Peter Lee: Computing Theory and Practice, and GPT-4's Impact

Peter Lee: Computing Theory and Practice, and GPT-4's Impact

Author: Daniel Bashir August 1, 2024 Duration: 1:01:48

Episode 133

I spoke with Peter Lee about:

* His early work on compiler generation, metacircularity, and type theory

* Paradoxical problems

* GPT-4s impact, Microsoft’s “Sparks of AGI” paper, and responses and criticism

Enjoy—and let me know what you think!

Peter is President of Microsoft Research. He leads Microsoft Research and incubates new research-powered products and lines of business in areas such as artificial intelligence, computing foundations, health, and life sciences. Before joining Microsoft in 2010, he was at DARPA, where he established a new technology office that created operational capabilities in machine learning, data science, and computational social science. Prior to that, he was a professor and the head of the computer science department at Carnegie Mellon University. Peter is a member of the National Academy of Medicine and serves on the boards of the Allen Institute for Artificial Intelligence, the Brotman Baty Institute for Precision Medicine, and the Kaiser Permanente Bernard J. Tyson School of Medicine. He served on President Obama’s Commission on Enhancing National Cybersecurity. He has testified before both the US House Science and Technology Committee and the US Senate Commerce Committee. With Carey Goldberg and Dr. Isaac Kohane, he is the coauthor of the best-selling book, “The AI Revolution in Medicine: GPT-4 and Beyond.” In 2024, Peter was named by Time magazine as one of the 100 most influential people in health and life sciences.

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

* (00:50) Basic vs. applied research

* (05:20) Theory and practice in computing

* (10:28) Traditional denotational semantics and semantics engineering in modern-day systems

* (16:47) Beauty and practicality

* (20:40) Metacircularity in the polymorphic lambda calculus: research directions

* (24:31) Understanding the nature of difficulties with metacircularity

* (26:30) Difficulties with reflection, classic paradoxes

* (31:02) Sparks of AGI

* (31:41) Reproducibility

* (38:04) Confirming and disconfirming theories, foundational work

* (42:00) Back and forth between commitments and experimentation

* (51:01) Dealing with responsibility

* (56:30) Peter’s picture of AGI

* (1:01:38) Outro

Links:

* Peter’s Twitter, LinkedIn, and Microsoft Research pages

* Papers and references

* The automatic generation of realistic compilers from high-level semantic descriptions

* Metacircularity in the polymorphic lambda calculus

* A Fresh Look at Combinator Graph Reduction

* Sparks of AGI

* Re-envisioning DARPA

* Fundamental Research in Engineering



Get full access to The Gradient at thegradientpub.substack.com/subscribe

Hosted by Daniel Bashir, The Gradient: Perspectives on AI moves beyond surface-level headlines to explore the intricate machinery and human ideas shaping artificial intelligence. Each episode is built on a foundation of deep research, leading to conversations that are both technically substantive and broadly accessible. You'll hear from researchers, engineers, and philosophers who are actively building and critiquing our technological future, discussing not just how AI systems work, but the larger implications of their integration into society. This isn't about speculative hype; it's a grounded examination of real progress, persistent challenges, and ethical considerations from those on the front lines. The discussions peel back layers on topics like model architecture, policy, and the fundamental science behind the algorithms becoming part of our daily lives. For anyone curious about the substance behind the buzz-whether you have a technical background or are simply keen to understand a defining technology of our age-this podcast offers a crucial and thoughtful resource. Tune in for a consistently detailed and nuanced take that treats artificial intelligence with the complexity it deserves.
Author: Language: English Episodes: 100

The Gradient: Perspectives on AI
Podcast Episodes
Judy Fan: Reverse Engineering the Human Cognitive Toolkit [not-audio_url] [/not-audio_url]

Duration: 1:32:39
Episode 136I spoke with Judy Fan about:* Our use of physical artifacts for sensemaking* Why cognitive tools can be a double-edged sword* Her approach to scientific inquiry and how that approach has developedEnjoy—and let…
L.M. Sacasas: The Questions Concerning Technology [not-audio_url] [/not-audio_url]

Duration: 1:47:20
Episode 135I spoke with L. M. Sacasas about:* His writing and intellectual influences* The value of asking hard questions about technology and our relationship to it* What happens when we decide to outsource skills and c…
Pete Wolfendale: The Revenge of Reason [not-audio_url] [/not-audio_url]

Duration: 2:52:57
Episode 134I spoke with Pete Wolfendale about:* The flaws in longtermist thinking* Selections from his new book, The Revenge of Reason* Metaphysics* What philosophy has to say about reason and AIEnjoy—and let me know wha…
Manuel & Lenore Blum: The Conscious Turing Machine [not-audio_url] [/not-audio_url]

Duration: 2:23:04
Episode 132I spoke with Manuel and Lenore Blum about:* Their early influences and mentors* The Conscious Turing Machine and what theoretical computer science can tell us about consciousnessEnjoy—and let me know what you…
Kevin Dorst: Against Irrationalist Narratives [not-audio_url] [/not-audio_url]

Duration: 2:15:21
Episode 131I spoke with Professor Kevin Dorst about:* Subjective Bayesianism and epistemology foundations* What happens when you’re uncertain about your evidence* Why it’s rational for people to polarize on political mat…
David Pfau: Manifold Factorization and AI for Science [not-audio_url] [/not-audio_url]

Duration: 2:00:52
Episode 130I spoke with David Pfau about:* Spectral learning and ML* Learning to disentangle manifolds and (projective) representation theory* Deep learning for computational quantum mechanics* Picking and pursuing resea…
Sergiy Nesterenko: Automating Circuit Board Design [not-audio_url] [/not-audio_url]

Duration: 1:03:35
Episode 128I 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 expertiseB…
C. Thi Nguyen: Values, Legibility, and Gamification [not-audio_url] [/not-audio_url]

Duration: 1:30:13
Episode 127I spoke with Christopher Thi Nguyen about:* How we lose control of our values* The tradeoffs of legibility, aggregation, and simplification* Gamification and its risksEnjoy—and let me know what you think!C. Th…