Comment by joshdavham
Comment by joshdavham 4 days ago
> Silurian builds foundation models to simulate the Earth, starting with the weather.
What else do you hope to simulate, if this becomes successful?
Comment by joshdavham 4 days ago
> Silurian builds foundation models to simulate the Earth, starting with the weather.
What else do you hope to simulate, if this becomes successful?
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.
Why is it difficult? Is it predicting the amount of rain that is difficult? Or the terrain that will cause x amount of rain to cause problems? Or something else?
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 more complex.
My advice is to drop the grid itself to the bottom of the list, and I say this as someone who worked at a national grid operator as the primary grid analyst. You'll never get access to sufficient data, and your model will never be correct. You're better off starting from a national 'adequacy' level and working your way down based on information made available via market operators.
Actually, it seems like a great time to get involved with the grid (at least in the US). In order to comply with FERC Order 881, all transmission operators need to adjust their line ratings based on ambient temperatures with hourly predictions 10 days into the future by mid 2025. Seems like that would present a great opportunity to work directly with the ISOs (which have regional models and live data) on improving weather data.
These are great resources, thank you. If you're open to it, we'd love to meet and chat about the energy space since we're newcomers to that arena. Shoot us an email at contact@silurian.ai
If their weather forecast is really the best, power traders would pay them large amounts just for the forecast.
Seems hard… weather is a structure in the Piagetian sense, with lots of individual elements influencing each other via static forces. Earthquakes are-AFAIU as a non-expert Californian-more about physical rock structures within the crust that we have only a vague idea of. Although hey, hopefully I’m wrong; maybe there’s a kind of pre-earthquake tremor for some kinds of quake that a big enough transformer could identify…
The Earth is a multi-physics complex system and OP claiming to "Simulate the Earth" is misleading. Methods that work on the atmosphere may not work on other parts. There are numerous scientific projects working on simulation earthquakes, both using ML and more "traditional" physics.
I suspect (possibly incorrectly) that earthquakes are a chaotic phenomenon resulting from a multilayered complex system, a lot like a lottery ball picker.
Essentially random outputs from deterministic systems are unfortunately not rare in nature…. And I suspect that because of the relatively higher granularity of geology vs the semicohesive fluid dynamics of weather, geology will be many orders of magnitude more difficult to predict.
That said, it might be possible to make useful forecasts in the 1 minute to 1 hour range (under the assumption that major earthquakes often have a dynamic change in precursor events), and if accuracy was reasonable in that range, it would still be very useful for major events.
Looking at the outputs of chaotic systems like geolocated historical seismographic data might not be any more useful than 4-10 orders of magnitude better than looking at previous lottery ball selections in predicting the next ones…. Which is to say that the predictive power might still not be useful even though there is some pattern in the noise.
Generative AI needs a large and diverse training set to avoid overfitting problems. Something like high resolution underground electrostatic distribution might potentially be much more predictive than past outputs alone, but I don’t know of any such efforts to map geologic stress at a scale that would provide a useful training corpus.
They’re empiricists — the only ~~real~~ conclusive way to answer that question is to try it, IMO!
The old ML maxim was “don’t expect models to do anything a human expert couldn’t do with access to the same data”, but that’s clearly going to way of Moore’s Law… I don’t think a meteorologist could predict 11km^2 of weather 10 days out very accurately, and I know for sure that a neuroscientists couldn’t recreate someone’s visual field based on fMRI data!
The actual killer thing would be flooding. Insurance has invested billions into trying to simulate risk here and models are still relatively weak.