Superintelligence, at least in an academic sense, has already been achieved. But Misha Laskin thinks that the next step towards artificial superintelligence, or ASI, should look both more user and problem-focused. ReflectionAI co-founder and CEO Misha Laskin joins Sarah Guo to introduce Asimov, their new code comprehension agent built on reinforcement learning (RL). Misha talks about creating tools and designing AI agents based on customer needs, and how that influences eval development and the scope of the agent’s memory. The two also discuss the challenges in solving scaling for RL, the future of ASI, and the implications for Google’s “non-acquisition” of Windsurf.
Sign up for new podcasts every week. Email feedback to
[email protected]
Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @MishaLaskin | @reflection_ai
Chapters:
00:00 – Misha Laskin Introduction
00:44 – Superintelligence vs. Super Intelligent Autonomous Systems
03:26 – Misha’s Journey from Physics to AI
07:48 – Asimov Product Release
11:52 – What Differentiates Asimov from Other Agents
16:15 – Asimov’s Eval Philosophy
21:52 – The Types of Queries Where Asimov Shines
24:35 – Designing a Team-Wide Memory for Asimov
28:38 – Leveraging Pre-Trained Models
32:47 – The Challenges of Solving Scaling in RL
37:21 – Training Agents in Copycat Software Environments
38:25 – When Will We See ASI?
44:27 – Thoughts on Windsurf’s Non-Acquisition
48:10 – Exploring Non-RL Datasets
55:12 – Tackling Problems Beyond Engineering and Coding
57:54 – Where We’re At in Deploying ASI in Different Fields
01:02:30 – Conclusion
New Zealand