Google's DeepMind Can Predict Wind Patterns a Day In Advance (engadget.com)

technology_dude writes: Google's DeepMind can predict wind patterns one day in advance. "Beginning last year, [Google and DeepMind] fed weather forecasts and existing turbine data into DeepMind's machine learning platform, which churned out wind power predictions 36 hours ahead of actual power generation," Engadget reports. "Google could then make supply commitments to power grids a full day before delivery." According to the report, this makes the energy generated by its wind turbines more valuable (by roughly 20%). Is this a blow to Big Blue who purchased The Weather Channel's Weather.com to showcase Watson, or is it news just because it's Google?

Comments

But can it predict..

[h33t l4x0r]

A butterfly flapping it's wings in Brazil?

So what? I can do that.

[alvinrod]

So what? I can do that.

My predictions rarely turn out to be correct, but that's besides the point.

I've got a question

[Artem S. Tashkinov]

Weather forecasting is a hard mathematical problem with thousands of variables which needs to be calculated to be precise.

The question is: does DeepMind AI/algorithm calculate the weather or it is just guessing it? If it's doing the latter then this guesswork is going to be pretty random and equally worthless, and I see no way it's gonna reach the precision of the known mathematical weather models. It might guess well in the short term (relatively few initial parameters), but in the long term I don't see it working well.

"trained on widely available weather forecasts"

[Namarrgon]

Best go direct to the source for your answers:

Using a neural network trained on widely available weather forecasts and historical turbine data, we configured the DeepMind system to predict wind power output 36 hours ahead of actual generation.

So no, it doesn't do weather forecasts itself, but it is based on those.

Not wind prediction

[Rutulian]

Ah, ok, so I actually read the article (no I'm not new here). And it even says it right there in the summary, but somehow it didn't register. They are predicting wind power generation, not the wind itself!

Definitely not as impressive as my first reading. They are even using existing weather forecast data, so nothing particularly innovative here. I think there is a lot of potential to do something really cool in this area with DeepMind, though. There is a ton of historical and real-time data available.

No one read the article

[supercell]

The amount of click bait on Slashdot is getting more than I can bear. It didn't predict make a meteorological forecast ie. wind. It made "wind power predictions" by combining wind forecasts and historical wind turbine power generation data. You can do this with a simple spread sheet.

We have been doing this for years and this is nothing new. This is news because it has Googles name in it and the AI buzz word.

Simply astonishing what people will write up as an article and it makes as "news"

Algorithms vs. models

[VeryFluffyBunny]

A little lesson in scientific literacy:

Algorithms, like Google's, that trawl through historical data & make predictions based on that work fine, as long as everything continues as before & nothing changes. It's a complex equivalent of continuing a straight line on a graph & calling it a prediction. It's completely useless & can even be destructive if you use it in combination with exerting influence on the system itself because biased or wrong predictions (which is always the case) create positive feedback loops that are self-reinforcing. A prime example of this abuse of data is so called predictive policing, which suffers from a negative form of the "Matthew Effect."

In contrast, a theoretical model makes predictions based on the properties, forces, & constraints on a system, be it complex (probabilities) or simple (rules). Even if conditions change, anomalies appear, etc., the model is more likely to remain reasonably predictive.

BTW, looking for patterns, regularities, etc. in data without testable hypotheses or research questions is a form of scientific malpractice called p-hacking.

Conclusions: AI is only predictive as long as nothing changes. Models or more reliable & flexible. We shouldn't stop thinking about how the world works & give it all over to mindless algorithms.