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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. ...

There is a growing and urgent need for a new kind of scientist – one who is skilled in applying not just knowledge but above all scientific skills to solve real-world problems, outside of academia or research. The fact is that many of our global drivers are complex, interrelated, dynamic, and strongly underpinned by science processes. Let’s see some examples: The Fourth Industrial Revolution and the compounding effect of interrelated progress in many fields from AI to Big Data, IoT, or cloud. Climate change and its impact (and opportunity) to drive positive change, like mitigation via more efficient factories or adaptation measures like green city spots that lower the temperature and improve our health. Sustainability and the need to play the interrelation of people and planet. These are just some quick examples. ...

I have recently finished the most important book I have read in decades. And I am not joking. But the core message is an old, plain, and simple thing: Telling stories works. As a scientist, I have heard that too often from communicators and journalists, usually when doing an interview where they struggle to understand what it is what we did and why would someone care. And I always considered stories a soft ball, inferior to telling science as it is: the facts and only the [amazing] facts. ...

The new Planetary Computer just dropped. Today is Release Day. Loads of data, enhancements, bug fixes and more. See the Changelog for details.

My paternity leave has easily been among the hardest things I’ve done. It’s also, without doubt, the most meaningful and rewarding things I’ve done. Far from a “nice long vacation” as someone told me, it’s been fully a deconstruction and reconstruction of so much of myself. The more I reflect on it, the more I realize both things: It is hard (and thankfully it’s getting easier) and it’s also rewarding (increasingly so). I also realize now it was crucially important not only for me, but for the baby, my wife, and for the whole family. Research (links below) has shown the extraordinary and literally lifelong impact of paternity leaves (specifically dads, besides maternity leaves). ...

Finally, I’m “40 under 40”. 40 days to my 40th birthday that is, not the Forbes award. I won’t lie that the number weighs on me, 40 years old is a big number. Entering the 20s was all about new worlds to explore. Life looked amazing, infinite, boundless. There wasn’t much thought of who I wanted to be, other than a scientist. That was the plan and that was the path, starting from my little corner of a rural village in northern Spain. Whatever it took. Move to the Canary Island to do Astrophysics? Check. Apply for a PhD I surely won’t get? Check. Pass the last uni exam at the last minute so I can accept the PhD position I somehow got? Check. Move to Germany without speaking a word of German? Check. Travel throughout Europe on a budget? Check. I was extremely opinionated as an atheist scientist, and I had a ton of fun as a graduate student in Gottingen, the perfect city for that decade. I made true friendships across Europe that I still keep today. It was also the decade I realized that my understanding of what science should be didn’t agree with what the world says. I felt research and academia could not be all that science is, I knew I had to steer away to find my path, but I was smart enough to enjoy the ride before jumping off the train: I finished my PhD, and accepted a postdoc as rocket scientist in Washington DC, knowing I would probably not finish it. I was also part of the astronaut selection process and was considering a job at fancy global consulting firm. I happily headed to DC, the world felt full of possibilities and time. I would figure out what Science meant for me. I wrapped my 20s as I decided to jump the academic train, without a plan B, without a visa, into the unknown, ready to adjust and learn. Aptly, I finished my 20s skydiving (twice), which was a crazy and boundless experience I loved. ...