to literature* (34:00) Fiction in other languages* (37:33) The Life Cycles of Genres* (38:00) The concept of “genre”* (41:00) Inflationary/deflationary views on natural kinds and genre* (44:20) Genre as a social and not a linguistic phenomenon* (46:10) Will causal models impact the humanities? * (48:30) (Ir)reducibility of cultural influences on authors* (50:00) Machine Learning and Human Perspective* (50:20) Fluent and perspectival categories—Miriam Posner on “the radical, unrealized potential of digital humanities.”* (52:52) How ML’s vices can become virtues for humanists* (56:05) Can We Map Culture? and The Historical Significance of Textual Distances* (56:50) Are cultures and other social phenomena related to one another in a way we can “map”? * (59:00) Is cultural distance Euclidean? * (59:45) The KL Divergence’s use for humanists* (1:03:32) We don’t already understand the broad outlines of literary history* (1:06:55) Science Fiction Hasn’t Prepared us to Imagine Machine Learning* (1:08:45) The latent space of language and what intelligence could mean* (1:09:30) LLMs as models of culture* (1:10:00) What it is to be a human in “the age of AI” and Ezra Klein’s framing* (1:12:45) Mapping the Latent Spaces of Culture* (1:13:10) Ted on Stochastic Parrots* (1:15:55) The risk of AI enabling hermetically sealed cultures* (1:17:55) “Postcards from an unmapped latent space,” more on AI systems’ limitations as virtues* (1:20:40) Obligatory GPT-4 section* (1:21:00) Using GPT-4 to estimate passage of time in fiction* (1:23:39) Is deep learning more interpretable than statistical NLP?* (1:25:17) The “self-reports” of language models: should we trust them?* (1:26:50) University dependence on tech giants, open-source models* (1:31:55) Reclaiming Ground for the Humanities* (1:32:25) What scientists, alone, can contribute to the humanities* (1:34:45) On the future of the humanities* (1:35:55) How computing can enable humanists as humanists* (1:37:05) Human self-understanding as a collaborative project* (1:39:30) Is anything ineffable? On what AI systems can “grasp”* (1:43:12) OutroLinks:* Ted’s blog and Twitter* Research* The literary uses of high-dimensional space* The Longue Durée of literary prestige* The Historical Significance of Textual Distances* Machine Learning and Human Perspective* The life cycles of genres* Can We Map Culture?* Cohort Succession Explains Most Change in Literary Culture* Other Writing* Reclaiming Ground for the Humanities* We don’t already understand the broad outlines of literary history* Science fiction hasn’t prepared us to imagine machine learning.* How predictable is fiction?* Mapping the latent spaces of culture* Using GPT-4 to measure the passage of time in fiction Get full access to The Gradient at thegradientpub.substack.com/subscribe

In episode 70 of The Gradient Podcast, Daniel Bashir speaks to Irene Solaiman.Irene is an expert in AI safety and policy and the Policy Director at HuggingFace, where she conducts social impact research and develops public policy. In her former role at OpenAI, she initiated and led bias and social impact research at OpenAI in addition to leading public policy. She built AI policy at Zillow group and advised poilcymakers on responsible autonomous decision-making and privacy as a fellow at Harvard’s Berkman Klein Center.Have suggestions for future podcast guests (or other feedback)? Let us know here or reach us at editor@thegradient.pubSubscribe to The Gradient Podcast:  Apple Podcasts  | Spotify | Pocket Casts | RSSFollow The Gradient on TwitterOutline:* (00:00) Intro* (02:00) Intro to Irene and her work* (03:45) What tech people need to learn about policy, and vice versa* (06:35) Societal impact—words and reality, Irene’s experience* (08:30) OpenAI work on GPT-2 and release strategies (yes, this was recorded on Pi Day)* (11:00) Open-source proponents and release* (14:00) What does a multidisciplinary approach to working on AI look like? * (16:30) Thinking about end users and enabling contributors with different sets of expertise* (18:00) “Preparing for AGI” and current approaches to release* (21:00) Who constitutes a researcher? What constitutes safety and who gets resourced? Limitations in red-teaming potentially dangerous systems. * (22:35) PALMS and Values-Targeted Datasets* (25:52) PALMS and RLHF* (27:00) Homogenization in foundation models, cultural contexts* (29:45) Anthropic’s moral self-correction paper and Irene’s concerns about marketing “de-biasing” and oversimplification* (31:50) Data work, human systemic problems → AI bias* (33:55) Why do language models get more toxic as they get larger? (if you have ideas, let us know!)