5 ms·

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 wha

by 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]
- YeGoblynQueenne 5y ago
  >> 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". This is according to whom? Who was it that 'downgraded [AI] to "weak AI"'?
  - jasode 5y ago
    >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 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.
    - YeGoblynQueenne 5y ago
      In other words, it's your personal opinion, correct? What you explain above are the reasons for which you hold this particular opinion, but it is still your opinion, yes?
      - jasode 5y ago
        >, but it is still your opinion, yes? 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?

by JCM9 5y ago

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]
YeGoblynQueenne 5y ago
>> 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". This is according to whom? Who was it that 'downgraded [AI] to "weak AI"'?
- jasode 5y ago
  >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 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.
  - YeGoblynQueenne 5y ago
    In other words, it's your personal opinion, correct? What you explain above are the reasons for which you hold this particular opinion, but it is still your opinion, yes?
    - jasode 5y ago
      >, but it is still your opinion, yes? 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?