-
1
2025 in AI, with Nathan Benaich
↗
-
2
Iason Gabriel: Value Alignment and the Ethics of Advanced AI Systems
↗
-
3
2024 in AI, with Nathan Benaich
↗
-
4
Philip Goff: Panpsychism as a Theory of Consciousness
↗
-
5
Some Changes at The Gradient
↗
-
6
Jacob Andreas: Language, Grounding, and World Models
↗
-
7
Evan Ratliff: Our Future with Voice Agents
↗
-
8
Meredith Ringel Morris: Generative AI's HCI Moment
↗
-
9
Davidad Dalrymple: Towards Provably Safe AI
↗
-
10
Clive Thompson: Tales of Technology
↗
-
11
Judy Fan: Reverse Engineering the Human Cognitive Toolkit
↗
-
12
L.M. Sacasas: The Questions Concerning Technology
↗
-
13
Pete Wolfendale: The Revenge of Reason
↗
-
14
Peter Lee: Computing Theory and Practice, and GPT-4's Impact
↗
-
15
Manuel & Lenore Blum: The Conscious Turing Machine
↗
-
16
Kevin Dorst: Against Irrationalist Narratives
↗
-
17
David Pfau: Manifold Factorization and AI for Science
↗
-
18
Dan Hart and Michelle Michael: Bringing AI to Students in New South Wales
↗
-
19
Kristin Lauter: Private AI, Homomorphic Encryption, and AI for Cryptography
↗
-
20
Sergiy Nesterenko: Automating Circuit Board Design
↗
-
21
C. Thi Nguyen: Values, Legibility, and Gamification
↗
-
22
Vivek Natarajan: Towards Biomedical AI
↗
-
23
Thomas Mullaney: A Global History of the Information Age
↗
-
24
Seth Lazar: Normative Philosophy of Computing
↗
-
25
Suhail Doshi: The Future of Computer Vision
↗
-
26
Azeem Azhar: The Exponential View
↗
-
27
David Thorstad: Bounded Rationality and the Case Against Longtermism
↗
-
28
Ryan Tibshirani: Statistics, Nonparametric Regression, Conformal Prediction
↗
-
29
Sasha Luccioni: Connecting the Dots Between AI's Environmental and Social Impacts
↗
-
30
Michael Sipser: Problems in the Theory of Computation
↗
-
31
Andrew Lee: How AI will Shape the Future of Email
↗
-
32
Joss Fong: Videomaking, AI, and Science Communication
↗
-
33
Kate Park: Data Engines for Vision and Language
↗
-
34
Ben Wellington: ML for Finance and Storytelling through Data
↗
-
35
Venkatesh Rao: Protocols, Intelligence, and Scaling
↗
-
36
Sasha Rush: Building Better NLP Systems
↗
-
37
Cameron Jones & Sean Trott: Understanding, Grounding, and Reference in LLMs
↗
-
38
Nicholas Thompson: AI and Journalism
↗
-
39
Subbarao Kambhampati: Planning, Reasoning, and Interpretability in the Age of LLMs
↗
-
40
Russ Maschmeyer: Spatial Commerce and AI in Retail
↗
-
41
Benjamin Breen: The Intersecting Histories of Psychedelics and AI Research
↗
-
42
Ted Gibson: The Structure and Purpose of Language
↗
-
43
Harvey Lederman: Propositional Attitudes and Reference in Language Models
↗
-
44
Eric Jang: AI is Good For You
↗
-
45
2023 in AI, with Nathan Benaich
↗
-
46
Kathleen Fisher: DARPA and AI for National Security
↗
-
47
Peter Tse: The Neuroscience of Consciousness and Free Will
↗
-
48
Vera Liao: AI Explainability and Transparency
↗
-
49
Thomas Dietterich: From the Foundations
↗
-
50
