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visit MIT OpenCourseWare at ocw.mit.edu. MARVIN MINSKY: If your have any
opinions about consciousness. There is one problem in the
artificial intelligence people, is there's a lot of pretty
smart people like Steve Pinker and others who think that
the problem of consciousness is maybe the most
important problem no matter what we do in
artificial intelligence. Anybody read Pinker? I can't figure out
what his basic view is. But there's a
feeling that if you can't solve this all
important mystery, then maybe whatever we
build will be lacking in some important property. There was another
family of AI skeptics, like Penrose, who's a physicist
and a very good physicist indeed, who wrote
more than I think three different books arguing
that AI is impossible because-- I'm trying to remember
what he thought was missing from machines. AUDIENCE: Quantum mechanics. MARVIN MINSKY: Quantum mechanics
was one and Godel's theorem, incompleteness was another. And for example,
if you try to prove Godel's theorem in
any particular logic, you'll find some sort
of paradox appearing where if you try to
formalize the proof, you can't prove it in
the logical system you're proving it about. I forget what that's called. So there are these
strange logical and semi-philosophical
problems that bother people. And Pinker's
particular problem is, he doesn't see how you could
make a machine be conscious. And in particular,
he doesn't see how a machine could have a
sense called qualia, which is having a different experience
from seeing something red and from seeing something green. | you make a machine
with two photo cells and put a green filter on
one and a red filter in front of the other and show them
objects of different colors, then they'll respond
differently and you can get the machine to print
out green and red and so forth. And he's worried that
no matter what you do, the machine will only have
some logical descriptions of these things, and it won't
have a different experience from the two things. So I'm not going
to get into that. I wonder if Word is going to
do this all the time until I kill something. What if I put it off screen? That's a good way to deal
with philosophical problems, just put them in back
where you can't see them. Oh, the picture disappeared. That's really annoying. Everything disappeared. OK, think of a good question
while I reboot this. Whoops. Well, how about pressing-- that did it. Whoops. That's the most
mysterious problem. Does anybody have an
explanation of why computers take the same amount
of time to reboot, even though they're 1,000
times faster than they were? AUDIENCE: They have to
load 1,000 times more stuff nowadays. MARVIN MINSKY: Yes,
but why can't it load the things that
were running last time? For some reason, they feel
they have to load everything. AUDIENCE: Maybe there is
a certain amount of time that they think humans
are willing to wait, so therefore, they will
load as much as they can during that time. Maybe that might be it. I think if they could, they
would upload even more, but they can't because that's
how the human patience. And so they always
run out of that one. MARVIN MINSKY: Does
the XO take time to do? AUDIENCE: Yes, it
takes several seconds. MARVIN MINSKY: So it
keeps it in memory. AUDIENCE: It doesn't
have it organized. MARVIN MINSKY: I'm serious. I guess it would. But it doesn't cost much to
keep a dynamic memory refreshed for a month or two. If anybody can
figure it out, I'd like to know because
it seems to me that it should be easy to make Unix
remember what state it was in. AUDIENCE: Well, if it remembered
exactly what state it was in, it wouldn't be very useful. We'd have to change the
statement every time. MARVIN MINSKY: Well,
I mean it could know which applications you've
been running or something. Anyway it's a mystery to me. For example, in time sharing
systems, you have many users. And the time shared system
keeps their state working fine. Let's see if this is actually
recovered from its bug. Maybe one of those forms
of Word doesn't work. That's a bad-- AUDIENCE: When computers
hibernate and stuff, they say if they have
to read the disk, it takes generally on a modern
system 30 to 45 seconds just to load its entire
memory content from disk. MARVIN MINSKY: That
could be the trouble. Why can't it load the
part that it needs? But never mind. I'm sure that there's
something wrong with this all. But now I've got another bug. That one seems better. Nope. Sorry about all this. I might have to use the backup. Anyway, I'll talk about
consciousness again. But I'm assuming that you've
read all or most of chapter 4. And we could start out with
this kind of question, which is I think of evolution as-- is this working? I think of us as part of the
result of a 400 million year-- 400 mega year process. And because the first
evidence for forms of life occurred about 400 million
years ago, which is pretty long. The earth appears to be
about 4 billion years. So life didn't
start up right away. And so there was a 100
million years of the first one celled animals. Maybe there were
some million years of molecules that didn't
leave any trace at all. So before there was
a cell membrane, you could imagine that there
was a lot of evolution. But nobody has posed
a plausible theory of what it could have been. There are about five or
six pretty good theories of how life might have started. There had to be some way of
making a complex molecule that could make copies of itself. And one standard theory is that
if you had just the right kind of muddy surface, you could
get some structure that would form on that, peel
away, and leave an imprint. But it sounds unlikely to
me because those molecules would have been much smaller
than the grains of mud. But who knows? Anyway that's 100 million
years of one celled things. And then there's 100
million years of things leading to the
various invertebrates, and 100 million years of
fish, reptile like things and mammals. And we're at the
very most recent part of that big fourth
collection of things. I think there's a-- whoops. Is this not going to work? All right, that's
my bug, not MIT's. So humans development,
splitting off from something between a
chimpanzee and a gorilla, has a history of
about 4 Million years. The dolphins developed, which
have very large brains somewhat like ours in that they
have a big cortex, developed before that. And I forget, does anybody know? My recollection is that
they stopped developing about 4 million years ago. So the dolphins brains
got to a certain size. The fossil ones of I think
about 4 million years ago are comparable to the present ones. So nobody knows
why they stopped. But there are a
lot of reasons why it's dangerous to
make a larger brain. And especially if
you're not a fish, because it would be slower
and hard to get around and you would have to eat more
and that's a bad combination. And other little bugs like
taking longer to mature. So if there are any
dangers, the danger of being killed
before you reproduce is a big hand
handicap in evolution. In fact, if you think of the
number of generations of humans since presumably
they've been living for sort of 20 year lifespan for
most of that 4 million years, like other primates. Compare that to bacteria. Some bacteria can
reproduce every 20 minutes instead of 20 years
or 10 years or whatever it is. So the evolution
of smaller animals is vastly faster, in fact, by
factors of order of hundreds. And so generally these big
slow long-lived animals have huge evolutionary
disadvantages. Anyway, here's four major ones. So what made up for that
and that's why chapter 4. I don't think I wrote anything
about this in chapter 4. But that's why it's
interesting to ask why are there so
many ways to think and how did we develop them? And a lot of that comes from
this evolutionary problem that as you got
smarter and heavier, it got more and more
difficult to survive. So your collection
of resourcefulness had to keep track. Well, in that four billion
years this only happened once. Well, the octopuses
are pretty smart. And the birds, just consider
