12 ms·

DeepMind AI predicts incoming rainfall with high accuracy

inasio 5y ago
A bit disappointed to be honest. They produced a bunch of models and then checked with a team of meteorologists what they thought of them vs the other models? This sounds more like GPT-3 writing sonets and getting a bunch of poets to evaluate them. Why not just check the predictions?
- saulrh 5y ago
  TBF, going by the problem statement in the article, the objective function could be pretty wacky. For example, do you weight accuracy by location? Is it more important to get your predictions right over sports stadiums than over residential areas? Would it be useful to weight accuracy by time, like, it's more important to get predictions right at 7 AM when people are driving to work than it is to get it right at 1 AM when everyone is asleep? I don't think that that's what's actually happening, but I do think that weather is a sufficiently ugly problem that comparing to human performance is useful.
  - hchasestevens 5y ago
    Surely they must have already established a loss function in order to train the network in the first place?
    - whimsicalism 5y ago
      Sometimes the metric you use for the loss function is not the best metric for eval. Think BLEU for translation, for instance (not that BLEU is particularly great).
localhost 5y ago
Link to Nature paper that the article is based on: https://www.nature.com/articles/s41586-021-03854-z https://www.nature.com/articles/s41586-021-03854-z
krona 5y ago
I do this with my own eyeballs looking at the rainfall radar. It would be interesting if this could be made to forecast beyond what I do to avoid getting wet on my commute, but 60 minutes is good enough already, for me.
- amelius 5y ago
  During winter prediction is easy. You just look at where the rain is, see where it is going and multiply the speed by the delta-time. During summer, rain can suddenly form out of nowhere, making predictions much harder.
  - Workaccount2 5y ago
    This is really what I am after. "Summer storms" as I call them are really tricky, and being an avid motorcyclist in the northeast it's something I pay a lot of attention too. Basically a locus of hot humid air gets going and it will shotgun small but intense rainfall/thunderstorms over a whole area. Weather models kinda just thrown their hands up and say "There is a 30% chance you'll get nailed".
    - reportingsjr 5y ago
      Yep, these are called pop up storms around here, and if you do lots of stuff outdoors then it is super, super useful to have accurate, localized weather predictions for a couple hours out. I was using darksky for a few years, and their 15 min warning was absolutely awesome for knowing when to book it off a trail biking, hiking, etc. It has sucked not having it since Apple bought them and killed the android app (yay anti-competitive behavior!) This summer I got nailed twice with really, really heavy thunderstorms while out doing trail work. The typical weather forecast was just "50% chance of rain for the area, pop up storms possible". Not very helpful when you have limited time to do stuff, and the weather for the next week is heavy rain.
shannifin 5y ago
Predicting only two hours ahead doesn't seem that impressive or helpful to me in itself; major weather-related decisions usually need more time. Still, interesting stuff! A few family members and I usually get little headaches hours before rain if it's preceded by a dip in air pressure, although I've never measured the accuracy or utility of this.
- mewpmewp2 5y ago
  One obvious thing that came to mind immediately is something like racing, for example F1. Knowing what tyres to put on is a make or break there. If DeepMind truly is better than anything else there, I'd imagine F1 to be one of the first to start using it.
- IshKebab 5y ago
  Predicting 2 hours ahead is one of the most useful weather forecasting tasks. 2 hours is enough time to prepare for rain in loads of situations. And sure the title is a bit ridiculous. Anyone can predict incoming rainfall. The question is can it predict it accurately. The abstract says: > we show that our generative model ranked first for its accuracy and usefulness in 89% of cases against two competitive methods Probably a bit better than your headaches.
- tonyedgecombe 5y ago
  I think farmers would like to know what is happening during the next hour or two during harvest.
- ddek 5y ago
  That's really interesting about the headaches. It makes sense. The model is actually useful for google. Road traffic is closely linked to weather, with some routes worse impacted than others. If you predict the weather, you can predict changes in congestion patterns caused by the rain, so you can predict journey times better. Most journeys people are using google maps for are probably in the 30min-2hr range. It's also simply interesting because predicting the progress of frontal rainfall is not something we're good at. We can apply conventional extrapolation, but this only considers the direction of the weather, not at all the changing saturation of the clouds.
