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Launch HN: Silurian (YC S24) – Simulate the Earth
91–100 of 156 posts
Re: Launch HN: Silurian (YC S24) – Simulate the Earth
#92> 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.
100% aggree. Flooding is the single costliest natural disaster.
But it's non-trivial to scale these new techniques into the field. A major factor is the scale of interest. FEMA's FIRMaps are typically at a 10m resolution not 11km.
Re: Launch HN: Silurian (YC S24) – Simulate the Earth
#93Earlier quoted context omitted.
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.
You'll need to subscribe to Alexa weather plus, for only 9.99$/month. Now seriously, yes, hyperlocal short-term weather forecast should be a commodity, even public utility?
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Re: Launch HN: Silurian (YC S24) – Simulate the Earth
#94Earlier quoted context omitted.
The actual killer thing would be flooding. Insurance has invested billions into trying to simulate risk here and models are still relatively weak.
100% aggree. Flooding is the single costliest natural disaster. But it's non-trivial to scale these new techniques into the field. A major factor is the scale of interest. FEMA's FIRMaps are typically at a 10m resolution not 11km.
Low-income neighborhoods are good signal indicator for flooding high risk zones. There's a demographic angle, too.
Re: Launch HN: Silurian (YC S24) – Simulate the Earth
#95Earlier quoted context omitted.
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…
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.
Re: Launch HN: Silurian (YC S24) – Simulate the Earth
#96> 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.
If anyone wants to get into flooding, I recently met the people of geosmart.space
They’re selling height maps of South-Africa, primary for flooding prediction for insurance companies.
Smart & friendly bunch.
Re: Launch HN: Silurian (YC S24) – Simulate the Earth
#97Fascinating. I have two quick questions, if you find the time:
…we’ve built our own foundation model, GFT (Generative Forecasting Transformer), a 1.5B parameter frontier model that simulates global weather…
Hence: simulate the Earth.
Best of luck, and thanks for taking the leap! Humanity will surely thank you. Hopefully one day you can claim a bit of the NWS’ $1.2B annual budget, or the US Navy’s $infinity budget — if you haven’t, definitely reach out to NRL and see if they’ll buy what you’re selling!
Oh and C) reach out if you ever find the need to contract out a naive, cheap, and annoyingly-optimistic full stack engineer/philosopher ;)
Re: Launch HN: Silurian (YC S24) – Simulate the Earth
#98Earlier quoted context omitted.
I just don't understand how can your produce new knowledge which it don't have access to. Are you you folks claiming the future weather is a function of previous weather and the model is capable of replicating the function?
No one is claiming that there is "new knowledge" here. The entire class of deep learning or AI-based weather models involves a very specific and simple modeling task. You start with a very large training set which is effectively a historical sequence of "4D pictures" of the atmosphere. Here, "4D" means that you have "pixels" for latitude, longitude, altitude, and time. You have many such pictures of these for relevan…
Thanks for the explanation. I am still a bit confused how this takes care of the errors? I can see how the weather prediction for tomorrow might have less errors. But shouldn't the errors accumulate as you feed the predicted weather as the input for the model? Wouldn't the results start diverging from reality pretty soon? Isn't that the reason why the current limit is close to 6 days? How exactly does this model fixed this issue?
Re: Launch HN: Silurian (YC S24) – Simulate the Earth
#99> Astonishingly, this approach, done correctly, produces better forecasts than traditional simulations of the physics of our atmosphere. It seems like this is another instance of The Bitter Lesson, no?
I'm not sure I buy The Bitter Lesson, tbh. Deep Blue wasn't a brute-force search. It did rely on heuristics and human knowledge of the domain to prune search paths. We've always known we could brute-force search the entire space but weren't satisfied with waiting until the heat death of the universe for the chance at an answer. The advances in machine learning do use various heuristics and techniques to solve particu…
Today's best chess models use no heuristics, I think starting with stockfish 16 they got rid of HCE (hand crafted evaluation), they're now neural nets and would absolutely eat Deep Blue
Re: Launch HN: Silurian (YC S24) – Simulate the Earth
#100Weather models are chaotic, are ML methods more numerically stable than a physics based simulation? And how do they compare in terms of compute requirements? the Aurora paper seemed to be promising, but I would love a summary of comparison better than what I get out of Claude. Once upon a time I converted spectral-transform-shallow-water-model (STSWM or parallelized as PSTSWM) from FORTRAN to Verilog. I believe this…
Great questions.
1. The truth is we still have to investigate the the numerical stability of these models. Our GFT forecast rollouts are around 2 weeks (~60 steps) long and things are stable in in that range. We're working on longer-ranged forecasts internally.
2. The compute requirements are extremely favorable for ML methods. Our training costs are significantly cheaper than the fixed costs of the supercomputers that government agencies require and each forecast can be generated on 1 GPU over a few minutes instead of 1 supercomputer over a few hours.
3. There's a similar floating-point story in deep learning models with FP32, FP16, BF16 (and even lower these days)! An exciting area to explore