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.
>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.
The New Atlas article has a link to the Nature journal paper it's based on. Your dismissal and summary of DeepMind's work described by the article as a "public relations move" is a disservice to readers.
The more detailed explanation in the Nature journal describes a new technique of "deep generative models" applied to weather radar. This was not available 25 years ago. In tests, their DGM forecasts became preferred by meteorologists 93% of the time for accuracy compared to previous "data analytics". Excerpt from Nature:
>We use a single case study to compare the nowcasting performance of the generative method DGMR to three strong baselines: PySTEPS, a widely used precipitation nowcasting system based on ensembles, considered to be state-of-the-art3,4,13; UNet, a popular deep learning method for nowcasting15; and an axial attention model, a radar-only implementation of MetNet19
>[...] When expert meteorologists judged these predictions against ground truth observations, they significantly preferred the generative nowcasts, with 93% of meteorologists choosing it as their first choice (Fig. 4b).
You're still misrepresenting the actual words in the NA article. NA didn't say "image extrapolation is new". What they actually wrote was the "generative modeling approach" was new. Excerpt:
>DeepMind set out to develop a machine-learning tool that can bring a new level of precision to these efforts, [...] It did so by using a generative modeling approach,
That's basically an accurate short summary of the longer Nature journal article.
>or “AI” is what’s not great and doesn’t help trend of people rushing to portray something as “AI” when it’s just the same sort of data analytics
This is really just the AI vs AGI[1] distinction. If you're not aware, the label "AI" (naked with no modifiers) has already been downgraded to "weak AI". So the more difficult "AI" that nobody solved yet is now elevated with a new label of "AGI" or "strong AI" to help sort out the confusion.
- "AI" which implies "weak AI" : analogous to "we don't care that planes and drones don't really 'fly' because even if they don't flap their wings like birds, it still solves a problem." Analogy is "AI":"fly"
--vs--
- "AGI" artificial general intelligence: solve the very hard problem of going from logic gates to "learning" everything like a child's brain does. In this definition, AlphaZero beating every human chess player and all previous computer chess engines is "not really AI".
[1] https://en.wikipedia.org/wiki/Artificial_general_intelligenc...
The DeepMind work is fantastic. The media spin isn't - and I don't mean DM's PR team, I mean the opinions shouted from the rooftops across blogs and popular media (including the MIT Technology Review [2]). DM's technique still falls squarely in the domain of extrapolation from recent imagery - exactly what some commenters here are pointing out was developed decades ago. There's little evidence that the new approach can robustly handle the development of new convection or non-linear evolution of mesoscale systems. That's obvious in the animation that's being shared - within the envelope of the linear system over the UK, the structure of storm cells is highly persistent and the overall motion is linear. But you can readily identify areas of unrealistic growth/decay (usually attributed to numerical diffusion in pure image processing techniques, e.g. semi-lagrangian advection of the background OF field).
That matters because the practical application(s) of precipitation nowcasting are really limited to things like, "it will rain in XX minutes at location YY". As long as there is rain on the radar, that problem is 'solved' about as precisely as you would ever need.
IMHO the biggest innovation here relates to the computational efficiency of the approach. Probably a total beast to train the DGMR system, but inferences in a handful of seconds? That's awesome - it opens up new possibilities for _analysis_ (e.g. sampling a large ensemble from the latent space of plausible future states of the radar imagery and producing highly-tuned probabilistic forecasts or incorporating stochastic mechanism that may yield more realistic projections of cellular growth/decay within linear systems) which have thus far been computationally intractable.
The next leap forward in nowcasting is convective initiation. That would be a legitimate game changer in meteorology.
[1]: https://www.mdpi.com/2073-4433/10/9/555/htm [2]: https://www.technologyreview.com/2021/09/29/1036331/deepmind...
This is according to whom? Who was it that 'downgraded [AI] to "weak AI"'?
The "who" is all of us. _We_ all collectively watered-down "AI" based on how we _used_ "AI" in mainstream news articles and VC-backed startups or any company today throwing around the word "AI" associated with technology. I was making a descriptive and not prescriptive statement.
