What are the Implications of Algorithmic Thinking? with Michael I. Jordan - #407

What are the Implications of Algorithmic Thinking? with Michael I. Jordan - #407

Today we’re joined by the legendary Michael I. Jordan, Distinguished Professor in the Departments of EECS and Statistics at UC Berkeley. Michael was gracious enough to connect us all the way from Italy after being named IEEE’s 2020 John von Neumann Medal recipient. In our conversation with Michael, we explore his career path, and how his influence from other fields like philosophy shaped his path. We spend quite a bit of time discussing his current exploration into the intersection of economics and AI, and how machine learning systems could be used to create value and empowerment across many industries through “markets.” We also touch on the potential of “interacting learning systems” at scale, the valuation of data, the commoditization of human knowledge into computational systems, and much, much more. The complete show notes for this episode can be found at. twimlai.com/go/407.

Jaksot(780)

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

AI Engineering Pitfalls with Chip Huyen - #715

AI Engineering Pitfalls with Chip Huyen - #715

Today, we're joined by Chip Huyen, independent researcher and writer to discuss her new book, “AI Engineering.” We dig into the definition of AI engineering, its key differences from traditional machi...

21 Tammi 202557min

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