"Искусственный интеллект", и Epic Fail оного
Есть такая дисциплина - "machine learning", иногда её называют "deep learning". Andrew Ng, (два года тому назад) профессор Станфорда, один из активно пиаривших модные онлайн-курсы, как раз практикует эту науку. Наум Хомский сказал обо всём этом бизнесе так:
On the other hand there's a lot work which tries to do sophisticated statistical analysis, you know bayesian and so on and so forth, without any concern for the uh actual structure of language, as far as I'm aware uh that only achieves success in a very odd sense of success.
There is a succ- notion of success which has developed in uh computational cognitive science in recent years which I think is novel in the history of science. It interprets success as uh approximating unanalyzed data. Uh so for example if your were say to study bee communication this way, instead of doing the complex experiments that bee scientists do, you know like uh having fly to an island to see if they leave an odor trail and this sort of thing, if you simply did extensive videotaping of bees swarming, OK, and you did you know a lot of statistical analysis of it, uh you would get a pretty good prediction for what bees are likely to do next time they swarm, actually you'd get a better prediction than bee scientists do, and they wouldn't care because they're not trying to do that. Uh but and you can make it a better and better approximation by more video tapes and more statistics and so on.
Uh I mean actually you could do physics this way, uh instead of studying things like balls rolling down frictionless planes, which can't happen in nature, uh if you uh uh took a ton of video tapes of what's happening outside my office window, let's say, you know, leaves flying and various things, and you did an extensive analysis of them, uh you would get some kind of prediction of what's likely to happen next, certainly way better than anybody in the physics department could do. Well that's a notion of success which is I think novel, I don't know of anything like it in the history of science.

И вот куры возвращаются на насест, как говорят в народе. Новая статья об одном из самых важных инструментов "machine learniтg" - нейронных сетях:

Second, we find that deep neural networks learn input-output mappings that are fairly discontinuous to a significant extend. Specifically, we find that we can cause the network to misclassify an image by applying a certain imperceptible perturbation, which is found by maximizing the network’s prediction error. In addition, the specific nature of these perturbations is not a random artifact of learning: the same perturbation can cause a different network, that was trained on a different subset of the dataset, to misclassify the same input.

Нейронная сеть - это сложная нелинейная функция, которая получается путём оптимизации (поиска минимума другой сложной нелинейной функции), "тренировки" на "учебном" наборе данных, чтобы классифицировать данные из "реального мира" - например, распознавать отсканированные буквы, находить заданное лицо в толпе, опознать человека по фотографии и т.д. Если после обучения нейронная сеть правильно классифицирует тестовый набор (который не входил в учебный набор), то считается, что нейронная сеть "обучена". А тут вдруг выясняется, что для любой правильно классифицируемой картинки можно найти визуально от неё не отличающуюся, которая классифицируется неправильно. Причём это свойство достаточно универсально, не зависит от "учебного" набора.

Кстати, директор исследовательской лаборатории Гугла Питер Норвиг ответил Хомскому так:
And while it may seem crass and anti-intellectual to consider a financial measure of success, it is worth noting that the intellectual offspring of Shannon's theory create several trillion dollars of revenue each year, while the offspring of Chomsky's theories generate well under a billion.

Аргумент настоящего либертарианца из Кремниевой долины - "я заработал больше денег, значит, я прав".