GorkSorry, can't do scarves.
I’m sad that the relevant xkcd is kinda obsolete now (because it’s been long enough for that research team to finish going its thing).
Reliability. We can do pretty much anything… with a 20% success rate.
Yeah, of course. I think I was misunderstood, which is probably why I got so many downvotes.
Most tasks are possible (and often trivial, given access to the right library) with traditional programming. If it’s possible to do them this way, this is by far the best approach.
Of the things that are not reasonably doable this way, like determining whether a photo is of a bird as in the comic, quite a lot of them are possible nowadays with machine learning (AKA “AI”), and often trivial given access to the right pre-trained model. And in this realm, I would say success rates are very often higher than that. Image recognition is insanely good.
What I’m asking is, what’s a task that’s virtually impossible both with programming and with machine learning?
“Mission critical” tasks which require very high and provable reliability, such as autonomous driving cars, technically fit this question but I think it’s ignoring the point of the question.
And if you were going to mention counterexamples where specially crafted images get mislabeled by AI: this is akin to attacking vulnerabilities in traditional software, which have always existed. If you’re making a low-stakes app or a game, this doesn’t matter.
I think if we’re looking at it conceptually, it has to be something that is too complex to do with traditional heuristics reliably and also doesn’t allow us to generate enough data for good DL results.
There’s also liability to consider, for cases like trains. Trains are dead simple to automate, but there needs to be someone there for long tail events, to make people feel safer, and as a fall guy in case of accidents. So in practice it’s impossible to automate beyond subways where you control the entire environment despite the tech being fully capable of it.