What I read from this article is that we're currently in a trend where the more compute you throw at machine learning, the better solutions you end up with. Nothing about general AI, it's just that you can train deeper, more complex neural networks that can handle broader and more complex versions of the problems they're designed to handle.

This can have implications down the line which still have nothing to do about AI becoming smarter (in the general intelligence kind of way) than it is today. If all input/output problems out there have very deep neural networks behind them, and those neural networks are constantly training simultaneously, and that has a positive economic output, we'll see tremendous amounts of FLOPS that pales cryptocurrency miners by comparison.

Just as an example, depending on how cloud solutions keep up, maybe startups on the cloud won't be so competitive anymore. It's an interesting trend to point out and keep track of. And yes, OpenAI's involvement will probably be to learn about how this can lead to unsafe use of AI, but again that's not what I saw the topic of this article being.

There are a limited number of paradigm shifts before AIs get "general" in their name. So "it already happened before" argument has a natural end. What makes you think it is still applicable?

Some organizations have access to a computational power reaching into the realm of a computational power of a human brain. We see commercial applications of systems which aren't programmed by hand like expert systems and which don't fail miserably like speech recognition engines of yore. How often do you hear about the curse of dimensionality today?

Some things have changed from then to now, the question is are they changed enough.

From a physics standpoint it’s not equivalent power actually, it’s equivalent energy.

Power is work per unit time. Work is energy expended which causes displacement.

These “brain-equivalent” computers can’t do nearly the work of a human brain in the amount of time. They use about the same amount of raw computational energy, but they don’t make nearly the same amount of structures waves in the information spaces around them. Their output can only even be seen in the absolute quietest of environments. They often run for long periods of time with no obviously informative output.

Human minds tuned for it are essentially incapable of not producing a constant stream of novel and disruptive insights (work) which is how you get a large computational power given a roughly constant computational energy.

In the sixties, chess was thought to be a problem that required intelligence since it couldn't be brute-forced. We now have machines which can play it well, without brute-forcing, and yet it's seen as entirely procedural.

As for style transfer, that is a very specific skill of making the patterns of one style map to the patterns of another. I am not particularly well versed with art, but that process seems well defined to me.

Perhaps your issue is with my more generalized definition of "clear" and "well-defined". I meant to use these terms to distinguish between autonomous driving and being a successful human. I really don't think there is anywhere close to a consensus on the latter. To the extent that there is, then yes, AI should be able to do it.

Of course we don't have human-level AI right now, but if that's the only thing you're claiming it's pretty vacuous.

Note that we have great raster-based deep visual effects, but vector is... not there yet (not saying it won't be) - vector is less structured than raster, so the choice of algorithm is less obvious.

As for well-defined criteria, I don't think that's really quite the right standard, I think the correct standard is that there is a way of metrizing success on a well-ordered set (like the [0,1] interval), even if it's noisy.

The hull size argument is apt. I think it's a lot more obvious that throwing more compute power at a neural networks isn't going to eventually make a general intelligence.

At the very least there will need to be significant breakthroughs in architecture design that will be at least as paradigm shifty as deep learning.

This is not true. The biggest battleship bought was the yamato

> Length: > 256 m (839 ft 11 in) (waterline) > 263 m (862 ft 10 in) (overall)

The biggest CV (aircraft carrier) right now should be the Nimitz class with

>Length: > 317m

which seems to emphasize your statement, but, battleships are not versatile and battleship development pretty much stopped after WWII. The germans had propsals of the H-class battleship, with a total length of the H-44 of 345m.

It is possible, it's just not strategically advantageous.

Just like throwing more compute at a neutral network isn't going to make general AI. The diminishing returns comes with how brittle their learning and representations are.

The issue with physical structures is that eventually the mass and stresses in the macro structure overcome the strength in the micro-structure. That is why nature stops at, eg, elephants.

It isn't obvious that intelligence suffers from such limits, as the only time the limit of intelligence has been tested (in terms of evolution) was when humans tried it. There is no evidence humans are pushing the limits of what intelligence can achieve. Quite the reverse, honestly, when you look at the performance of computers so far.

It isn't just the computer power that allows us to be intelligent. It's also the bandwidth (which is always an order of magnitude or more behind the computer power) and the algorithm (which we don't have a clue how to create).

State of the art visual processing that gets so touted in the press is brittle -- it has to see very similar examples or it will fail. Neural networks don't transfer to new problem domains well at all.

Neural Networks have no sense of self or agency and they never will. There are key parts missing (like the ability to experiment with the environment). I'm not saying we will never have general intelligence, just that it's quite a ways away and the algorithms will be significantly different to the neural networks we use today. That said there will probably be many recognizeable components, like backpropagation, recurrent nodes, bayesian estimations, etc.

The Japanese actually invested heavily in the biggest battleship in the world. It was soundly defeated at sea by an inferior force with an aircraft carrier at Leyte Gulf during World War II.

I would argue that if you want to identify the mechanism of military dominance that the US has used to assert itself in the world you should look to Trident and the Ohio class.