how much a bird does with its sub pea sized brain. But it seems to me that
it's hard to generalize from the evolution of humans
to anything else because-- because what? We must have been
unbelievably lucky. William Calvin has
an interesting book. He's a neurologist who writes
pretty interesting things about the development
of intelligence. And he attributes a lot
of human superiority to a series of dreadful
accidents, namely five or six ice ages, in which the human
population was knocked down, nobody knows to how small. But it could have been as
small as tens of thousands. And we just squeaked by. And only the very, very,
very smartest of them managed to get
through a few hundred years of terrible weather and
shortage of food and so forth. So that's-- anybody
remember the title of-- have you read any
William Calvin? Interesting neurologist out
in California somewhere. There is very small handful
of people, including William Calvin,
that I think have good ideas about intelligence
in general and how it evolved and so forth. And Aaron Sloman
is a philosopher at the University of
Birmingham in England, who has theories that are
maybe the closest to mine. And he's a very good
technical philosopher. So if you're interested
in anything about AI, if you just search for Aaron
Sloman, he's the only one. So Google will find
him instantly for you. And he's got dozens
of really deep essays about various aspects of
intelligence and problem solving. The only other philosopher
I think is comparable is Daniel Dennett. But Dennett is more concerned
with classical philosophical issues and a little less
concerned with exactly how does the human mind work. So to put it another way
Aaron Sloman writes programs and Dennett doesn't. AUDIENCE: He's basically
a classical philosopher. MARVIN MINSKY: What's that? AUDIENCE: If you're
in an argument with a classical
philosopher about issues in classical philosophy,
Dennett's arguments can back you. MARVIN MINSKY: Yeah. But I'm not sure we
can learn very much. AUDIENCE: No. MARVIN MINSKY: I love
classical philosophy. But the issues they discuss
don't make much sense anymore. Philosophy is where
science has come from. But philosophy departments
keep teaching what they were. What chapter does this
story first appear in? Joan is part way
across the street. She's thinking about the future. She sees and hears a
car coming and makes a quick decision about whether
to back up or run across. And she runs across. And I have a little essay
about the kinds of issues there, if you ask what was
going on in Joan's mind? This is a short version
of a even larger list that I just got
tired of writing. And I don't know how different
all of these 20 or 30 things are. But when you see discussions
of consciousness in Pinker and everyone except
Dennett and Sloman, they keep insisting
that consciousness is a special phenomenon. And my view is that
consciousness is-- there certainly a lot
of questions to ask. But there isn't one big one. I think Pinker very artistic-- art-- I can't think
of the right word. He says this is the
big central problem. What is this amazing thing
called consciousness. And he calls that the hard
question of psychology. But if you look at this and
say, how did she select the way to choose among options? Or how did she describe
her body's condition? Or how did she describe
her three most noticeable recent mental
states or whatever? Each of those are
different questions. And if you look at it as
from the point of view of a programmer, you could
say, how could a program that's keeping push down lists and
various registers and caches and blah, blah, blah, how
would a program do this one? How do you think about
what you've recently done? Well, you must have made
a representation of it. Maybe you had a push
down list and we're able to back up and
go to the other state. But then the state of
you that's wondering how to describe that other
state wouldn't be there anymore. So it looks like
you need to have two copies of a process or some
way to timeshare the processor or whatever. And so if you dwell on this
kind of question for a while, then you say there's
something wrong with Pinker. Yes, he's talking about
a very hard problem. But he's got blurred maybe
20, 30, 100, I don't know, pretty hard problems. And each of these
is fairly hard. But on the other hand,
for each of them, you can probably think
of a couple of ways to program something that
does something a little bit like that. How do you go from
a verbal description to block supporting a third
block to a visual image if you have one? Well, you could think
of a lot of ways those-- I didn't say what shape the
blocks were and so forth. And you can think of your mind. One part of your mind
can see the other part trying to figure out which
way to arrange those blocks. Maybe all three blocks
are just vertically like this, this and this. That's two blocks
supporting a third block. And so instead of
saying consciousness is the hard problem, you
could say consciousness is 30 pretty hard problems. And I bet I could make some
progress on each of them if I spent two or three years
or if I had 30 students spending or whatever. Actually, that's
what you really want to do is fool some
professors into thinking about your problem when you're
a student That's the only way to actually get anything done. Well, I'm being a
little dismissive. And another thing that
Pinker and the other people of his ilk, the philosophers who
try to find a central problem, do is say, well, there's
another hard problem which is the problem
called qualia, which is what is the psychological
difference between something that's red and green? And I usually feel
uncomfortable about that because I was in
such a conversation when I discovered that Bob Fono
who is one of our professors was color blind. And he didn't have that qualia,
so sort of embarrassing. In the Exploratorium, how
many of you have been at the-- a few. Maybe the best science museum
in the world, and somewhere near San Francisco. But one trouble or
one feature of it, it was designed by Frank
Oppenheimer, who is Robert Oppenheimer's brother. He quite a good physicist. And I used to hang
around there when I spent a term at Stanford. And it had a lot
of visual exhibits with optical illusions
and colored lights doing different things and
changes of perspective and a lot of binocular
vision tricks. And there's a problem with
that kind of exhibit-- we have them here in
the science museum too-- which is that about
15% or 20% of people don't see stereo very well. And at least 10% don't
view stereo images at all. And some of these is because
one eye's vision is very bad. But actually if one eye is 20/20
and the other eye is 20/100, you see stereo fine anyway. It's amazing how blurred
one of the images can be. Then some people just
can't fuse the images. They don't have separate
eye control or whatever. And a certain percentage don't
fuse stereo for no reason that anybody can
measure and so forth. But that means that if
a big family is looking at this exhibit,
probably one of them is only pretending that he
or she can see the illusion. And I couldn't figure out
any way to get out of that. But I thought if
you make a museum, you should be sure to include
some exhibits for the-- what's the name for
a person who only-- is there a name for non-fusers? When you get a
pilot's license, you have to pass a binocular
vision test, which seems awfully pointless to me,
because if you need stereo, which only works for
about 30 feet, then you're probably dead
anyway, maybe the last half second of landing. So anyway, so much for the
idea of consciousness itself. You might figure
out something to say about the difference
between blue and green and yellow and
brown and so forth. But why is that really more
important than the difference between vanilla and chocolate? Why do the philosophers pick
on these particular perceptual distinctions as being
fundamentally hard mysteries whereas they don't seem to-- they're always picking on color. Beats me. So what does it mean to say-- going back to that little
story of crossing the street-- to say that Joan is
conscious of something? And here's a little
