Why Google failed to make GPT-3 -- with David Luan of Adept

Latent Space · Beginner ·🧠 Large Language Models ·2y ago
David Luan has been at the center of the modern AI revolution: he was the ~30th hire at OpenAI, he led Google's LLM efforts and co-led Google Brain, and then started Adept in 2022, one of the leading companies in the AI agents space. In today's episode, we asked David for some war stories from his time in early OpenAI (including working with Alec Radford ahead of the GPT-2 demo with Sam Altman, that resulted in Microsoft’s initial $1b investment), and how Adept is building agents that can “do anything a human does on a computer" — his definition of useful AGI. Full show notes: https://www.latent.space/p/adept 00:00:00 Introduction of David Luan, CEO and co-founder of Adept 00:01:14 David's background and career trajectory 00:03:20 Transition from reinforcement learning to transformers in the AI industry 00:05:35 History and development of GPT models at OpenAI and Google 00:13:08 Adept's $420 million funding rounds 00:13:38 Explanation of what Adept does and their vision for AI agents 00:19:20 Reasons for Adept becoming more public-facing 00:21:00 Adept's critical path and research directions (Persimmon, Fuyu, Act One) 00:26:23 How AI agents should interact with software and impact product development 00:30:37 Analogies between AI agents and self-driving car development 00:32:42 Balancing reliability, cost, speed and generality in AI agents 00:35:11 Adept's unique positioning and advantages in the AI industry 00:37:30 Potential of foundation models for robotics 00:39:22 Core research questions and reasons to work at Adept 00:40:57 David's closing thoughts on the AI agent space and industrialization of AI
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Chapters (15)

Introduction of David Luan, CEO and co-founder of Adept
1:14 David's background and career trajectory
3:20 Transition from reinforcement learning to transformers in the AI industry
5:35 History and development of GPT models at OpenAI and Google
13:08 Adept's $420 million funding rounds
13:38 Explanation of what Adept does and their vision for AI agents
19:20 Reasons for Adept becoming more public-facing
21:00 Adept's critical path and research directions (Persimmon, Fuyu, Act One)
26:23 How AI agents should interact with software and impact product development
30:37 Analogies between AI agents and self-driving car development
32:42 Balancing reliability, cost, speed and generality in AI agents
35:11 Adept's unique positioning and advantages in the AI industry
37:30 Potential of foundation models for robotics
39:22 Core research questions and reasons to work at Adept
40:57 David's closing thoughts on the AI agent space and industrialization of AI
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