* (35:45) The gradient of generative AI release, Irene’s experience with the open-source world, tradeoffs along the release gradient* (38:40) More on Irene’s orientation towards release* (39:40) Pragmatics of keeping models closed, dealing with open-source by force* (42:22) Norm setting for release and use, normalization of documentation on social impacts* (46:30) Race dynamics :(* (49:45) Resource allocation and advances in ethics/policy, conversations on integrity and disinformation* (53:10) Organizational goals, balancing technical research with policy work* (58:10) Thoughts on governments’ AI policies, impact of structural assumptions* (1:04:00) Approaches to AI-generated sexual content, need for more voices represented in conversations about AI* (1:08:25) Irene’s suggestions for AI practitioners / technologists* (1:11:24) OutroLinks:* Irene’s homepage and Twitter* Papers* Release Strategies and the Social Impacts of Language Models* Hugh Zhang’s open letter in The Gradient from 2019* Process for Adapting Large Models to Society (PALMS) with Values-Targeted Datasets* The Gradient of Generative AI Release: Methods and Considerations Get full access to The Gradient at thegradientpub.substack.com/subscribe

In episode 69 of The Gradient Podcast, Daniel Bashir speaks to Drago Anguelov.Drago is currently a Distinguished Scientist and Head of Research at Waymo, where he joined in 2018. Earlier, he spent eight years at Google working on 3D vision and pose estimation for StreetView, then leading a research team that developed computer vision systems for annotating Google Photos. He has been involved in developing popular neural network methods such as the Inception architecture and the SSD detector. Before joining Waymo, he also led the 3D perception team at Zoox.Have suggestions for future podcast guests (or other feedback)? Let us know here or reach us at editor@thegradient.pubSubscribe to The Gradient Podcast:  Apple Podcasts  | Spotify | Pocket Casts | RSSFollow The Gradient on TwitterOutline:* (00:00) Intro* (02:04) Drago’s background in AI and self-driving, work with Daphne Koller + Sebastian Thrun, computer vision / pose estimation* (14:20) One- and two-stage object detectors* (15:15) Early experiences and thoughts on self-driving and its prospects* (21:00) An introduction to the “self-driving stack”: mapping & localization, perception, behavior modeling & planning, simulation* (29:25) From Stuart Russell’s comments on early Waymo’s “old-fashioned” approach* (37:34) Scaling 3D Detection: challenges and architectural innovations* (43:20) Behavior modeling: making decisions and modeling interactions in multi-agent environments* (52:42) Distributional RL (+ imitation learning) in self-driving?* (54:10) The Waymo Open Dataset* (1:01:48) Looking forward in self-driving* (1:04:36) OutroLinks:* Drago’s LinkedIn and Twitter* Research* SSD: Single-Shot Multibox Detector* SCAPE: Shape completion and animation of people* Behavior Models for Autonomous Driving* Wayformer* Symphony: Learning Realistic and Diverse Agents for Autonomous Driving Simulation* Imitation Is Not Enough* Scaling 3D Detection to the Long Tail Get full access to The Gradient at thegradientpub.substack.com/subscribe

In episode 68 of The Gradient Podcast, Daniel Bashir speaks to Professor Joanna Bryson.Professor Bryson is Professor of Ethics and Technology at the Hertie School, where her research focuses on the impact of technology on human cooperation and AI/ICT governance. Professor Bryson has advised companies, governments, transnational agencies, and NGOs, particularly in AI policy. She is one of the few people doing this sort of work who actually has a PhD and work experience in AI, but also advanced degrees in the social sciences. She started her academic career though in the liberal arts, and publishes regularly in the natural sciences.Have suggestions for future podcast guests (or other feedback)? Let us know here!Subscribe to The Gradient Podcast:  Apple Podcasts  | Spotify | Pocket Casts | RSSFollow The Gradient on TwitterOutline:* (00:00) Intro* (01:35) Intro to Professor Bryson’s work* (06:37) Shifts in backgrounds expected of AI PhDs/researchers* (09:40) Masters’ degree in Edinburgh, Behavior-Based AI* (11:00) PhD, differences between MIT’s engineering focus and Edinburgh, systems engineering + AI* (16:15) Comments on ways you can make contributions in AI* (18:45) When definitions of “intelligence” are important* (24:23) Non- and proto-linguistic aspects of intelligence, arguments about text as a description of human experience* (31:45) Cognitive leaps in interacting with language models* (37:00) Feelings of affiliation