Deep Unsupervised Learning for Climate Informatics with Claire Monteleoni - #497

Deep Unsupervised Learning for Climate Informatics with Claire Monteleoni - #497

Today we continue our CVPR 2021 coverage joined by Claire Monteleoni, an associate professor at the University of Colorado Boulder. We cover quite a bit of ground in our conversation with Claire, including her journey down the path from environmental activist to one of the leading climate informatics researchers in the world. We explore her current research interests, and the available opportunities in applying machine learning to climate informatics, including the interesting position of doing ML from a data-rich environment. Finally, we dig into the evolution of climate science-focused events and conferences, as well as the Keynote Claire gave at the EarthVision workshop at CVPR “Deep Unsupervised Learning for Climate Informatics,” which focused on semi- and unsupervised deep learning approaches to studying rare and extreme climate events. The complete show notes for this episode can be found at twimlai.com/go/497.

Avsnitt(781)

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...

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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...

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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...

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Infrastructure Scaling and Compound AI Systems with Jared Quincy Davis - #740

Infrastructure Scaling and Compound AI Systems with Jared Quincy Davis - #740

In this episode, Jared Quincy Davis, founder and CEO at Foundry, introduces the concept of "compound AI systems," which allows users to create powerful, efficient applications by composing multiple, o...

22 Juli 20251h 13min

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