nm0938: can i just back that up Cumberland Lodge is the most brilliant deal you 
could possibly imagine it's the er it's owned by the er the royal family it's 
actually owned by the Queen Mother i think and it's where the royal family keep 
a small portion of their fabulous er art collection er it's in the middle of 
Windsor Great Park which is er tremendous as a er a place to go walking and 
we've got a good er a good programme sort of shaping up we've got er er 
Professor er er Ray Ball from York University who talks about political er 
rhetoric about how different politicians er conceal their intentions and tell 
you lies all the time er we've got er somebody coming to talk about er 
cognitive rehabilitation er that is how how you help people to er get back on 
their feet after they've had brain damage and so on and there are lots of other 
lots of other events er that er that that we do at Cumberland Lodge and 
generally speaking and people have a great time and if if you are going 
there as a part of a it's it's the place is sort of hired out to companies and 
if if er if a company like I-C-I er send somebody to Cumberland Lodge they pay 
a thousand pounds per person per day and you get three days there for fifty 
quid er again courtesy of something called the Saint Catherine's Foundation 
which again is financed by the the royal family so i i strongly recommend it 
and er we we've got er places are limited but you know the more people apply 
the more places we can have okay now i'm to-, today er i'm going to be talking 
about artificial life and er er i'm afraid that the the sound system has packed 
up in here so i'm afraid once again i this this mike is for the er camera here 
it's it's not er er the the audio system in here er so if if people at the back 
can't hear me er just just shout out during the lecture and i'll i'll raise the 
er i'll raise the volume a bit i'm also going to 
er slow show some slides and videos so i'm afraid there's going to be a bit of 
er er fiddling about with er with with technical stuff 'cause i haven't had 
time to er put it all together because the lecture theatre was full okay for a 
long time er artificial intelligence has er dominated er the the study of of 
intelli-, of of of the human mind this is what i was telling you in the first 
lecture that i gave that we have as it were a a a new discipline called 
cognitive science which brings together all sorts of different disciplines 
neuroscience anthropology linguistics psychology philosophy and artificial 
intelligence and you'll notice that there's there's quite a strong link between 
artificial intelligence and psychology and artificial intelligence has as it 
were had a good run for its money about er thirty years' worth really and er 
you'll find remarks like this being made artificial intelligence A-I is the 
single most important 
d-, development in the history of psychology er the computer is the last 
metaphor for the mind er we shall ever need and at last we know what the mind 
is it's a symbol it's a physical symbol system now this is this is the central 
idea in artificial intelligence and and and what does that what does that idea 
really mean well what it what it means actually refers to that picture i put up 
a while ago at the beginning you remember that in the fifteenth century the 
Christian mystic John Flood he asked us to imagine that the mind somehow 
reflected the external world and the external world was ordered structured by 
the Creator and that somehow the the microcosm what was in the head reflected 
the macrocosm the the universe outside it the senses were related to the 
different planets and and a whole integrated view of the mind and how it fitted 
into the universe but following the industrial scientific and intellectual 
revolutions following the enlightenment the past three-hundred years of 
European history instead we tend to think of ourselves now as er machines who 
think the brain is some sort of information processing device and artificial 
intelligence is the most er if you like er extended attempt to sorry i'm i'm 
i'm faffing about here because i've realized i've forgotten to bring some er 
some overhead transparencies but i i can do without them the idea behind the 
the mind as computer actually can be traced back to the the philosopher Hobbes 
Thomas Hobbes whose great er political treatise the Leviathan actually began 
with er a long discourse about the nature of the mind his idea of the ideal 
political system was based on the idea that first we must understand what human 
beings are really like and how their minds work in order to devise a system 
within which they can live together safely and without going into his political 
theory i just draw your 
attention to these er remarks in effect er by ratiocination thinking i mean 
computation and that is Hobbes speaking in whenever it is about the sixteen-
fifties that was the the centre of that diagram by Robert Flood ratio 
ratiocination rationality a Cartesian idea which says that the essence of the 
human mind is the ability to manipulate clear and distinct ideas and we have a 
a machine nowadays which does just that that's exactly what the computer does 
notice ratio right in the middle there and so the project of artificial 
intelligence er and these are the slides i'm afraid i've forgotten to bring 