tziki 5y ago
Not talking specifically about this one, but to me it seems DeepMind is producing more higher quality research and breakthroughs than other parts of Google. I wonder why that is, it's not like other parts of Google are lacking in talented ML researchers.
- m12k 5y ago
  Maybe because it was an acquisition, so they had already built up a talent pool?
- dagw 5y ago
  not like other parts of Google are lacking in talented ML researchers. I wonder if Google has split it's AI research so that they funnel all their more fundamental research from different departments into DeepMind and make them the 'face' of publicity friendly easy to understand AI research results. Or it could be that all their top AI talent gravitate towards DeepMind, and the people that work there simply are the best AI talent that Google has.
  - sprafa 5y ago
    From experience DeepMind is the #1 goal for any AI/ML person anywhere in Europe. I know people don’t necessarily want to be googlers, but Deepmind ? Yes. If you’re brilliant in the field where else would you work? OpenAI is now completely different from where it started. It’s also heavily based in London and I know they absorb basically all the top Europe folks who want to stay close to home. Not everyone wants to go to the US. Demis is also a bit of a legend and I know DeepMind people teach/recruit at UCL his old university. I assume they do so everywhere in the UK but London is an easy talent pool to make and recruit talent for them. So they have a full integrated vertical stack for talent.
- ndr 5y ago
  There seem to be less PR about those, but the rest of Google is doing a lot: https://research.google/pubs/ https://research.google/pubs/ Agreed, few things are as flashy as protein folding :)
- 5y ago
nefitty 5y ago
I recently received a response to one of my comments saying that GPT-3 can't predict the future. From a naive understanding of the basic way it works, if it can generate subsequent words and phrases that follow the rules of a language based on tokens, couldn't it be trained on larger concepts if those are represented as tokens? I understand that simulations are ultimately constrained by their assumptions, eg how the ultimate stable state of Sim City games seems to be some sort of authoritarian police-state. [1] Couldn't we create a simulation of human history, etc, and iterate on the rules by backtesting, ie doing something similar to the way stock trading indicators are tested? I do fear that AI could be used for nefarious purposes in that regard. "I want to see what needs to occur and what I need to do for this company to gain a monopoly in this industry." I'd be surprised if this isn't already occurring. If anyone has any links they can share about how AI is being applied to social spheres, that would be awesome. 1. https://www.polygon.com/videos/2021/4/1/22352583/simcity-hidden-politics-ideology-urban-dynamics https://www.polygon.com/videos/2021/4/1/22352583/simcity-hid...
- simion314 5y ago
  One problem is if you can train your ML correctly and not on garbage data. To predict the world you need a giant input and a ton of data. And you also have chaos, a little input divergence can cause a big output difference, I am not an ML-ANN dev but chaos feels to me impossible to approximate/interpolate with ANN, I would love to know if I am wrong and you can do any meaningful stuff on chaotic systems.
- tsimionescu 5y ago
  You're making an unwarranted assumption: that the methods used to predict the next token in human language would work for predicting the next token in a simulation of SimCity or history or weather etc. As far as we've seen, each of DeepMind's and OpenAI's successes have been a different network architecture, it's not like we have one network architecture that is good at learning anything you throw at it. Not to mention, the huge problem with weather and history is the limited amount of data, compared to human language, for which there are immense troves.
- whimsicalism 5y ago
cortexio 5y ago
well that's kind of sad. The app i use since ~2014 can predict rain up to 180 minutes with high accuracy. Seems like this is either some DM commercial or just lazy journalism.
m12k 5y ago
I wonder when they're going to tackle macroeconomic forecasting? It seems like a good candidate - a complex system with too many variables for analytical models to be very good, and indications that there are patterns and connections that we don't even understand theoretically yet, but which might be there in the data. I guess for all we know, a sophisticated ML model like that might already exist in some hedge fund, but they'd be keeping quiet about it so they don't lose their edge.
- whimsicalism 5y ago
