I think AlphaGo is specialized to play Go. You must be thinking DeepMind, on which AlphaGo is based.

The real question would be is there a goal of the AI. The true superhuman AI, that is problematic, is when AI decides to learn something on its own. And I think that is a long road.

This is simply not true, and there are many tasks the architecture would fail with and the networks presented are architecturally different from the highest performing vision networks like GoogLeNet. The techniques behind AlphaGo have no memory component holding state between moves, such as a hidden state in an RNN. It is completely reactionary.

A simple game that this architecture would fail at is Simon [0], where you are presented a sequence and then are tasked to replay the sequence.

[0] https://en.wikipedia.org/wiki/Simon_(game)

Of course, but it has to be trained anew, with new data. You can't train it on Go data and expect it to perform at all well on vision tasks.

The same statement holds for humans too. I don't think you can teach a person how to play Go and expect him/her to learn anything else than how to play Go.

I'm not talking about learning the rules of the game. You don't need an AI to model the rules of the game. You need an AI to model the winning strategy. Winning strategy is what humans generalise to other domains and computers don't.

There's a lot that suggests that humans and machine learning algorithms learn in very different ways. For instance, by the time a human can master a game like Go they can also perform image processing, speech recognition, handwriten digit recognition, word-sense disambiguation and other similar cognitive tasks. Machine learning algorithms can only do one of those things at a time. A system trained to do image processing might do it well, but it won't be able to go from recognising images to recognising the senses of words in a text without new training, and not without the new training clobbering the previous training.

To make it perfectly clear: I'm talking about separate instances of possibly the same algorithm, trained on a different task every time. I'm not saying that CNNs can't do speech recognition because they're good at image processing. I'm saying that an instance of a CNN that's learned to tag images must be trained on different data in a different time if you also want it to do word-sense disambiguation.

And that that is a limitation, that stands in the way of machine learning algorithms achieving general intelligence.

I was just contradicting your statement.

A human brain (or any other animal brain for that matter) is almost infinitely more advanced and computationally efficient than state of the art machine intelligence, even without taking things like thoughts, emotions and dreams - which we currently do not understand at all - into account.

It's a huge accomplishment for machines to be able to win over humans in games like chess and go and <insert game here>, but these are games originally designed for humans - by humans - to be played recreationally and I think we shouldn't read too much into it.

Didn't you see KarateKid: wax on, wax off. Jokes aside, as someone said before. The human mind is far better at generalizing and can reuse learned skills in different fields.

How does that differ from a human brain?

A lot. A brain is much bigger and much slower.