See that the gp complains that a "deep generative model neural net" is _not_ "AI". My point is that virtually all uses of naked "AI" is now understood to be examples of "weak AI". Therefore, making a meta-comment on every article that mentions "AI" (instead of "AGI") as "that's not really AI" ... has become superfluous.
Consider the phrase "YC-backed AI startup": https://www.google.com/search?q=yc-backed+%22ai+startup%22
Let's imagine if each of those stories was submitted to HN. Do we really need to make a meta-comment in each thread saying, "What they're doing is not really AI and I hate how the AI label is slapped on everything!" ?
We already know that Real Generalized Artificial Intelligence is not actually here (maybe not for decades) -- and yet -- people we don't control keep using the label "AI". Now what do we do? If one remembers they're talking about "weak AI" whenever they use the naked "AI" terminology, we just let it go and move on.
No, it's not my opinion. I'm making factual statements of language evolution which doesn't care about my opinion. A bunch of other people we have no control over have already used "AI" the way it's being used now. I think you're trying to be combative and argumentative about the word "AI" and I don't know why.
I ask you to click on the google link for "YC-backed AI startup". What would be your definition of "AI" such that all those headlines can be interpreted correctly? How is everyone using that term? And when Amazon/Apple/Google/Microsoft announce that they have a new feature "AI assisted this or AI-powered that" ... what do they mean? This thread's article used the term "AI" and "AI system" to refer to DeepMind's GAN neural network. What did the author mean by "AI" in his text? Certainly not AGI. So what's left?
I'm pointing out that it is your opinion that 'the label "AI" (naked with no modifiers) has already been downgraded to "weak AI".' You explained why you think so, but that's just ...why you think so. You're not some kind of authority on how terms should be used and you have no reason to admonish the other user to use it in the way you like.
Btw, note that I'm not interested in your disagreement with the OP about "weak" vs. "strong" AI. As far as I'm concerned, you're both trying to apply what you know from Science Fiction to the real world. The only thing that exists in the real world today that's called "AI" by any authoritative source is the field of research in artificial intelligence. It is common in the lay press to describe systems created by AI researchers as "AI" or "AIs" and it's even more common to refer to deep learning reserach synechdochically as "AI", or "machine learning", but those are terminological mistakes that are to be expected from people outside a field of research as varied and broad as AI. Researchers in the field, of course, don't ever call their work "AI"! Well, not in published research at least. I mean, that's a three-strong-reject offence. One'd be laughed out of the field...
So I hope this clarifies the confusion and the motivation for my comment. You have an opinion, strongly held, based on poor, irrelevant knowledge and you forcefully support it. I thought, since I have a bit of knowledge in the matter, I should set the record straight: That's just, like, your opinion, man.
Yes, I agree I'm not and I previously said, "I was making a descriptive and not prescriptive statement." If you're not familiar with descriptive-vs-prescriptive: https://en.wikipedia.org/wiki/Is%E2%80%93ought_problem
>to admonish the other user to use it in the way you like.
To be clear, I didn't admonish him. I thought he misunderstood how _others_ were (mis)using the "AI" term. I didn't disagree with the OP about "AI" and did not instruct him to use it differently.
>It is common in the lay press to describe systems created by AI researchers as "AI" or "AIs" and it's even more common to refer to deep learning reserach synechdochically as "AI", or "machine learning", but those are terminological mistakes that are to be expected
When you write, "It is common in the lay press to describe systems created ..." you just restated the same descriptive-vs-prescriptive explanation I did.
And yes! Others we don't control keep repeating "terminological mistakes" as you call it. That's my point and you're restating it in different words. I just happened to use the word "downgraded" instead of "terminological mistakes". Maybe there's a language barrier and it's that particular word that bothers you?
>Researchers in the field, of course, don't ever call their work "AI"!
Exactly! So now we must hold two contradictory facts (not opinions) in our head:
(1) academic researchers don't call their work "AI"
(2) ~2 billion search results for the term "AI" of which most usage are "terminological mistakes" : https://www.google.com/search?q=%22AI%22+software
So the reality is that we still have 2 billion pages using "AI" that didn't obey any authority such as academic researchers telling them how to use it. Now what? I guess we can complain, "I wish 2 billion webpages didn't slap "AI" on everything!"
