41–50 of 156 posts
Am skeptical about the business case for this given the huge government investment in part of this. What will your differentiators be? Are you paying for weather data products?
Better weather predictions are worth money, plain and simple.
Earlier quoted context omitted.
I didn't know it was open source. I thought it was a ripoff.
A ripoff of the visualization layer? Even if it was, who cares? That's not the work. What's next, you think a new chess engine is a ripoff because they use a standard chess board for visualization? A new protein prediction model is a ripoff because they use the standard visualization?
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…
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 skimboarding.
Basically, I needed AI. And this looks incredible. Love your website and even the name and concept of "Generative Forecasting Transformer (GFT)" - very cool. I imagine the likes of Surfline, The Weather Channel, and NOAA would be interested to say the least.
> 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…
It's certainly true that "just throw a bunch of GPUs at it" is wasteful, but it does achieve results.
1. How will you handle one-off events like volcanic eruptions for instance? 2. Where do you start with this too? Do you pitch a meteorology team? Is it like a "compare and see for yourself"?