4 ms·

That sounds like petty criticism to me. What exactly is "true weather prediction" then? Using a crystal ball? And yes, extrapolation is not new, but deep lear

That sounds like petty criticism to me. What exactly is "true weather prediction" then? Using a crystal ball? And yes, extrapolation is not new, but deep learning is just much better at it. > labeling things as “AI” that are just rather basic data analytics They are using a GAN to do this, you can't get more AI than this. If this is not AI, then what is? Getting accurate short term predictions is very valuable for e.g. prediction of solar panels energy production to avoid starting coal peaker plants, or to predict flooding. I get that it's popular to dunk on anything called AI to show that you have not fallen prey to the PR bullshit machine, but in that case, it's 100% unwarranted in my opinion.

dr-detroit 5y ago
the point is that when academics and private entities start throwing around the word "AI" for anything digital they are cashing in on other people's hard work and diluting the positive effects of actual AI research.
scoopertrooper 5y ago
I generally agree with your points, but nowcasting is definitely the less exciting problem for ML to solve. Also, applying a GAN to nowcasting is definitely not a novel idea. You'd hope that given that modern ML methods can handle crazy high dimensional data that someone would be able to do something with weather forecasting beyond current models. However, there are many factors that render this a 'hard' problem. I guess, starting off with nowcasting is a neat way for DeepMind to become acquainted with the topic matter, but I'd hope they have their ambitions set a little higher. Here's an interesting paper on this topic: https://royalsocietypublishing.org/doi/10.1098/rsta.2020.0097 https://royalsocietypublishing.org/doi/10.1098/rsta.2020.009...
- sailingparrot 5y ago
  Sure, that might not be a Nobel prize worthy breakthrough, but it is still very much AI, and it is still very much "true" weather prediction. I just don't understand where the gatekeeping on both point is coming from.
  - scoopertrooper 5y ago
    Some folks around Hacker News are convinced that AI is just another hype-cycle. Yes, this paper is an incremental improvement over the state of the art of hand crafted algorithms. However, it should be viewed as an incremental step towards greater things! I'm sure DeepMind, the team that recently (basically) solved the protein folding problem, will deliver exciting advancements in the field of weather forecasting in the coming years.
    - Joker_vD 5y ago
      The AI has been another hype-cycle for at least two times already. "AI winter" is a term from the seventies, and it re-emerged the second time in the late eighties. But maybe the third time the charm, who knows.
      - scoopertrooper 5y ago
        I think it's fair to say that we've moved forward leaps and bounds since the the 80s. There have been major advances in compute and AI methods since that era. There's certainly much further to go and we may end up hitting another wall sooner than later, but AI is delivering tangible results today in ways that it never could in previous eras.
        Joker_vD 5y ago
        This same argument was just as valid in 1970 (AI has never been done before at all) and in 1985 (they did actually improve it, and the hardware, drastically). Still, in both cases walls were hit and hype died. So, learning from this dataset, I would predict something similar. In any case, the weather on a scale larger than 2 months is effectively can't be modelled because butterfly effect is real: there is just not enough raw data being collected, and it's not precise enough; and IIRC the required amount of data and floating-point precision for more or less accurate modelling grows exponentially wrt the time period being modelled.
        5y ago
JCM9 5y ago
“True weather prediction” as in the actual computer forecast modes that have been around for decades. Taking atmospheric measurements and creating 3D predictions of weather conditions in the future, including wind speed, precip type and amount, and so on. The example here is just basic image extrapolation analytics, which as pointed out by many, is not new.

dr-detroit 5y ago

the point is that when academics and private entities start throwing around the word "AI" for anything digital they are cashing in on other people's hard work and diluting the positive effects of actual AI research.

scoopertrooper 5y ago

I generally agree with your points, but nowcasting is definitely the less exciting problem for ML to solve. Also, applying a GAN to nowcasting is definitely not a novel idea. You'd hope that given that modern ML methods can handle crazy high dimensional data that someone would be able to do something with weather forecasting beyond current models. However, there are many factors that render this a 'hard' problem. I guess, starting off with nowcasting is a neat way for DeepMind to become acquainted with the topic matter, but I'd hope they have their ambitions set a little higher. Here's an interesting paper on this topic: https://royalsocietypublishing.org/doi/10.1098/rsta.2020.0097 https://royalsocietypublishing.org/doi/10.1098/rsta.2020.009...

sailingparrot 5y ago
Sure, that might not be a Nobel prize worthy breakthrough, but it is still very much AI, and it is still very much "true" weather prediction. I just don't understand where the gatekeeping on both point is coming from.
- scoopertrooper 5y ago
  Some folks around Hacker News are convinced that AI is just another hype-cycle. Yes, this paper is an incremental improvement over the state of the art of hand crafted algorithms. However, it should be viewed as an incremental step towards greater things! I'm sure DeepMind, the team that recently (basically) solved the protein folding problem, will deliver exciting advancements in the field of weather forecasting in the coming years.
  - Joker_vD 5y ago
    The AI has been another hype-cycle for at least two times already. "AI winter" is a term from the seventies, and it re-emerged the second time in the late eighties. But maybe the third time the charm, who knows.
    - scoopertrooper 5y ago
      I think it's fair to say that we've moved forward leaps and bounds since the the 80s. There have been major advances in compute and AI methods since that era. There's certainly much further to go and we may end up hitting another wall sooner than later, but AI is delivering tangible results today in ways that it never could in previous eras.
      - Joker_vD 5y ago
        This same argument was just as valid in 1970 (AI has never been done before at all) and in 1985 (they did actually improve it, and the hardware, drastically). Still, in both cases walls were hit and hype died. So, learning from this dataset, I would predict something similar. In any case, the weather on a scale larger than 2 months is effectively can't be modelled because butterfly effect is real: there is just not enough raw data being collected, and it's not precise enough; and IIRC the required amount of data and floating-point precision for more or less accurate modelling grows exponentially wrt the time period being modelled.
        5y ago

JCM9 5y ago

“True weather prediction” as in the actual computer forecast modes that have been around for decades. Taking atmospheric measurements and creating 3D predictions of weather conditions in the future, including wind speed, precip type and amount, and so on. The example here is just basic image extrapolation analytics, which as pointed out by many, is not new.