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> Silurian builds foundation models to simulate the Earth, starting with the weather. What else do you hope to simulate, if this becomes successful?
The actual killer thing would be flooding. Insurance has invested billions into trying to simulate risk here and models are still relatively weak.
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Hi, this looks really cool! Can we meet? Shoot us an email at contact@silurian.ai
Maybe between the two of you, you can tell me why my Alexa is telling me there’s no rain today, but it’s raining right now.
If you've ever heard of the Lorenz/Butterfly Effect/Strange Attractors, those chaotic systems were discovered because of a discrepancy between two parallel weather simulations. One preserved the original simulation's calculation train while the other started off with simply the previous results (out to like 10 decimals) and suffered from a rounding error and thus both simulations diverged hugely.
Lorenz was trying to simulate weather by subdividing the atmosphere into tons and tons of cubes. Really interesting reading/video watching tbh.
Haha. The old NLP saying "every time I fire a linguist, my performance goes up", now applies to the physicists....
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We want to branch out to industries which are highly dependent on weather. That way we can integrate their data together with our core competency: the weather and climate. Some examples include the energy grid, agriculture, logistics, and defense.
you'll have trouble simulating the grid, but for energy data you might want to look at (or get in touch with) these people: https://app.electricitymaps.com/map They're a cool little team based in Copenhagen. Would be useful, for example, to look at the correlation between your weather data and regional energy production (solar and wind). Next level would be models to predict national hydro storage, but that is a lot…
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Low-income neighborhoods are good signal indicator for flooding high risk zones. There's a demographic angle, too.
Are you suggesting that flood prevention only happens in higher income neighbourhoods? Flood prevention tends to lie on the county engineers. Not so much private individuals to dictate. Doesn't matter how much money you have, you can't just dig up a road to put in proper flood prevention measures like drainage and grade.
99 Percent Invisible did an episode about this recently:
https://99percentinvisible.org/episode/nbft-05-the-little-le...
I'm hoping the singularity will coincide with a large-scale AI achieving simulated Earth consciousness. Human intelligence is only a spec compared to all the combined intelligence of nature.
What is "simulated Earth consciousness"?
Large Language Model + Large Earth Model
Wow, so excited for this. I had a web app online in 2020-22 called Skim Day that predicted skimboarding conditions on California beaches that was mostly powered by weather APIs. The tide predictions were solid, but the weather itself was almost never right, especially wind speed. Additionally there were some missing metrics like slope of beach which changes significantly throughout the year and is very important for…
Have specific industries reached out to you for your commerical potential – natural resource exploration, for example?
Glad to see that you can make ensemble forecasts of tropical cyclones! This absolutely essential for useful weather forecasts of uncertain events, and I am a little dissapointed by the frequent comparisons (not just you) of ML models to ECMWF's deterministic HRES model. HRES is more of a single realization of plausible weather, rather than an best estimate of "average" weather, so this is a bit of apples vs oranges.…
HN never disappoints, jeez. Thanks for chiming in with some expert context! I highly recommend any meteoronoobs like me to check out the pdf version of the linked paper, the diagrams are top notch — https://www.nature.com/articles/s41586-024-07744-y.pdf Main takeaway, gives me some hope: Our results provide strong evidence for the disputed hypothesis that learning to predict short-term weather is an effective way to…
(Former Google employee, but I have no inside knowledge; this is just my speculation from public data.)
Owning your own data and serving systems can also make previously impossible features possible. When I was a Google intern in 2007 I attended a presentation by someone who had worked on Google's then-new in-house routing system for Google Maps (the system that generates directions between two locations). Before, they licensed a routing system from a third party, and it was expensive ($) and slow.
The in-house system was cheap enough to be almost free in comparison, and it produced results in tens of milliseconds instead of many hundreds or even thousands of milliseconds. That allowed Google to build the amazing-at-the-time "drag to change the route" feature that would live-update the route to pass through the point under your cursor. It ran a new routing query many times per second.