Neural Compression of Atmospheric States

Can AI revolutionize climate research? In this episode, we sit down with Piotr Mirowski from Google DeepMind to explore groundbreaking research that slashes the amount of data needed for climate modeling—without losing the crucial details. The compression ratio they’ve achieved is astonishing, but the real challenge? Preserving rare, high-impact events like typhoons. Get it wrong, and the data becomes useless for predicting exactly the disasters we most need to understand. Listen to find out how AI is revolutionising the way huge climate science datasets are lowering one of the barriers to working in this field.

Paper: [2407.11666] Neural Compression of Atmospheric States

Guests:

• Piotr Mirowski, Senior Staff Research Scientist, Google DeepMind

PhD in computer science in 2011 at New York University, with a thesis on “Time Series Modeling with Hidden Variables and Gradient-based Algorithms” supervised by Prof. Yann LeCun.  Areas of academic focus include navigation-related research, on scaling up autonomous agents to real world environments, on weather and climate forecasting and now on human–centered AI, and the use of AI for artistic human and machine-based co-creation.

Chapters:

00:00   Introduction

01:23   Aye Aye Fact of the Day

02:20   The Evolution of AI and Personal Experiences

08:31   AI over the last 15 years

10:50   Weather research and Climate Change

13:56   Understanding Data Volume: The Petabyte Challenge

18:21   Modelling Climate: The Complexities of Variables

20:11   The Cost of Climate Science: Data and Resources

26:16   Compression Techniques: Lossy vs Lossless

40:30   Neural Compression: A New Frontier in Data Handling

45:15   Understanding Compression Representations in AI

48:34   Challenges of Representing Spherical Data

56:21   Applying Compression Techniques to Other Data Sets

59:05   Lightning Round

1:03:51   Close out

 

Music: "Fire" by crimson.