The Gradient: Perspectives on AI

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 [email protected] for feedback, ideas, guest suggestions.

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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)

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 [email protected] for feedback, ideas, guest suggestions.

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

Outline:

* (00:00) Intro

* (00:35) 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)

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 [email protected] 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

Episode 124

You may think you’re doing a priori reasoning, but actually you’re just over-generalizing from your current experience of technology.

I spoke with Professor Seth Lazar about:

* Why managing near-term and long-term risks isn’t always zero-sum

* How to think through axioms and systems in political philosphy

* Coordination problems, economic incentives, and other difficulties in developing publicly beneficial AI

Seth is Professor of Philosophy at the Australian National University, an Australian Research Council (ARC) Future Fellow, and a Distinguished Research Fellow of the University of Oxford Institute for Ethics in AI. He has worked on the ethics of war, self-defense, and risk, and now leads the Machine Intelligence and Normative Theory (MINT) Lab, where he directs research projects on the moral and political philosophy of AI.

Reach me at [email protected] for feedback, ideas, guest suggestions.

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

Outline:

* (00:00) Intro

* (01:32) The allocation of attention — attention, moral skill, and algorithmic recommendation

* (03:53) Attention allocation as an independent good (or bad)

* (08:22) Axioms in political philosophy

* (11:55) Explaining judgments, multiplying entities, parsimony, intuitive disgust

* (15:05) AI safety / catastrophic risk concerns

* (22:10) Superintelligence arguments, reasoning about technology

* (28:42) Attacking current and future harms from AI systems — does one draw resources from the other?

* (35:55) GPT-2, model weights, related debates

* (39:11) Power and economics—coordination problems, company incentives

* (50:42) Morality tales, relationship between safety and capabilities

* (55:44) Feasibility horizons, prediction uncertainty, and doing moral philosophy

* (1:02:28) What is a feasibility horizon?

* (1:08:36) Safety guarantees, speed of improvements, the “Pause AI” letter

* (1:14:25) Sociotechnical lenses, narrowly technical solutions

* (1:19:47) Experiments for responsibly integrating AI systems into society

* (1:26:53) Helpful/honest/harmless and antagonistic AI systems

* (1:33:35) Managing incentives conducive to developing technology in the public interest

* (1:40:27) Interdisciplinary academic work, disciplinary purity, power in academia

* (1:46:54) How we can help legitimize and support interdisciplinary work

* (1:50:07) Outro

Links:

* Seth’s Linktree and Twitter

* Resources

* Attention, moral skill, and algorithmic recommendation

* Catastrophic AI Risk slides

Episode 123

I spoke with Suhail Doshi about:

* Why benchmarks aren’t prepared for tomorrow’s AI models

* How he thinks about artists in a world with advanced AI tools

* Building a unified computer vision model that can generate, edit, and understand pixels.

Suhail is a software engineer and entrepreneur known for founding Mixpanel, Mighty Computing, and Playground AI (they’re hiring!).

Reach me at [email protected] for feedback, ideas, guest suggestions.

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

Outline:

* (00:00) Intro

* (00:54) Ad read — MLOps conference

* (01:30) Suhail is *not* in pivot hell but he *is* all-in on 50% AI-generated music

* (03:45) AI and music, similarities to Playground

* (07:50) Skill vs. creative capacity in art

* (12:43) What we look for in music and art

* (15:30) Enabling creative expression

* (18:22) Building a unified computer vision model, underinvestment in computer vision

* (23:14) Enhancing the aesthetic quality of images: color and contrast, benchmarks vs user desires

* (29:05) “Benchmarks are not prepared for how powerful these models will become”

* (31:56) Personalized models and personalized benchmarks

* (36:39) Engaging users and benchmark development

* (39:27) What a foundation model for graphics requires

* (45:33) Text-to-image is insufficient

* (46:38) DALL-E 2 and Imagen comparisons, FID

* (49:40) Compositionality

* (50:37) Why Playground focuses on images vs. 3d, video, etc.

* (54:11) Open source and Playground’s strategy

* (57:18) When to stop open-sourcing?

* (1:03:38) Suhail’s thoughts on AGI discourse

* (1:07:56) Outro

Links:

* Playground homepage

* Suhail on Twitter

Episode 122

I spoke with Azeem Azhar about:

* The speed of progress in AI

* Historical context for some of the terminology we use and how we think about technology

* What we might want our future to look like

Azeem is an entrepreneur, investor, and adviser. He is the creator of Exponential View, a global platform for in-depth technology analysis, and the host of the Bloomberg Original series Exponentially.

Reach me at [email protected] for feedback, ideas, guest suggestions.

