The Gradient: Perspectives on AI – Details, episodes & analysis

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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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