ÜBER DIESE EPISODE
Emmanuel Ameisen, an interpretability researcher at Anthropic, joins the podcast to demystify how large language models work.
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Detailed show notes - with links to many references - can be found on The Data Exchange web site.
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VERGANGENE FOLGEN SUCHEN
Vorhandene Folgen von The Data Exchange with Ben Lorica durchsuchen.
WEITERE EPISODEN IN DIESEM PODCAST
Ben Lorica talks with Abi Aryan, AI engineer, author of LLMOps: Managing Large Language Models in Production, about how deploying generative AI is turning into a distributed systems problem.
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Richard Ho, VP of Hardware at OpenAI, explains how the team built Jalapeño, its first custom AI accelerator, and why controlling more of the hardware stack could lower inference costs and improve performance.
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Ben Lorica talks with David Fattal, founder and CTO of Leia Inc., about why stereo data could become an important ingredient for AI systems that need to understand the physical world.
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Ben Lorica speaks with Andrew Dai, co-founder and CEO of Elorian AI, about why today’s frontier models still struggle with complex visual reasoning and why simply scaling language-centric architectures may not solve the problem.
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Ben Lorica is joined by Evangelos Simoudis for a wide-ranging discussion on how AI is reshaping knowledge work: from the Navier-Stokes controversy and the need to protect intellectual property to whether workers should still be expected to understand and defend AI-generated work.
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BEARBEITEN
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