AlphaGo Can't Beat Me, Says Chinese Go Grandmaster Ke Jie

Games at this level are decided by a few points. The closer two players come to optimal play, the harder it is to be very much more efficient than your opponent over the course of a game. To be so much more efficient, at this level, that you could overcome a 4 stone handicap is unthinkable.

I would very much like to see computers improve to the level that they could take on a top professional giving a 2 stone handicap. That would be a sight!

For example a CNN does well at object recognition from photos, but does poorly at recognizing a pencil sketch of an object it hasn't previously seen a sketch of. Or recognizing a scene at night that it has only seen daytime photos of. Those tasks, humans do very well because the trained human brain combines cultural understanding, physics intuition, and visual cortex at the same time, and would be able to use this to their advantage to easily beat, say, a massively trained CNN image recognition program that lacks cultural understanding and physics intuition.

Some other more complex NN structure may be able to tackle these kind of tasks, but as long as its neural structure is rigid, it will have yet other deficiencies. The human brain is still able to structurally adapt in ways that NNs cannot, yet.

That's your wishful thinking.

As we've seen the matches with Fan Hui in the past, unfortunately and fortunately, the CNN will improve drastically game after game and making less mistakes whereas human always do at a certain level.

Another big point I want to point out is human emotion and state of the mind. With 0:2, Lee Sedol lost his confidence ("speechless" in his own words) [1] and quite possibly increasing fear of losing in a landslide 0:5. Losing confidence and fear of losing are VERY TERRIBLE things in any competitive sports.

[1] http://www.theverge.com/2016/3/10/11191184/lee-sedol-alphago...

(I'm convinced that programmers will take the AI-automating-all-the-jobs idea more seriously once it's their own jobs that are on the line)

There are a few ways I can think of to object to this line of reasoning:

1) You could argue that programming will be a job that never gets automated away. But this seems unlikely--previous intellectual tasks (Chess, Jeopardy, Go) were thought to be "essentially human", and once they were solved by computers, got redefined as not "essentially human" (and therefore not AI). My opinion: In the same way we originally thought tool use made humans unique, then realized that other animals use tools too, we'll eventually learn that there's no fundamental uniqueness to human cognition. It'll be matter & algorithms all the way down. Of course, the algorithms could be really darn complicated. But the fact the Deepmind team won at both Go and Atari using the same approach suggests the existence of important general-purpose algorithms that are within the reach of human software engineers.

2) You could argue that programming will be automated away but in a sense that isn't meaningful (e.g. you need an expensive server farm to replace a relatively cheap human programmer). This is certainly possible. But in the same way calculators do arithmetic much faster & better than humans do, there's the possibility that automated computer programmers will program much faster & better than humans. (Honestly humans suck so hard at programming https://twitter.com/bramcohen/status/51714087842877440 that I think this one will happen.) And all the jobs that we've automated 'til now have been automated in this "meaningful" sense.

Neither of these objections hold much water IMO, which is why I take the intelligence explosion scenario described by Oxford philosopher Nick Bostrom seriously: http://www.amazon.com/Superintelligence-Dangers-Strategies-N...

One day we'll be tempted to write a more serious, more complex "game", with real world consequences. For those, we'd better specify our goals precisely, lest we face unforeseen unfortunate consequences —there will be reasons why the machine does it better, ranging from faster computation, exquisite motor control, or a total lack of ethics —assuming ethics wasn't part of the programming.

Next thing you know, you need to solve the Friendly AI problem.

We should rejoice in that fact. We are woefully unprepared for true learning programs as a species. Let us hope that between now and the time we do manage to create one that we mature to the point where we don't create these thinking entities for malicious purposes.

With the danger of sounding stupid, isn't that what Deep-Q did?

http://arstechnica.com/science/2015/02/ai-masters-49-atari-2...

> Scientists tested Deep Q’s problem-solving abilities on the Atari 2600 gaming platform. Deep-Q learned not only the rules for a variety of games (49 games in total) in a range of different environments, but the behaviors required to maximize scores. It did so with minimal prior knowledge, receiving only visual images (in pixel form) and the game score as inputs.

Sure, the problem space is still fairly limited, but the AI did learn new games without much guidance at all.