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Shameless plug: recently we've built a demo that allows you to search for objects in San Francisco using natural language. You can look for things like Tesla cars, dry patches, boats, and more. Link: https://demo.bluesight.ai/
We've tried using Clay embeddings but we quickly found out that they perform poorly for similarity search compared to embeddings produced by CLIP fine tuned on OSM captions (SkyScript).
> Silurian builds foundation models to simulate the Earth, starting with the weather. What else do you hope to simulate, if this becomes successful?
This really, really looks like a nullschool clone ( https://earth.nullschool.net/ ). Is it not?
I don’t think I understand what your issue is with them. They used an open-source project to visualize their data, were open about doing so, and cited the creator of the project. What more did you want from them? (Genuine question.)
nullschool is obscure enough to the general audience that when I saw it there was an immediate red flag.
If only specialized scientists can see the difference between the sites, it's a presentation problem.
Earlier quoted context omitted.
I don’t think I understand what your issue is with them. They used an open-source project to visualize their data, were open about doing so, and cited the creator of the project. What more did you want from them? (Genuine question.)
something within the interface that more clearly illustrates their product differentiation. nullschool is obscure enough to the general audience that when I saw it there was an immediate red flag. If only specialized scientists can see the difference between the sites, it's a presentation problem.
Does anyone predict economy/population/... by simulating individual people based on real census information? Monte carlo simulation of major events (births, death, ...) based on known statistics based on age, economic background, location, education, profession, etc.? It seems there are not that many people that this would be computationally infeasible, and states and companies have plenty of data to feed into such s…
Can we help you? We build the equivalent for land, as a non-profit. It's basically a geo Transformer MAE model (plus DINO, plus matrioska, plus ...), but largest and most trained (35 trillion pixels roughly). Most importantly fully open source and open license. I'd love to help you replace land masks with land embeddings, they should significantly help downscale the local effects (e.g. forest versus city) that afaik…
Hi, this looks really cool! Can we meet? Shoot us an email at contact@silurian.ai
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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…
Deep blue had to use the techniques it did due to the limitations of the hardware of the time. Deep blue would almost certainly lose against AlphaZero, even if you tuned it to modern hardware. All you have to do 'manually' is teach it the rules/give it a loss function, then you just let it do its thing. It's certainly true that "just throw a bunch of GPUs at it" is wasteful, but it does achieve results.
And even though solutions to many such problems were in the NP or NP-hard categories it didn’t mean that we couldn’t get useful results.
But it still gave us better results by applying what we know about search strategies and reinforcement to provide guidance and heuristics. Even Alpha didn’t use the most general algorithms and throw hardware at the problem. Still took quite a lot of specialized software and methods to fine-tune the overall system to produce the results we want.
> 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?
Notably forecast skill is quantifiable, so we'd need to see a whole lot of forecast predictions using what is essentially the stochastic modelling (historical data) approach. Given the climate is steadily warming with all that implies in terms of water vapor feedback etc., it's reasonable to assume that historical data isn't that great a guide to future behavior, e.g. when you start having 'once every 500 year' floods every decade, that means the past is not a good guide to the future.