with me but G-P-S stands for General Problem Solver is a classic example of a 
program which behaves intelligently solves problems and generally speaking er 
can be applied to a large variety of different situations er on the ba-, on the 
simple basis that it makes a representation of the world in terms of statements 
in a simple language and these statements can 
be manipulated to produce different representations of of the world as it might 
be and these are then checked against whether this representation of the world 
as it might be is getting you towards your goal you give the program a 
statement of the world as it is a statement of what you want and you let the 
program work out how to get from where you are to where you want to be so the 
classic example here might be something like a chessboard you tell the machine 
this is the way the chessboard is and you give it a er a general abstract 
description of what you want which is a position where you control the game if 
it's chess where the opponent's king is in check and can't move out of check 
and you win that's a statement an abstract statement of the goal you want to 
get to and the general problem solver would get from where it is to where you 
want it to be through a process of recognizing subgoals making moves and 
generally transforming the world until you get 
what you want now this is artificial intelligence it can be applied in all 
sorts of and and has been applied in all sorts of different er areas to solve 
problems to give medical diagnosis to er generally speaking do what human 
beings do when they're acting intelligently and there have been some tremendous 
successes but for one reason or another that project has run into trouble what 
we have discovered and that's the point i was making with those sentences 
remember er time flies like an arrow the point i was making with that example 
in the first lecture is that when you get into real human intelligence you find 
that even the simplest things like parsing a sentence requires a huge depth of 
knowledge and if you're going to approach intelligence on the basis of 
manipulating symbolic structures which represent that knowledge the machine has 
to get enormous huge now some people are still working on that basis they think 
they are going to produce er real intelligence 
and Allen Newell in fact o-, on that er that slide there this fellow Allen 
Newell er he died recently but just before he died he he wrote a book called er 
A Unified Theory of Cognition and he and his fellow research workers are still 
working on the idea that if you produce a machine with some symbolic 
manipulation capacity and a huge memory full of symbolic structures which 
represent everything it knows about the world it will like a k-, like a nuclear 
pile you keep on throwing in knowledge and eventually it sort of goes critical 
and begins to glow and begins to be intelligent now other people for different 
reasons say this is bound to fail we simply can't do it it isn't going to be 
that easy and a different er two different approaches have well a number of 
different approaches have arisen i want to wa-, talk about one very quickly and 
that's connectionism and then i want to get on to the last but the the the 
focus 
of this week's lecture which is er artificial life and i have a a video to show 
of er well very nearly the latest a a a humanoid er robot connectionism is er a 
simple but radical idea which is that instead of trying to make artificial 
intelligence by programming it instead we can in effect we can grow it 
connectionism is sometimes called parallel distributed processing P-D-P and 
sometimes it's called neural networks what these networks are like is er a 
series of units some of which are connected to the outside world so these units 
which are could be each one could be a little computer or it could be some er 
bundle of electronics or it could be some simulation of er electronics are 
connected to something like a camera or a er a microphone or in some way they 
are driven by the outside world in between there are a number of units which 
are connected to the inputs and to each other and they're also connected to 
output units and in effect you allow these 
units to adjust their con-, their connectivity with each other you nobody 
intervenes in this network the network learns and what what connectio-, ha-, 
these connectionist systems work by having networks of units with dense 
connections between them which can be excitatory or inhibitory or they can be 
simply er there or not there as the network as it were experiences inputs from 
here it also er is trained by inputs which are not shown on this diagram which 
in effect er tell the units what is right and what is wrong and after a while 
the network has learned it it learns to for example er identify faces there's 
there's er er Professor Igor Alexander at er Imperial College er has built a 
number of networks which for example er i could er i could take somebody's face 
er take a number of er different photographs of it train the network up to ne-, 
to recognize that face it would then recognize that face in er profile for 
example where it hadn't seen it before or it might 
recognize it upside down or you can put on a false beard and glasses and it 
