Machine Learning

Last week I was invited to speak at the Aarhus Symposium to technical and business students exploring the theme “Beyond the AI Hype: AI for Earth.” To make sense of this broad brief, I divided my class into three parts: a very candid view of what AI is (and isn’t) today in general, then AI applied to understanding Earth, and finally the impact of AI on Earth (its environmental footprint). My goal was simple: to cut through both hype and doom, showing where the technology genuinely shines, where it falls short, and why it matters for Earth. ...

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. ...

We foolishly think AI for Earth is yet another try to deliver the same vision geospatial has promised for decades. The reality is much harsher. This was the provocative prompt I wanted to articulate on my plenary at SatSummit Lisbon. That room, and the network I can reach here, has some of the main shapers and doers of AI for Earth, both those that fully engage and build it, and also those that refuse to and by omission let others build without the experience and perspective they have. I strongly believe this window of opportunity to shape AI for Earth is closing in the next 12-24 months, so I will lean here with strong opinions loosely held. Do not aim to be comprehensive, but provocative, and encourage you to disagree, rebut, question and comment on this. The goal is the conversation to understand what’s happening, or not happening and should. ...

AI is changing the world of text, images, audio, … but not Earth data. Yes, its hard to work with, but not only we are dropping the [globe] ball, AI for Earth has outsized benefits (impact and profits alike), specially if done fully in the open. Last year Dan Hammer and I noticed a glaring oversight on the current tsunami of AI: the largely untapped potential for Earth. We have amazing breakthroughs in AI with text, images, video, and audio — but not Earth data. This is deeply disappointing considering the massive global challenges we face related to nature, climate change, and sustainability. I think part of the reason for this gap in AI is that AI and geospatial skills are the bottleneck. Earth data is very difficult to store, process, and work with. So AI+Geospatial is an extremely niche set of skills. ...

AI dominates every digital space. More specifically OpenAI with ChatGPT and GPT4, clearly outperforming [at least in public perception] usual suspects like Google who claimed to be “AI first”, since 2016. The irony is that OpenAI uses Transformers for its star product ChatGPT and friends, which is a Google invention from 2017. There are many reports that Google sounded the alarm to catch up, and yesterdays Google IO shows they are all-in. But a comparison between OpenAI and Google strategies, in my opinion, reveals starkly contrasting strategies: OpenAI goes loudly and boldly all in on generative text, and Google (more silently) on embeddings. ...