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Intelligent content that gives practitioners, innovators, and leaders an inside look at the present and future of ML & AI technologies.
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Why Image Generation Needs More Than Bigger Models with Fatih Porikli - #773
Text-to-image models have become remarkably good at producing realistic images. But realism isn't the same as correctness. Ask for several distinct people, a specific composition, or a high-resolution image generated locally, and today's models still stru
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Why Models Are AI's Next Training Dataset with Damian Borth - #772
For more than a decade, AI has advanced by training ever-larger models on ever-larger datasets. But as high-quality training data becomes harder to find and pretraining grows increasingly expensive, researchers are looking for new ways to keep foundation
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How AI Learns to Smell with Alex Wiltschko - #771
In this episode, Alex Wiltschko, founder and CEO of Osmo, joins the show to discuss his goal of giving computers a sense of smell and what it takes to build olfactory intelligence. We explore the science behind smell, from the hundreds of olfactory recept
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Why AI Agents Break the GenAI Security Model with Devvret Rishi - #770
In this episode, Sam talks with Dev Rishi, GM of AI at Rubrik, about what happens when agents move beyond answering questions and start taking action across tools, systems, and business processes. We explore why the enterprise playbook of static guardrail
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Is RAG Dead? Lessons from Building AI for Tax Law with Alex Bowcut - #769
As context windows grow into the millions of tokens, many AI practitioners are questioning whether retrieval-augmented generation (RAG) is still necessary. If modern models can ingest entire libraries of documents, why bother with retrieval at all? In thi
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