still recognizes the face and you can put the network which is trained to 
recognize this face into a camera swing it around the crowd and ding it picks 
you out and if you wonder what those cameras which you now see increasingly 
hanging below the er bridges of motorway u-, u-, under motorways are doing what 
they're doing is using neural networks to recognize number plates and these 
things are extremely effective they can recognize fingerprints they can 
recognize voices they can recognize handwriting er and this is something that 
that good old-fashioned artificial intelligence that's what GOFAI stands for 
good old-fashioned artificial intelligence which recognize things on the basis 
of making a description of your face in some sort of er symbolic language and 
then trying to recognize it it turned out to be extremely difficult but 
training artificial neural networks to recognize things 
turns out to be much easier and much more powerful 
sm0939: does one camera search for one number plate or does it 
nm0938: no you can 
sm0939: well does one network search for one number plate 
nm0938: no i mean once you know let let's let's er let's assume that you had a 
network for every face that you know you there's n-, there's no there's no 
problem in multiplying the networks so then you take one camera parallel the 
output of that camera to all these networks and the one that lights up that's 
the person you've got in front of you 
sm0939: do you have one network for each face or whatever you don't have 
networks which can recognize a number of different things 
nm0938: well in fact a number of networks are capable of for example one of the 
networks that er er ic-, er Alexander has trained er can distinguish between 
men and women so there's one network you show it a whole load of women and you 
say that's a woman that's a woman that's a woman and then you show them a whole 
load of m-, men 
that's a man that's a man that's a man then you show them er a gender specific 
person er who they haven't seen before and they say that's a woman and it's 
right so networks are extremely flexible let's t-, let's take this on in the 
the seminars i mean al-, all i'm putting in front of you here is the idea that 
instead of programming intelligence we can grow it instead of somehow 
understanding intelligence in a formal Cartesian way writing down the the the 
the procedures that underlie our ability to play chess or to er have a 
conversation we are getting to the point where we're we're actually simulating 
the the the a b-, a biologically plausible model of what the brain is doing and 
notice there's s-, there's some really quite interesting philosophical 
conundrums here because say you get some artificial network which does 
something really quite interesting like recognize things and if someone comes 
up to you and says how does the network do it you 
actually can't say all you can say is well it's trained to do it and if someone 
says can you point to where the knowledge is in this network that allows us to 
recognize that face or that face you you can't you can just say well there's a 
whole mass of connections in there er and they do the job er i mean here's 
here's a er an example of er the idea that you c-, you can have networks to 
recognize letters and then these letters as it were are connected in the 
excitatory and inhibitory ways to a whole set of word recognition nodes in the 
network and simulating what human beings do in experiments in recognizing words 
and letters er can be done using networks really quite effectively so i'm 
putting in front of you here the idea that we're moving from er understanding 
intelligence by programming it symbolically towards understanding intelligence 
in a more biologically plausible way and in connectionism the the the knowledge 
in a 
connectionist system is distributed there's no particular place where it is it 
runs in parallel and it doesn't depend upon symbols and in many people have 
said actually when you look at it that's the way the brain works when you open 
up the brain you don't see the sort of serial processor central processor 
structure that you find inside a normal computer instead you see a dense web 
which you can't understand simply by looking at it and good old-fashioned 
artificial intelligence is very much localized that is to say if you lose a bit 
of an old computer's memory you've lost that memory completely if you lose a 
bit of a connectionist network you haven't lost anything but the whole thing 
has er lost a little bit er good old-fashioned er artificial intelligence is 
definitely serial it's very fast but it's just one thing after another very 
quickly in a central processor and it depends essentially on symbolic 
representations of the 
environment and we strongly suspect that many good perfectly good cognitive 
beings like animals don't make symbolic representations of their environments 
at all but their nervous systems are tuned up to be able to act effectively 
this is a quotation from one of the people who er invented P-D-P they say 
connectionist systems don't contain knowledge just connections and er another 
quotation a similar one is that connectionist systems don't have any rules 
inside them they just behave as if they did however compared to connectionism 