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

Outline:

* (00:00) Intro

* (00:32) Ad read — MLOps conference

* (01:05) Problematizing the term “exponential”

* (07:35) Moore’s Law as social contract, speed of technological growth and impedances

* (14:45) Academic incentives, interdisciplinary work, rational agents and historical context

* (21:24) Monolithic scaling

* (26:38) Investment in scaling

* (31:22) On Sam Altman

* (36:25) Uses of “AGI,” “intelligence”

* (41:32) Historical context for terminology

* (48:58) AI and teaching

* (53:51) On the technology-human divide

* (1:06:26) New technologies and the futures we want

* (1:10:50) Inevitability narratives

* (1:17:01) Rationality and objectivity

* (1:21:13) Cultural affordances and intellectual history

* (1:26:15) Centralized and decentralized AI systems

* (1:32:54) Instruction tuning and helpful/honest/harmless

* (1:39:18) Azeem’s future outlook

* (1:46:15) Outro

Links:

* Azeem’s website and Twitter

* Exponential View

Episode 122

I spoke with Professor David Thorstad about:

* The practical difficulties of doing interdisciplinary work

* Why theories of human rationality should account for boundedness, heuristics, and other cognitive limitations

* why EA epistemics suck (ok, it’s a little more nuanced than that)

Professor Thorstad is an Assistant Professor of Philosophy at Vanderbilt University, a Senior Research Affiliate at the Global Priorities Institute at Oxford, and a Research Affiliate at the MINT Lab at Australian National University. One strand of his research asks how cognitively limited agents should decide what to do and believe. A second strand asks how altruists should use limited funds to do good effectively.

Reach me at [email protected] for feedback, ideas, guest suggestions.

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

Outline:

* (00:00) Intro

* (01:15) David’s interest in rationality

* (02:45) David’s crisis of confidence, models abstracted from psychology

* (05:00) Blending formal models with studies of the mind

* (06:25) Interaction between academic communities

* (08:24) Recognition of and incentives for interdisciplinary work

* (09:40) Movement towards interdisciplinary work

* (12:10) The Standard Picture of rationality

* (14:11) Why the Standard Picture was attractive

* (16:30) Violations of and rebellion against the Standard Picture

* (19:32) Mistakes made by critics of the Standard Picture

* (22:35) Other competing programs vs Standard Picture

* (26:27) Characterizing Bounded Rationality

* (27:00) A worry: faculties criticizing themselves

* (29:28) Self-improving critique and longtermism

* (30:25) Central claims in bounded rationality and controversies

* (32:33) Heuristics and formal theorizing

* (35:02) Violations of Standard Picture, vindicatory epistemology

* (37:03) The Reason Responsive Consequentialist View (RRCV)

* (38:30) Objective and subjective pictures

* (41:35) Reason responsiveness

* (43:37) There are no epistemic norms for inquiry

* (44:00) Norms vs reasons

* (45:15) Arguments against epistemic nihilism for belief

* (47:30) Norms and self-delusion

* (49:55) Difficulty of holding beliefs for pragmatic reasons

* (50:50) The Gibbardian picture, inquiry as an action

* (52:15) Thinking how to act and thinking how to live — the power of inquiry

* (53:55) Overthinking and conducting inquiry

* (56:30) Is thinking how to inquire as an all-things-considered matter?

* (58:00) Arguments for the RRCV

* (1:00:40) Deciding on minimal criteria for the view, stereotyping

* (1:02:15) Eliminating stereotypes from the theory

* (1:04:20) Theory construction in epistemology and moral intuition

* (1:08:20) Refusing theories for moral reasons and disciplinary boundaries

* (1:10:30) The argument from minimal criteria, evaluating against competing views

* (1:13:45) Comparing to other theories

* (1:15:00) The explanatory argument

* (1:17:53) Parfit and Railton, norms of friendship vs utility

* (1:20:00) Should you call out your friend for being a womanizer

* (1:22:00) Vindicatory Epistemology

* (1:23:05) Panglossianism and meliorative epistemology

* (1:24:42) Heuristics and recognition-driven investigation

* (1:26:33) Rational inquiry leading to irrational beliefs — metacognitive processing

* (1:29:08) Stakes of inquiry and costs of metacognitive processing

* (1:30:00) When agents are incoherent, focuses on inquiry

* (1:32:05) Indirect normative assessment and its consequences

* (1:37:47) Against the Singularity Hypothesis

* (1:39:00) Superintelligence and the ontological argument

* (1:41:50) Hardware growth and general intelligence growth, AGI definitions

* (1:43:55) Difficulties in arguing for hyperbolic growth

* (1:46:07) Chalmers and the proportionality argument

* (1:47:53) Arguments for/against diminishing growth, research productivity, Moore’s Law