Martin Wattenberg: ML Visualization and Interpretability
↗
-
51
Laurence Liew: AI Singapore
↗
-
52
Michael Levin & Adam Goldstein: Intelligence and its Many Scales
↗
-
53
Jonathan Frankle: From Lottery Tickets to LLMs
↗
-
54
Nao Tokui: "Surfing" Musical Creativity with AI
↗
-
55
Divyansh Kaushik: The Realities of AI Policy
↗
-
56
Tal Linzen: Psycholinguistics and Language Modeling
↗
-
57
Kevin K. Yang: Engineering Proteins with ML
↗
-
58
Arjun Ramani & Zhengdong Wang: Why Transformative AI is Really, Really Hard to Achieve
↗
-
59
Miles Grimshaw: Benchmark, LangChain, and Investing in AI
↗
-
60
Shreya Shankar: Machine Learning in the Real World
↗
-
61
Stevan Harnad: AI's Symbol Grounding Problem
↗
-
62
Terry Winograd: AI, HCI, Language, and Cognition
↗
-
63
Gil Strang: Linear Algebra and Deep Learning
↗
-
64
Anant Agarwal: AI for Education
↗
-
65
Raphaël Millière: The Vector Grounding Problem and Self-Consciousness
↗
-
66
Peli Grietzer: A Mathematized Philosophy of Literature
↗
-
67
Ryan Drapeau: Battling Fraud with ML at Stripe
↗
-
68
Shiv Rao: Enabling Better Patient Care with AI
↗
-
69
Hugo Larochelle: Deep Learning as Science
↗
-
70
Jeremie Harris: Realistic Alignment and AI Policy
↗
-
71
Antoine Blondeau: Alpha Intelligence Capital and Investing in AI
↗
-
72
Joon Park: Generative Agents and Human-Computer Interaction
↗
-
73
Christoffer Holmgård: AI for Video Games
↗
-
74
Riley Goodside: The Art and Craft of Prompt Engineering
↗
-
75
Talia Ringer: Formal Verification and Deep Learning
↗
-
76
Brigham Hyde: AI for Clinical Decision-Making
↗
-
77
Scott Aaronson: Against AI Doomerism
↗
-
78
Ted Underwood: Machine Learning and the Literary Imagination
↗
-
79
Irene Solaiman: AI Policy and Social Impact
↗
-
80
Drago Anguelov: Waymo and Autonomous Vehicles
↗
-
81
Joanna Bryson: The Problems of Cognition
↗
-
82
Daniel Situnayake: AI on the Edge
↗
-
83
Soumith Chintala: PyTorch
↗
-
84
Sewon Min: The Science of Natural Language
↗
-
85
Richard Socher: Re-Imagining Search
↗
-
86
Joe Edelman: Meaning-Aligned AI
↗
-
87
Ed Grefenstette: Language, Semantics, Cohere
↗
-
88
Ken Liu: What Science Fiction Can Teach Us
↗
-
89
Hattie Zhou: Lottery Tickets and Algorithmic Reasoning in LLMs
↗
-
90
Kyunghyun Cho: Neural Machine Translation, Language, and Doing Good Science
↗
-
91
Steve Miller: Will AI Take Your Job? It's Not So Simple.
↗
-
92
Blair Attard-Frost: Canada’s AI strategy and the ethics of AI business practices
↗
-
93
Linus Lee: At the Boundary of Machine and Mind
↗
-
94
Suresh Venkatasubramanian: An AI Bill of Rights
↗
-
95
Pete Florence: Dense Visual Representations, NeRFs, and LLMs for Robotics
↗
-
96
Melanie Mitchell: Abstraction and Analogy in AI
↗
-
97
Marc Bellemare: Distributional Reinforcement Learning
↗
-
98
François Chollet: Keras and Measures of Intelligence
↗
-
99
Yoshua Bengio: The Past, Present, and Future of Deep Learning
↗
-
100
Kanjun Qiu and Josh Albrecht: Generally Intelligent
↗
