[K0SM0S]

I think the word you're looking for is "familiarity", insofar as it describes a particularly efficient means of recognition. E.g. humans have become pretty good at identifying cats.

I don't see a fundamental difference between biological and electronic neural nets; so please take the following with a physicalist grain of salt. Imho, precisely because NNs will be fed with nothing else than the reality (physical or virtual) we live in, it should gradually develop the same familiarity as humans have; i.e. nothing more and nothing less than elements of our lives/civs. Visually lots of cats, lots of cars, mountains and coasts; functionally all the tasks we accomplish daily, like driving or cooking or cleaning.

I don't really think you can hard-code "human bias" as it's an emergent property of our biology: too complex (we don't really understand much of it, imho you're bound to miss the mark and induce subjective biases), and somewhat contradictory to how NNs are supposed to evolve (thinking long term here). Basically, I don't think it would be practical nor cost efficient to induce too much perturbations in deep learning, better work on refining the process itself. Think of plants: you can tweak the growing all you want, but the root deciding factors lie in genetics (their potential, and in understanding how to maximize it).

I realize another wording is that we should apply sound evolutionary (Darwin etc.) principles in "growing" AI at large. Because AI and humans share the same environment, we should see converging "intelligence" (skills, familiarity, etc). It's a quite fascinating time from an ontological perspective.

[jim-greer]

It's interesting to think about what the limits of an AI that doesn't have a full human experience are. I think you're probably right that machine vision will be competitive with human vision. It's already much better in specialized areas.

General purpose machine translation is harder, for instance. Brute force algorithms have gotten decent, but aren't in the same ballpark as humans (though professional translation services now often work by correcting a machine translation). However, MT systems trained on a specific domain do much better (medical or legal docs, etc).

What would be the hardest task for machines that's trivial for humans? Maybe deciding if a joke is funny or not?

[rspeer]

Perhaps not the hardest, but one where there's tons of room for improvement: the Story Cloze Test [1] is a test involving very simple, five-sentence stories, where you pick the ending that makes sense out of two endings.

A literate human scores 100% on this test. No computer system so far scores better than 60%. (And remember that random guessing gets 50%.)

[1] http://cs.rochester.edu/nlp/rocstories/

[johnlbevan2]

Interesting study; whilst it's possible to guess which ending is expected as correct, the alternate could be easily argued. For example, in the case of Jim's getting a new credit card, I recall during my uni days many students took that exact approach to debt...

[johnlbevan2]

Good point; I'd not considered whether the human imprint would be down to familiarity (individual's) or in-built through evolution (inherited familiarity); likely a combination of both. In fact, I recently read that chimpanzees raised by humans are believed to identify as human rather than chimp; so individual familiarity does seem stronger.

The book, "We are all Completely Beside Ourselves" is fiction, but refers to findings from real studies.