diagram of a mind at work. And I picked out four
kinds of processes that are self models, mock
whatever you're doing. There are probably a
few parts of your brain that are telling little
stories or making visual representations
or whatever, showing what you've been
doing mentally or physically or emotionally or whatever
distinctions you want to draw. Different parts
of your brain are keeping different historical
narrations and representations maybe over different
time scales. And so I'm imagining. I'm just picking on
four different things that are usually happening
at any time in your mind. And these two diagrams are
describing or representing two mental activities. One of which is actually
doing something. You make some decision
to get something done and you have to write a program
and start carrying it out. And the program involves
descriptions of things that you might want to
change, and looking at records of what usually happens
when you do this so you can avoid accidents. So one side of your mind, which
is sort of performing actions, could be having four processes. And I'm using pretty
much the same-- they're not quite. Wonder why I changed
one and not the others. And then there's another
part of your mind that's monitoring the results
of these little actions as you're solving a problem. And those involve pretty
much the same kinds of different processes,
making models of how you've changed yourself
or deciding what to remember. As you look at the situation
that you're manipulating, you notice some features and
you change your descriptions of the parts so that you were-- in other words, in the
course of solving a problem, you're making all sorts
of temporary records and learning little
things, stuffing them away. So the processes that we
lump into being conscious involve all sorts of
different kinds of activities. Do you feel there's
a great difference between the things you're
doing that you're conscious of and the often equally
complicated things that you're doing that you
can say much less about? How do you recognize the two? Do you say I've noticed this
interval and that interval, and then in the
next four measures we swap those intervals
and we put this one before that instead of after? If you look at Twinkle,
Twinkle, Little Star, there's a couple of inversions. And if you're a musician,
you might, in fact, be thinking geometrically
as these sounds are coming in and processing them. Some composers know a great
deal about what they're doing. And some don't have
the slightest idea, can't even write it down. And I don't know if they produce
equally complicated music. What's this slide for? Anyway, when you
look at the issues that philosophers discuss like
qualia and self-awareness, they usually pick what seem
to be very simple examples like red and green. But they don't-- but
what am I trying to say? But someone like Pinker
a philosopher talking about qualia tend say
there's something very different about red and green. What is the difference? I'm just saying, why did I
have a slide that mentioned commonsense knowledge? Well, if you've ever cut
yourself, it might hurt. And there's this red thing. And you might remember,
unconsciously, for the rest of your life
that something red signifies pain and uncertainty and
anxiety and injury and so forth. And very likely you don't have
any really scary associations with green things. So when people say
the quality of red, it's so different from green. Well maybe it's
like the differences over being stabbed or not. And it's not very subtle. And philosophically
it's hard to think of anything puzzling about it. You might ask, why
is it so hard to tell the difference between pleasure
and pain or to describe it? And the answer is you could
go on for hours describing it in sickening and
disgusting detail without any philosophical
difficulty at all. So what do you think of redness? You think of tomatoes and blood. And what are the 10
most common things? I don't know. But I don't see that in
the discussion of qualia. And the qualia of
philosophers try to say there's something
very simple and indescribable and absolute about these
primary sensations. But in fact, if you look
at the visual system, there are different cells
for those, which are sensitive to different spectra. But the color of a region
in the visual field does not depend on the color
of that region, so much as the difference between it
and other regions near it. So I don't have any
slides to show that. But the first time you see
some demonstrations of that, it's amazing because
you always thought that when you look at a patch
of red, you're seeing red. But if the whole visual
field is red slightly, you hardly can tell at
all after a few seconds what the background color is. So I'm going to stop
talking about those things. Who has an idea
about consciousness and how we should
think about it? Yeah. AUDIENCE: Maybe it's just the
K-lines that are in our brain, so the K-lines are different
for an average person. MARVIN MINSKY:
That's interesting. If you think of K-lines as
gadgets in your brain which-- each K-line turns on
a different activity in a lot of different
brain centers perhaps. And I'm not sure what-- AUDIENCE: So like at a moment
you have a set of K-lines that are active. MARVIN MINSKY: Right, but as you
mentioned in different people, they're probably different. AUDIENCE: Yeah, yeah. MARVIN MINSKY: So when
you say red and I say red, how similar are they? That's a wonderful question. And I don't know what to say. How would we measure that? AUDIENCE: I know I
can receive some-- so, for example, a
frog can receive some like with his eyes like pixels. And like these
structures are the same. Like we can perceive
some automatic things. And like this would
be the same for us. But when we're growing, we
probably create these K-lines for like red or green. MARVIN MINSKY: Right. The frog probably
has them built in. AUDIENCE: Yeah. And probably it's very
similar because we have centers in our brain. So, for example, for
vision, we have a center. And probably like
things that are close by will have a tendency
to blend together. And so red would be
similar to each one of us because it's very
low level concept. But if you go higher, it
probably, for example, for numbers to have different
representation than red. I think there's started off
by learning that we represent numbers by saying, like there
is another person that presents just by seeing the number. And then you got to see it. MARVIN MINSKY: He has an
interesting idea that maybe in the first few layers of
visual circuits, we all share. They're pretty similar. And so for the primary-- for the first three or four
levels of visual processing, the kinds of events that
happen for when red and green are together or blue and yellow. Those are two different
kinds of events. But the processes in for most
of us are almost identical. The trouble is when you
get to the level of words that might be 10 or 20
processes away from that. And when you say the
word red, then that has probably closer connections
to blood and tomatoes than two patches of-- anyway it's a nice-- AUDIENCE: So like
animals still have most of this because they
don't have the K-lines. For example, monkeys or
dogs, but when you filter, these animals doesn't have the
ability to break K-line out of consciousness. And so you will
have some kind of-- with the animals you have
like less social visualization or linear function
representation. MARVIN MINSKY:
Yes, well, I guess if you're make
discrimination tests, then people would be very
similar in which color patterns. Did I mention that
some fraction of women have two sets of red cones? You know, there
are three colors. AUDIENCE: It's between
the red and green. MARVIN MINSKY: I thought it was
very close to the red, though. AUDIENCE: Very close to red. MARVIN MINSKY: So
some women have four different primary colors. And do you know
what fraction it is? I thought it was only
about 10% of them. AUDIENCE: Yeah, it's 5%
of people, 10% of women. MARVIN MINSKY: I
thought it's only women. AUDIENCE: It might be. MARVIN MINSKY: Oh, well, AUDIENCE: We could look it up. MARVIN MINSKY: One of my