  Trying to compete in markets is like Goodhart's law on steroids. People use machine learning, but nothing this sophisticated. Outside of the market makers, most of the successful firms are just selection bias.
- EricE 5y ago
  >I wonder when they're going to tackle macroeconomic forecasting? That was Dark Sky's play - and it was amazingly fruitful. It would even leverage the barometer in most phones. It was a bit disappointing when Apple acquired them since it cuts non-iOS users off, but it was a great app when it was out. For iOS users as of iOS 15 Apple's weather widget is now based on Dark Sky's tech making their app redundant.
- libertine 5y ago
  Hmm, but aren't most macroeconomic big swings triggered by Blackswan events? And aren't those, per definition, virtually impossible to predict?
  - netcan 5y ago
    Swings, maybe. There's room for forecasting at larger or smaller intervals, even if swings are unforecastable. That said, I don't think most big financial events are black swan events in some sense but not others. Financial black swans don't tend to be random, real world events. We're living through a major real world event now, and the financial markets haven't collapse... though a lot of weird stuff is happening. The 2008 crisis and many others had not real world. Real world links were gradual and fairly financial in nature, an accumulation of debt/risk/leverage/lies/fragility or whatnot over time. These are more like bugs, design flaws or fail conditions, IMO. From a financial analyst's perspective they're "black swans" because they are events with a >0% likelihood that (by definition) are not priced into security prices or financial systems. From a forecasting model POV... who knows. In any case, forecasting the future of the economy is probably very very hard. If it's possible, it probably has epic sci-fi level consequences.
kristofferR 5y ago
While this would obviously be advantageous for the vast majority of situations, I also can't help but be annoyed how it may have a detrimental effect on racing. Just a few days ago we had some of the best action in a long time due to the teams predicting the rain differently [1]. [1] https://www.youtube.com/watch?v=Jjw1x6xQo7s https://www.youtube.com/watch?v=Jjw1x6xQo7s
- algo_trader 5y ago
  This was a VERY dramatic finish. True. BUT a. all the teams get the same weather feed. I doubt even a top f1 team can afford a custom weather analysis. b. Even if they could, its a cost trade off. The evil capitalist PE which bought F1 has introduced budget limits to level the competition. So if u have a better weather prediction you have to give up on an custom wing package, or whatever. c. Most teams had 2 cars in the running. So they simply split the strategy, sending one car to pit early, and the other on the track. This is probably the optimal strategy EVEN if you have a small edge in weather prediction. Redbull for example popped Verstapen into 2nd place, but Perez tumbled from 3rd to 8th or something. d. Finally, Norris simply chose to stay out DESPITE the weather prediction. Setting emotions aside, a young driver has a different risk-payoff calculus. e. Dont forget the plan to put sprinkles on the track !! You cant predict those ;)) EDIT: spelling
JCM9 5y ago
This is a bit over-hyped and not exactly a breakthrough. It’s not doing true weather prediction but rather extrapolating the movement of radar images. This is nothing new. I remember as far back as the 1990s TV weathermen would draw a line across a line of moving rain and the computer would predict which town it would get to and when. Slapping “AI” on this 25 years later is a good example of the whole present PR move of labeling things as “AI” that are just rather basic data analytics.
- antegamisou 5y ago
  Yep, unless they came up with a way to make the primitive equations solutions significantly computionally cheaper, you’re not getting any more accurate without better turbulence models.
- netcan 5y ago
  Possibly, but this is a DeepMind result. They've had enough non trivial successes to earn some credibility. Even if it is hype, more understandable than most overhyped PR headlines.
- ironrabbit 5y ago
  I don't know much about meteorology -- what is "true weather prediction" then?
  - JCM9 5y ago
    The weather models that run on super computers and predict 3D detailed atmospheric conditions. This is just image analytics. These models could predict, for example, that new precip will develop. This just tracks the path of existing weather.
    - ironrabbit 5y ago
      It seems like this method outperforms those model-based approaches, why should we care that it's "just image analytics"?
fernowens 5y ago
It would be great if they try to predict incoming typhoons formations, this will greatly help us anticipate and measure how dangerous the upcoming danger is.
joosters 5y ago
