5 ms·

I’m a regular software dev but I’ve had to do ML stuff by necessity. I wonder how “real” ML people deal with the stochastic/gradient results and people’s expec

I’m a regular software dev but I’ve had to do ML stuff by necessity. I wonder how “real” ML people deal with the stochastic/gradient results and people’s expectations. If I do ordinary software work the thing either works or it doesn’t, and if it doesn’t I can explain why and hopefully fix it. Now with ML I get asked “why did this text classifier not classify this text correctly?” and all I can say is “it was 0.004 points away to meet the threshold”, and “it didn’t meet it because of the particular choice of words or even their order” which seems to leave everyone dissatisfied.

xtagon 2y ago
Not all ML is built on neural nets. Genetic programming and symbolic regression is fun because the resulting model is just code, and software devs know how to read code.
- nchfgsj1 2y ago
  Genetic programming however isn’t machine learning, but instead it’s an AI algorithm. An extremely interesting one as well! It was fun to have my eyes opened after being taught genetic algorithms, to then be brought into genetic programming
- aiforecastthway 2y ago
  Symbolic regression has the same failure mode; the reasons why the model failed can be explained in a more digestible way, but the actual truth of what happened is fundamentally similar -- some coefficient was off by some amount and/or some monomial beat out another in some optimization process. At least with symbolic regression you can treat the model as an analyzable entity from first principles theories. But that's not really particularly relevant to most failure modes in practice, which usually boil down to either missing some qualitative change such as a bifurcation or else just parameters being off by a bit. Or a little bit of A and a little bit of B.
hkt 2y ago
This seems to be the absolute worst of all worlds: the burden of software engineering with the tools of an English Language undergrad.
- gopher_space 2y ago
  The English degree helps explain why word choice and order matter, giving you context and guidelines for software design.

xtagon 2y ago

Not all ML is built on neural nets. Genetic programming and symbolic regression is fun because the resulting model is just code, and software devs know how to read code.

nchfgsj1 2y ago
Genetic programming however isn’t machine learning, but instead it’s an AI algorithm. An extremely interesting one as well! It was fun to have my eyes opened after being taught genetic algorithms, to then be brought into genetic programming
aiforecastthway 2y ago
Symbolic regression has the same failure mode; the reasons why the model failed can be explained in a more digestible way, but the actual truth of what happened is fundamentally similar -- some coefficient was off by some amount and/or some monomial beat out another in some optimization process. At least with symbolic regression you can treat the model as an analyzable entity from first principles theories. But that's not really particularly relevant to most failure modes in practice, which usually boil down to either missing some qualitative change such as a bifurcation or else just parameters being off by a bit. Or a little bit of A and a little bit of B.

hkt 2y ago

This seems to be the absolute worst of all worlds: the burden of software engineering with the tools of an English Language undergrad.

gopher_space 2y ago
The English degree helps explain why word choice and order matter, giving you context and guidelines for software design.