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>This is a bit over-hyped and not exactly a breakthrough. [...] This is nothing new. >Slapping “AI” on this 25 years later is a good example of the whole prese

by jasode 5y ago

>This is a bit over-hyped and not exactly a breakthrough. [...] This is nothing new. >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).

JCM9 5y ago
The underlying research is indeed very interesting. Passing this (image extrapolation as a means of tracking rain movements) off as something new 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 that’s been going on for a long time. Granted a lot of that concern falls on the press and PR folks more so than the underlying research.
- jasode 5y ago
  >Passing this (image extrapolation as a means of tracking rain movements) off as something new 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_intelligence https://en.wikipedia.org/wiki/Artificial_general_intelligenc...
  - counters 5y ago
    To be extremely pedantic, their general approach isn't new. Folks in the meteorology community have been toying with generative modeling approaches for the past five years. For example [1] used GANs for super-resolution reconstruction of radar-derived imagery and if you skim the past 4-5 AI conferences at the American Meteorological Society Annual Meeting you'll see multiple people working with similar (albeit simpler - usually weather folks aren't domain experts in AI) modeling approaches. 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 https://www.mdpi.com/2073-4433/10/9/555/htm [2]: https://www.technologyreview.com/2021/09/29/1036331/deepminds-ai-predicts-almost-exactly-when-and-where-its-going-to-rain/ https://www.technologyreview.com/2021/09/29/1036331/deepmind...
  - deleted 5y ago
    [deleted]

by jasode 5y ago

JCM9 5y ago

The underlying research is indeed very interesting. Passing this (image extrapolation as a means of tracking rain movements) off as something new 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 that’s been going on for a long time. Granted a lot of that concern falls on the press and PR folks more so than the underlying research.

jasode 5y ago
>Passing this (image extrapolation as a means of tracking rain movements) off as something new 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_intelligence https://en.wikipedia.org/wiki/Artificial_general_intelligenc...
- counters 5y ago
  To be extremely pedantic, their general approach isn't new. Folks in the meteorology community have been toying with generative modeling approaches for the past five years. For example [1] used GANs for super-resolution reconstruction of radar-derived imagery and if you skim the past 4-5 AI conferences at the American Meteorological Society Annual Meeting you'll see multiple people working with similar (albeit simpler - usually weather folks aren't domain experts in AI) modeling approaches. 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 https://www.mdpi.com/2073-4433/10/9/555/htm [2]: https://www.technologyreview.com/2021/09/29/1036331/deepminds-ai-predicts-almost-exactly-when-and-where-its-going-to-rain/ https://www.technologyreview.com/2021/09/29/1036331/deepmind...
- deleted 5y ago
  [deleted]