* (1:50:08) On progress studies

* (1:52:40) Improving research productivity and technology growth

* (1:54:00) Mistakes in the moral mathematics of existential risk, longtermist epistemics

* (1:55:30) Cumulative and per-unit risk

* (1:57:37) Back and forth with longtermists, time of perils

* (1:59:05) Background risk — risks we can and can’t intervene on, total existential risk

* (2:00:56) The case for longtermism is inflated

* (2:01:40) Epistemic humility and longtermism

* (2:03:15) Knowledge production — reliable sources, blog posts vs peer review

* (2:04:50) Compounding potential errors in knowledge

* (2:06:38) Group deliberation dynamics, academic consensus

* (2:08:30) The scope of longtermism

* (2:08:30) Money in effective altruism and processes of inquiry

* (2:10:15) Swamping longtermist options

* (2:12:00) Washing out arguments and justified belief

* (2:13:50) The difficulty of long-term forecasting and interventions

* (2:15:50) Theory of change in the bounded rationality program

* (2:18:45) Outro

Links:

* David’s homepage and Twitter and blog

* Papers mentioned/read

* Bounded rationality and inquiry

* Why bounded rationality (in epistemology)?

* Against the newer evidentialists

* The accuracy-coherence tradeoff in cognition

* There are no epistemic norms of inquiry

* Permissive metaepistemology

* Global priorities and effective altruism

* What David likes about EA

* Against the singularity hypothesis (+ blog posts)

* Three mistakes in the moral mathematics of existential risk (+ blog posts)

* The scope of longtermism

* Epistemics

Episode 121

I spoke with Professor Ryan Tibshirani about:

* Differences between the ML and statistics communities in scholarship, terminology, and other areas.

* Trend filtering

* Why you can’t just use garbage prediction functions when doing conformal prediction

Ryan is a Professor in the Department of Statistics at UC Berkeley. He is also a Principal Investigator in the Delphi group. From 2011-2022, he was a faculty member in Statistics and Machine Learning at Carnegie Mellon University. From 2007-2011, he did his Ph.D. in Statistics at Stanford University.

Reach me at [email protected] for feedback, ideas, guest suggestions.

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

Outline:

* (00:00) Intro

* (01:10) Ryan’s background and path into statistics

* (07:00) Cultivating taste as a researcher

* (11:00) Conversations within the statistics community

* (18:30) Use of terms, disagreements over stability and definitions

* (23:05) Nonparametric Regression

* (23:55) Background on trend filtering

* (33:48) Analysis and synthesis frameworks in problem formulation

* (39:45) Neural networks as a specific take on synthesis

* (40:55) Divided differences, falling factorials, and discrete splines

* (41:55) Motivations and background

* (48:07) Divided differences vs. derivatives, approximation and efficiency

* (51:40) Conformal prediction

* (52:40) Motivations

* (1:10:20) Probabilistic guarantees in conformal prediction, choice of predictors

* (1:14:25) Assumptions: i.i.d. and exchangeability — conformal prediction beyond exchangeability

* (1:25:00) Next directions

* (1:28:12) Epidemic forecasting — COVID-19 impact and trends survey

* (1:29:10) Survey methodology

* (1:38:20) Data defect correlation and its limitations for characterizing datasets

* (1:46:14) Outro

Links:

* Ryan’s homepage

* Works read/mentioned

* Nonparametric Regression

* Adaptive Piecewise Polynomial Estimation via Trend Filtering (2014) 

* Divided Differences, Falling Factorials, and Discrete Splines: Another Look at Trend Filtering and Related Problems (2020)

* Distribution-free Inference

* Distribution-Free Predictive Inference for Regression (2017)

* Conformal Prediction Under Covariate Shift (2019)

* Conformal Prediction Beyond Exchangeability (2023)

* Delphi and COVID-19 research

* Flexible Modeling of Epidemics

* Real-Time Estimation of COVID-19 Infections

* The US COVID-19 Trends and Impact Survey and Big data, big problems: Responding to “Are we there yet?”

In episode 120 of The Gradient Podcast, Daniel Bashir speaks to Sasha Luccioni.

Sasha is the AI and Climate Lead at HuggingFace, where she spearheads research, consulting, and capacity-building to elevate the sustainability of AI systems. A founding member of Climate Change AI (CCAI) and a board member of Women in Machine Learning (WiML), Sasha is passionate about catalyzing impactful change, organizing events and serving as a mentor to under-represented minorities within the AI community.