friends has a 12 color printer. He says it costs hundreds of
dollars to replace the ink. And I can't see any difference. On my printer, which
is a tectonics phaser, this is supposed to be red. But it doesn't look
very red to me. Does that look
red to any of you? AUDIENCE: Reddish. MARVIN MINSKY: Yeah. AUDIENCE: Purple brownish. MARVIN MINSKY: It's
a great printer. It has-- you feed it four
bars of wax as your solid and it melts them and puts
them on a rotating drum. And the feature is that it
stays the same for years. But it's not very good. AUDIENCE: It might look
red on different paper. MARVIN MINSKY: No, I tried it. AUDIENCE: I'm sure if you
put it up to a light bulb, we could make it
all sorts of colors. MARVIN MINSKY: I think
what I'll do is-- I saw a phaser on the
third floor somewhere. Maybe I'll borrow
their red one and see if it's different from mine. Well, let me
conclude because I-- I think this really raises
lots of wonderful questions. And I wonder if we wouldn't-- does this make things too easy? I think what happens in the
discussions of the philosophers like Pinker and
most of the others is that they feel there's a
really hard problem, which is what is the sense of being? What does it mean to
have an experience, to perceive something? And how they want to
argue that somehow-- they are saying they can't
imagine how anything that has an explanation, how any
program or any process or any mechanical system, could
feel pain or sorrow or anxiety or any of these things
that we call feelings. And I think this is
a curious idea that is stuck in our culture,
which is that if something is hard to express, it
must be because it's so different from anything
else, that there's no way to describe it. So if I say, exactly how
does it feel to feel pain? Well, if you look at literature,
you'll see lots of synonyms like stabbing or griping or
aching or you might find 50 or-- I mentioned this in
first lecture I think, that there are lots of
words about emotional or-- I don't know what to call them-- states. But that doesn't mean
that they're simple. That means-- The reason you have so
many words for describing simple states,
feelings, and so forth is that not that they are simple
and a lot of different things that have nothing to do with one
another, but that each of those is a very complicated process. What does it mean when
something's hurting? It means it's hard
to get anything done. I remember when I
first got this insight because I was driving down
from Dartmouth to Boston and I had a toothache. And it was really
getting very bad. That's why I was driving down
because I didn't know what to do and I had a dentist here. And after a while, it's
sort of fills up my mind. And I'm saying this is very
dangerous because maybe I shouldn't be driving. But if I don't drive,
it will get worse. So I really should
drive very fast. So what is pain? Pain is a reaction of
some very smart parts of your mind to the
malfunctioning of other very smart parts. And to describe
it you would have to have a really big
theory of psychology with more parts than in
Freud or in my Society Of Mind, book which has only
about 300 pages, each of which describes some different
aspect of thinking. So if something takes 300
pages to describe, this fools you into thinking, oh,
it's indescribable. It must be elemental. It couldn't be mechanical. It's too simple. If pain were like the four
gears in a differential. Well, most humans don't-- if you show them a
differential, and say what happens if you do this? The average intelligent human
being is incapable of saying, oh, I see, this
will go that way. A normal person can't understand
those four little gears. So, of course, pain
seems irreducible, because maybe it involves 30 or
40 parts and another 30 or 40 of your little society of mind
processes are looking at them. And none of them know much
about how the others work. And so the way you get your
PhD in philosophy is by saying, oh, I won't even try. I will give an explanation
for why I can't do it, which is that it's too
simple to say anything about. That's why the word
qualia only appears once in The Emotion Machine book. And a lot of people
complained about that. They said, why don't
you-- why doesn't he-- they say, you should read,
I forget what instead. Anyway. I don't think I have anything
else in this beautiful set of-- how did it end? If you look on my web page,
which I don't think I can do. Oh, well it will
probably-- there. I just realized I
could quit Word. Well, there's a paper
called "Causal Diversity." And it's an interesting
idea of how do you explain-- how do you answer questions? If there's some
phenomenon going on and something like being
in pain is a phenomenon, what do you want
to say about it? And here's a little diagram
that occurred to me once, which is what kinds
of sciences or what kinds of disciplines
or ways of thinking do you use for answering
different kinds of questions? So I got this little matrix. And you ask, suppose something
happens and think of it in terms of two dimensions. Namely the world is
in a certain stage. Something happens and the world
gets into a different state. And you want to know
why things change? Like if I stand this up-- oh, I can even balance it. I don't know. No I can't. Anyway, what happened there? It fell over. And you know the reason. If it were perfectly centered,
it might stand there forever. Or even if it were
perfectly balanced, there's a certain
quantum probability that its position and
momentum are conjugate. So even if I try to
position it very precisely, it will have a certain momentum
and eventually fall over. It might take a billion years
or it might be a few seconds. So if we take any
situation, we could ask how many things
are affecting the state of this system
and how large are they? So how many causes, a
few causes or a lot? And what are the effects
of each of those? So a good example
is a gas, if you add a cylinder and a piston. And if it's this
size, then there would probably be
a few quadrillion or trillion anyway molecules of
air, mostly oxygen and nitrogen and argon there. And every now and
then, they would all happen to be going this
way instead of this way. And the piston would move out. And it probably wouldn't move
noticeably in a billion years. But eventually it would. But anyway, there
is a phenomenon where there is a very large
number of causes, each of which has a very small effect. And what kind of science or
what kind of computer program or whatever would you need to
do to predict what will happen in each of those situations? So if there's a very few causes
and their effects are small, then you just add them up. Nothing to it. If there is a very
large number of causes and each has a large
effect, then go home. There's nothing to say
because any of those causes might overcome all the others. So I found nine states. And if there are a large
number of small causes, then neural networks
and fuzzy logic might be a way to handle
a situation like that. And if there is a very small
number of large causes, then some kind of
logic will work. Sometimes there are two
causes that are XOR-ed. So if they're both
on, nothing happens. If they're both off,
nothing happens. And if just one is on,
you get a large effect. And you just say it's
X or Y, and analogies and example-based reasoning. So these are where
AI is good, I think. And for lots of everyday
problems like the easy ones or large numbers
of small effects, you can use statistics. And small numbers
of large effects, you can use common sense
reasoning and so forth. So this is the realm of AI. And of course, it
changes every year as you get better or worse at
handling things like these. If you look at artificial
intelligence today, it's mostly stuck up here. There are lots of places
you can make money by not using symbolic reasoning. And there are lots
of things, which are pretty interesting
problems here. And of course, what we want
to do is get to this region where the machines
start solving problems that people are no good at. So who has a question
or a complaint? AUDIENCE: I have a question. MARVIN MINSKY: Great. AUDIENCE: That
