Scientists at DeepMind just created a revolution in weather forecasting with their new AI.
This paper is history in the making.
Why?
Well, you see, weather forecasting is typically done by not people.
And not even government agencies, but often intergovernmental organizations.
Why is that?
Well because it’s just too hard.
To be able to measure all the information needed for a good weather prediction costs
billions and billions of dollars.
Almost no one can do it.
You have to take into consideration temperature, precipitation, wind, humidity, pressure, vortical
velocities and so much more, and then, simulate their physics into the future.
This needs physicists, meteorologists, computer scientists, and of course, billions and billions
of dollars.
Dear Fellow Scholars, this is Two Minute Papers with Dr. Károly Zsolnai-Fehér.
So, if it costs so much, we get the results instantly, right?
Well, not quite.
This is very expensive and it still takes a long time.
But sometimes you don’t have time and need a quick prediction.
You can’t wait for 24 hours for the algorithm to tell you what’s going to happen in the
next 10 hours.
During that time, storms, hurricanes, floods may happen, and we have to know as much in
advance as possible.
So we need something faster.
Now, we are experienced Fellow Scholars over here, so we already know that AI techniques
exist that can perform physics simulations.
We start running a fluid or smoke simulation, and we say, okay, we stop the simulation here,
now, little AI, tell me how it continues!
And magically so it does.
However, the main goal of these simulations is to be pleasing to the eye and not be that
accurate, so they don’t have the accuracy for weather predictions.
Until now.
Yes, here it is.
An absolutely amazing new paper that promises, wow, that is crazy.
They promise a prediction for the next 10 days in just one minute.
One minute.
Wow.
I will immediately say that okay, I get that, but then, some tradeoffs surely have to be
made.
The quality can’t be so good, right?
I mean, just matching the quality of the billion-dollar system in a minute would be absolutely impossible.
But in any case, let’s have a look.
What?
Are you seeing what I am seeing?
Holy mother of papers - we are measuring the root mean squared error here, so lower is
better here, and the new, super quick technique not only matches the industry standard high-resolution
forecast, but it even outperforms it.
And it is better than the current techniques on about 90 to 99% of the test cases.
That is insanity.
It takes the current state of the quantities we can measure, and then it advances time
by 6 hours accurately.
Then, we can take this predicted model as the current state, and advance that with another
6 hours, and so on, thereby we can make predictions for even 10 days into the future.
And the list of good news just keeps going.
I have three more incredible things for you about this paper.
One, what I am also really shocked by is this.
Now hold on to your papers Fellow Scholars, because it uses less than 40 million parameters.
That sure sounds like a lot, right?
Well, remember, GPT-4, the system that powers ChatGPT has more than 1.5 trillion parameters
as far as I know.
Yes, you are hearing correctly, this system is 50,000 times smaller than GPT-4 and yet
can pull off this absolute miracle.
I will note that the paper did not claim that this would run on your phone, but the size
and and efficiency of this system could potentially allow us to do that.
My goodness.
This kind of superpower right in your pocket.
I am out of words.
And it gets even better.
Two, it is already being used out there in the wild.
Not so long ago, going from a paper to a product out there in the real world took several years,
sometimes even decades.
And this, this is already out there.
This paper is simply so good no one can ignore it.
Loving it.
Three, the models are open source.
Yes!
With a little expertise, you can download and play with them right now or even use them
in an online notebook.
I also loved how they used a graph neural network to accomplish this.
These are typically used for things that have a graph-like structure, like people and their
relationships in a social network, or molecular dynamics.
However, here, instead, the temperature, pressure and other quantities are measured in these
little points, and the graph neural network ensures that there can be communication between
the neighbors in this mesh.
They can pass messages to each other and the surprise is that such a tiny network can be
so powerful at weather prediction that it rivals, and often beats billion dollar systems.
Bravo DeepMind.
So, an AI-based technique that you can run in your pocket soon, and it will give some
of the best warnings for extreme weather events, flood risk and extreme temperatures.
This is a paper that is going to save lives.
This is history in the making.
What a time
to be alive!