I am highly skeptical of this approach. Not only has this been attempted before, it appears to be on course to repeat the same technical design mistakes that caused prior attempts to fail. You can't decompose weather/climate style supercomputing models below a certain resolution because the fundamental characteristics of the computation are not meaningfully representable that way. Scalable models that correctly and efficiently capture the macro effects of many types of sparse, high-resolution dynamics require much more sophisticated computer science, systems engineering, and ways of representing data. You can't brute force it.
In particular, this is a notoriously poor approach to modeling complex large-scale interactions between humans and their environments. There was a study I was involved in last year to determine why one of the epidemiological models for COVID was so badly off target. The root cause was modeling human behavior in the same way you would model weather, which is quite inappropriate but the implementors of the epidemiological model did not have the expertise to know better.
The selection of GPUs is also not appropriate for the nominal objectives of the program. When modeled correctly, i.e. not as weather, these kinds of things aren't the kind of workload GPUs are good at. They tend to look more like very high dimensionality sparse graphs -- latency-hiding is more important than computational throughput. CPUs are actually quite good at this.
This looks more like a program more designed to produce press releases than useful results.
I know it's off topic but I'd love to learn more about how the modeling of COVID went wrong. You can pm me if you want to take the discussion elsewhere.
The models tended to overshoot the number of deaths by huge amounts. For example, the Imperial College of London estimated 40m deaths in 2020 instead of the 2m that occurred.
The model authors have since argued that the data was correct, but people responded to the pandemic by changing the way we live. That's OP point: that feedback cycles and corrections exist and they make modeling dynamic systems very difficult.
This seems unsurprising, and the correct way to model a situation like this to me.
"Lots of people could die if you keep behaving as you currently are"
"Okay, lets behave differently"
And then less people die.
Trying to frame it as "they modelled it wrong" is nonsense. What even is the point of predictions like this if not to change behaviour - predicting outcomes based on everyone taking precautions and not telling people what might happen if they don't would be dangerous and irresponsible.
> Trying to frame it as "they modelled it wrong" is nonsense.
It's not that. It's that when the system you model responds to the existence of your model, it becomes anti-inductive. It's no longer like weather, but is now like the stock market[0]. Your model suddenly can't predict the system anymore[2], it can at best determine its operating envelope by estimating the degree to which the system can react to the existence of the model.
--
[0] - I use the term anti-inductive per LessWrong nomenclature[1], but I've also been reading "Sapiens" by Yuval Noah Harari, and there he uses terms "first order chaotic" for systems like weather, and "second order chaotic" for systems like the stock market.
Edit: I think this is not the first time the good people from lesswrong dug up some well known idea and gave it a new name.
Good thing, too, giving this important concept more attention. Too often we forget how many people have dealt with the problem of modeling complex systems in the past. And while we can not read everything, it's often a good idea to have at least a glance at where they failed!
Too often I read/review some new "revolutionary" paper based on the idea that hey, we can model this process (involving people) like XYZ from physics, where this stuff works great! Surely, this is better than the plebian approaches in the literature!
And then, to the shock of all involved, it doesn't work great....
fun fact comment, Asimov thought about this, I'm quoting the short part that is relevant from this article on wikipedia :https://en.wikipedia.org/wiki/Foundation_series
"One key feature of Seldon's theory, which has proved influential in real-world social science,[3] is the uncertainty principle: if a population gains knowledge of its predicted behavior, its self-aware collective actions become unpredictable."
The issue is that the example nostromo gave (40m) was not intended to be predictive of what would actually happen. It was based on a worst case / left unchecked scenario (useful for establishing an upper bound), and therefore irrelevant w/r/t the system responding to the model.
> The models tended to overshoot the number of deaths by huge amounts. For example, the Imperial College of London estimated 40m deaths in 2020 instead of the 2m that occurred.
The very article you cited pointed out that the 40m figure was based on a "left unchecked" scenario. It was not an attempt to predict the actual number of deaths that would occur. Claiming that this is indicative of overshooting because the actual number of deaths is 2m is completely wrong.
But they never learn either, the ICL react studies are still getting it spectacularly wrong, 4 weeks ago they claimed cases were rising in the UK, that R was above 1.
Even a cursory glance at the actual data, even the data available at the time, shows they were completely and utterly wrong.
> The model authors have since argued that the data was correct, but people responded to the pandemic by changing the way we live. That's OP point: that feedback cycles and corrections exist and they make modeling dynamic systems very difficult.
Were they actually so naïve that their model did not allow for the possibility that human beings change their behavior in fear of death by pandemic?
Of course people changed their behavior because of Covid models, that's the fucking point. The modelling wasn't wrong it was just predicting what would happen if people didn't follow any precautions, in order to work what precautions were necessary.
Snarky, but true point. Also consider that scientific efforts geared towards attracting funding tend to be the ones that get funded.
This hasn’t always been the case so it’s fair to consider what circumstances might foster a better situation, where research can be directed to areas most promising to add progress and value to society at large.
I honestly find it rather interesting how much it is possible for scientific research to gain funding though it be completely bereft of any practical benefit for mankind, or profit margin.
Consider palæontology as an entire field; there is no financial benefit nor practical application to be had for it, yet it seems to find ways to attract funding all the same, most likely because it does have a habit of generating spectacular news articles which sponsors would probably enjoy the publicity of.
But there is truly no practical benefit for mankind to be had in trying to answer to what extent various dinosaur species were endothermic and feathered.
> But there is truly no practical benefit for mankind to be had in trying to answer to what extent various dinosaur species were endothermic and feathered.
Except we’re an insatiably curious species and it sates our curiosity.
It's a good example of how curiosity is sated not by truth, but by anything purporting as such.
The image of dinosaurs that became canonically entrenched in popular culture is almost certainly completely wrong, but the truth is of little consequence, exactly because it is not used for anything that might depend on it's veracity.
It really does not matter whether it be accurate or completely false, for this purpose.
In particular, this is a notoriously poor approach to modeling complex large-scale interactions between humans and their environments. There was a study I was involved in last year to determine why one of the epidemiological models for COVID was so badly off target. The root cause was modeling human behavior in the same way you would model weather, which is quite inappropriate but the implementors of the epidemiological model did not have the expertise to know better.
The selection of GPUs is also not appropriate for the nominal objectives of the program. When modeled correctly, i.e. not as weather, these kinds of things aren't the kind of workload GPUs are good at. They tend to look more like very high dimensionality sparse graphs -- latency-hiding is more important than computational throughput. CPUs are actually quite good at this.
This looks more like a program more designed to produce press releases than useful results.