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Courtesy of New Scientist Magazine
Dr. Mark Humphrys
University of Edinburgh
Artificial Intelligence (AI) is a perfect example of how sometimes science moves more
slowly than we would have predicted. In the first flush of enthusiasm at the invention of
computers it was believed that we now finally had the tools with which to crack the
problem of the mind, and within years we would see a new race of intelligent machines. We
are older and wiser now. The first rush of enthusiasm is gone, the computers that
impressed us so much back then do not impress us now, and we are soberly settling down to
understand how hard the problems of AI really are.
What is AI? In some sense it is engineering inspired by biology. We look at animals, we
look at humans and we want to be able to build machines that do what they do. We want
machines to be able to learn in the way that they learn, to speak, to reason and
eventually to have consciousness. AI is engineering but, at this stage, is it also
science? Is it, for example, modeling in cognitive science? We would like to think that is
both engineering and science but the contributions that is has made to cognitive science
so far are perhaps weaker than the contributions that biology has given to the
The confused history of AI
Looking back at the history of AI, we can see that perhaps it began at the wrong end of
the spectrum. If AI had been tackled logically, it would perhaps have begun as an
artificial biology, looking at living things and saying "Can we model these with
machines?". The working hypothesis would have been that living things are physical
systems so let's try and see where the modeling takes us and where it breaks down.
Artificial biology would look at the evolution of physical systems in general, development
from infant to adult, self-organization, complexity and so on. Then, as a subfield of
that, a sort of artificial zoology that looks at sensorimotor behavior, vision and
navigation, recognizing, avoiding and manipulating objects, basic, pre-linguistic learning
and planning, and the simplest forms of internal representations of external objects. And
finally, as a further subfield of this, an artificial psychology that looks at human
behavior where we deal with abstract reasoning, language, speech and social culture, and
all those philosophical conundrums like consciousness, free will and so forth.
That would have been a logical progression and is what should have happened. But what did
happen was that what people thought of as intelligence was the stuff that impresses us.
Our peers are impressed by things like doing complex mathematics and playing a good chess
game. The ability to walk, in contrast, doesn't impress anyone. You can't say to your
friends, "Look, I can walk", because your friends can walk too.
So all those problems that toddlers grapple with every day were seen as unglamorous,
boring, and probably pretty easy anyway. The really hard problems, clearly, were things
demanding abstract thought, like chess and mathematical theorem proving. Everyone ignored
the animal and went straight to the human, and the adult human too, not even the child
human. And this is what `AI' has come to mean - artificial adult human intelligence. But
what has happened over the last 40-50 years - to the disappointment of all those who made
breathless predictions about where AI would go - is that things such as playing chess have
turned out to be incredibly easy for computers, whereas learning to walk and learning to
get around in the world without falling over has proved to be unbelievably difficult.
And it is not as if we can ignore the latter skills and just carry on with human-level AI.
It has proved very difficult to endow machines with `common sense', emotions and those
other intangibles which seem to drive much intelligent human behavior, and it does seem
that these may come more from our long history of interactions with the world and other
humans than from any abstract reasoning and logical deduction. That is, the animal and
child levels may be the key to making really convincing, well-rounded forms of
intelligence, rather than the intelligence of chess-playing machines like Deep Blue, which
are too easy to dismiss as `mindless'.
In retrospect, the new view makes sense. It took 3 billion years of evolution to produce
apes, and then only another 2 million years or so for languages and all the things that we
are impressed by to appear. That's perhaps an indication that once you've got the mobile,
tactile monkey, once you've got the Homo erectus, those human skills can evolve fairly
quickly. It may be a fairly trivial matter for language and reasoning to evolve in a
creature which can already find its way around the world.
The new AI, and the new optimism That's certainly what the history of AI has served to
bear out. As a result, there has been a revolution in the field which goes by names such
as Artificial Life (AL) and Adaptive Behavior, trying to re-situate AI within the context
of an artificial biology and zoology (respectively). The basic philosophy is that we need
much more understanding of the animal substrates of human behavior before we can fulfil
the dreams of AI in replicating convincing well-rounded intelligence.