artificial life is a much more fundamental break with artificial intelligence 
and i'd like to spend the rest of the lecture talking about that now what i 
mean by artificial life is anticipated a bit by connectionism for example some 
people have tried to build small walking robots and on the old idea what you 
did was you wrote a program which had instructions in it like lift the left leg 
move it forward drop it again and when you're stable do the same with the right 
leg and so on there's some sort of program inside the machine and you could 
point to different bits of the program and say that's what moves the leg like 
this and that's what moves the leg like that instead people have begun to build 
machines which have legs they have ways of moving those legs but they have no 
program instead they have a er a a dense set of connections inside them which 
gradually learn to control the organism let me give you er a brief illustration 
of this er ne-, never mind the text for the moment there's there's er a fish 
called a lionfish which er when it emerges from the egg it's simply got no idea 
it swims up down any which way it looks if they're very small and they look as 
if they're simply er mess in the water just floating about but after er some 
hours you see that their movements become less random and in 
effect what the what the the fish is doing is flapping its control surfaces at 
random and finding out the results of doing that and then gradually becoming 
less random to the point where it can swim straight and level now it has to 
have a balance organ to do that but er that's what it's doing it's learning 
about its own body well without going into detail this is the control structure 
of er an aquatic robot which does the same thing you toss it in the water it 
thrashes about you come back next morning and it's swimming around straight and 
level nobody has programmed it to do that it's learned how to do it this is 
artificial life now i'll i'll expand that with some examples in effect the 
program for artificial life is captured in these two quotations here the 
project is to capture the logical form of life in artefacts now logical form 
might mean things as er straightforward as let's say the growth 
patterns of plants now these these are plants which are generated on a computer 
screen but the manner in which they're generated we're now discovering can be 
described by very simple rules what you see as the complex structure of an 
organism actually may be the product of rather simple growth rules and recent 
developments in mathematics particularly to do with fractals and chaos er are 
helping us to understand that what we might think of as being the the result of 
a complex genetic program actually might be the result of rather simpler growth 
patterns and here you have er plants which fundamentally the same formula with 
a few parameter changes produces different organisms and you can even account 
for the direction of the wind now these things are just structures inside 
computers but they could be actually built as well likewise here we have er 
real structures built by social insects wasps and bees and programming 
virtual insects as it were and allowing them to interact with each other 
produces structures which are beginning to capture the the sorts of patterns 
that we we see in nature these again are virtual patterns inside a machine but 
once again they could be built quite easily what we're doing is we're we're 
moving towards capturing the logical form of life in artefacts and well i'll 
come on to this er at the end of the lecture but not only the life that we know 
about but the life that might be that is to say we may be on the brink of 
creating life forms which in a sense go beyond the D-N-A based life forms that 
we know and love and which we are ourselves so er i'm actually on to this this 
point here the examples of artificial life discovering the laws of growth and 
form that's what plants are about genetic algorithms and comfuter computer 
viruses these i mean you know about computer viruses these are information 
structures which reproduce themselves in the 
computer domain and er Thomas Ray has actually produced what he calls the 
Tierra Project which are th-, these are not viruses these are actually 
computational organisms that live on the Internet and they they transmit 
themselves and reproduce themselves in different computers wherever they can 
get and he sort of generated them and let them loose and now they are they're 
out there reproducing er with slight variations evolving some of them die some 
of them find it easier to survive if they mutate slightly er what are these 
things well they're they're they're digital organisms er it's been found that 
they tend to follow the shadow of they they they tend to hover the the the the 
the Internet covers the globe and they tend to be found in the dark part now 
why is that it's because then people go to sleep and the computers have got 
more room to host these organisms so they've actually developed organic 
patterns robots that learned to control their 
bodies that was that was the er the fish and once you er begin to er play with 
these sorts of things you can actually model individual fish like that real 
ones er and inside there's some sort of er set of program structures er a-, a 