Have suggestions for future podcast guests (or other feedback)? Let us know here or reach Daniel at [email protected]

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

Outline:

* (00:00) Intro

* (00:43) Sasha’s background

* (01:52) How Sasha became interested in sociotechnical work

* (03:08) Larger models and theory of change for AI/climate work

* (07:18) Quantifying emissions for ML systems

* (09:40) Aggregate inference vs training costs

* (10:22) Hardware and data center locations

* (15:10) More efficient hardware vs. bigger models — Jevons paradox

* (17:55) Uninformative experiments, takeaways for individual scientists, knowledge sharing, failure reports

* (27:10) Power Hungry Processing: systematic comparisons of ongoing inference costs

* (28:22) General vs. task-specific models

* (31:20) Architectures and efficiency

* (33:45) Sequence-to-sequence architectures vs. decoder-only

* (36:35) Hardware efficiency/utilization

* (37:52) Estimating the carbon footprint of Bloom and lifecycle assessment

* (40:50) Stable Bias

* (46:45) Understanding model biases and representations

* (52:07) Future work

* (53:45) Metaethical perspectives on benchmarking for AI ethics

* (54:30) “Moral benchmarks”

* (56:50) Reflecting on “ethicality” of systems

* (59:00) Transparency and ethics

* (1:00:05) Advice for picking research directions

* (1:02:58) Outro

Links:

* Sasha’s homepage and Twitter

* Papers read/discussed

* Climate Change / Carbon Emissions of AI Models

* Quantifying the Carbon Emissions of Machine Learning

* Power Hungry Processing: Watts Driving the Cost of AI Deployment?

* Tackling Climate Change with Machine Learning

* CodeCarbon

* Responsible AI

* Stable Bias: Analyzing Societal Representations in Diffusion Models

* Metaethical Perspectives on ‘Benchmarking’ AI Ethics

* Measuring Data

* Mind your Language (Model): Fact-Checking LLMs and their Role in NLP Research and Practice

In episode 119 of The Gradient Podcast, Daniel Bashir speaks to Professor Michael Sipser.

Professor Sipser is the Donner Professor of Mathematics and member of the Computer Science and Artificial Intelligence Laboratory at MIT.

He received his PhD from UC Berkeley in 1980 and joined the MIT faculty that same year. He was Chairman of Applied Mathematics from 1998 to 2000 and served as Head of the Mathematics Department 2004-2014. He served as interim Dean of Science 2013-2014 and then as Dean of Science 2014-2020.

He was a research staff member at IBM Research in 1980, spent the 1985-86 academic year on the faculty of the EECS department at Berkeley and at MSRI, and was a Lady Davis Fellow at Hebrew University in 1988. His research areas are in algorithms and complexity theory, specifically efficient error correcting codes, interactive proof systems, randomness, quantum computation, and establishing the inherent computational difficulty of problems. He is the author of the widely used textbook, Introduction to the Theory of Computation (Third Edition, Cengage, 2012).

Have suggestions for future podcast guests (or other feedback)? Let us know here or reach Daniel at [email protected]

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

Outline:

* (00:00) Intro

* (01:40) Professor Sipser’s background

* (04:35) On interesting questions

* (09:00) Different kinds of research problems

* (13:00) What makes certain problems difficult

* (18:48) Nature of the P vs NP problem

* (24:42) Identifying interesting problems

* (28:50) Lower bounds on the size of sweeping automata

* (29:50) Why sweeping automata + headway to P vs. NP

* (36:40) Insights from sweeping automata, infinite analogues to finite automata problems

* (40:45) Parity circuits

* (43:20) Probabilistic restriction method

* (47:20) Relativization and the polynomial time hierarchy

* (55:10) P vs. NP

* (57:23) The non-connection between GO’s polynomial space hardness and AlphaGo

* (1:00:40) On handicapping Turing Machines vs. oracle strategies

* (1:04:25) The Natural Proofs Barrier and approaches to P vs. NP

* (1:11:05) Debates on methods for P vs. NP

* (1:15:04) On the possibility of solving P vs. NP

* (1:18:20) On academia and its role

* (1:27:51) Outro

Links:

* Professor Sipser’s homepage

* Papers discussed/read

* Halting space-bounded computations (1978)

* Lower bounds on the size of sweeping automata (1979)

* GO is Polynomial-Space Hard (1980)

* A complexity theoretic approach to randomness (1983)

* Parity, circuits, and the polynomial-time hierarchy (1984)

* A follow-up to Furst-Saxe-Sipser

* The Complexity of Finite Functions (1991)