Everything in AI seems to be focused on LLMs lately. But in my opinion, powerful problem-specific models like this are even more interesting. The SOTA AI models used in weather forecasting are already outperforming the classic NWP models while being orders of magnitude more efficient (inference). Most are based on multi scale (hierarchical) Graph Neural Networks, an architecture which is not often talked about. The original Graphcast paper is worth a read if you think this is interesting: https://arxiv.org/abs/2212.12794
Maybe was this that was the last drop for Sundar.
Demis: "I have a new amazing breakthrough"
Sundar: "Great! We really need a answer to Sol and Fable"
Demis: "They are completely owned in typhoon forecasting"
Ironically typhoon forecasting, at this moment, is more valuable. These predictions are matters of life, death, and billions of dollars in damage.
I know this is uncharitable and I am wrong but I am having trouble coming up with concrete scenarios where you die with 2 days notice but survive with 3. I am nonethless a believer that more accurate forecasting has value.
Could you imagine a scenario where from warning to complete evacuation takes more than two days? Evacuating a whole area is a hard task, particularly once you start looking at more complex problems (elderly, prisons, hospitals).
I feel like the details of this are highly dependent on the confidence of the warning; moving large numbers of people (particularly elderly) will result in some deaths regardless. I guess more time to do it should help though.
The 2 vs 3 days makes less of an impact on personal decision making but has massive benefits for decision making at the country wide response level.
Hurricane Maria went from Cat 2 to Cat 5 in less than 24 hours, and turned making a direct hit to Dominica in 2017.
Valuable, agreed. But lucrative?
Are Sol and Fable lucrative? I suspect they also are valuable (to clients) but not lucrative (yet).
I think both have value, but in opposite ways. While WeatherNext prevents costs, models like fable or sol "create profit".
I can think of 10 examples how one could make money with fable. With WeatherNext? Only 10 examples of preventing costs.
Taking this, maybe naive, thought further, profits have no upper limit (except resources) while costs can only save so much?
I've always assumed that insurance and/or government departments that would spend money due to storms would be the ones funneling money to these sorts of efforts. It's not exactly something you can easily sell directly to individuals who would benefit. It would be pretty dystopian for them to sell subscriptions for an extra 24 hrs notice on the next typhoon :P
I agree, but the shareholder mentality undervalues the heck out of that.
This is really cool, please more of this from the AI folks! That’s way more impactful and interesting than another coding agent
I just discovered typhoon/cyclone predictions and they're insane. I get mine via https://zoom.earth (whose iPhone app is terrific).
Here's a selection from Typhoon Dolphin, currently sitting off the east coast of China.
Dolphin continues its slow, trochoidal Z motion, generally heading westward deeper into the East China Sea. Over the past 12 hours, the system completed another cyclonic loop and has decelerated, exhibiting continued meandering prior to establishing a sustained westward track.
The erratic motion witnessed over the past two days is attributable to a weak steering environment produced by a break in the subtropical ridge 2 over Korea, combined with the dynamics where the inner core is cocooned within a much larger parent circulation.
While the general steering pattern is weak, a mesoscale deep-layer ridge is seen building over southern Japan.
Here's Chan-hom, which threatens to make my birthday a windy day here in northern Japan.
Intensity guidance is in good agreement overall. However, the JTWC forecast is placed lower than all the guidance save for Google DeepMind over the next 36 hours, before joining the consensus envelope (which peaks at 95 km/h (50 knots) at 60 hours) through the remainder of the forecast.
If you’re just getting into this tropical tidbits[0] is my go to for more raw data. Less pretty than zoom earth but also an interesting place to see what the models are predicting on each of their runs which is then interesting to compare to actual forecast guidance.
For atlantic basin hurricanes (and the occasional one that could impact Hawaii he also does fantastic youtube videos
I am getting into cyclone predictions (for maritime scheduling)
Is there a basic/freemium resource for past events? Mostly just very coarse spatial/temporal maps of past events
From the tagline in the article: "WeatherNext enables accurate cyclone forecasts that can give an extra day of warning. Now we are open sourcing the model."
> We can now generate a single 15-day forecast in less than a minute on a TPU, empowering forecasters to quickly evaluate the probability distribution of potentially devastating tail-risks.
Crazy
"This has surprised scientists, and it remains an open research question to fully understand how our models produce such accurate predictions at this resolution."
Also crazy.
Seems important to understand why something does what it does, in the very least to know when it might not?
You can't, and it's one of the biggest problems when trying to use AI for anything.
Next step: steering them. (As in Permutation City's "Operation Butterfly".)
They should try to forecast earthquakes, that would really be a breakthrough If anything better than random comes out
Google has the early warning system that gives people maybe 20-30s to e.g. turn off gas, stop vehicles, get under something solid. There was a lot of news recently about how this saved many thousands of lives in Venezuela I think it was.
But hey let's all keep shitting on Google because their coding agent is slightly worse than SOTA.
> But hey let's all keep shitting on Google because their coding agent is slightly worse than SOTA.
Reminder, we can do two or even more things. In fact, we can even simultaneously hold contradictory opinions.
This needs to be tied to a whole house shutoff system because if I get an alert I’m not thinking about shutting off my gas or water. Having a system that shut those off immediately would be great
This was my immediate hope too, as fault line resident
Is this even feasible with our current sensor data?
Accurate weather forecasting has been one of the major achievements of the 20th and 21st century. Computing power is a central piece of this story, but it's also important to remember that the government infrastructure in place to collect ground-truth current weather data is utterly critical to these model's successes. From launching weather balloons to running global weather-monitoring satellites, the scientists and systems at NOAA/NWS (and in this case, the UK counterparts) provide critical expertise and data.
I say this because it seems that earlier announcements where industrial deep neural nets "outperformed NOAA" likely encouraged the slash-and-burn Trump administration in its gutting of critical activities and centers of expertise at NOAA. The impression that industry can predict weather better than the government agencies totally misses that the industrial models utterly rely on government data for inputs. In fact, almost all weather reports you see---weather.com, TV, etc.---are just lightly repackaged products that NOAA provides for free on weather.gov (which you can access for free without ads).
It seems especially useful for cargo ships, with better predictions they could save some fuel and be safer.
Wake me up once commercial airplanes can take advantage of this and take us across the Atlantic in less than 5 hours.
I don't mean to be disrespectful... but, why would you consider tech intended to save lives and resources less of a deal, than slightly faster flights over the Atlantic?
planes fly above the weather, so kinda irrelevant. you can cross the atlantic fast with something like the Concorde
Well, that explains why even cyclones don't make us faster!
Obviously the technology I'm talking about would involve the planes going into the cyclone so plane can go faster.
I keep relearning this every few months
You say this as if you don't need he MWP models to train the AI models? The accuracy of the AI Prediction depends entirely on the quality of the training dataset...
I would imagine this would be trained on actual historical weather data instead?