consciousness again. Would it have been easier-- MARVIN MINSKY: Is this working? No. AUDIENCE: It goes to the camera. MARVIN MINSKY: Oh. AUDIENCE: You can
hand it to him. MARVIN MINSKY: OK, well
I'll try to repeat it. AUDIENCE: Would
it have bit easier if we never created the
suitcase, as you put it in the papers, the
suitcase of consciousness, and just kept those
individual concepts? The second part of
that question is, how do we know this is
what they had in mind when they initially created
the word consciousness? MARVIN MINSKY: That's
a nice question. Where did the word
consciousness come from? And would we be better off
if nobody had that idea? I think I talked about
that a little bit the other day that there's
the sort of legal concept of responsibility. And if somebody decided that
they would steal something, then they become a thief. And so it's a very
useful idea in society for controlling
people to recognize which things people do
are deliberate and involve some reflection and which things
are because they're learnable. It's a very nice question. Would it be better if we
had never had the word? I think it might be better if
we didn't have it in psychology. But it's hard to get rid
of it for social reasons, just because you have
to be able to write down a law in some form that
people can reproduce. I'm trying to think of
a scientific example where there was a
wrong term that-- can anybody think of an example
of a concept that held science back for a long time? Certainly the idea that
astronomical bodies had to go in circles,
because the idea of ellipses didn't occur much till Kepler. Are there ellipses-- Euclid knew about
ellipses, didn't he? Anybody know? If you take a string and
you put your pencil in there and go like that, that's
a terrible ellipse. people knew about ellipses. Certainly Kepler knew
it, but didn't invent it. So I think the idea of
free will is a social idea. And well, we certainly
still have it. Most educated people think
there is such a thing. It's not quite as-- just as most people
think there's such a thing as consciousness,
instead of 40 fuzzy sets. How many of you
believe in free will? AUDIENCE: My free will. MARVIN MINSKY: It's
the uncaused cause. Free will means you can do
something for no reason at all. And therefore you're
terribly proud of it. It's a very strange concept. But more important, you
can blame people for it and punish them. If they couldn't help
doing it, then there's no way you can get even. AUDIENCE: It has the implication
that there is a choice. MARVIN MINSKY: Yeah. I suppose for each
agent in the brain, there's a sort of little choice. But it's it has several inputs. but I don't think the word
choice means anything. AUDIENCE: Well, you have the
relationship between free will and randomness. Certainly there are some things
that start as random processes and turn out to be causes. MARVIN MINSKY:
Well, random things have lots of small causes. So random is over here,
many small causes. And so you can't figure
out what will happen, because even if you
know 99 of those causes, you don't know what
the 100th one is. And if they all got
XOR-ed by a very simple deterministic logic,
then you're screwed. So but again, it's illegal
the freedom of will is. It just doesn't make
sense to punish people for things they
didn't decide to do, if it happened in a part
of the nervous system that can't learn. If they can't learn, then
you can put them in jail so that they won't be
able to do it again. But you'd have to-- but the chances
are it's not going to change the chance
that they'll try to do it if it's in fact a random. Did you have-- yeah. AUDIENCE: So machine learning
has been on for a long time and like processors are
really fast right now, like computers are really fast. Do you believe there is
some mistake like people that do research should learn? I mean the-- MARVIN MINSKY: Well,
machine learning is to me it's an
empty expression. Do you mean, are they doing
some Bayesian reasoning or-- I mean nobody does
machine learning. Each person has
some particular idea about how to make
a machine improve its performance by experience. But it's a terrible expression. AUDIENCE: So like, statistical
methods like improving methods to machine learning
to the machine to infer like what point
will belong to a data set or whatever? MARVIN MINSKY: Sure. AUDIENCE: People
that do that, do you think they are
doing some mistake? Like do you think there would be
more advance into representing intelligence in another way
and try to program that? MARVIN MINSKY: The
problem is this. Suppose you have--
here's some system that has a bunch of gadgets
that affect each other, just a lot of interactions
and dependencies. And you want to know if it's
in a certain state, what will be the next state. So suppose you put a lion
and a tiger in a cage. And how do you predict
what will happen? Well, what you could
do is if you've got a million lions and a
million tigers and a million cages, then you could put a
lion and a tiger in each cage. And then you could say the
chances that the tiger will win is 0.576239 because, that's
how many cases the tiger won. And the lion will win-- I don't know-- that many. So to me, that's what
statistical learning is. It has no way to make
smart hypotheses. So to me, anybody who's
working on statistical learning is very smart. And he's doing
what we did in 1960 and quit, 50 years out of date. What you need is a smart
way to make a hypothesis about what's going on. Now if nothing's going on
except rounding and motion, then statistical
learning is fine. But if there's an intricate
thing like a differential, which is this thing and
that thing summing up in a certain way,
how do you decide to find the conditional
probability of that hypothesis? And so in other words, you can
skim the cream off the problem by finding the things that
happened with high probability, but you need to have
a theory of what's happening in there to
conjecture that something of low probability on
the surface will happen. And I just-- So here's the thing. If you have a theory of
statistical learning, then your job is to find an
example that it works on. It's the opposite of what you
want for intelligence, which is, how do you make progress on
a problem that you don't know the answer to or
what kind of answer? So how did they generate? I don't know. Are you up on-- how do the statistical
Bayesian people decide which conditional
probability to score? Suppose these 10
variables, then there's 2 to the 10th or 1,000
conditional probabilities to consider. If there's 100 variables-- and so you can do it. 2 to the 10th is nothing. And a fast computer can do many
times 1,000 things per second. But suppose it is 100 variables
2 the 100 is 10 to the 30. No computer can do that. So I'm saying statistical
learning is great. It's so smart. How do-- I'm repeating myself. Anybody have an
argument about that? I bet several of you
are taking courses in statistical learning. What did they say
about that problem? AUDIENCE: Trial and error. MARVIN MINSKY: What? AUDIENCE: Largely
trial and error. MARVIN MINSKY: Yeah, but what
do you try when it's 10 to 30th? Yeah. So do they say, I quit,
this theory is not going to solve hard problems. So once you admit
that, and say I'm working on something
that will solve lots of easy problems,
more power to you. But please don't teach
it to my students. AUDIENCE: What do you think
about the relationship of statistical
inference methods? MARVIN MINSKY: I can't hear you. So in other words, the
statistical learning people are really in this place,
and they're wasting our time. However, they can make
billions of dollars solving easy problems. There's nothing wrong with it. It just has no future. AUDIENCE: What do you think
about the relationship between statistical
learning methods? MARVIN MINSKY: Of what? AUDIENCE: The relation between
statistical learning method and maybe something-- MARVIN MINSKY: I couldn't
get the fourth one. AUDIENCE: Relationship
of statistical-- MARVIN MINSKY: Statistical, oh. AUDIENCE: --to more
abstract ideas like boosting or something where the
method they are using at one and they-- MARVIN MINSKY: There's a
