This is the article version of the invited talk I gave at the BSC AI Factory of Barcelona, during a CARTO event. The title ‘Earth Intelligence’ sounds like science fiction or philosophy. It isn’t. This is not a wish list for future data or tech I wish we had. We do in fact have enough data and tools. This is about the simplest, fastest way to access planetary insights right now. What a time to be working on this! ...
TL;DR: Training Clay v1.5 was “carbon neutral” and actually emitted ~10 tonnes of CO₂e. Moreover, focusing on lower emissions during geoAI training is a climate distraction compared to understanding geoembeddings. A year ago we trained Clay model v1.5 — still one of the most capable geoAI models today: open-source, open-data, open-license. At the time we promised to publish its emissions. I just updated the docs, but sharing this longer post since it proved harder — and had deeper pragmatic implications — than I expected. ...
My key message on Monday at the geo day at AWS in London was that a fundamental shift in how we approach Earth Observation (EO) data is not just possible, but essential, with Geo Embeddings at its core. Phil Cooper, the host, introduced the session around the question of whether GIS has an identity crisis. I think that is not only true, but also that is because GIS has had an EO product problem for decades and finally AI seems to provide the stack to solve it… We must change GIS EO from pixel math and widgets, to frictionless semantic navigation through embeddings. ...
From a keynote at the European Rover Challenge last week in Krakow, and celebrating the 150th anniversary of “The Lunar Trilogy” by Jerzy Zulawski. Video at the end. The conference hall was buzzing. Outside, teams of students around the world were competing with their DIY robots on a Mars landscape, driven by the same age-old question that has fueled explorers for centuries: “What is where?” ...