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2:01:56
The Gradient

The Gradient: Perspectives on AI

Interviews with various people who research, build, or use AI, including academics, engineers, artists, entrepreneurs, and more.
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2:01:56
Thomas Dietterich: From the Foundations
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2:01:56
Thomas Dietterich: From the Foundations
On developing theories in science, what "learning" means, robustness and sustainability, and what we should be building instead of LLMs.
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daniel bashir
1:42:04
Martin Wattenberg: ML Visualization and Interpretability
On the principles and practice of interaction design, visualization and interpretability for ML systems, and how to understand and explain language…
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daniel bashir
2
50:28
Laurence Liew: AI Singapore
On the AI Singapore program, developing generational AI talent, and Singapore's place in the global AI ecosystem.
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daniel bashir
3
57:20
Michael Levin & Adam Goldstein: Intelligence and its Many Scales
On diverse intelligence in unconventional embodiments and spaces, the concept of a Self, regenerative medicine, and more imaginative notions of AI.
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daniel bashir
7
1:08:21
Jonathan Frankle: From Lottery Tickets to LLMs
On sparsity in neural networks, what matters in research, and what AI practitioners and society should be thinking about.
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daniel bashir
2
1:02:18
Nao Tokui: "Surfing" Musical Creativity with AI
On human-computer interaction in art, the AI DJ Project, and creativity.
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daniel bashir
2
1:17:43
Divyansh Kaushik: The Realities of AI Policy
On why AI and nuclear weapons are not the same, what developing (AI) policy looks like, and the ever-present AI governance / innovation tradeoff.
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daniel bashir
2
1:14:50
Tal Linzen: Psycholinguistics and Language Modeling
On syntax acquisition in language models, evaluation paradigms, LM representations, and what psycholinguistics and deep learning can do for each other.
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daniel bashir
2
1:00:00
Kevin K. Yang: Engineering Proteins with ML
On the generative models for proteins, and the promise and methods of protein engineering.
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daniel bashir
1:49:32
Arjun Ramani & Zhengdong Wang: Why Transformative AI is Really, Really Hard to Achieve
On why "AI will change the world" narratives might be less true than you think.
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daniel bashir
4
1:00:46
Miles Grimshaw: Benchmark, LangChain, and Investing in AI
On investing in AI through the deep learning decade, Benchmark's LangChain investment, and emerging opportunities for AI.
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daniel bashir
1
1:16:36
Shreya Shankar: Machine Learning in the Real World
On what makes ML engineering hard and how to think about it.
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daniel bashir
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