for robots, phenomenological experience in humans and (not) in AI systems* (42:00) Language models and technological systems as cultural artifacts, expressing agency through machines* (44:15) Capabilities development and moral patient status in AI systems* (51:20) Prof. Bryson’s perspectives on recent AI regulation* (1:00:55) Responsibility and recourse, Uber self-driving crash* (1:07:30) “Preparing for AGI,” “Living with AGI,” how to respond to recent AI developments* (1:12:18) OutroLinks:* Professor Bryson’s homepage and Twitter* Papers* Systems AI* Behavior Oriented Design, action selection, key differences in methodology/views between systems AI researchers and e.g. connectionists* Agent architecture as object oriented design (1998)* Intelligence by design: Principles of modularity and coordination for engineering complex adaptive agents (2001)* Cognition* Age-Related Inhibition and Learning Effects: Evidence from Transitive Performance (2013)* Primate Errors in Transitive ‘Inference’: A Two-Tier Learning Model (2007)* Skill Acquisition Through Program-Level Imitation in a Real-Time Domain* Agent-Based Models as Scientific Methodology: A Case Study Analysing Primate Social Behaviour (2008, 2011)* Social learning in a non-social reptile (Geochelone carbonaria) (2010)* Understanding and Addressing Cultural Variation in Costly Antisocial Punishment (2014)* Polarization Under Rising Inequality and Economic Decline (2020)* Semantics derived automatically from language corpora contain human-like biases (2017)* Evolutionary Psychology and Artificial Intelligence: The Impact of Artificial Intelligence on Human Behaviour (2020)* Ethics/Policy* Robots should be slaves (2010)* Standardizing Ethical Design for Artificial Intelligence and Autonomous Systems (2017)* Of, For, and By the People: The Legal Lacuna of Synthetic Persons (2017)* Patiency is not a virtue: the design of intelligent systems and systems of ethics (2018)* Other writing* Reflections on the EU’s AI Act* Is There an AI Cold War?* Living with AGI* One Day, AI Will Seem as Human as Anyone. What Then? Get full access to The Gradient at thegradientpub.substack.com/subscribe

In episode 67 of The Gradient Podcast, Daniel Bashir speaks to Daniel Situnayake. Daniel is head of Machine Learning at Edge Impulse. He is co-author of the O’Reilly books "AI at the Edge" and "TinyML". Previously, he’s worked on the Tensorflow Lite team at Google AI and co-founded Tiny Farms, an insect farming company. Daniel has also lectured in AIDC technologies at Birmingham City University.Have suggestions for future podcast guests (or other feedback)? Let us know here!Subscribe to The Gradient Podcast:  Apple Podcasts  | Spotify | Pocket Casts | RSSFollow The Gradient on TwitterOutline:* (00:00) Intro* (1:40) Daniel S Origin Story: computer networking, RFID/barcoding, earlier jobs, Tiny Farms, Tensorflow Lite, writing on TinyML, and Edge Impulse* (15:30) Edge AI and questions of embodiment/intelligence in AI* (21:00) The role of hardware, other constraints in edge AI* (25:00) Definitions of intelligence* (29:45) What is edge AI?* (37:30) The spectrum of edge devices* (43:45) Innovations in edge AI (architecture, frameworks/toolchains, quantization)* (53:45) Model compression tradeoffs in edge* (1:00:30) Federated learning and challenges* (1:09:00) Intro to Edge Impulse* (1:20:30) Feature engineering for edge systems, fairness considerations* (1:25:50) Edge AI and axes in AI (large/small, ethereal/embodied)* (1:37:00) Daniel and Daniel go off the rails on panpsychism* (1:54:20) Daniel’s advice for aspiring AI practitioners* (1:57:20) OutroLinks:* Daniel’s Twitter and blog* Edge Impulse Get full access to The Gradient at thegradientpub.substack.com/subscribe

In episode 66 of The Gradient Podcast, Daniel Bashir speaks to Soumith Chintala.Soumith is a Research Engineer at Meta AI Research in NYC. He is the co-creator and lead of Pytorch, and maintains a number of other open-source ML projects including Torch-7 and EBLearn. Soumith has previously worked on robotics, object and human detection, generative modeling, AI for video games, and ML systems research.Have suggestions for future podcast guests (or other feedback)? Let us know here!Subscribe to The Gradient Podcast:  Apple Podcasts  | Spotify | Pocket Casts | RSSFollow The Gradient on TwitterOutline:* (00:00) Intro* (01:30) Soumith’s intro to AI journey to Pytorch* (05:00) State of computer vision early in Soumith’s career* (09:15) Institutional inertia and sunk costs in academia, identifying fads* (12:45) How Soumith started working on GANs, frustrations* (17:45) State of ML frameworks early in the deep