Weakly Supervised Causal Representation Learning with Johann Brehmer - #605

Weakly Supervised Causal Representation Learning with Johann Brehmer - #605

Today we’re excited to kick off our coverage of the 2022 NeurIPS conference with Johann Brehmer, a research scientist at Qualcomm AI Research in Amsterdam. We begin our conversation discussing some of the broader problems that causality will help us solve, before turning our focus to Johann’s paper Weakly supervised causal representation learning, which seeks to prove that high-level causal representations are identifiable in weakly supervised settings. We also discuss a few other papers that the team at Qualcomm presented, including neural topological ordering for computation graphs, as well as some of the demos they showcased, which we’ll link to on the show notes page. The complete show notes for this episode can be found at twimlai.com/go/605.

Episoder(782)

Powering AI with the World's Largest Computer Chip with Joel Hestness - #684

Powering AI with the World's Largest Computer Chip with Joel Hestness - #684

Today we're joined by Joel Hestness, principal research scientist and lead of the core machine learning team at Cerebras. We discuss Cerebras’ custom silicon for machine learning, Wafer Scale Engine 3...

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AI for Power & Energy with Laurent Boinot - #683

AI for Power & Energy with Laurent Boinot - #683

Today we're joined by Laurent Boinot, power and utilities lead for the Americas at Microsoft, to discuss the intersection of AI and energy infrastructure. We discuss the many challenges faced by curre...

7 Mai 202449min

Controlling Fusion Reactor Instability with Deep Reinforcement Learning with Aza Jalalvand - #682

Controlling Fusion Reactor Instability with Deep Reinforcement Learning with Aza Jalalvand - #682

Today we're joined by Azarakhsh (Aza) Jalalvand, a research scholar at Princeton University, to discuss his work using deep reinforcement learning to control plasma instabilities in nuclear fusion rea...

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GraphRAG: Knowledge Graphs for AI Applications with Kirk Marple - #681

GraphRAG: Knowledge Graphs for AI Applications with Kirk Marple - #681

Today we're joined by Kirk Marple, CEO and founder of Graphlit, to explore the emerging paradigm of "GraphRAG," or Graph Retrieval Augmented Generation. In our conversation, Kirk digs into the GraphRA...

22 Apr 202447min

Teaching Large Language Models to Reason with Reinforcement Learning with Alex Havrilla - #680

Teaching Large Language Models to Reason with Reinforcement Learning with Alex Havrilla - #680

Today we're joined by Alex Havrilla, a PhD student at Georgia Tech, to discuss "Teaching Large Language Models to Reason with Reinforcement Learning." Alex discusses the role of creativity and explora...

16 Apr 202446min

Localizing and Editing Knowledge in LLMs with Peter Hase - #679

Localizing and Editing Knowledge in LLMs with Peter Hase - #679

Today we're joined by Peter Hase, a fifth-year PhD student at the University of North Carolina NLP lab. We discuss "scalable oversight", and the importance of developing a deeper understanding of how ...

8 Apr 202449min

Coercing LLMs to Do and Reveal (Almost) Anything with Jonas Geiping - #678

Coercing LLMs to Do and Reveal (Almost) Anything with Jonas Geiping - #678

Today we're joined by Jonas Geiping, a research group leader at the ELLIS Institute, to explore his paper: "Coercing LLMs to Do and Reveal (Almost) Anything". Jonas explains how neural networks can be...

1 Apr 202448min

V-JEPA, AI Reasoning from a Non-Generative Architecture with Mido Assran - #677

V-JEPA, AI Reasoning from a Non-Generative Architecture with Mido Assran - #677

Today we’re joined by Mido Assran, a research scientist at Meta’s Fundamental AI Research (FAIR). In this conversation, we discuss V-JEPA, a new model being billed as “the next step in Yann LeCun's vi...

25 Mar 202447min

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