[learndeeply]

I can't think of anything that neural nets can't beat, except small tabular data with boosted decision trees. Can you give some examples?

[zelphirkalt]

Explicability is a big part of it It is often worth being a percent less accurat but having an explainable result.

[adamsmith143]

I've been on a lot of ML teams and outside of Finance and a few other sensitive topics explainability has always been irrelevant.

[zelphirkalt]

What happens, when your model exhibits a discriminating bias? How do you find out, what is going wrong? Knowing, what the model pays attention to can be pretty helpful.

[adamsmith143]

Not aware of any court cases where someone successfully sued because they were shown one product recommendation or Ad on a webpage instead of another.

[jstx1]

(I don't really agree with GP's point but for the sake of answering your question)

1. Collaborative filtering based on a sparse dataset of implicit interactions.

2. Many time series applications.

[marcyb5st]

Didn't all recommendations engines move to two-towers like models? I remember that it "solved" the freshness problem (ie when adding a new item to your catalog how do you recommend it to users if there are no ratings/interactions). Of course as long as you have a good model that creates items embeddings.

Regarding time series, don't everyone moved to attention based models?

Not challenging your answer, just curious. I work mostly with Graph NNs and quite a bit out of touch with the rest of the field.

[niemandhier]

Small data problems, where’re never the less have a really good idea of how things are causally related.

[insane_dreamer]

> we often use ML over DL in scientific analysis because we need models that can be inspected/explained not just results

> also, DL generally requires more data whereas you can get by with ML on less data if you have domain knowledge

[patrick451]

The black box nature of a neural net is a problem. For model based design, a bit more accuracy out of a black box doesn't really help when you need, for example, state space matrices in a control design.