it's probably like nuclear fusion you
know yours people when will the first
nuclear fusion power be available and
the answer is always in 50 years time
and the answer is and when will there be
genuinely intelligent machines well I
might be very wrong here but some optimists
tell within 10 years I said well
maybe it's 50 years maybe it will always
be 50 years I just do not know Turing
was fascinated with the idea of how
intelligent could you make a machine be
the imitation game itself was a game
Turing devised to what yes the idea
was that if you're at the other end of a
telephone line and you gotta, I think in
those days of course it was a teletype
or a dumb terminal you know you type
questions at something and the answer
comes back and if after an agreed period
of time you're then asked was that a
human you were talking to or was it a
machine if you can't decide that it's
not human then it's passed the test it's
masqueraded as a human I think what he
realized all along perhaps teasing
as a little and it's always seemed to me
is that with a limited bandwidth for
interaction like that it's very probably
one of Turing's own undecidable problem
you can't get enough information to come
to a rational decision as to whether
it's a human or a machine but this
doesn't stop there being i think, is this
right, there's a yearly competition now
they normally give scores like you know
sixty percent thought it was human forty
percent thought it was machine then
answer is it's machine and stuff like
that to me you see it really is it as i
was saying a case of undecidability if
you need to visit the other end of the
line and see whether there's somebody
typing teletype to come to a decision
but then of course that spoils the whole
nature of the test it was a topic that
fascinated Turing and one that he
really really cared about and it's
interesting had he lived i'd love to
know what he makes of the present day
artificial intelligence scene and just
what is easy to do in the AI field and
what is hard and my own observation and
i'm not an expert sorry
fully expect to be put in my place and told
her royal is that the computer could do
a fixed task in a narrow domain very
differently to a human and be super
batted example chess absolutely superb
example I think deep blue be Kasparov
way back when but it absolutely did not
use the pattern matching analysis type
thing with the Grandmaster would do it's
brute force you know the whole game tree
as it were chess if I do this don't do
that then do that I mean your average
chess player might be able to look six
or seven moves ahead over all
possibilities blast deep blue can go I
lord knows how many moves ahead and
actually this I think is is it is a good
examplar of this because what
grandmasters were missing and actually
what I think ken Thompson's chess
machine Bell help to fill in is he I
think he did all five piece endings or
something like that and the ones that
the grandmasters didn't know about
because they needed tons of look ahead
I think that is right is the ones way you
absolutely do something stupid you go
miles or what he and the rest of them say
don't do that no no you don't do that
this machine's stupid it's gonna lose it's
gonna lose you know then right at the
very end a tiny little thing that nobody
had noticed and from the absolute depths
of near defeat he suddenly finds a
sneaky little way to make this pawn do
that matter now before we know where you
are it's a squeaker narrow victory in
only 10,000 moves or something like that
you know I'm exaggerating but you get
the idea that it's those where you would
have to go miles away from the solution
into an area where human said there's no
hope it's everything is lost but it
isn't there's a sneaky little Holly way
through to getting a victory that's the
kind of things a machine can do but a
human can't. Then the other side of that is the travelling salesman
problem where there are so many
possibilities but a human would just
look at an opera go well i'm going to
try that well there is a connection
there's an interesting one too you're
quite right that it's one of these
classic NP complete problems you
it is believed that there is no way to
get the perfect answer than simply to
look at every single possibility and the
monumental way the combinations of those
add up is just mind-boggling however
what I think people in the field will
tell you is there is a very good set of
algorithms which will give you a pretty
darn good answer even though it might
not be absolutely perfect and that's the
differences between you know good enough
within five or six percent in the best
versus must be absolutely the best route
so that kind of brings you back to the
whole human versus computer Turing
thing which is if I'm on the end of the
phone or the teletype and I say I'd know
what color is music yeah this is the
problem about that Turing test game you
know you could get either end of the
equation trying to play silly games I
think there was a program called Perry
probably invented was it a Stanford
somewhere in America Perry was going to
replicate the speech patterns of a
paranoid schizophrenic you say and you
can imagine if you're up against that
and you don't know whether the other end
is actually a real schizophrenic person
or a program simulating that behavior
that is far harder to tell the
difference or on because you know you
you ask some perfectly ordinary question
like what is two plus two I don't know
why you're focusing on two it's my
hated number and all this kind of stuff
it makes it very very difficult to start
saying that is a human but one with a
mental problem versus that is a program
that's simulating that sort of thing so
I think it's the thing that AI always
runs into is this that getting a part
way answer but by very different routes
than a human would I'm giving you enough
information about navigating to return
to be useful fine is when you start
wanting total perfection and fantastic
subtlety but because things fall apart
because that's the thing as well
ambiguity that humans are accustomed to
and the idea of inflection in town
yeah so for instance ok Google how do
you pronounce something in Hungarian
so it's given us the word for something
in Hungarian and bess it it's told us it
pronounced it like this follow me but
I'm being more general about it so I
want to know how Hungarians pronounce
things on the air fabulous things like
ambiguity and use of similes and
metaphors is a classic for throwing
things off there's a real classic from
the seventies I think it is is you know
okay artificial intelligence program
analyze the difference between time
flies like an arrow and fruit flies like
a banana now that's a beauty because you
know time flies like an arrow it's a
simile and all this kind of thing
extension of a metaphor but on the other
hand the other one which on the surface
has got a very similar grammatical structure
Time flies is a noun phrase like ah it's
not like you've been used in the similar
sense it's like is a joy and a clearly
eating a banana you say now it's that
kind of subtlety and that kind of
knowledge of how the real world works
allied to syntactic analysis that's what
you and I use all the time you know and
what computer systems would find so very
very difficult but of course you're
inviting the computer program to sort of
say hey there must be a thing called a
time fly and they live on an exclusive
diet of a truce oh yeah we know that's
impossible so we're using that kind of
knowledge over this whole domain of what
the real world is like in order to
disambiguate between those two all the
successes of artificial intelligence
tend to be a narrow domains you know if
you're like so play chess is one example
there's a thing they did it stanford
mice in i think it was about antibiotics
and their effect and the great advantage
of course are you with a computer is if
it is all done by logic then it will do
it and it will not
get tired and it will not usually make
mistakes unless you're counting up
Gangnam style hits but that's the point
you know you don't get bored you don't
make mistakes if you're a computer so therefore
that's why they often outperform humans
roger penrose very famous mathematician
at oxford got really rather hot under
the collar about all this I think he
wrote a book called something like the
emperor's new mind had a real go at
artificial intelligence basically said
the trouble is you're assuming that the
human brain is just like a very complex
Turing machine what I am convinced it's
more than that i Roger Penrose think
there are quantum effects and if only my
neurological medical friends would tell
me how to find them and how to
categorize them I'm able to tell you
more about the computational model that
is really going on in the human brain
but I've not read anything about that
recently so it's on the to-do list I
think
friendly environment by enveloping
around the agent in question Lemaitre
every time that had got up to nine it
would nudge the dial to the left of it
to move on one plus they get stuck in
what's called a local maximum or a local
minimum on the left will be 2 to the
power zeros