very useful thing intention generators i you know i wish i had one every time 
you're at a loss you can just turn to your intention generator and have 
something new to do well these these things er actually are not so er 
extraordinary as they might sound er here's an intention generator er are are 
you er [sniff] are you on your own er sorry are you inside the pack is really 
what that statement is saying well no if you're not er find somebody and get 
close to them if you are stay roughly speaking near the centre of gravity of 
the whole pack you're in so if you if we produce a thousand virtual fish with 
these little intention generators inside them what they do is they flock they 
shoal they they behave in an absolutely natural way which is if you if you 
frighten them they they scatter and then regroup again now nobody programmed 
them to do that all you did was put those little instructions inside each one 
and they they produce the the resultant structure er emerges [sniff] so the 
point i'm making there is that you can get what might appear to be complex 
behaviour from simple rules flocks of birds swarms of bees shoals of fishes 
seem to behave in an intelligent collective way well that collective 
intelligence emerges from very simple intelligence in each member of the flock 
or swarm or herd or whatever this is what the A-I project is about capturing 
the logical form of life in artefacts now what i want to er finish up with er 
is the the most er radical attempt to produce artificial life namely to produce 
a humanoid robot and this this is the the work of of Rodney Brooks who er at a 
at a conference a while back was asked generally what how he would describe his 
own work because sometimes he calls himself a psychologist sometimes he calls 
himself an engineer what he's doing is building a humanoid robot which i'll 
show you on a video in a minute and er somebody asked him well what sort of a 
thing are you what are you what are you doing are you an engineer are you a 
psychologist are you a philosopher what are you doing and he said well i'm a 
bit of everything and if you want me to describe my work i'd i'd put it like 
this i'm making a hope i'm making a home for the mind and hoping that the mind 
will come what he does is he builds he calls it behaviour based robotics he 
builds complete creatures and he described his er work a little bit like this a 
project to capitalize on computation to understand human cognition we will 
build an integrated physical system including vision sound input output 
manipulation er the resulting system will learn to think by building on its 
bodily experiences this is the Cog Project it's it's come to be known as the 
Cog project and what i'd like to do now is to show you it now i don't know 
whether 
this is er how well this is going to work but let's have a go [sniff] okay i'll 
have to have the lights out to er to do this properly Cog is er a torso it 
starts at the hips ends at the head it's got a pair of arms un like most 
robotic research up to now it doesn't live in a laboratory it stands in the 
corridor at M-I-T and people who come by play with it and that's how it learns 
and what we've got here is er a video er this is Rod Brooks' work er a video 
which shows what Cog can do and i'll i'll it takes about five minutes it's got 
no sound i'll talk you through it so humanoid intelligence requires humanoid 
interactions with the world you'll see Cog in a second er it's that head there 
it is there's there's the head up there it's er it's not to clear in this er 
thing but this i-, this is Cog's arm and it's got er it's got something in it 
which er it's trying to hand 
to Rod Brooks but it's not doing too well at the moment there's its head and 
notice that as as it moves around the eyes and the head can move independently 
notice here the eyes follow objects around if you move it slowly the head 
follows it around if you move it quickly its eyes flick if you move it slowly 
it follows it with its head if you move its head its eyes stay still you can't 
you can't see this but its its eyes are looking in one direction it's got in 
effect er an optical reflex such as we have if we move your eyes quickly your 
head quickly sorry your eyes stay in the same place lots of animals have got it 
er this is Cog i think trying to find something it's looking for something 
[laughter] here we are found it and if you make a move it picks up you i've 
heard people who've interacted it with it saying that it's the first time 
they've felt like they've they've been in the room with something it feels like 
you're with something to be with Cog now most industrial 
robots are very dangerous you have to put them behind 'cause they're so 
powerful you have to put them behind screens Cog is safe in the corridor it's 
actually quite cuddly [laughter] er it doesn't mind being wrenched about a bit 
bit like a sort of friendly dog [laughter] and if you wiggle it the actual 
mechanics of its body are sort of humanoid too and if given nothing to do it 
just sort of nuh-huh-nuh-huh-nuh-huh [laughter] however if you leave it alone 
once it's been doing something it practises what it's been doing and you'll see 
in a second what it's practising but this is Cog having a think about what it's 
been doing just a moment ago [laughter] oh well we can fast-forward this little 
