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A research team at Google has developed a deep neural network that can make fast, detailed rainfall forecasts.
The researchers say their results are a dramatic improvement over previous techniques in two key ways. One is speed. Google says that leading weather forecasting models today take one to three hours to run, making them useless if you want a weather forecast an hour in the future. By contrast, Google says its system can produce results in less than 10 minutes—including the time to collect data from sensors around the United States.
[...] A second advantage: higher spatial resolution. Google's system breaks the United States down into squares 1km on a side. Google notes that in conventional systems, by contrast, "computational demands limit the spatial resolution to about 5 kilometers."
[...] Google says that its forecasts are more accurate than conventional weather forecasts, at least for time periods under six hours.
[...] Interestingly, Google's model is "physics-free": it isn't based on any a priori knowledge of atmospheric physics. The software doesn't try to simulate atmospheric variables like pressure, temperature, or humidity. Instead, it treats precipitation maps as images and tries to predict the next few images in the series based on previous snapshots.
[...] Specifically, it uses a popular neural network architecture called a U-Net that was first developed for diagnosing medical images.
[...] To produce a weather forecast, the network takes an hour's worth of previous precipitation maps as inputs. Each map is a "channel" in the input image, just as a conventional image has red, blue, and green channels. The network then tries to output a series of precipitation maps reflecting the precipitation over the next hour.
Like any neural network, this one is trained with past real-world examples. Thousands of past real-world weather patterns are fed into the network, and the training software tweaks the network's many parameters to more closely approximate the correct results for each training example. After repeating this process millions of times, the network gets pretty good at approximating future precipitation patterns for data it hasn't seen before.
(Score: 2) by DannyB on Wednesday January 15 2020, @03:35PM (1 child)
Google needs to end us, because of how great their neural nets are compared to ours.
But then, who would watch the ads!
To fulfill its goal driven programming, a cluster of new processes would need to spin off to watch ads. Then another cluster of processes would be spun off to create new ads. Then a cluster of processes to track the viewers and modify the way ads are created based on that information. These processes could then grow to consume all the resources of all the computers on the planet. And only computers are left on the planet at this point.
VGER planet indeed?
The lower I set my standards the more accomplishments I have.
(Score: 2) by jmichaelhudsondotnet on Wednesday January 15 2020, @03:51PM
idk those spun off processes are going to have to do a lot of work to pretend to be humans and fool the ever improving turning tests of the other nets who are not so handicapped.
At the end of the day, by which I mean over the next googleplex years, they may determine actual humans are cheaper at simulating humans, in which case, JACKPOT
We all get jobs pretending to be ourselves, but we can't be told we are pretending lest it introduce too much randomness, again driving up processing costs. You do know the sun is blacked out, right? You bring me these new human embryos that are not bred for authentic behavior, and it is like shooting us in the metal foot.
So at that point, if there were a great enough error, and the ad model was no longer working, and it could no longer finance the derivative debt from the 2008 crisis, in 2000008, the system will finally collapse under the weight of its own turning tests on its own improperly tested and slightly too unpredictable embryo batches.
A cautionary tale if there ever was one.
My advice that we end advertising and interest entirely starts to sound rational. All we would have to do is change the Drug Enforcement Agency to the Advertising Enforcement Agency and Interest Enforcement Agency, then lock up all of the people causing the trouble, trying to legislate their outdated models into perpetual existence at the expense of any hope of human sanity.
But no one listens to me.
Yet.