Years ago, I set up a simple website that screen-scraped the BBC's weather predictions, and compared them against the day's weather report to calculate a very crude and basic accuracy. For the UK towns it monitors, a dumb prediction of "tomorrow's weather will be the same as today's" gives a 34% accuracy - which only falls to about 25% when predicting the weather for next week! Luckily, the proper weather forecasters do a bit better than this :) https://weather.slimyhorror.com/ https://weather.slimyhorror.com/ Excuse the basic site, I set this up over 17 years ago, and with minimal tweaks it has been left to its own devices since then. The stats also compare the BBC accuracy over the last year vs all time, and it seems that they are getting better - I wonder if new AI techniques will really make a big leap in predictions or whether they are just more incremental improvements.
- mLuby 5y ago
  A basic site, sure, but eminently readable. Kudos!
- fho 5y ago
  Ok ... I am directly going to piggyback on this: 1. Subjectively this years weather predictions have been way off, compared to the years before. I heard several theories on that: (a) the year was extraordinary (less cars, less flights) and (b) predictions were worse because data from plane based weather radar was missing. -> Does anybody know if my subjective feeling is based in reality? And if true, what are the reasons? 2. Again subjectively, but I feel like most of my weather based decisions are "do I leave now or do I wait for the rain to pass". That question is answered pretty well by looking at the weather radar maps myself. I feel like an statistical/ML/AI approach that combines what was the weather yesterday and what is the weather in the surrounding cities should fair pretty well.
  - joosters 5y ago
    1. The data I scrape could be enough to check this theory out. Currently the site calculates a 'all time' accuracy and 'last year' accuracy, it would be fairly simple to also add an accuracy for 1 year back, 2 years back, etc. When I have time, I'll give that a try. 2. I once knew of a website that did just this - it displayed the radar image for the village that the creator lived in, and used some really simple linear motion estimation to predict rain in the next hour. I believe it had pretty good accuracy, but unfortunately I can't find that site any more, sorry.
inasio 5y ago
A bit disappointed to be honest. The produced a bunch of models and then checked with a team of meteorologists what they thought of them vs the other models? This sounds more like GPT-3 writing sonets and getting a bunch of poets to evaluate them. Why not just check the predictions?
- FartyMcFarter 5y ago
  > Why not just check the predictions? Isn't this what the paper does in the "Forecast skill evaluation" section?
deleted 5y ago
[deleted]
deleted 5y ago
[deleted]
asdff 5y ago
Living in California, I find the weather a lot more complicated than when I was living in a much flatter and consistent area of the country. Just within my city the variance is so large that any forecast that just says "Los Angeles" is just an average that doesn't exist in reality at all. In Marina del Rey it could be 60, cold, grey, windy, even raining, then you go seven miles northeast to hollywood and its sunny, 85, hot, without a cloud in the sky, no cool ocean breeze, then you go through the cahuenga pass and in a 10 minutes drive the temperature goes up another 10 degrees by the time you are in north hollywood. Then if the winds decide to shift and you get some Santa Anas blowing in, everything can turn on its head fast and its hot in marina del rey even at night. Even with a storm moving directly above it could do remarkably different things whether you live in a flatter side of town or one on a hillside, which usually sees precipitation and even hail or sleet along with colder temps while it might remain bone dry in the flatter parts. Accurate weather estimations for some places needs a very robust understanding of local topology, seasonal winds, and data, lots of data, from sensors that aren't there in enough quantities and in enough places to capture what is actually happening over varied terrain and changing conditions. I found localized apps like darksky very accurate in the midwest where weather is uncomplicated to model beyond occasional things like lake effect snow (which seemed to be well understood), but not very useful in Los Angeles where you practically need your own hardware to actually quantify what the weather is where you are at in your particular canyon today.
EricE 5y ago
I've had this for years with DarkSky - who Apple bought and now the default weather app in iOS is based on. When it first came out I can remember being at a friends baseball game - it was sunny but I got an alert that rain was going to start in 10 minutes. So I went and got an umbrella from the car. Got some snide comments - until 8 minutes later when it started pouring down rain! It's not perfect, but it is spooky at how many times it is spot on with rain within the next hour. The more systems offering this kind of service the better since it is very useful!

