No Priors: Artificial Intelligence | Technology | Startups
Asimov: Building An Omniscient RL Oracle with ReflectionAI’s Misha Laskin
At this moment of inflection in technology, co-hosts Elad Gil and Sarah Guo talk to the world's leading AI engineers, researchers and founders about the biggest questions: How far away is AGI? What markets are at risk for disruption? How will commerce, culture, and society change? What’s happening in state-of-the-art in research? “No Priors” is your guide to the AI revolution. Email feedback to show@no-priors.com.
Sarah Guo is a startup investor and the founder of Conviction, an investment firm purpose-built to serve intelligent software, or "Software 3.0" companies. She spent nearly a decade incubating and investing at venture firm Greylock Partners.
Elad Gil is a serial entrepreneur and a startup investor. He was co-founder of Color Health, Mixer Labs (which was acquired by Twitter). He has invested in over 40 companies now worth $1B or more each, and is also author of the High Growth Handbook.
Show Notes
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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.
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Chapters:
– Misha Laskin Introduction
– Superintelligence vs. Super Intelligent Autonomous Systems
– Misha’s Journey from Physics to AI
– Asimov Product Release
– What Differentiates Asimov from Other Agents
– Asimov’s Eval Philosophy
– The Types of Queries Where Asimov Shines
– Designing a Team-Wide Memory for Asimov
– Leveraging Pre-Trained Models
– The Challenges of Solving Scaling in RL
– Training Agents in Copycat Software Environments
– When Will We See ASI?
– Thoughts on Windsurf’s Non-Acquisition
– Exploring Non-RL Datasets
– Tackling Problems Beyond Engineering and Coding
– Where We’re At in Deploying ASI in Different Fields
– Conclusion
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