[unfamiliar]

Humans are really good at taking a vague description of a task and using a small number of examples to disambiguate it.

For example, "sort all of the folders, so that it Alan goes before Amy, etc". The rule ("sort") is pretty ambiguous, but one simple example in the context gives enough information to realise you probably mean alphabetically by first name.

Is there something like this example that could be combined with NLP to make things like these "intelligent assistants" we have now much more useful for data processing tasks?

It would be great to describe data manipulation to a machine the way that I would describe it to a colleague: give an overview of an algorithm, watch how they interpret it, and correct with a couple of examples in a feedback loop. Currently describing such things for a machine requires writing the algorithm manually in a programming language.

[Spiritus]

Isn't just because we've already been trained on that since learning the alphabet? Imagine giving a human the same question, but sort "aa" before "bb" but after "cc".

[TuringTest]

Maybe it's "just" because of previous training, but it's still a very useful ability, which programs do not have.

Being able to solve quickly the most common cases (which rely in such "common knowledge") would automate a lot of work that now requires writing a complex program in advance, and would allow the user to concentrate on the outliers that require more thought.

[paulddraper]

> Humans are really good at taking a vague description of a task and using a small number of examples to disambiguate it.

IIRC this is tested heavily in IQ tests.

[jdironman]

Abstract thought really.