very simple answer for that. It's inductive probability. There is a theory. I wonder if anybody could
summarize that nicely. Have you tried? AUDIENCE: Basically-- MARVIN MINSKY: I can
try it next time. AUDIENCE: You should
assume that everything is generated by a program. And your prior over the
space possible program should be the description
length of the program. MARVIN MINSKY: Suppose
there is a set of data, then what's the shortest
description you can make of it? And that will give you a
chance of having a very good explanation. Now what Solomonoff did was
say, suppose that something's happened, and you make all
possible descriptions of what could have happened, and then
you take the shortest one, and see if that works
and see what it predicts will happen next. And then you take-- say, it's all
binary, then there's two possible descriptions
that are one bit longer. And maybe one of
them fits the data. And the other doesn't. So you give that
one half the weight. And so Solomonoff
imagines an infinite sum where you take all
possible computer programs and see which of them
produce that data set. And if they produce
that data set, then you run the program one
more step and see what it does. In other words,
suppose your problem is you see a bunch of data
about the history of something, like what was the price
of a certain stock for the last billion
years, and you want to see will it go
up or down tomorrow. Well, you make all possible
descriptions of that data set and weight the
shortest ones much more than the longer descriptions. So the trouble with that
is that you can't actually compute such things because
it's sort of an uncomputable. However, you can use
heuristics to approximate it. And so there are
about a dozen people in the world who are
making theories of how to do Solomonoff induction. And that's where-- Now another piece of
advice for students is if you see a lot of
people doing something, then if you want to be sure
that you'll have a job someday, do what's popular, and
you've got a good chance. If you want to
win a Nobel Prize, or solve an important
problem, then don't do what's popular because
the chances are you'll just be a frog in a big pond of frogs. So I think there's probably
only half a dozen people in the world working on
Solomonoff induction, even though it's been
around since 1960. Because it needs a few more
ideas on how to approximate it. But unless you want
to make a living, don't do Bayesian learning. Yeah. AUDIENCE: I don't know
if this actually works. But if you take like Bayesian
learning and we kind of advice sometimes like let's
say we see something with very small
probability and we type just like this part of that
is never considered any good. Would that kind of
like be like what we're trying to do with getting
representations and things? I mean-- MARVIN MINSKY: Yeah, I think-- AUDIENCE: Would this make
it much more discrete and kind of make it much more
easier and more attractable? Or is it like-- my question would
be, is it really representations
for things saying, this chair has this
representation. Isn't that kind of
doing the same like kind of statistical model,
but just throwing away a lot of the stuff that
we might not want look at, what we consider as things
that shouldn't be looked at? MARVIN MINSKY: I think-- say there's the
statistical thing and there's the question of-- suppose there's a lot
of variables x1, x2, x 10 to the ninth,
10 to the fifth. Let's say there's
100,000 variables. Then, there's 2 to
the 100,000 Pijs. But it isn't ij, it's ij
up to 10,000 subscript. So what you need is a good idea
for which things to look at. And that means you want to
take commonsense knowledge and jump out of the
Bayesian knowledge. The problem with a
Bayesian learning system is you're estimating the values
of conditional probabilities. But you have to decide which
conditional probabilities to estimate the values. And the answer is-- oh, look at it another way. Look at history and you'll
see 1,000 years go by, what was the
population of the world between 500 AD, between
the time of Augustine and the time of Newton, or 1500,
like O'Brien, those people, 1,000 years? And I don't know is there 100
million people in the world, anybody know? About how many people
were there in 1500? Don't they teach any history? I think history starts-- I changed schools
around third grade. So I never-- there was
no European history. So to me American
history is recent and European history is old. So 1776 is after 1815. That is, to me, history
ends with Napoleon, because then I got
into fourth grade. Don't you all have that? You've got gaps in your
knowledge because the curricula aren't-- somebody should
make a map of those. AUDIENCE: There were about
half a billion people in 1500. MARVIN MINSKY: That's a lot. AUDIENCE: Yeah, I found
it on the internet. MARVIN MINSKY: This
is from Google? AUDIENCE: This is
from Wikipedia. MARVIN MINSKY: Well. AUDIENCE: It's on the
timeline of people. MARVIN MINSKY: OK. So there's half
a billion people, not thinking of the
planets going in eclipses. So why is that? How is a Bayesian person going
to make the right hypothesis if it's not in the algebraic
extension of the things they're considering? I mean, it could go and it
could look it up in Wikipedia. But Bayesian thing
doesn't do that. RAIs will. Yeah. AUDIENCE: But when we are kids,
don't we learn the common sense knowledge? MARVIN MINSKY: Well-- I'm saying what happened
in the 1,000 years? You actually have to
tell people to consider. I'm telling the Bayesians
to quit that and do something smart. Somebody has to tell them. I don't meet up with Newton. But they need one. What are they doing? What do they hope to accomplish? How are they going to
solve a hard problem. Well, they don't have to. The way you predict
the stock market today is Bayesian with the reaction
time or the millisecond. And you can get all the money
from the poor people that were investing in your bank. It's OK, who cares? But maybe it
shouldn't be allowed. I don't know. Yeah. AUDIENCE: Do you
think the goal is to replace human
intelligence that can create a
computer that will be able to reason by itself or
is there also the ability to create a system-- MARVIN MINSKY: It have to
stop getting sick and dying and becoming senile. Yes. Now there are several
ways to fix this. One is to freeze you and
just never thaw you out. But we don't want to be
stuck with people like us for the rest of all
time, because, you know, there isn't much time left. The sun is going to be a red
giant in three billion years. So we have to get out of here. And the way to get out
of here is make yourself into smart robots. Help. Let's get out of this. We have to get out
of these bodies. Yeah. AUDIENCE: So you talked
a lot about emotions. But emotions you described
as like states of mind. And if you have
like, for, example n states of mind
that represent-- I don't know-- log n
bits of information, why should we spend so
much time talking about like so new information? MARVIN MINSKY: Talking about? AUDIENCE: Little information. Like if we had n
states or n emotions, they would represents log
n bits of information. And like that's very different
information that they will see. So for example if
I'm happy or sad, like if I had just two
states, happy or sad? MARVIN MINSKY: If we
just had two states, you couldn't compute anything. I'm not sure what
you're getting at. AUDIENCE: Like emotions
seem too little information. They don't represent much
information inside our brain. Why should they be so important
in intelligence since they-- MARVIN MINSKY: I don't think-- I think emotions generally
are important for lizards. I don't think they're
important for humans. AUDIENCE: Like if we-- MARVIN MINSKY: You have
to stay alive to think. So you've got a lot of
machinery that makes sure that you don't starve to death. So there's gadgets that
measure your blood sugar and things like that and
make sure that you eat. So those are very nice. On the other hand,
if you simplified it, you just need three
volts to run the CPU. And then you don't
need all that junk. AUDIENCE: So they're not