A bit disappointed to be honest. They produced a bunch of models and then checked with a team of meteorologists what they thought of them vs the other models? This sounds more like GPT-3 writing sonets and getting a bunch of poets to evaluate them. Why not just check the predictions?

saulrh 5y ago
TBF, going by the problem statement in the article, the objective function could be pretty wacky. For example, do you weight accuracy by location? Is it more important to get your predictions right over sports stadiums than over residential areas? Would it be useful to weight accuracy by time, like, it's more important to get predictions right at 7 AM when people are driving to work than it is to get it right at 1 AM when everyone is asleep? I don't think that that's what's actually happening, but I do think that weather is a sufficiently ugly problem that comparing to human performance is useful.
- hchasestevens 5y ago
  Surely they must have already established a loss function in order to train the network in the first place?
  - whimsicalism 5y ago
    Sometimes the metric you use for the loss function is not the best metric for eval. Think BLEU for translation, for instance (not that BLEU is particularly great).

localhost 5y ago

Link to Nature paper that the article is based on: https://www.nature.com/articles/s41586-021-03854-z https://www.nature.com/articles/s41586-021-03854-z

krona 5y ago

I do this with my own eyeballs looking at the rainfall radar. It would be interesting if this could be made to forecast beyond what I do to avoid getting wet on my commute, but 60 minutes is good enough already, for me.

amelius 5y ago
During winter prediction is easy. You just look at where the rain is, see where it is going and multiply the speed by the delta-time. During summer, rain can suddenly form out of nowhere, making predictions much harder.
- Workaccount2 5y ago
  This is really what I am after. "Summer storms" as I call them are really tricky, and being an avid motorcyclist in the northeast it's something I pay a lot of attention too. Basically a locus of hot humid air gets going and it will shotgun small but intense rainfall/thunderstorms over a whole area. Weather models kinda just thrown their hands up and say "There is a 30% chance you'll get nailed".
  - reportingsjr 5y ago
    Yep, these are called pop up storms around here, and if you do lots of stuff outdoors then it is super, super useful to have accurate, localized weather predictions for a couple hours out. I was using darksky for a few years, and their 15 min warning was absolutely awesome for knowing when to book it off a trail biking, hiking, etc. It has sucked not having it since Apple bought them and killed the android app (yay anti-competitive behavior!) This summer I got nailed twice with really, really heavy thunderstorms while out doing trail work. The typical weather forecast was just "50% chance of rain for the area, pop up storms possible". Not very helpful when you have limited time to do stuff, and the weather for the next week is heavy rain.

shannifin 5y ago

Predicting only two hours ahead doesn't seem that impressive or helpful to me in itself; major weather-related decisions usually need more time. Still, interesting stuff! A few family members and I usually get little headaches hours before rain if it's preceded by a dip in air pressure, although I've never measured the accuracy or utility of this.

mewpmewp2 5y ago
One obvious thing that came to mind immediately is something like racing, for example F1. Knowing what tyres to put on is a make or break there. If DeepMind truly is better than anything else there, I'd imagine F1 to be one of the first to start using it.
IshKebab 5y ago
Predicting 2 hours ahead is one of the most useful weather forecasting tasks. 2 hours is enough time to prepare for rain in loads of situations. And sure the title is a bit ridiculous. Anyone can predict incoming rainfall. The question is can it predict it accurately. The abstract says: > we show that our generative model ranked first for its accuracy and usefulness in 89% of cases against two competitive methods Probably a bit better than your headaches.
tonyedgecombe 5y ago
I think farmers would like to know what is happening during the next hour or two during harvest.
ddek 5y ago
That's really interesting about the headaches. It makes sense. The model is actually useful for google. Road traffic is closely linked to weather, with some routes worse impacted than others. If you predict the weather, you can predict changes in congestion patterns caused by the rain, so you can predict journey times better. Most journeys people are using google maps for are probably in the 30min-2hr range. It's also simply interesting because predicting the progress of frontal rainfall is not something we're good at. We can apply conventional extrapolation, but this only considers the direction of the weather, not at all the changing saturation of the clouds.