learning era, differentiators* (23:50) Frameworks and leveling the playing field, exceptions* (25:00) Contributing to Torch and evolution into Pytorch* (29:15) Soumith’s product vision for ML frameworks* (32:30) From product vision to concrete features in Pytorch* (39:15) Progressive disclosure of complexity (Chollet) in Pytorch* (41:35) Building an open source community* (43:25) The different players in today’s ML framework ecosystem* (49:35) ML frameworks pioneered by Yann LeCun and Léon Bottou, their influences on Pytorch* (54:37) Pytorch 2.0 and looking to the future* (58:00) Soumith’s adventures in household robotics* (1:03:25) Advice for aspiring ML practitioners* (1:07:10) Be cool like Soumith and subscribe :)* (1:07:33) OutroLinks:* Soumith’s Twitter and homepage* Papers* Convolutional Neural Networks Applied to House Numbers Digit Classification* GANs: LAPGAN, DCGAN, Wasserstein GAN* Automatic differentiation in PyTorch* PyTorch: An Imperative Style, High-Performance Deep Learning Library Get full access to The Gradient at thegradientpub.substack.com/subscribe

In episode 65 of The Gradient Podcast, Daniel Bashir speaks to Sewon Min.Sewon is a fifth-year PhD student in the NLP group at the University of Washington, advised by Hannaneh Hajishirzi and Luke Zettlemoyer. She is a part-time visiting researcher at Meta AI and a recipient of the JP Morgan PhD Fellowship. She has previously spent time at Google Research and Salesforce research.Have suggestions for future podcast guests (or other feedback)? Let us know here!Subscribe to The Gradient Podcast:  Apple Podcasts  | Spotify | Pocket Casts | RSSFollow The Gradient on TwitterOutline:* (00:00) Intro* (03:00) Origin Story* (04:20) Evolution of Sewon’s interests, question-answering and practical NLP* (07:00) Methodology concerns about benchmarks* (07:30) Multi-hop reading comprehension* (09:30) Do multi-hop QA benchmarks actually measure multi-hop reasoning?* (12:00) How models can “cheat” multi-hop benchmarks* (13:15) Explicit compositionality* (16:05) Commonsense reasoning and background information* (17:30) On constructing good benchmarks* (18:40) AmbigQA and ambiguity* (22:20) Types of ambiguity* (24:20) Practical possibilities for models that can handle ambiguity* (25:45) FaVIQ and fact-checking benchmarks* (28:45) External knowledge* (29:45) Fact verification and “complete understanding of evidence”* (31:30) Do models do what we expect/intuit in reading comprehension?* (34:40) Applications for fact-checking systems* (36:40) Intro to in-context learning (ICL)* (38:55) Example of an ICL demonstration* (40:45) Rethinking the Role of Demonstrations and what matters for successful ICL* (43:00) Evidence for a Bayesian inference perspective on ICL* (45:00) ICL + gradient descent and what it means to “learn”* (47:00) MetaICL and efficient ICL* (49:30) Distance between tasks and MetaICL task transfer* (53:00) Compositional tasks for language models, compositional generalization* (55:00) The number and diversity of meta-training tasks* (58:30) MetaICL and Bayesian inference* (1:00:30) Z-ICL: Zero-Shot In-Context Learning with Pseudo-Demonstrations* (1:02:00) The copying effect* (1:03:30) Copying effect for non-identical examples* (1:06:00) More thoughts on ICL* (1:08:00) Understanding Chain-of-Thought Prompting* (1:11:30) Bayes strikes again* (1:12:30) Intro to Sewon’s text retrieval research* (1:15:30) Dense Passage Retrieval (DPR)* (1:18:40) Similarity in QA and retrieval* (1:20:00) Improvements for DPR* (1:21:50) Nonparametric Masked Language Modeling (NPM)* (1:24:30) Difficulties in training NPM and solutions* (1:26:45) Follow-on work* (1:29:00) Important fundamental limitations of language models* (1:31:30) Sewon’s experience doing a PhD* (1:34:00) Research challenges suited for academics* (1:35:00) Joys and difficulties of the PhD* (1:36:30) Sewon’s advice for aspiring PhDs* (1:38:30) Incentives in academia, production of knowledge* (1:41:50) OutroLinks:* Sewon’s homepage and Twitter* Papers* Solving and re-thinking benchmarks* Multi-hop Reading Comprehension through Question Decomposition and Rescoring / Compositional Questions Do Not Necessitate Multi-hop Reasoning* AmbigQA: Answering Ambiguous Open-domain Questions* FaVIQ: FAct Verification from Information-seeking Questions* Language Modeling* Rethinking the Role of Demonstrations* MetaICL: Learning to Learn In Context* Towards Understanding CoT Prompting* Z-ICL: Zero-Shot In-Context Learning with Pseudo-Demonstrations* Text representation/retrieval* Dense Passage Retrieval* Nonparametric Masked Language Modeling Get full access to The Gradient at thegradientpub.substack.com/subscribe