very important for us. It's just-- MARVIN MINSKY: They are only
important to keep you alive. AUDIENCE: Yeah. MARVIN MINSKY: But they don't
help you write your thesis. I mean, the people who consider
such questions are the science fiction writers. So there are lots of thinking
about what kind of creatures could there be besides humans. And if you look at
detective stories or things, then you find that there are
some good people and bad people and stuff like that. But to me, general
literature is all the same. When you've read 100 books,
you've read them all, except for science fiction. That's my standard joke, that I
don't think much of literature except-- because the science
fiction people say what would happen if people
had a different set of emotions or different ways to think? Or one of my favorite ones
is Larry Niven and Jerry Pournelle, who just wrote a
couple of volumes about what about a creature that has one
big hand in two little hands. Do you remember
what it's called? The Gripping Hand. This is for holding the
work, while this one holds the soldering
iron and the solder. That's right. That's how the book
sort of begins. And there is imagination. On the other hand, you
can read Jane Eyre. And it's lovely. But do you end up better than
you are or slightly worse? And if you read
hundreds of them-- luckily she only
wrote 10, right? I'm serious. You have to look at
Larry Niven and Robert Heinlein and those people. And when you look at the
reviews by the literary people, they say the characters
aren't developed very well. Well, foo, the last thing
you want in your head is a well-developed
literary character. What would you do with her? Yes. I love your questions. Can you wake them up? AUDIENCE: When we
are small babies, like we kind of are creating
this common sense knowledge. And we have a lot
of different inputs. So for example I'm
talking to you, there is this input of
the sound, the vision, like all these different inputs. Aren't we so involved
when we are babies, like in very positive
relations between these inputs? For example, the K-lines, is it
like the machine learning guys argue that with a
lot of variables and maybe 10 to the
third was small set. What would be the difference
if you go deep down? Are they trying to find
like a very simple path? MARVIN MINSKY: I think you're
right in the sense that I'll bet that if you take each of
those highly advanced brain centers, and say, well it's
got something generating hypotheses maybe or something. But underneath it, you
probably have something very like a Bayesian
reinforcement thing. So they're probably all
over the place and maybe of 90% of your machinery
is made of little ones. But it's the symbolic things
and the K-lines that give them the right things to learn. But I think you raise
another question, which I'm very sentimental about
because of the history of how our projects got started, namely
nobody knew much about how children develop in 1900. For all of human history,
as far as I know, generally babies are regarded
as like ignorant adults. There isn't I there
aren't much theories of how children develop. And it isn't till 1930 that we
see any real substantial child psychology. And the child
psychology is mostly that one Swiss
character, Jean Piaget. It's pronounced John
for some reason. And he had three children
and observed them. I think his first publication
was something about mushrooms. He had been in botany. Is that right? Can anybody remember? Cynthia, do you remember
what Piaget's original? AUDIENCE: Biology. MARVIN MINSKY: Something. But then he studied
these children and he wrote several books
about how they learned. And as far as I know, this is
about the first time in history that anybody tried to
observe infants very closely and chart how they
learned and so forth. And my partner, Seymour
Papert, was Piaget's assistant for several years
before he came to MIT. And we started the-- I started the artificial
intelligence group with John McCarthy
who had been one of my classmates in graduate
school at Princeton in math, actually. then McCarthy went to start
another AI group in Stanford and Seymour Papert appeared on
my scene just the same time. And it was a kind of miracle
because we had both-- we met in some meeting in
London where we both presented the same machine learning
paper on Bayesian probabilities in some linear learning system. We both hit it off because
we obviously the same way. But anyway Piaget had been
one of the principal people conducting the experiments
on young children in Piaget's group. And when Piaget got older and
retired in about 1985, Cynthia, do you remember when
did Piaget quit? It's about when we started. AUDIENCE: Didn't he die
in 1980 or something. MARVIN MINSKY: Around then. There were several
good researchers there. AUDIENCE: He was trying to
get Seymour to take over. MARVIN MINSKY: He wanted Seymour
to take over at some point. And there were several good
people there, amazing people. But the Swiss government sort
of stopped supporting it. And the greatest laboratory on
child psychology in the world faded away. It's closed now. And nothing like it
ever started again. So there's a strange thing,
maybe the most important part of human psychology is
what happens the first 10 years, first 5 years? And if you're
interested in that, you could find a few
places where somebody has a little grant to do it. But what a tragedy. Anyway, we tried to
do some of it here. But Papert got more
interested in-- and Cynthia here-- got
more interested in how to improve early education than
find out how children worked. Is there any big laboratory
at all doing that any? Where is child psychology? There are a few places,
but none of them are famous enough to notice. AUDIENCE: For a while
there was stuff in Ontario, and Brazelton. MARVIN MINSKY: Brazelton Yeah. Anyway. It's curious because
you'd think that would be them one of the
most important things, how do humans develop? It's very strange. Yeah. AUDIENCE: So like infants,
when they are about a year old, I think there's a
favorite moment, like they learn how their goal,
like how to achieve goals, like rock the knees. And then after one year,
they learn how to clap, how to achieve a means. So for example, I think
they do the experiment of putting like a hand in
their ear, like left ear. And then chimpanzees do the
same as one-year-old infants. And somehow I believe
that, for example, reflexes between infants and
chimpanzees are very similar. We tend to represent
things better, because like we have this-- MARVIN MINSKY: You're
talking about chimps? AUDIENCE: Chimpanzees. MARVIN MINSKY: Yep. AUDIENCE: They are
like apes in general. MARVIN MINSKY: Right. AUDIENCE: I believe
there are some apes that can learn sign language. I am not sure if that's right. But they can take the goals. And, for example, dogs
can achieve a goal. But they can't imagine
themselves like each moment. Maybe that's because of
how they represent things, maybe they represent badly. They don't have good hierarchy. MARVIN MINSKY: There's some
very interesting questions about that. That's why we need
more laboratories. But here's an example. We had a researcher at
MIT named Richard Heald. And he did lots of interesting
experiments on young animals. So for example, he
discovered that if you take a cat or a dog, if
you have a dog on a leash and you take it
somewhere, there's a very good chance it will
find its way back because it remembers what it did. But he discovered if
you take a cat or a dog and you take it for a
walk and go somewhere, it won't learn because
it didn't do it itself. So in other words, if you
take it on a road passively, even a dozen times
or 100 times, it won't learn that path,
if it didn't actually have any motor reactions. So that was very convincing. And the world became convinced
that for spatial learning, you have to participate. Many years later,
we were working with a cerebral palsy
guy who had never locomoted himself very much. I'm trying to remember
his name-- well, name doesn't matter. But the Logo
project had started. And he by putting a hat
with a stick on his head, he was able to type
keys, which is really very boring and tedious. And believe it or not, even