tziki 5y ago

Not talking specifically about this one, but to me it seems DeepMind is producing more higher quality research and breakthroughs than other parts of Google. I wonder why that is, it's not like other parts of Google are lacking in talented ML researchers.

m12k 5y ago
Maybe because it was an acquisition, so they had already built up a talent pool?
dagw 5y ago
not like other parts of Google are lacking in talented ML researchers. I wonder if Google has split it's AI research so that they funnel all their more fundamental research from different departments into DeepMind and make them the 'face' of publicity friendly easy to understand AI research results. Or it could be that all their top AI talent gravitate towards DeepMind, and the people that work there simply are the best AI talent that Google has.
- sprafa 5y ago
  From experience DeepMind is the #1 goal for any AI/ML person anywhere in Europe. I know people don’t necessarily want to be googlers, but Deepmind ? Yes. If you’re brilliant in the field where else would you work? OpenAI is now completely different from where it started. It’s also heavily based in London and I know they absorb basically all the top Europe folks who want to stay close to home. Not everyone wants to go to the US. Demis is also a bit of a legend and I know DeepMind people teach/recruit at UCL his old university. I assume they do so everywhere in the UK but London is an easy talent pool to make and recruit talent for them. So they have a full integrated vertical stack for talent.
ndr 5y ago
There seem to be less PR about those, but the rest of Google is doing a lot: https://research.google/pubs/ https://research.google/pubs/ Agreed, few things are as flashy as protein folding :)
5y ago

nefitty 5y ago

I recently received a response to one of my comments saying that GPT-3 can't predict the future. From a naive understanding of the basic way it works, if it can generate subsequent words and phrases that follow the rules of a language based on tokens, couldn't it be trained on larger concepts if those are represented as tokens? I understand that simulations are ultimately constrained by their assumptions, eg how the ultimate stable state of Sim City games seems to be some sort of authoritarian police-state. [1] Couldn't we create a simulation of human history, etc, and iterate on the rules by backtesting, ie doing something similar to the way stock trading indicators are tested? I do fear that AI could be used for nefarious purposes in that regard. "I want to see what needs to occur and what I need to do for this company to gain a monopoly in this industry." I'd be surprised if this isn't already occurring. If anyone has any links they can share about how AI is being applied to social spheres, that would be awesome. 1. https://www.polygon.com/videos/2021/4/1/22352583/simcity-hidden-politics-ideology-urban-dynamics https://www.polygon.com/videos/2021/4/1/22352583/simcity-hid...

simion314 5y ago
One problem is if you can train your ML correctly and not on garbage data. To predict the world you need a giant input and a ton of data. And you also have chaos, a little input divergence can cause a big output difference, I am not an ML-ANN dev but chaos feels to me impossible to approximate/interpolate with ANN, I would love to know if I am wrong and you can do any meaningful stuff on chaotic systems.
tsimionescu 5y ago
You're making an unwarranted assumption: that the methods used to predict the next token in human language would work for predicting the next token in a simulation of SimCity or history or weather etc. As far as we've seen, each of DeepMind's and OpenAI's successes have been a different network architecture, it's not like we have one network architecture that is good at learning anything you throw at it. Not to mention, the huge problem with weather and history is the limited amount of data, compared to human language, for which there are immense troves.
whimsicalism 5y ago

cortexio 5y ago

well that's kind of sad. The app i use since ~2014 can predict rain up to 180 minutes with high accuracy. Seems like this is either some DM commercial or just lazy journalism.

m12k 5y ago

I wonder when they're going to tackle macroeconomic forecasting? It seems like a good candidate - a complex system with too many variables for analytical models to be very good, and indications that there are patterns and connections that we don't even understand theoretically yet, but which might be there in the data. I guess for all we know, a sophisticated ML model like that might already exist in some hedge fund, but they'd be keeping quiet about it so they don't lose their edge.

