Michael - Thursday
It’s a favorite
theme in Science Fiction that thinking machines lie in our future, some for
good, some for ill. Remember Hal 9000 in 2001 - A Space Odyssey? The moral of the
story always seemed much deeper than the technology. It seems to be that creating a
thinking being – no matter of what material – is God’s provenance and we
trespass on that at our peril. Well, we’re finally catching up with the
SF. No less a person than Stephen Hawking has warned of the dangers to humanity
of intelligent machines.
And yet so called
Artificial Intelligence - once the hottest area of computer science - seemed to
over claim and under deliver. There are a number of benchmarks out there and
they’ve been hard to reach. One was Turing’s test. The idea is that you talk
over the phone to either a computer or a human and you cannot distinguish
between them. That challenge stood
unfulfilled from Turing’s time until a few years ago. And yet this is not the
test of a thinking machine. It’s a test to see if a computer can be programmed
to impersonate a human. That’s very
different. (Not that it’s not hard. Smart
as she is, chat to Siri on your iPhone for a few seconds. No one would be
fooled into thinking it’s a real woman.)
Another benchmark was chess. Chess is a difficult game with a
well-defined set of rules and chance isn’t involved. The skill is in thinking
ahead and recognizing that one position will be better than another. The target was to design a computer which
could beat a chess grandmaster.
IBM’s Deep Blue
did so twenty years ago beating Gary Kasparov in the second of their match series.. It was a super achievement. The trick was to evaluate
the various positions in terms of a favorability analysis, and then provide the
computing power and memory which allowed the machine to think further ahead
than its opponent. Impressive, BUT let’s analyze this “thinking ahead.” It’s
not thought at all. It simply involves flowing the possible sequence of reasonable
moves from where you are now into the future. This isn’t trivial. There may be
around 40 moves available to each side in any position. Some will be rejected
almost at once, so say there are 10 realistic moves. After you and your
opponent have made five moves each, you have reached over 10 billion
possibilities! Human grandmasters often think ten moves into the future of the
game, but they don’t evaluate 10 billion different positions. They see that
only a few of those are worth following because the others will clearly not be
to their advantage. Clear to them…
Think about Deep Blue’s remarkable achievement. At the crudest level it was a matter of
computing many possible situations and evaluating them by means of matching to
good and bad templates produced for it by humans. This is not learning at all.
In the machine learning
world, deep learning means the ability of the computer to do the same sort of
thing we talk about in human learning. Given the rules of the game it can
arrange its own “neurons” to reflect the rewards it gets for good moves and the
penalties it gets for bad moves. This mimics deep learning in people – at least
at some level.
Last week deep
learning had its greatest triumph so far. Google has a whole division – Deep
Mind - working on it. They designed a computer to play Go.
Go is regarded as
a much more complex game than chess. It offers the players hundreds of
different moves in each position and the efficacy of a move is unclear for
quite a way into the future. Deep Blue doesn’t know how to play Go, and if it
did it would lose. We don’t understand the game well enough to do good
evaluations of the positions and we can’t play the moves far enough into the
future. Ten moves into the future would lead to 100 billion billion
possibilities! Forget about it.
The Google machine
– AlphaGo – already had a pretty impressive record of success. But over the
last week it beat the world champion Lee Sedol by 4 games to 1 in a five match $1
million series. This is a thinking
machine. It taught itself to play Go.
The Google programmers don’t know how it does it.
After AlphaGo won
its first game, the New York Times carried an article made up of four short pieces
from experts in different aspects of computer science. You can read the whole
piece HERE. Interesting that much of the emphasis is on how humans should
respond to this new development.
Here are a couple of quotes:
Google’s AlphaGo is demonstrating for the first
time that machines can truly learn and think in a human way…The real question,
now that machines are capable of approximating human intuition in decision
making, is: How should we cultivate human talents going forward? Because it's
clear that the human advantage is eroding fast - Howard Yu, professor of strategic management
and innovation.
The third critical [human] ability, somewhat
surprisingly, is storytelling, which has not traditionally been valued by
organizations. Charts, graphs and data analysis will continue to be important,
but that’s exactly what technology does so well. To change people’s minds or
inspire them to act, tell them a story - Geoff Colvin, a senior editor at large for Fortune Magazine.
Of
course I couldn’t resist that second one! Maybe writers will be the new elite!
I won’t hold my breath.
Another
of the experts points out that it’s a long way from learning to play a formal
game with fixed rules and no downside for mistakes to thinking in the uncertain
real world environment in which we operate every day. And that brings us back
to HAL. It wasn’t thinking that was
the issue there, it was consciousness.
And that’s a whole other ball park.
