[sgt101]

I'd elaborate by saying that for the focus of ML to be on mathematics risks creating technology that isn't useful at great cost. I think that software engineering followed a similar arc; the mathematical appeal and authority of formal methods was very great, in the absence of wide and deep experience of the reality of system development there was a huge focus on trying to use formal methods as a fundamental part of system development with very marginal pay off in terms of current practice. I think that the impact of this can be seen in the way that software development is essentially an artisanal practice now. AI and ML risk the same sort of diversionary excursion where mathematical fundamentals are the focus of the field and the real world is demoted/externalised. We do not understand the transformations between data and a deployed application, they are semantic, organisational, system dependent and very human. We can't characterise the type of mistakes that classifiers will make, or why those mistakes are, or aren't significant in an application. We can't engineer a machine learning system in the sense that we can't evaluate or certify that it'll work reliably or consistently.

Right now ML and AI are like airships in the 1920's if and when something goes wrong and lots of people die (or are blinded) the community isn't even in a position to properly investigate what's happened. Before we get to focusing on the equivalent of hydrodynamics we need to move to an organisation and practice of engineering discipline - that's what the aircraft people did, and that's why the windows in jetliners aren't square, and that's why you can fly off on holiday.

If AI and ML don't do this and instead everyone spends their hours and days doing maths that isn't absolutely at the core of the real issues of application then watch as confidence and trust evaporates and be ready to wait 20 years to see any value arise.

But - maths that achieves results like those in compiler design and optimisation, I'll buy that for $1!

[Analog24]

"We can't characterise the type of mistakes that classifiers will make, or why those mistakes are, or aren't significant in an application. We can't engineer a machine learning system in the sense that we can't evaluate or certify that it'll work reliably or consistently."

These statements are false. It sounds like your extrapolating what you've read in a few blog posts and assuming that's how the entire industry operates. You don't read headlines about people digging through the data and error logs on a daily basis b/c it's not headline worthy but that doesn't mean it's not being done.