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Introducing the Ndea podcast - Abstract Synthesis. Hear the stories behind interesting academic papers in the world of program synthesis. Episode 1 features Mark Santolucito, Barnard College/Columbia University, discussing his paper "Grammar Filtering for Syntax-Guided Synthesis".

Ndea

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15,917 görüntüleme • 8 ay önce •via X (Twitter)

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Today, we're joined by Yejin Choi, professor and senior fellow at Stanford University University in the Computer Science Department and Stanford UniversityHAI. In this conversation, we explore Yejin’s recent work on making small language models reason more effectively. We discuss how high-quality, diverse data plays a central role in closing the intelligence gap between small and large models, and how combining synthetic data generation, imitation learning, and reinforcement learning can unlock stronger reasoning capabilities in smaller models. Yejin explains the risks of homogeneity in model outputs and mode collapse highlighted in her “Artificial Hivemind” paper, and its impacts on human creativity and knowledge. We also discuss her team's novel approaches, including reinforcement learning as a pre-training objective, where models are incentivized to “think” before predicting the next token, and "Prismatic Synthesis," a gradient-based method for generating diverse synthetic math data while filtering overrepresented examples. Additionally, we cover the societal implications of AI and the concept of pluralistic alignment—ensuring AI reflects the diverse norms and values of humanity. Finally, Yejin shares her mission to democratize AI beyond large organizations and offers her predictions for the coming year. 🗒️ For the full list of resources for this episode, visit the show notes page: 📖 CHAPTERS =============================== 00:00 - Introduction 04:44 - "Snowball effect" in AI investments 06:58 - Approaches to smaller models 08:58 - Importance of “better data” 14:07 - Imitation learning 18:24 - Artificial Hivemind paper 25:25 - AI risks 27:50 - Spectrum tuning 28:53 - Future of AI on humanity 33:08 - Reasoning in small models 34:58 - Prismatic Synthesis 48:20 - Reinforcement as a Pretraining Objective 55:04 - Pluralistic alignment 1:03:30 - Predictions

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How Much Protein Should You Really Eat Daily? | David Allison, Ph.D. This clip is from episode # 368 of The Drive which was released today (10/13/25). In the full episode, we cover: -Why the protein RDA may be inadequate for most people and how to think about optimal intake -What current evidence shows about muscle protein synthesis and high-protein diets -The limitations and biases of nutrition research, including economic and methodological constraints -How processed and ultra-processed foods fit into the broader discussion of obesity and public health -More Watch or listen (Ep. # 368) to the full episode on my website or your favorite podcast player.

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