2 ms·
I suspect it's the same reason it grinds my gears that it's called a "learning rate" instead of "step size" in ML. Not only is it less precise term, but it giv
by sillysaurusx 4y ago
I suspect it's the same reason it grinds my gears that it's called a "learning rate" instead of "step size" in ML.
Not only is it less precise term, but it gives the wrong implications.
Personally, I'm on the side of releasing training data. Let everybody train on everything. But it's always felt absurd to say that the ML models are "learning" things.
But hey, none of us know how learning works anyway, right? So maybe it's not such a big distinction. As you say, none of us can pinpoint why a model isn't learning vs why we are.
- godelski 4y agoI think the problem with these words is that the vernacular has different meanings with the common lexicon. But this is quite true for any field. "Field" is even a good example of this as mathematicians use it in a drastically different way than I just used it now. This can make people think they understand technicals more than they do. But if you're making the argument that ML needs to learn more math and needs more rigor, then I'd defend that claim. It is a personal pet peeve of min (fuck man, how often I have to explain to my research group what a covariance matrix is and why it is essential to diffusion is absurd).
- wnkrshm 4y agoA lot of what we see is cargo cult engineering and not fundamental research in ML. Most of it is applied research or engineering - there is a little bit of fundamental research that actually expands our own knowledge about how things work and what their limits are, while applied science keeps marching on (maybe towards funamentally impossible goals).