Aardvark beats groundhogs and supercomputers in weather forecasting Aardvark, a novel machine learning-based weather prediction system, teases a future where supercomputers are optional for forecasting - but don't pull the plug just yet. Academics affiliated with the Alan Turing Institute in the UK and other institutions claim they have developed a weather prediction model that can be trained …
Or get the famous weather forecasting rock…
And Train the AI on these rules
If the rock is wet, it's raining.
If the rock is swinging, the wind is blowing.
If the rock casts a shadow, the sun is shining.
If the rock does not cast a shadow and is not wet, the sky is cloudy.
If the rock is difficult to see, it is foggy.
If the rock is white, it is snowing.
If the rock is coated with ice, there is a frost.
If the ice is thick, it's a heavy frost.
If the rock is bouncing, there is an earthquake.
If the rock is under water, there is a flood.
If the rock is warm, it is sunny.
If the rock is missing, there is a tornado.
If the rock is wet and swinging violently, there is a hurricane.
If the rock can be felt but not seen, it is night time.
If the rock has white splats on it, watch out for birds.
If the rock is levitating, you're stoned.
If there are two rocks, you're drunk.
The forecast for this evening/tonight where I am is full of dire warnings of thunderstorms, gusting winds, and hailstones the size of golfballs.
So I spent a long-overdue hour cleaning my wife’s #%^@! out of the garage to allow me to put my car away (hers has been squeezed into one of the barns) juuuuuust in case.
Can’t decide if in the event of no hail I will be annoyed or relieved, after going to all that effort!
But glass half full, I now have that rarest and most precious of things - a garage with a car in it rather than just junk.
Can't read the paper, but this looks like another example that picking the right parameters is key to any statistical exercise, which underpin most of modern AI systems. Starting from relatively simple machine learning, LLMs just added parameters as they came along in the race to be "first". Having now more or less reached the "complexity horizon", other approaches are required.
Thank you !!!
'AI' has run into the 'Complexity Horizon' at full tilt ... and discovered that it is surprisingly 'Hard' in all senses, like a 'gag' from 'Looney Tunes'.
The resulting 'collision' has resulted in an 'Headache' we are ALL feeling !!!
Maybe when our 'collective heads' clear we will recover enough sense to recognize that it is all an 'oversold' scam !!!
[One can hope that the 'bubble' WILL burst and sooner rather than later !!!]
:)
Yerrrs, but doesn't this weather forecasting system represent a move to addressing that Complexity Horizon (or at least pushing it a little further into the future)?
They appear to be taking on board that, with a bit of domain-specific knowledge and input you can do better than the current default of just throwing a shit-ton of stuff into a ginormous ML.
Perhaps the real future of ML is not so much tilting at AGI, but effective domain-specific systems.
I'm only accurate out about four days, but some old-timers get pretty good accuracy out almost a week.
It works by using the bridge as a kind of barometer. The fog hits the bridge at different heights depending on barometric highs and lows, Their relative movements can be visually tracked, both how far off the water, and how much of the towers can be seen above it. Couple that with observing the wind direction, if one tower is clear and the other is in fog (changing wind directions) and various other factors combine to make for a very useful tool to the observant local. Knowing what the water temp readings readings are at the off-shore buoys are help, too, but some say that's cheating.
Yes, I live in a mostly boring flat area (Norfolk) though, as I live on one of the more bumpier parts, I can either get a clear view of the Norwich Southern Bypass as it passes over the river or not depending on the weather (or, in summer, possibly the local trees).
One of my cats will bury herself underneath the top covers on my bed if it is or is going to be cold. Not exactly forecasting but probably as good as any aardvark that I know of!
I've just started with a company that deals with building management systems. Worldwide.
Thousands of buildings, and hundreds of thousands of data points (outside temperature is a given, humidity is common) that can be pulled together in a second.
That is a shitload of "free" data to work with.
I have already suggested throwing it into a learning engine to see what happens
I doubt anyone knows, or expects to know. A neural net isn't an algorithm. Although the different stages may often be inspired by the kinds of categorisation that humans apply when doing the same task "by hand", there's no hard and fast rule requiring this. You could just start with a layer containing all the raw measurements, end with a layer delivering the time-series of the forecasts for different places, and stick a few layers of "mix it all up" in between and see how you get on.
If it works, it's useful. Eventually, one might study the best of the working models and see if the layers are doing something specific that no-one had thought to try before. It could give you clues that eventually lead to genuine scientific progress. In the meantime, not needing a super-computer to create a forecast might even save you money. (Perhaps not, mind, since you probably do still need something quite chunky for the training.)
Indeed. There is in fact already a considerable academic push, though, (including at my own research centre) into developing techniques to "peer into the black box" and gain insight into how opaque ML systems (yes, they are algorithms!) achieve the functionality that they do.
A neural network is essentially a system that can encode patterns in data during the training phase, and then recognize them during the inference phase. Meteorological data has patterns, and those patterns correlate with future weather.
It works similarly to the old folk who know nothing of atmospheric physics, but have observed the weather in their town every day for fifty years. They can probably make a decent forecast on just the data they get from their senses, although they won't be able to explain it much better than "because it's always like that". That literally means "because the current data matches a pattern I recognize".
The NN works in a similar fashion, except that it has observed a lot more data on a lot more weather in a lot more locations.
"It works similarly to the old folk who know nothing of atmospheric physics"
The old Red sky at nigh is a very generalised version of this. It works because the weather in the UK usually arrives from the west. What it doesn't say is that it won't work when we're in a run of easterlies. If this is what ML accomplishes it will have the same limitations. As someone said up-thread predicting tomorrow will be like today has a pretty good success rate without any form of computation.
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