Has that changed anything? Was that complaint about others' language (mis)usage productive to the discussion? In my opinion, I don't think so.
As analogy, if you insist that peanuts/cashews/almonds correct definition is "legumes" and not "nuts" -- you still have to simultaneously hold another contradictory definition in your head to understand that others are still referring to those as "nuts". Descriptive-vs-Prescriptive.
EDIT reply to: >"So we agree that 'the label "AI" (naked with no modifiers) has already been downgraded to "weak AI"' is just your opinion, correct?"
You're playing argument games trying to trap me in "my opinion" instead of noticing that you said the same thing I did. No it's not my opinion; it's a factual observation of what people are doing. It's also not my opinion that you explained what the world does as "terminological mistakes" -- a factual observation -- which was a restatement of what I already said. This means we're going around in circles.
In any case, I would like to ask you why you think academic researchers in AI field don't call their work "AI"?
As far as "It’s not doing true weather prediction but rather extrapolating the movement of radar images.", both the paper and article say the paper is tackling short term rain prediction ('precipitation nowcasting'), so it's not oversold as far as I can see.
Did it do it as accurately as this new method?
You are making this sound way too simplistic - and sort of insulting to scientists who study weather patterns, it is not nearly as "draw a straight line and be right 100% of the time" as you're making it out to be, especially over larger timescales.
These models do not use AI, they work by extrapolating, like you say.
If you have access to DWD's RADOLAN image data, for example as rendered images through the DWD WarnWetter-App (you need to pay a small one-time fee to access the radar data), you can clearly see how much this extrapolation leaves to be desired (even though it is extremely useful as it is). Actually, almost every German weather data provider which offers radar precipitation predictions is based on the raw data provided by DWD, this raw data can also be downloaded for free at https://opendata.dwd.de/weather/radar/radolan/rw/
Anyway, if you look at the predictions, they are pretty simple. As if the wind direction at two different altitudes is determined for each point, and then applied to the current precipitation data.
These wind vectors don't change during the (short term, max 2h) prediction, so you see the parts of the image moving at a constant velocity as soon as you're talking about the future.
This neglects two things: wind direction will change during these two hours, which is why you as the app user need to check often to verify if it is still accurate, but most importantly this simple model does not take into account the humidity in the air. So sometimes the rain will arrive sooner not because the wind got faster, but because new clouds are starting to build faster in your direction than the old cloud systems get to travel towards you with the wind.
And in both these cases AI provides a significant potential of improvement. By looking at more of the surrounding weather dynamics it will be able to predict better what is actually happening in the weather system. Currently we can only improve this by adding more sensors and more frequent radar scans, but AI can really start to interpret the past one-hour-weather and "understand" what is happening there in order to predict what will happen later. And there is a ton of data available for training.
The steering currents really don't change much over 2 hours for an organized system. You can get some rotational motion with a large cyclone but modern OF methods do just fine with that, and you can always remove the divergent component of the flow field. An example of (b) can be found in any Spring season convective outbreak in the Central US; once a squall line congeals along a front you'll see pioneer convection propagate along a vector somewhat orthogonal to the squall line's motion (there are heuristics for the propagation vector that work OK for curved hodographs except in inhomogeneous environments, e.g. Fig 8.10 from Markowski and Richardson). It's the 3D wind shear that matters here, augmented with the lapse rate / profile for (a).
It's hard to bullish on the AI applications here until we see them start to account for these larger input parameter spaces. But of course, where is this data going to come from? Mesoscale or convection-permitting models. And if you already have the capability to run these models in a cost-efficient manner, do you need the AI system in the first place?
"Ensemble numerical weather prediction (NWP) systems, which simulate coupled physical equations of the atmosphere to generate multiple realistic precipitation forecasts, are natural candidates for nowcasting as one can derive probabilistic forecasts and uncertainty estimates from the ensemble of future predictions7. For precipitation at zero to two hours lead time, NWPs tend to provide poor forecasts as this is less than the time needed for model spin-up and due to difficulties in non-Gaussian data assimilation8,9,10."
Sounds like the detailed models are too heavy for this particular job, and that the existing methods to deal with it are too coarse. And there's lots of training data, so it's a really natural place to drop in a generative model.
It's a special corner case of weather forecasting, but a real result.