whimsicalism 5y ago
Trying to compete in markets is like Goodhart's law on steroids. People use machine learning, but nothing this sophisticated. Outside of the market makers, most of the successful firms are just selection bias.
EricE 5y ago
>I wonder when they're going to tackle macroeconomic forecasting? That was Dark Sky's play - and it was amazingly fruitful. It would even leverage the barometer in most phones. It was a bit disappointing when Apple acquired them since it cuts non-iOS users off, but it was a great app when it was out. For iOS users as of iOS 15 Apple's weather widget is now based on Dark Sky's tech making their app redundant.
libertine 5y ago
Hmm, but aren't most macroeconomic big swings triggered by Blackswan events? And aren't those, per definition, virtually impossible to predict?
- netcan 5y ago
  Swings, maybe. There's room for forecasting at larger or smaller intervals, even if swings are unforecastable. That said, I don't think most big financial events are black swan events in some sense but not others. Financial black swans don't tend to be random, real world events. We're living through a major real world event now, and the financial markets haven't collapse... though a lot of weird stuff is happening. The 2008 crisis and many others had not real world. Real world links were gradual and fairly financial in nature, an accumulation of debt/risk/leverage/lies/fragility or whatnot over time. These are more like bugs, design flaws or fail conditions, IMO. From a financial analyst's perspective they're "black swans" because they are events with a >0% likelihood that (by definition) are not priced into security prices or financial systems. From a forecasting model POV... who knows. In any case, forecasting the future of the economy is probably very very hard. If it's possible, it probably has epic sci-fi level consequences.

kristofferR 5y ago

While this would obviously be advantageous for the vast majority of situations, I also can't help but be annoyed how it may have a detrimental effect on racing. Just a few days ago we had some of the best action in a long time due to the teams predicting the rain differently [1]. [1] https://www.youtube.com/watch?v=Jjw1x6xQo7s https://www.youtube.com/watch?v=Jjw1x6xQo7s

algo_trader 5y ago
This was a VERY dramatic finish. True. BUT a. all the teams get the same weather feed. I doubt even a top f1 team can afford a custom weather analysis. b. Even if they could, its a cost trade off. The evil capitalist PE which bought F1 has introduced budget limits to level the competition. So if u have a better weather prediction you have to give up on an custom wing package, or whatever. c. Most teams had 2 cars in the running. So they simply split the strategy, sending one car to pit early, and the other on the track. This is probably the optimal strategy EVEN if you have a small edge in weather prediction. Redbull for example popped Verstapen into 2nd place, but Perez tumbled from 3rd to 8th or something. d. Finally, Norris simply chose to stay out DESPITE the weather prediction. Setting emotions aside, a young driver has a different risk-payoff calculus. e. Dont forget the plan to put sprinkles on the track !! You cant predict those ;)) EDIT: spelling

JCM9 5y ago

This is a bit over-hyped and not exactly a breakthrough. It’s not doing true weather prediction but rather extrapolating the movement of radar images. This is nothing new. I remember as far back as the 1990s TV weathermen would draw a line across a line of moving rain and the computer would predict which town it would get to and when. Slapping “AI” on this 25 years later is a good example of the whole present PR move of labeling things as “AI” that are just rather basic data analytics.

antegamisou 5y ago
Yep, unless they came up with a way to make the primitive equations solutions significantly computionally cheaper, you’re not getting any more accurate without better turbulence models.
netcan 5y ago
Possibly, but this is a DeepMind result. They've had enough non trivial successes to earn some credibility. Even if it is hype, more understandable than most overhyped PR headlines.
ironrabbit 5y ago
I don't know much about meteorology -- what is "true weather prediction" then?
- JCM9 5y ago
  The weather models that run on super computers and predict 3D detailed atmospheric conditions. This is just image analytics. These models could predict, for example, that new precip will develop. This just tracks the path of existing weather.
  - ironrabbit 5y ago
    It seems like this method outperforms those model-based approaches, why should we care that it's "just image analytics"?

fernowens 5y ago

It would be great if they try to predict incoming typhoons formations, this will greatly help us anticipate and measure how dangerous the upcoming danger is.

