A recent article in New Scientist (paywalled, I don't have an alternative) suggests that 8 km is about the limit for fish. The problem, apparently, is that the pressure distorts protein shapes, eventually preventing them from working properly. The tissue (particularly muscle) of deep-sea fishes contains trimethylamine oxide, which may protect against this problem, and the deeper you go, the more of it the fish have, but by about 8km they are saturated with it.

Invertebrates have been found deeper, so presumably they have a different mechanism.

I could be by design...
http://en.wikipedia.org/wiki/S...

There is a huge difference: humans pick relevant features guided by a deep understanding of the world, while machine learning, unguided by any understanding, only does so by chance.

Hand-coded fixes are not AI - that would be as if we have we had a higher-level intelligent agent in our heads to correct our mistakes (see the homunculus fallacy).

And minds are just charges flipping around in some brain (at one level of abstraction, it is chemical, but chemistry is explained by the movement of charges.)

If you look at John Searle's arguments in detail, they ultimately end up as nothing more than "I can't believe that this is just physics." Searle's view is actually rather more speculative than the one he rejects, as it implies an unknown extension to atomic physics.

Nevertheless, none of what I write here should be construed as a claim that artificial intelligence has been achieved.

No. Learning that the "exact images" presented here are tricks would not be a solution to the problem revealed by this study. The goal in any form of machine learning is software that can effectively extrapolate beyond the training set.

Once you understand the problem, you will see what the story is.

It seems highly unlikely that such an elementary mistake was made: "Clune used one of the best DNNs, called AlexNet, created by researchers at the University of Toronto, Canada, in 2012 – its performance is so impressive that Google hired them last year."

The fact that the net returns a confidence level implies that it does have a way to return a result of 'not recognized'.

This is not some network hastily trained by people who are ignorant of a very basic and long-known problem: "Clune used one of the best DNNs, called AlexNet, created by researchers at the University of Toronto, Canada, in 2012 – its performance is so impressive that Google hired them last year." From a paper by the developers of AlexNet: "To reduce overfitting in the globally connected layers we employed a new regularization method that proved to be very effective."

It does not seem plausible that this result can be explained away as an elementary mistake.

Right, and it has created a great deal of misery throughout human history. Just because it is prevalent does not mean it is not a problem.

More specifically, the overconfidence displayed by the networks here should lead to a corresponding skepticism, in a rational observer, to the notion that they have cracked the image recognition problem.

I know what you mean, but did you see a peacock before you read the label?

You seem to be suggesting that it is 'simply' a case of overfitting, but overfitting is a problem that has been recognized for some time. I don't doubt that the developers of these networks have thought long and hard about the issue, so this study suggests that it is a hard and as-yet unsolved problem in this domain.

One thing that humans use, but which these systems do not seem to have developed, is a fairly consistent theory of reality. Combine this with analytical reasoning, and we can take a difficult image and either work out what is being depicted, or realize that we are not succeeding in doing so.

No, clearly not - that would be an unjustified extrapolation of an unwarranted generalization of a simplistic reading of the point under discussion.

While Mr Rosenberg claims that Go is distinguished by its approach to concurrency, his section 'The Essence of Go' is almost entirely devoted to the trivia of braces and semicolons. Yo won't learn anything about Go's approach to concurrency here.

I think the fact that Apple did not indicates that it did not think there was much chance of success, and was not, by then, expecting (or even much hoping) to ship with a sapphire screen.

My guess is that at some point Apple decided the new manufacturing technology was unlikely to work in their timescale, and was not going to make its plans dependent on it (I imagine this was shortly before it backed out of acquiring the manufacturing equipment.) At this point, GT became the party desperately seeking a deal, and Apple effectively said 'show us, and we will consider it.'