- AI is not about trying
to create life, right? That's not what it's about, at all. But it's kind of, very
much feels like that. I mean, if we ever achieved
the ultimate dream of AI, which I call the "Hollywood dream of AI," the kind of thing that we
see in Hollywood movies, then we will have
created machines that are conscious, potentially, in the same way that human beings are. So it's very like that kind
of dream of creating life- and that, in itself, is a very old dream. It goes back to the ancient Greeks: The Greeks had myths about the blacksmiths to the gods who could create
life from metal creatures. In medieval Prague they had
the myth of the 'Golem,' which was a creature that
was fashioned from clay and brought to life. You know, the dream of
creating life from nothing. So, it's a fascinating idea. It's an idea that's been there
throughout human history, but it's an idea that we seem to now have the tools to potentially make real. Hi, my name's Mike Wooldridge. I'm a professor of computer
science at the University of Oxford and an AI
researcher, and most recently, I'm the author of "A Brief History of AI" out now in Flatiron. So John McCarthy was
an American researcher, and he applied for funding
from the Rockefeller Foundation for a summer school at Dartmouth. What he had to do for this
funding bid was to give a name for what they wanted to do. And so he picked the term
Artificial Intelligence, and it's the name that stuck. So what McCarthy was
working in was a trend in artificial intelligence,
which is called 'Symbolic AI.' When we consider what we should do, we kind of have a
conversation with ourselves: "I should do this because X and Y and Z, no I shouldn't do it
because A and B and so on." And the Symbolic AI is
about trying to recreate that kind of reasoning. So, how do we approach
artificial intelligence? How do we go about doing it? We wanna build a machine
that can do some task which requires intelligence in humans, let's say translating French into English. So the Symbolic AI view of this is that what you do is you go and find somebody who's
really expert and you find out from them all the knowledge that they use when they translate
from French to English, and you code it up in what are computer
versions of sentences. And if you do that right, so is the idea, then the machine will
have that human expertise. That's the Symbolic AI approach, right, that human intelligent behavior
is a problem of knowledge. If you give the machine
the right knowledge, it will be able to do the problem. But there's a different trend. It says, "Look, forget about
trying to tell the machine how to do it by giving it the knowledge. Just show the machine
what you want it to do, and get the machine to learn." In the French to English
translation example, you're not telling it how
to do the translation. You're just saying, "Look, for this input, this is what I would want
you to produce as the output. For this French input, I would
want this English output." And you give it lots
of examples like that. And the idea is it will
learn how to do it. So that's machine learning,
is what that's all about. And the techniques themselves
are not a new thing. Two researchers called
McCulloch and Pitts, in the 1940s, came up with this idea for what are now called 'neural networks,' but throughout the 60s and early 70s, really progress stalled. And so there was a backlash
against AI in the mid-1970s, and that was called 'The AI Winter.' It turned out that to
make neural networks work, you needed lots and lots of data- but also, these things are
computationally very expensive. You need lots of compute power in order to make these
neural networks work. And that's the area where we've seen lots of progress over the last 15 years. That's really the reason that we're having this conversation today. That's the reason that AI
is such an important field at the moment. So what most of contemporary
AI is about is focused on getting AI systems to
do very, very narrow tasks, very, very specific things. And in those specific
tasks, it might be better than any living human being,
but it can't do anything else. You can drive a car, I can drive a car, I can then get out of
the car and play a game of football, rather badly
in my case, and then make a good meal and tell
a joke, and I can do that- the whole range of things. You consider a driverless car, however good it is at driving, it's doing one tiny narrow thing. So, the grand dream of AI, it's not kind of formalized anywhere, there's no very specific version
of it, but nowadays it goes by the name of 'Artificial
General Intelligence,' AGI. And basically what it
means if AGI succeeds, if we achieve with that grand dream, then we'll have machines
that have the same intellectual capabilities
that human beings do- but there's one other
fascinating part of the puzzle. So a colleague of mine here at the University of
Oxford called Robin Dunbar, he's an evolutionary psychologist, and he was interested in
the following question: Why do human beings have big brains? It's a very natural question. Why do human beings have big brains? What Dunbar became
convinced by was the idea that we have big brains
because we are social animals, and we have big brains to be able to cope with many social relationships. You know, where I keep
track of: 'What Bob thinks about what Alice thinks
about Bob, you know,' that kind of thing- how these stand in
relation to one another. And what I found about
that so fascinating is that it means that human intelligence is, in a fundamental way, social intelligence. Back in the 1950s when John McCarthy and his contemporaries
were thinking about AI, what they wanted to do was to demonstrate that machines could do things
like learn and solve problems. And it's only much more recently that AI has become concerned
with these social aspects. What happens if you have two AI systems that can start to
interact with one another? Then how do we give them social skills, skills like cooperation, the
ability to work as a team, to coordinate with each other,
to negotiate with each other? So, how might we get there,
to conscious machines? One of the steps along
that path is the idea that we will be able to build machines, which can put themselves
in another's mind. I think that's a step
in the right direction, but the truth is we don't know
how to even take that step at the moment. Human beings are wonderful creations. I mean, they are the
most incredible creations in the entire Universe, but there's nothing magic about them. We are a bunch of atoms that are bumping up against each other. For that reason, I don't
think there should be any logical reason that says that conscious machines aren't possible. But saying that something is
logically possible and saying that we know how to do it are
completely different things. Do we know how to do it? Absolutely not. And actually, one of
the fundamental problems is that consciousness itself in human beings is really
not remotely understood. It is one of the big mysteries in science. How do that large number of
neurons that are connected in all those kind of weird
ways create consciousness and self-awareness, the human experience? So the path ahead I think is
gonna be slow and torturous. These are fearsomely complex
things that are being created. But, one of the fascinating
things, not about AI, but about computing generally, is that the limits to computing: they're not the limits
of concrete or steel or anything like that
in the physical world. You're really bounded only
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