[doctorM]

I'm a bit sceptical of the exponentially harder debugging claim.

First it looks polynomially harder for the given example :p.

Second other engineering domains arguably have additional dimensions which correspond to the machine learning ones mentioned in the article. The choice of which high level algorithm to implement is another dimension to traditional software engineering that seemingly exists and corresponds to the model dimension. This is often codified as 'design'.

The data dimension often exists as well in standard learning software engineering. [Think of a system that is 'downstream' of other].

It's probably a lot simpler to deal with these dimensions in standard software engineering - but then this is what makes machine learning harder, not that there are simply 'more dimensions'.

The delayed debugging cycles point seems a lot more valid.

[grantpitt]

I had similar thoughts on this. It looks polynomial because he classified errors into certain groups and spread them across an axis which we implicitly think is a dimension. But it's not... as he says, "Along any one dimension we might have a combination of issues (i.e. multiple implementation bugs)". So looking at the grid and thinking you're at a single point is wrong... but so is thinking you could be any configuration of locations (you couldn't be at points (1, 1) and (2, 2), you also need to be at points (1, 2) and (2, 1), i.e. the set of points you're at is transitive). Thus this is a bad way to "enumerate the failure cases" and makes his notion of "adding a dimension" pretty unintuitive. It makes more sense to see every point of his dimensions as itself a dimension. Each of these dimensions could be either correct or incorrect, 1 or 0. So you're configuration could be visualized as a string of 1's and 0's. Thus, the number of possible configurations grows exponentially (2^n).

[jolt42]

The article also pretends that there is only one correct answer, which seems atypical of the domain. The 1 green spot should extend somewhat fuzzily in each dimension in the ML case.

[janalsncm]

There isn’t only one correct answer. Quite the opposite actually, many configurations give you local maximas. The difficulty is that it can be hard to explain from first principles why one local maxima is good.

[pizzaknife]

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