bit this is this is a Cog's eye view of the world let me er aagh [laughter] no 
stop [laughter] [laughter] right [laughter] this i-, 
this is Cog reaching towards something it sees picks it up with its head and 
brings its hand out and actually this i-, this is a that's what it was that's 
what it was practising now here's somebody just playing around with Cog putting 
something in front of it and Cog sort of looks at the person looks at the 
object now my kids grew up pretty normal but that reminds me exactly of one of 
my daughters when she was about eighteen months old [laughter] she'd sort of 
push things around do it and then [laughter] wait for me to pick it up 
[laughter] but this is this is Cog learning to know its body here okay now this 
is what was that was that a was that psychology engineering er what was it well 
i i suggest it's er i i suggest it it's you might say cybernetic philosophy 
that is to say the Cog project can stand for a number of of projects around the 
world now which are attempts to create what in popular fiction would be called 
the the cyborg 
the cybernetic organism that is to say Cog begins to look like a a humanoid it 
has nothing inside it which has anything to do with artificial intelligence 
there are no representations no Cartesian ratio right in the middle it's just a 
a seething mass of lots of different connectionist systems different ways of 
getting different aspects of intelligence to interact with with each other and 
the the way that they interact is structured by Cog's interaction with the 
social world so what i'm putting in front of you is the proposition that what 
we are creating is artificial life which will in some sense share our social 
world and we will create artificial intelligence not by programming anything in 
explicit symbolic terms but by building machines which are broadly speaking 
organisms we are as it were making the artificial er natural by creating 
artefacts that can learn a-, to be part of our social world now i want to 
finish this lecture by putting in front 
of you what may seem a rather odd proposition but it it is the proposition that 
actually we are such artefacts already this is Jonathan Kingdon who is a er a 
biologist who's very interested in evolution and what he claims is that human 
beings you know that our er genetic material overlaps with that of our close 
evolutionary relatives like the bonobo chimpanzees to something like ninety-
nine per cent we are very similar genetically speaking to chimpanzees and yet 
we are completely different we have language we have culture and so on now i i 
should say incidentally that ninety-nine per cent might sound like a lot but 
we're we're thirty- three or so per cent identical with mushrooms [laughter] so 
it it it isn't er quite the drama that it might first appear but what a lot of 
biologists are now saying and Jonathan Kingdon is one of them is that we don't 
need to look inside human beings for what makes us unique and different 
from animals instead we perhaps might look at what is outside and what we grow 
up within and Jonathan Kingdon puts it like this that human beings are in 
effect artefacts of their own artefacts now let me explain that if you think 
about er i-, what i what i'm playing with here is the idea that artificial life 
may be creating cyborgs but we may be cyborgs already cultural evolution is 
much more important than biological evolution for us biological evolution 
stopped about two-hundred-thousand years ago it hasn't stopped but it's so slow 
compared to the evolutionary process that was unleashed once human beings could 
transmit information from generation to generation using culture that if you 
think about it wherever you look you find something that is made by human 
beings for human beings and babies grow up like the the lionfish there finding 
out what their body will do within an environment which is structured to bring 
out certain capacities from 
bodies so all the things that surround us from Stone Age stone tools up to 
digital watches are products of the human mind cultural artefacts are produced 
by minds but minds the human minds are produced by interacting with those 
cultural artefacts and so human the human condition is in fact intrinsically 
artificial and i'd invite you to think about this from the point of view of 
literature if we go back to the sixteenth century Cervantes' Don Quixote we 
find technology the windmills turning peacefully in the wind charged at by the 
sad knight on the basis that they are giants which he has to slay but the the 
technology is out there and human meanings are projected onto it if we go about 
three-hundyear years later to Charles Dickens in Hard Times we find that the 
machine is the central metaphor of Hard Times machines have now got their own 
meanings which they project onto human beings human beings have to serve the 
machines the 
machines are a a current theme within the a recurrent theme within the novel 
showing how people's lives are manipulated and warped by the powers of the 
machine and the needs of the machine and if we come up to date with 
contemporary cyberpunk fiction somebody like William Gibson in his novel 
Neuromancer or Mona Lisa Overdrive you'll find that now the image is of human 