(Incidentally, the reader should note that the terminology is in chaos, as fields re-group
and re-define themselves. For example, I work on artificial zoology but describe myself
casually as doing AI. This chaos can, however, be seen as a healthy sign of a field which
has not yet stabilized. Any young scientist with imagination should realize that these are
the kind of fields to get into. Who wants to be in a field where everything was solved
So AI is not dead, but re-grouping, and is still being driven, as always, by testable
scientific models. Discussions on philosophical questions, such as `What is life?' or
`What is intelligence?', change little over the years. There have been numerous attempts,
from Roger Penrose to Gerald Edelman, to disprove AI (show that it is impossible) but none
of these attempted revolutions has yet gathered much momentum. This is not just because of
lack of agreement with their philosophical analysis (although there is plenty of that),
but also perhaps because they fail to provide an alternative paradigm in which we can do
science. Progress, as is normal in science, comes from building things and running
experiments, and the flow of new and strange machines from AI laboratories is not remotely
exhausted. On the contrary, it has been recently invigorated by the new biological
In fact, the old optimism has even been resurrected. Professor Kevin Warwick of the
University of Reading has recently predicted that the new approach will lead to
human-level AI in our lifetimes. But I think we have learned our lesson on that one. I,
and many like me in new AI, imagine that this is still Physics before Newton, that the
field might have a good one or two hundred years left to run. The reason is that there is
no obvious way of getting from here to there - to human-level intelligence from the rather
useless robots and brittle software programs that we have nowadays. A long series of
conceptual breakthroughs are needed, and this kind of thinking is very difficult to
timetable. What we are trying to do in the next generation is essentially to find out what
are the right questions to ask.
It may never happen (but not for the reasons you think)
I think that people who are worried about robots taking over the world should go to a
robotics conference and watch these things try to walk. They fall over, bump into walls
and end up with their legs thrashing or wheels spinning in the air. I'm told that in this
summer's Robotic Football competition, the losing player scored all five goals - 2 against
the opposing robot, and 3 against himself. The winner presumably just fell over.
Robots are more helpless than threatening. They are really quite sweet. I was in the MIT
robotics laboratory once looking at Cog, Rodney Brooks' latest robot. Poor Cog has no
legs. He is a sort of humanoid, a torso stuck on a stand with arms, grippers, binocular
vision and so on. I saw Cog on a Sunday afternoon in a darkened laboratory when everyone
had gone home and I felt sorry for him which I know is mad. But it was Sunday afternoon
and no one was going to come and play with him. If you consider the gulf between that and
what most animals experience in their lives, surrounded by a tribe of fellow infants and
adults, growing up with parents who are constantly with them and constantly stimulating
them, then you understand the incredibly limited kind of life that artificial systems
The argument I am developing is that there may be limits to AI, not because the hypothesis
of `strong AI' is false, but for more mundane reasons. The argument, which I develop
further on my website, is that you can't expect to build single isolated AI's, alone in
laboratories, and get anywhere. Unless the creatures can have the space in which to evolve
a rich culture, with repeated social interaction with things that are like them, you can't
really expect to get beyond a certain stage. If we work up from insects to dogs to Homo
erectus to humans, the AI project will I claim fall apart somewhere around the Homo
erectus stage because of our inability to provide them with a real cultural environment.
We cannot make millions of these things and give them the living space in which to develop
their own primitive societies, language and cultures. We can't because the planet is
already full. That's the main argument, and the reason for the title of this talk.
So what will happen?
So what will happen? What will happen over the next thirty years is that will see new
types of animal-inspired machines that are more `messy' and unpredictable than any we have
seen before. These machines will change over time as a result of their interactions with
us and with the world. These silent, pre-linguistic, animal-like machines will be nothing
like humans but they will gradually come to seem like a strange sort of animal. Machines
that learn, familiar to researchers in labs for many years, will finally become mainstream
and enter the public consciousness.
What category of problems could animal-like machines address? The kind of problems we are
going to see this approach tackle will be problems that are somewhat noise and error
resistant and that do not demand abstract reasoning. A special focus will be behavior that
is easier to learn than to articulate - most of us know how to walk but we couldn't
possibly tell anyone how we do it. Similarly with grasping objects and other such skills.
These things involve building neural networks, filling in state-spaces and so on, and
cannot be captured as a set of rules that we speak in language. You must experience the
dynamics of your own body in infancy and thrash about until the changing internal numbers
and weights start to converge on the correct behavior. Different bodies mean different
dynamics. And robots that can learn to walk can learn other sensorimotor skills that we
can neither articulate nor perform ourselves.
