4 ms·

that's exactly the thing I'm seeing in my field (computational materials science). Basically a simple regression model (with very simple features) brings you 90

by fock 7y ago

that's exactly the thing I'm seeing in my field (computational materials science). Basically a simple regression model (with very simple features) brings you 90% there, still people compete on publishing (on ONE shitty benchmark dataset) ever better results – the most-cited people are using KRR, e.g. each fit uses 2TB of RAM and "days" of CPU (features of length O(1000) and 100000 samples). The sample data is probably a 30s calculation (and still only a rough estimation). Sometimes wants you to question science, but hey, writing proposals with "ML" in it gives at least a chance on that grant...

1000units 7y ago
--- guy who writes "O(1000)" telling you how ML research is flawed
KaoruAoiShiho 7y ago
I haven't thought as much about it as you have probably, but ok you get 90% of the way there, but now what? How do you get that last 10%? It would be a huge amount of work right?
- hef19898 7y ago
  Isn't that usually the case? That the first 90% are as difficult to achieve as the next 5%, which are again as hard as the next 3%?
  - KaoruAoiShiho 7y ago
    Basically the argument is that ML would be able to get there easier.
- fock 7y ago
  you get the last 10% by running a calculation for that (which itself is not necessarily accurate...). The whole situation is a little bit like Plato's cave... And also from what I've seen so far, those models either are 90% there or they are overfitting like hell.
nilkn 7y ago
This is great if you only need 90%. But often that extra 10% is the difference between a state of the art commercially viable product and vaporware that nobody will pay for. It’s sort of like production software in general. Sometimes the core product is pretty easy to prototype... but serving it to all your users with very high reliability and uptime is not so easy, and that’s what actually gets them to pull out the credit card.

by fock 7y ago

1000units 7y ago

--- guy who writes "O(1000)" telling you how ML research is flawed

KaoruAoiShiho 7y ago

I haven't thought as much about it as you have probably, but ok you get 90% of the way there, but now what? How do you get that last 10%? It would be a huge amount of work right?

hef19898 7y ago
Isn't that usually the case? That the first 90% are as difficult to achieve as the next 5%, which are again as hard as the next 3%?
- KaoruAoiShiho 7y ago
  Basically the argument is that ML would be able to get there easier.
fock 7y ago
you get the last 10% by running a calculation for that (which itself is not necessarily accurate...). The whole situation is a little bit like Plato's cave... And also from what I've seen so far, those models either are 90% there or they are overfitting like hell.

nilkn 7y ago

This is great if you only need 90%. But often that extra 10% is the difference between a state of the art commercially viable product and vaporware that nobody will pay for. It’s sort of like production software in general. Sometimes the core product is pretty easy to prototype... but serving it to all your users with very high reliability and uptime is not so easy, and that’s what actually gets them to pull out the credit card.