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?” ...
The end of the pixel king Fig 1. A screenshot of Google Maps with a search for “grass” returns with “No results” even when it’s obvious to find the grass on the satellite image. Google Maps excels at providing detailed information on restaurants, live updates on public transport, and more. Yet, it struggles to identify something as simple as grass or an entire forest, which appears as a gray blob: ...
Last week I taught on a PhD-level summer course on geoAI, or AI for Earth, at the University of Copenhagen. I saw other speakers give their fantastic technical talks, some on fundamentals, and some on real specific applications. The organizers did a fantastic job to equip these selected and global group of PhD students with truly the latest and best in the field; depth and breadth. So after listening to other speakers, I decided to scrap my prepared presentation. I had tweaked the one I usually do about Clay with more technical parts. I was going to present our Clay v1, the choices, the architecture, the training, the results. Instead I chose to talk straight to the most important point. The “so what”. ...
Would you defend the position that “AI, and not humans, hold the key for understanding risk today”? and would you do it at the plenary of the largest and most important conference about disaster risks, hosted by the World Bank? That’s what I just did in Himeji, Japan. On a debate where the opposing side was Ivan Gayton, from Humanitarian OpenStreetMap Team, who does tremendously important work empowering communities where it matters most, in most difficult places. Someone who is very much on top of all the hype of AI, and part of his job is to extract the real value. So he did have great arguments that humans should hold the key. ...
We have a very clear set of needs to understand Earth’s climate and nature, including measuring biodiversity, mapping disasters, and monitoring crops, to mention just a few. Yet despite having an enormous amount of diverse, Earth data (both open and commercial), most insights about the Earth are locked in the data, requiring very advanced technical skills, resources, time, and tools to use it. We believe we finally have the missing piece to unlock this situation. To transition Earth Observation beyond being the scribes of doom, and instead support the change we want to see in the world, removing complexity and adding clarity and speed. That last piece we needed is AI. AI for Earth. ...
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. ...
In the field of genetic ancestry, services like 23andMe enable us to trace our roots back 100,000 years. However, the recent ancestry — the closer generations to us — are paradoxically harder to trace. Why? This is in part due to advent of increasingly common, fast, and cheap travel: horses, ships, trains, cars, planes, … It basically mixes populations in much deeper, complex, dynamic ways. The technological revolution made genetic ancestry possible, but also complicates the assumption that one can easily relate location with DNA. Similarly, today, a comparable shift is taking place in the digital world. As I explain below, we can no longer assume that any digital record is a factual representation of the present or the past. This was the case before. It is the end of presumption of digital veracity. ...
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. ...