existence passing from flesh into the biolog-, into the digital domain of the 
Internet this theme this image of the human condition somehow becoming 
technologized is a dominant theme in cyberpunk fiction as technology has 
developed so it has approached and been assimilated by the body let me just er 
give you a couple of examples if you think about what we er currently find 
around us in the the media we find an enormous enormously problematic control 
of let's say human reproduction we can now choose the genetic make-up of the 
next er generation er we are playing around with the idea that er 
i mean thi-, this was taken from the Guardian a few weeks ago happened to be on 
the same page er spy camera matches faces to police files that's what neural 
networks are doing but brain implants now allow patients to work computers 
without touching them which is true you can put brains you can put er chips 
into the brain er in such a way that they're biologically incorporated into the 
workings of the nervous system and then they transmit er through a radio link 
to a computer and you for example can drive the cursor around the screen by 
thinking about it which is pretty handy if your body's paralysed which is what 
they're for this is er Kevin Warwick who's a professor of Computer Science at 
Reading er that little thing that he's holding up there is a silicon implant 
which er he put into his arm and whenever he approaches his department the 
computer in the department knows he's coming turns on his computer warms up his 
room opens the door for him er and generally gets the er intelligent department 
ready to er receive him and er as he puts it here 
cybernetics is all about humans and technology interacting for a professor of 
cybernetics to become a true cyborg part man part machine is rather appropriate 
so as we are technologizing the biological con-, condition so we are 
biologizing the technologable b condition and as we assimilate technology so it 
it somehow disappears for example i find that people of my generation get 
bugged by computers er by telephone systems which are sort of complicated 
answering machines that er tell you if you want this press that if you want 
that press this and so on but i find that er younger people and particularly 
very young people are beginning not to care whether they talk on the phone with 
a human being or a machine they don't make that distinction and in many ways i 
i think this assimilation of technology is moving down the age scale at an 
increasing rate and the idea that somehow the machine will become human may 
sound like a 
contemporary cyberpunk fiction invention but in fact it was seen many many 
years ago particularly by Samuel Butler a Victorian er novelist and er literary 
figure friendly critic of the theory of evolution er and in his famous er 
satire on Victorian society Erewhon which i-, which stands for nowhere he he 
says there's there's no reason why machines couldn't develop consciousness in 
fact he he was the guy who said that a po-, even a potato has a sort of low 
form of cunning inside it [laughter] and he he warned against this he said 
machines will become as it were a threat and if you look around now you'll find 
that machines are becoming intensely personalized people's personal computers 
are really personal that is to say they have their own er voice recognition 
routines which recognizes your voice but not somebody else it corrects your 
spelling mistakes but not somebody else it has your diaries but not somebody 
else and very soon well now people commit 
themselves so strongly to partnership with machines in which most of the 
artificial intelligence research is dumped that's that's where most of the M-I-
T research used to be funded by the military but it's not any longer it's 
funded by Microsoft who want to make machines people and so cyborgs don't have 
to be as it were built in laboratories like Cog they will they will appear by 
humans integrating their everyday lives with machines which know them as people 
and you can see that happening in the shape of electronic pets what are those 
little things called 
ss: Tamagotchi 
nm0938: tam-, Tamagotchi things well imagine you know imagine what they're 
going to be like in fifty years when they actually run around you know they'll 
be furry they'll have big eyes like this [laughter] and they'll know your voice 
and not somebody else's you'll be able to teach them tricks which nobody else 
will be able to make 
them do and for a lonely kid they will be extremely powerful companions and 
they will not be machines any longer they will be part of the social world in 
fact somebody called Moravec who was one of Brooks' er research partners has 
written a book called Mind Children in which he claims that it won't take very 
long before artefacts computer artefacts in a sense continue the life of 
individuals after they've died computers will make us immortal so i i will 
finish with this this idea that artificial life in its many forms is actually 
the making of artefacts which are organic and it's no accident that it's 
happening at a time when organic things are being made into mechanisms but all 
these things require some sort of control and human beings have the ability to 
control themselves we call it paying attention and we'll deal with that next 
week