AI Agents: Substance or Snake Oil with Arvind Narayanan - #704

AI Agents: Substance or Snake Oil with Arvind Narayanan - #704

Today, we're joined by Arvind Narayanan, professor of Computer Science at Princeton University to discuss his recent works, AI Agents That Matter and AI Snake Oil. In “AI Agents That Matter”, we explore the range of agentic behaviors, the challenges in benchmarking agents, and the ‘capability and reliability gap’, which creates risks when deploying AI agents in real-world applications. We also discuss the importance of verifiers as a technique for safeguarding agent behavior. We then dig into the AI Snake Oil book, which uncovers examples of problematic and overhyped claims in AI. Arvind shares various use cases of failed applications of AI, outlines a taxonomy of AI risks, and shares his insights on AI’s catastrophic risks. Additionally, we also touched on different approaches to LLM-based reasoning, his views on tech policy and regulation, and his work on CORE-Bench, a benchmark designed to measure AI agents' accuracy in computational reproducibility tasks. The complete show notes for this episode can be found at https://twimlai.com/go/704.

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Scaling Up Test-Time Compute with Latent Reasoning with Jonas Geiping - #723

Scaling Up Test-Time Compute with Latent Reasoning with Jonas Geiping - #723

Today, we're joined by Jonas Geiping, research group leader at Ellis Institute and the Max Planck Institute for Intelligent Systems to discuss his recent paper, “Scaling up Test-Time Compute with Late...

17 Maalis 202558min

Imagine while Reasoning in Space: Multimodal Visualization-of-Thought with Chengzu Li - #722

Imagine while Reasoning in Space: Multimodal Visualization-of-Thought with Chengzu Li - #722

Today, we're joined by Chengzu Li, PhD student at the University of Cambridge to discuss his recent paper, “Imagine while Reasoning in Space: Multimodal Visualization-of-Thought.” We explore the motiv...

10 Maalis 202542min

Inside s1: An o1-Style Reasoning Model That Cost Under $50 to Train with Niklas Muennighoff - #721

Inside s1: An o1-Style Reasoning Model That Cost Under $50 to Train with Niklas Muennighoff - #721

Today, we're joined by Niklas Muennighoff, a PhD student at Stanford University, to discuss his paper, “S1: Simple Test-Time Scaling.” We explore the motivations behind S1, as well as how it compares ...

3 Maalis 202549min

Accelerating AI Training and Inference with AWS Trainium2 with Ron Diamant - #720

Accelerating AI Training and Inference with AWS Trainium2 with Ron Diamant - #720

Today, we're joined by Ron Diamant, chief architect for Trainium at Amazon Web Services, to discuss hardware acceleration for generative AI and the design and role of the recently released Trainium2 c...

24 Helmi 20251h 7min

π0: A Foundation Model for Robotics with Sergey Levine - #719

π0: A Foundation Model for Robotics with Sergey Levine - #719

Today, we're joined by Sergey Levine, associate professor at UC Berkeley and co-founder of Physical Intelligence, to discuss π0 (pi-zero), a general-purpose robotic foundation model. We dig into the m...

18 Helmi 202552min

AI Trends 2025: AI Agents and Multi-Agent Systems with Victor Dibia - #718

AI Trends 2025: AI Agents and Multi-Agent Systems with Victor Dibia - #718

Today we’re joined by Victor Dibia, principal research software engineer at Microsoft Research, to explore the key trends and advancements in AI agents and multi-agent systems shaping 2025 and beyond....

10 Helmi 20251h 44min

Speculative Decoding and Efficient LLM Inference with Chris Lott - #717

Speculative Decoding and Efficient LLM Inference with Chris Lott - #717

Today, we're joined by Chris Lott, senior director of engineering at Qualcomm AI Research to discuss accelerating large language model inference. We explore the challenges presented by the LLM encodin...

4 Helmi 20251h 16min

Ensuring Privacy for Any LLM with Patricia Thaine - #716

Ensuring Privacy for Any LLM with Patricia Thaine - #716

Today, we're joined by Patricia Thaine, co-founder and CEO of Private AI to discuss techniques for ensuring privacy, data minimization, and compliance when using 3rd-party large language models (LLMs)...

28 Tammi 202551min

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