On AI's past, on AI's future

So much of what has been labelled as AI, when projecting into the future, has had the sticker torn off once the accomplishment has been achieved. 
There's an interesting article (http://bit.ly/MBmLSA) from American Scientist that covers some of the early days with Minsky and McCarthy, through to the work on checkers with Schaeffer, language translation and knowledge systems such as Watson and the online AI courses that are now available for anyone to sign up to.

The main point of the article is that much of the successes of AI are from the point of "shallow understanding" rather than "deep understanding."  Language translation employs word and phrase lookups, checkers has endgame databases and move lookaheads.
Each time we think of problems that would require "true thinking" we later seem to move the goalposts and decide that the solution didn't, in fact, truly require any deep insights to be made by the computer.

And yet progress marches ever on.  More and more AI systems are being employed in ever more diverse fields, whether it's refining city management or controlling satellites or aiding diagnosis - or even at the level of knowing which ads you'd most like, books you'd buy or which people are most influential in certain circles for certain products. 

The question will be, at what point does the illusion go away? 
At what point does it become impossible to tell ourselves it's not really smart, it's just applying some basic rules, iterated over a lot of flops? 

Because given the non-linear rates of advancement, by the time it starts getting notice, what's on the cutting edge will be what's otherwise around the corner, and what's around the corner from that, well ...

I think there will be some interesting times ahead where we end up with systems that cross over the following divides:
- of teaching itself
- of sentience
- of self-awareness

And even in those areas, it's not a binary situation, and I think we'll see debate over systems that have limited learning vs a more flexible and unlimited learning system.  And the terminology will be hotly debated.  What are the bounds?  What is the environment?  What is being learned?

I suspect in some ways that debate will continue all the way up to the point where machines start clearly surpassing humans and have ticked off all three boxes above: being auto-didactic, sentient and self-aware.

And then there's the complex issue of creativity.

And because I'd argue that none of those are entirely binary, and that progress is more continuous, I think the discussion will carry on for some time, and merely start asymptoting to zero without actually having a moment where we just stop.  Though it's likely that there will be drop-off points where we go, see, this particular machine/accomplishment is strong evidence...

In a way, that's decades off, depending on how you like to put your marks on curves.  But one way or another, we're in for some upheaval.

To quote William Gibson: the future is already here, it's just not very evenly distributed.