Jeremie Harris

Jeremie Harris and Edouard Harris, co-founders of Gladstone AI, dive deep into the world of artificial intelligence and national security. They discuss the urgent need for informed governance in light of AI's rapid advancements. The duo emphasizes the risks of AI, including potential mass manipulation via social media. They explore the relationships between AI and human emotions, the ethical implications of AI decision-making, and the importance of regulatory frameworks to ensure safety. Additionally, they highlight innovations in AI technology and their transformative potential for society.

In episode 79 of The Gradient Podcast, Daniel Bashir speaks to Jeremie Harris.Jeremie is co-founder of Gladstone AI, author of the book Quantum Physics Made Me Do It, and co-host of the Last Week in AI Podcast. Jeremy previously hosted the Towards Data Science podcast and worked on a number of other startups after leaving a PhD in physics.Have suggestions for future podcast guests (or other feedback)? Let us know here or reach us at editor@thegradient.pubSubscribe to The Gradient Podcast:  Apple Podcasts  | Spotify | Pocket Casts | RSSFollow The Gradient on TwitterOutline:* (00:00) Intro* (01:37) Jeremie’s physics background and transition to ML* (05:19) The physicist-to-AI person pipeline, how Jeremie’s background impacts his approach to AI* (08:20) A tangent on inflationism/deflationism about natural laws (I promise this applies to AI)* (11:45) How ML implies a particular viewpoint on the above question* (13:20) Jeremie’s first (recommendation systems) company, how startup founders can make mistakes even when they’ve read Paul Graham essays* (17:30) Classic startup wisdom, different sorts of startups* (19:35) OpenAI’s approach in shipping features for DALL-E 2 and generation vs. discrimination as an approach to product* (24:55) Capabilities and risk* (26:43) Commentary on fundamental limitations of alignment in LLMs* (30:45) Intrinsic difficulties in alignment problems* (41:15) Daniel tries to steel man / defend anti-longtermist arguments (nicely :) )* (46:23) Anthropic’s paper on asking models to be less biased* (47:20) Why Jeremie is excited about Anthropic’s Constitutional AI scheme* (51:05) Jeremie’s thoughts on recent Eliezer discourse* (56:50) Cheese / task vectors and steerability/controllability in LLMs* (59:50) Difficulty of one-shot solutions in alignment work, better strategies* (1:02:00) Lack of theoretical understanding of deep learning systems / alignment* (1:04:50) Jeremie’s work and perspectives on AI policy* (1:10:00) Incrementality in convincing policymakers* (1:14:00) How recent developments impact policy efforts* (1:16:20) Benefits and drawbacks of open source* (1:19:30) Arguments in favor of (limited) open source* (1:20:35) Quantum Physics (not Mechanics) Made Me Do It* (1:24:10) Some theories of consciousness and corresponding physics* (1:29:49) OutroLinks:* Jeremie’s Twitter* Quantum Physics Made Me Do It* Gladstone AI Get full access to The Gradient at thegradientpub.substack.com/subscribe

Guest Jeremie Harris discusses AI Safety and risks of AGI, potential for AGI to destroy or benefit humanity, tips on becoming an AI safety expert, insights on his work at Mercurius, differences in Canadian vs American start-up ecosystems, and the importance of mentorship in data science career development.

Jeremie Harris, host of the Towards Data Science podcast, dives into the pivotal AI trends of 2021. He highlights the shift towards efficiency in foundational models, exemplified by DeepMind's Gopher outpacing GPT-3 in performance while requiring fewer resources. The conversation also touches on the importance of procedural environment generation in reinforcement learning, showcasing how it can lead to more adaptable AI agents. Jeremie reflects on the implications for ethics in AI development and the exciting future of open-ended learning.

Jeremie Harris, co-founder of ShapestMinds and host of the Towards Data Science Podcast, dives into the notable advancements in AI during 2021. The conversation explores the fascinating avocado armchair AI, the ethical dilemmas surrounding facial recognition after a false arrest in New Jersey, and even a new Nirvana song generated by AI. They discuss the transformative potential of multimodal models and the broader implications for creativity, copyright, and ethical practices in the fast-evolving landscape of artificial intelligence.