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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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WYSZUKAJ MINIONE ODCINKI
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INNE ODCINKI W TYM PODCASCIE
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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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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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 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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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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Zastrzeżenie: Podcast i grafika osadzone na tej stronie pochodzą z Ben Lorica, który jest własnością jego właściciela i nie jest powiązany ani wspierany przez Listen Notes, Inc.
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