What are examples of these type of problems? Well, for example, there are already
autonomous lawnmowers that will wander around gardens all afternoon. The next step might
be autonomous vacuum cleaners inside the house (though clutter and stairs present
immediate problems for wheeled robots). These are all sorts of other uses for artificial
animals in areas where people find jobs dangerous or tedious - land-mine clearance, toxic
waste clearance, farming, mining, demolition, finding objects and robotic exploration, for
example. Any jobs done currently or traditionally by animals would be a focus. We are
familiar already from the Mars Pathfinder and other examples that we can send autonomous
robots not only to inhospitable places, but also send them there on cheap one-way
`suicide' missions. (Of course, no machine ever `dies', since we can restore its mind in a
new body on earth after the mission.)
Whether these type of machines may have a future in the home is an interesting question.
If it ever happens, I think it will be because the robot is treated as a kind of pet, so
that a machine roaming the house is regarded as cute rather than creepy. Machines that
learn tend to develop an individual, unrepeatable character which humans can find quite
attractive. There are already a few games in software - such as the Windows-based game
Creatures, and the little Tamagotchi toys - whose personalities people can get very
attached to. A major part of the appeal is the unique, fragile and unrepeatable nature of
the software beings you interact with. If your Creature dies, you may never be able to
raise another one like it again. Machines in the future will be similar, and the family
robot will after a few years be, like a pet, literally irreplaceable.
What will hold things up? There are many things that could hold up progress but hardware
is the one that is staring us in the face at the moment. Nobody is going to buy a robotic
vacuum cleaner that costs £5000 no matter how many big cute eyes are painted on it or
even if it has a voice that says, "I love you". Many conceptual breakthroughs
will be needed to create artificial animals. The major theoretical issue to be solved is
probably representation: what is language and how do we classify the world. We say `That's
a table' and so on for different objects, but what does an insect do, what is going on in
an insect's head when it distinguishes objects in the world, what information is being
passed around inside, what kind of data structures are they using. Each robot will have to
learn an internal language customized for its sensorimotor system and the particular
environmental niche in which it finds itself. It will have to learn this internal language
on its own, since any representations we attempt to impose on it, coming from a different
sensorimotor world, will probably not work.
Finally, what will be the impact on society of animal-like machines? Let's make a few
predictions that I will later look back and laugh at.
First, family robots may be permanently connected to wireless family intranets, sharing
information with those who you want to know where you are. You may never need to worry if
your loved ones are alright when they are late or far away, because you will be
permanently connected to them. Crime may get difficult if all family homes are full of
half-aware, loyal family machines. In the future, we may never be entirely alone, and if
the controls are in the hands of our loved ones rather than the state, that may not be
such a bad thing.
Slightly further ahead, if some of the intelligence of the horse can be put back into the
automobile, thousands of lives could be saved, as cars become nervous of their drunk
owners, and refuse to get into positions where they would crash at high speed. We may look
back in amazement at the carnage tolerated in this age, when every western country had
road deaths equivalent to a long, slow-burning war. In the future, drunks will be able to
use cars, which will take them home like loyal horses. And not just drunks, but children,
the old and infirm, the blind, all will be empowered.
Eventually, if cars were all (wireless) networked, and humans stopped driving altogether,
we might scrap the vast amount of clutter all over our road system - signposts, markings,
traffic lights, roundabouts, central reservations - and return our roads to a soft,
sparse, eighteenth-century look. All the information - negotiation with other cars,
traffic and route updates - would come over the network invisibly. And our towns and
countryside would look so much sparser and more peaceful.
I've been trying to give an idea of how artificial animals could be useful, but the reason
that I'm interested in them is the hope that artificial animals will provide the route to
artificial humans. But the latter is not going to happen in our lifetimes (and indeed may
never happen, at least not in any straightforward way).
In the coming decades, we shouldn't expect that the human race will become extinct and be
replaced by robots. We can expect that classical AI will go on producing more and more
sophisticated applications in restricted domains - expert systems, chess programs,
Internet agents - but any time we expect common sense we will continue to be disappointed
as we have been in the past. At vulnerable points these will continue to be exposed as
`blind automata'. Whereas animal-based AI or AL will go on producing stranger and stranger
machines, less rationally intelligent but more rounded and whole, in which we will start
to feel that there is somebody at home, in a strange animal kind of way. In conclusion, we
won't see full AI in our lives, but we should live to get a good feel for whether or not
it is possible, and how it could be achieved by our descendants.
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