Superfast Microsoft AI is first to predict air pollution for the whole world
nature.com
nature.com
It's cool that their model has learned about this relationship, but a greater editorial point is that _we already have global air pollution models_ - particularly, the one this model was trained (fine-tuned) against! That's not to discount all the really novel and interesting new developments with this paper, but the editorialization around AI applications in the weather and climate space is getting way out of hand, especially when most of these applications are incremental improvements at best over the existing technology in the field (with the key exception of cost/efficiency of generating forecasts - that's a true revolution).
The Aurora paper is really cool, but issues like these greatly temper my enthusiasm for it. I hope that the research team seeks input and collaboration with domain experts in meteorology and atmospheric chemistry in the future to find ways to leverage this new technology in impactful and useful ways that actually will benefit society.
From my perspective ML in general has a weird relationship with cross-domain collaboration. The discussions around linguistics feel like they're full of similar examples and it comes off as a lack of curiosity.
> The researchers trained Aurora on more than a million hours of data from six weather and climate models. After training the model, the team tweaked it to predict pollution and weather globally. The model generates a ten-day global weather forecast alongside the air-pollution prediction.
Edit: Added the quote from the article where they confirm the model is trained on existing legacy models.
This is the same broad plan used for Alphafold — and for things like Stable Diffusion turbo.
It’s rare that these efforts yield interesting new output; they’re usually made specifically for the space/time speedup. In this case, weather predictions that are crazy fast/cheaper to run are seen as a public good, and so sending around a ‘compressed’ version of the big simulations is great.
So we'd still need to keep working on them anyway, or not?
Why would training an AI on many of the models that get mediocre results somehow produce a more reliable output than the current natural intelligences are able to get from the same models?
Like, from a layman's perspective, getting the forecasted high and low within 5 degrees and accurately predicting if it's going to rain (and if so, when) are probably the factors we're evaluating. We don't really notice or care if something like the atmospheric pressure forecast is right or wrong. We care about whether we're well-prepared for the temperature and whether we get wet, and we know from constant experience that the forecast 14 days out can change by 10+ degrees (F) and/or more than 50% chance of rain by the time it becomes tomorrow's forecast, so from our perspective, the forecast is "usually wrong" when it's more than a couple days out.
Are we really, en masse, that wrong about what seems to be constant and universal experience? Or is "precision" measured very differently by professionals than what laypeople mean when they talk about "whether the weather forecast is right or wrong"?
But, having said that, temperature predictions seem to be fairly ok up to a week out. Rain is something else, but since we got our dog, I don't care any longer so much. I need to go out anyway ;-) And then I just take a look at the actual rain-radar data for my location to check the actual situation and the outlook for the next hour or so.
[1] "the gridboxes in weather and climate models have sides that are between 5 kilometers (3 mi) and 300 kilometers (200 mi) in length" https://en.wikipedia.org/wiki/Numerical_weather_prediction
1. any percentage prediction like "99,999999999999%" chance of rain is still 100% correct if it doesn't rain.
2. its my understanding (from a trip to the local weather bureau) that an "80% change of rain" means something like "when conditions in the past were like they are now, it rained 4 out of 5 times".
Happy to be educated otherwise.
While there are some factors that influence predictability in the weather forecast, as the fortran code is based on physics (at least in a broad sense), it doesn't suffer from those issues in the same way.
This doesn't mean that the ML forecasts are wrong (obviously), just different. Given the relative computational simplicity of running them, I wonder if the issue is not just expertise, but also understanding how they can best be used to generate reliable weather forecasts?
but you have to admit, they are really gross to work with
Oh wait, that's right. IBM owns weather.com.
In my experience stuff like this is often vastly overestimated but if it really can do what it says it's great but I would like to see some examples.
predict the levels of carbon monoxide, nitrogen oxide, nitrogen dioxide, sulfur dioxide, ozone and particulate matter. Its predictions span five days...at [much less] cost than...ECMWF [model], which predicts global air-pollution levels
Aurora’s predictions were of a similar quality to those of the conventional model.
After 1st read-thru, I believed the article was touting promise as result.
As I wrote the above synopsis, I zeroed in on the part about the performance comparison to the conventional model. Not sure why I couldn't pick it out at first.
1. First, models will predict pollution. The outcomes will help shape urban policy. But these won't solve crime or stop people from driving.
2. Second, models will predict individual behavior and track person level emissions. The outcomes will force behavior changes, mostly freedom limiting.
3. Third, and finally, models will predict thoughts. The the thought of driving instead of walking might trigger a response.
It's a slippery slope and we need to draw a line between prediction and policy.
Even allowing for the ridiculously massive technical leap from 1 to 2 and then 2 to 3, it doesn't make much sense.
For one thing, if states are determined to enforce individual emissions limits, they can do it today with legislation. You don't need a predictive model. What does the model add?
Also, the only difference between 2 and 3 is whether a person acts on a thought.
So are you suggesting with #3 that predicted thoughts (e.g. not literal mind reading) which a person doesn't act upon will prompt state action?
To be honest, I feel the latter sense of the word is a bit of a stretch - semantically, not politically.
But you see it because "freedom" is a powerful word in politics, and rather than argue against "freedom", pundits go up the ladder of abstraction and argue the definition instead.