though he could barely talk, he quickly learned to
control the turtle, a floor turtle, which you could
tell its turn left and right and go forward one
unit, stuff like that. And the remarkable thing
was that no sooner did he start controlling
this turtle, than the turtle went over
here and he turned it around and he wanted it
to go back to here. And everybody
predicted that he would get left and right reversed
because he had never had any experience in the world. But right off, he knew
which way to do it. So he had learned
spatial navigation pretty much never having
done much of it himself. And Richard Heald
was very embarrassed, but had to conclude that what
you learned from cats and dogs might not apply to people. We ran into a little
trouble because there was another psychologist we
tried to convince of this. And that psychologist
said, well, maybe this was-- it took three years for
him to develop a lot of skills. And the psychologist said,
well, maybe that's a freak. I won't approve your PhD thesis
until you do a dozen of them. I didn't mention the
psychologist name, because-- Anyway, so we had a sort
of Piaget like laboratory. But we never worked
with infants. Did we? You think it would
be a big industry. Nixon once came
around and asked. There was a great
senator in Massachusetts, I forget his name. He said, what can
we do for education? The senator said, research
on children how they learn. And Nixon said,
that's great idea. Let's put a billion
dollars into it. And he couldn't convince
anybody in his party to support this idea. The only good thing I've heard
about Nixon except for opening China, I guess. He was determined
to do something about early education. Oh, the teachers union
couldn't stand it. He didn't get any support
from the education business I'll probably remember
the senator next. Who's next? Yes. AUDIENCE: So it's kind
of along the same lines. So if we come out thinking
about how we represent things, even if we think
about language itself, so the early, early stages
of learning the language obviously have a lot of this
statistical learning involved where we learned
morphology of the language, rather than learning
that language is actually representing things. So for example, if
we're going to learn like how certain letters
come one after the other or how they go,
we kind of listen and we see that it's the
way everyone else does it. And there are certain
words that exist and certain words don't exist,
even if they could exist. I guess these are all
like statistical learning. And then like after
this structure is there, we use this structure to
make this representation. So isn't it-- like
wouldn't it kind of be right to say that
these two are basically the same thing, just that
the representation, the more complex one is just
another version of the statistical learning
where we've just done it? MARVIN MINSKY: Well there's
context free grammar. And there's the grammars that
have push down lists and stacks and things like that. So you actually need something
like a programming language to generate and parse sentences. There's a little
recursive quality. I don't know how you can-- it's hard to represent
that in a Bayesian network unless you have a
push down stack. The question is
does the brain have push down stacks or are they
only three deep or something? Because if you say
this is the dog that bit the cat that chased
the rat that so on, nobody has any trouble. And that's a recursion. But if you say this is
the dog that the cat that the rat bit ate,
people can't that. AUDIENCE: It's
empirical evidence that the brain got
its tail cut off. MARVIN MINSKY: That it's what? AUDIENCE: The brain influenced
tail cut off representation. MARVIN MINSKY: Yeah. Why is language restricted in
that you can't embed clauses past the level of two or three,
which Chomsky never admitted. AUDIENCE: Can't it be the
case that we also learn that? Like we also learned that
certain patterns can only exist between words. We do parse it using
your parse tree. We learn using a parse tree. Like we learn that when
you hear a sentence, go after trying
to parse it using three words, two words, four
words and just try that, see if it works. If it doesn't try another way. It can't distance itself
from like learning the number of words that
usually happen in a clause. Is it this type of learning? MARVIN MINSKY: Well
I'm not sure why is it very different
from learning that you have to open a bottle-- open the box before
you take the thing out. We learn procedures. I'm not sure-- I don't believe in
grammar, that is. AUDIENCE: If we were
trying to teach a machine to be like a human being, would
we just lay out the very basics and let it grow like
a child with learning or would we put these
representations in there, like put the representations-- MARVIN MINSKY: Well, a
child doesn't learn language unless there are
people to teach it. AUDIENCE: Right. MARVIN MINSKY: However-- AUDIENCE: So maybe we can expose
the machine to that white board to-- or we can expose it to the world
somehow to some kind of input. MARVIN MINSKY: I'm not sure
what question you're asking. Is all children's learning
of a particular type or are they learning frames or
are they learning grammar rules or do you want a uniform
theory of learning? AUDIENCE: I think which
one is a better approach, that the machine has very
basic things and it learns? So there's a machine, should
we makes machines as infants and let them learn things,
by for example giving them a string that's
from the internet, from communication
over the internet or communication among
other human beings, just like a child learns
from seeing his parents talk. MARVIN MINSKY:
Several people have-- AUDIENCE: Is it
better to actually inject all that knowledge
into the machine, and then expect it to act
on it from the beginning? MARVIN MINSKY: Well, if
you look at the history, you'll find that-- I'm not sure how to look it up. But quite a few
people have tried to make learning systems
that start with very little and keep developing. And the most impressive ones
were the ones by Douglas Lenat. But eventually he gave up. And he had systems that
learned a few things. But they petered out. And he changed his orientation
to trying to build up commonsense libraries. But I'm trying to
think of the name for self-organizing systems. There are probably a dozen. If you're interested, I'll
try to find some of them. But for some reason people
have given up on that, and so certainly worth a try. As for language, I think the
theory that language is based on grammar is just plain wrong. I suspect it's based on certain
kinds of frame manipulation things. And the idea of abstract syntax
is really not very productive or it hasn't-- anyway. Because you want it to be
able to fit into a system for inference as well. I'm just bluffing here. Did you have a question? AUDIENCE: I was
just going to say it seems that what
you're saying might be considered to be a form
of example-based reasoning. You just have lots
and lots of examples, which are not unlike
the work that DuBois does with a child the word water
from hearing lots of people use that word in different
contexts and examples. MARVIN MINSKY:
While you're here, Janet Baker was a pioneer
in speech recognition. How come the latest system
suddenly got better? Are they just bigger databases? AUDIENCE: That's a lot of it. MARVIN MINSKY: Of
course, the early ones you had to train for an hour. AUDIENCE: But we now have
so many more examples and exemplars that you can
much better characterize their ability, which is
tremendous, between the people. And you typically have
multiple models, a lot of different models of how-- so it knows in a space of how
people say different things and allowing you to
characterize it really well, so it will do a much better job. You always do better if you
have models of a given person speaking and
modeling their voice. But you can now model a
population much better when you have so much more data. MARVIN MINSKY: They're
really getting useful. AUDIENCE: Oh, dear. MARVIN MINSKY: OK,
unless somebody has a really urgent question. Thanks for coming.
