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.

Avsnitt(782)

Inside Nano Banana 🍌 and the Future of Vision-Language Models with Oliver Wang - #748

Inside Nano Banana 🍌 and the Future of Vision-Language Models with Oliver Wang - #748

Today, we’re joined by Oliver Wang, principal scientist at Google DeepMind and tech lead for Gemini 2.5 Flash Image—better known by its code name, “Nano Banana.” We dive into the development and capab...

23 Sep 20251h 3min

Is It Time to Rethink LLM Pre-Training? with Aditi Raghunathan - #747

Is It Time to Rethink LLM Pre-Training? with Aditi Raghunathan - #747

Today, we're joined by Aditi Raghunathan, assistant professor at Carnegie Mellon University, to discuss the limitations of LLMs and how we can build more adaptable and creative models. We dig into her...

16 Sep 202558min

Building an Immune System for AI Generated Software with Animesh Koratana - #746

Building an Immune System for AI Generated Software with Animesh Koratana - #746

Today, we're joined by Animesh Koratana, founder and CEO of PlayerZero to discuss his team’s approach to making agentic and AI-assisted coding tools production-ready at scale. Animesh explains how rap...

9 Sep 20251h 5min

Autoformalization and Verifiable Superintelligence with Christian Szegedy - #745

Autoformalization and Verifiable Superintelligence with Christian Szegedy - #745

In this episode, Christian Szegedy, Chief Scientist at Morph Labs, joins us to discuss how the application of formal mathematics and reasoning enables the creation of more robust and safer AI systems....

2 Sep 20251h 11min

Multimodal AI Models on Apple Silicon with MLX with Prince Canuma - #744

Multimodal AI Models on Apple Silicon with MLX with Prince Canuma - #744

Today, we're joined by Prince Canuma, an ML engineer and open-source developer focused on optimizing AI inference on Apple Silicon devices. Prince shares his journey to becoming one of the most prolif...

26 Aug 20251h 10min

Genie 3: A New Frontier for World Models with Jack Parker-Holder and Shlomi Fruchter - #743

Genie 3: A New Frontier for World Models with Jack Parker-Holder and Shlomi Fruchter - #743

Today, we're joined by Jack Parker-Holder and Shlomi Fruchter, researchers at Google DeepMind, to discuss the recent release of Genie 3, a model capable of generating “playable” virtual worlds. We dig...

19 Aug 20251h 1min

Closing the Loop Between AI Training and Inference with Lin Qiao - #742

Closing the Loop Between AI Training and Inference with Lin Qiao - #742

In this episode, we're joined by Lin Qiao, CEO and co-founder of Fireworks AI. Drawing on key lessons from her time building PyTorch, Lin shares her perspective on the modern generative AI development...

12 Aug 20251h 1min

Context Engineering for Productive AI Agents with Filip Kozera - #741

Context Engineering for Productive AI Agents with Filip Kozera - #741

In this episode, Filip Kozera, founder and CEO of Wordware, explains his approach to building agentic workflows where natural language serves as the new programming interface. Filip breaks down the ar...

29 Juli 202546min

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