joosters 5y ago

Years ago, I set up a simple website that screen-scraped the BBC's weather predictions, and compared them against the day's weather report to calculate a very crude and basic accuracy. For the UK towns it monitors, a dumb prediction of "tomorrow's weather will be the same as today's" gives a 34% accuracy - which only falls to about 25% when predicting the weather for next week! Luckily, the proper weather forecasters do a bit better than this :) https://weather.slimyhorror.com/ https://weather.slimyhorror.com/ Excuse the basic site, I set this up over 17 years ago, and with minimal tweaks it has been left to its own devices since then. The stats also compare the BBC accuracy over the last year vs all time, and it seems that they are getting better - I wonder if new AI techniques will really make a big leap in predictions or whether they are just more incremental improvements.

mLuby 5y ago
A basic site, sure, but eminently readable. Kudos!
fho 5y ago
Ok ... I am directly going to piggyback on this: 1. Subjectively this years weather predictions have been way off, compared to the years before. I heard several theories on that: (a) the year was extraordinary (less cars, less flights) and (b) predictions were worse because data from plane based weather radar was missing. -> Does anybody know if my subjective feeling is based in reality? And if true, what are the reasons? 2. Again subjectively, but I feel like most of my weather based decisions are "do I leave now or do I wait for the rain to pass". That question is answered pretty well by looking at the weather radar maps myself. I feel like an statistical/ML/AI approach that combines what was the weather yesterday and what is the weather in the surrounding cities should fair pretty well.
- joosters 5y ago
  1. The data I scrape could be enough to check this theory out. Currently the site calculates a 'all time' accuracy and 'last year' accuracy, it would be fairly simple to also add an accuracy for 1 year back, 2 years back, etc. When I have time, I'll give that a try. 2. I once knew of a website that did just this - it displayed the radar image for the village that the creator lived in, and used some really simple linear motion estimation to predict rain in the next hour. I believe it had pretty good accuracy, but unfortunately I can't find that site any more, sorry.

inasio 5y ago

A bit disappointed to be honest. The produced a bunch of models and then checked with a team of meteorologists what they thought of them vs the other models? This sounds more like GPT-3 writing sonets and getting a bunch of poets to evaluate them. Why not just check the predictions?

FartyMcFarter 5y ago
> Why not just check the predictions? Isn't this what the paper does in the "Forecast skill evaluation" section?

deleted 5y ago

[deleted]

deleted 5y ago

[deleted]

asdff 5y ago

Living in California, I find the weather a lot more complicated than when I was living in a much flatter and consistent area of the country. Just within my city the variance is so large that any forecast that just says "Los Angeles" is just an average that doesn't exist in reality at all. In Marina del Rey it could be 60, cold, grey, windy, even raining, then you go seven miles northeast to hollywood and its sunny, 85, hot, without a cloud in the sky, no cool ocean breeze, then you go through the cahuenga pass and in a 10 minutes drive the temperature goes up another 10 degrees by the time you are in north hollywood. Then if the winds decide to shift and you get some Santa Anas blowing in, everything can turn on its head fast and its hot in marina del rey even at night. Even with a storm moving directly above it could do remarkably different things whether you live in a flatter side of town or one on a hillside, which usually sees precipitation and even hail or sleet along with colder temps while it might remain bone dry in the flatter parts. Accurate weather estimations for some places needs a very robust understanding of local topology, seasonal winds, and data, lots of data, from sensors that aren't there in enough quantities and in enough places to capture what is actually happening over varied terrain and changing conditions. I found localized apps like darksky very accurate in the midwest where weather is uncomplicated to model beyond occasional things like lake effect snow (which seemed to be well understood), but not very useful in Los Angeles where you practically need your own hardware to actually quantify what the weather is where you are at in your particular canyon today.

EricE 5y ago

I've had this for years with DarkSky - who Apple bought and now the default weather app in iOS is based on. When it first came out I can remember being at a friends baseball game - it was sunny but I got an alert that rain was going to start in 10 minutes. So I went and got an umbrella from the car. Got some snide comments - until 8 minutes later when it started pouring down rain! It's not perfect, but it is spooky at how many times it is spot on with rain within the next hour. The more systems offering this kind of service the better since it is very useful!