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visit MIT OpenCourseWare at ocw.mit.edu. MARVIN MINSKY: What do you think
AI people should work on next? AUDIENCE: There were
lots of questions I was going to ask you before
you said your question. I was going to ask you,
what kind of questions do you think that AI people
should be asking right now? MARVIN MINSKY: That's right. Anybody have a meta question? One good question is-- oops. I could focus it better
or make it bigger. Is that enough layers? It's possible that I got
the idea from Sigmund Freud. Who knows what Sigmund
Freud's three layers were? Sure. AUDIENCE: Id, ego, super-ego. MARVIN MINSKY: I can't here. AUDIENCE: Id, ego, super-ego. MARVIN MINSKY: Freud
wrote about 30 books. I know because I had a
graduate student once who decided to quit
computer science and go into emergency medicine. He had an MD, which
he wasn't using, and he suddenly got fed
up with computer science. So he sold me his set of
Freud books, which is-- But in Freud's vision, these
don't seem to be a stack. But there is this thing,
which is basic instincts. I shouldn't say basic-- maybe, to a large
extent, built in. And this is learned socially. And I think the nice thing
about Freud's concept, which as far as I know,
doesn't appear much in earlier psychology,
is that these conflict. And when a child grows up, a
lot of what we call civilization or socialization or whatever
comes from taking the built-in instincts-- which is, if you see
something you like, take it-- and the social constraints
that say, you should negotiate. And if you want something
someone else has, you should fool them into
wanting to give it to you, or whatever. So in fact, when I make this
big stack of mechanisms, that really-- well, that's actually
not the organization that the book starts
to develop chapter by chapter, which
was, instincts, learned reactions, and so
forth, up to self-conscious. What's the next to top layer? I've forgotten. AUDIENCE: Ideals? MARVIN MINSKY: Yeah, I guess so. It's hard to tell. The reason I had six layers
is that, unlike what people do when making
theories in science, is, I assume that whatever ideas
I have, they're not enough. That is, instead of
trying to reduce the mind to as few mechanisms
as possible, I think you want to leave room. If your theory is to
live for a few years, you want to leave
room for new ideas. So the last three
layers, beginning with reflective thought and
self-reflective and the ideals, it's hard to imagine
any clear boundaries. And at some point, when
you make your own theory, maybe you can squeeze it into
these extra boxes that I have. A little later in the book,
another hierarchy appears. This is imagining, as
a functional diagram, to show how knowledge
or skills or abilities or whatever you
want to call them, the things that people
do, might be arranged. And as far as I know, because
virtually every psychologist in the last 100 years has
suffered from physics envy-- they want to make something
like Newton's laws-- the result has been that--
except for the work of people like Newell and Simon and other
pioneers who started research in artificial
intelligence, you've heard me complain
about neuroscientists, but there's pretty
much the same complaint to be leveled against most
cognitive scientists, who, for example, try to say,
maybe the entire human mind is a collection of
rules, if-then rules. Well, in some sense, you
could make anything out of if-then rules. But the chances
are, if you tried to make a learning machine
that just tried to add rules in some beautifully
general fashion, I suspect the chances are
it would learn for a while and then get stuck. And indeed, that's what
happened maybe five or six times in the history of
artificial intelligence. Doug Lenat's system, called
AM, Automated Mathematician, was a wonderful PhD thesis. And it learned arithmetic
completely by itself. He just set up the
thing biased so that it would have some
concept of numbers, like 6 is the successor
of 5 and stuff like that. And it fooled around
and discovered various regularities. I think I mentioned
it the other day. It first developed
the concept of number, which wasn't very
hard, because he wrote the whole thing in
the AI language called Lisp. How many of you know Lisp? That's a lot. In looking at the biographies
of the late John McCarthy all week, there were lots
of attempts by the writers to say what Lisp was. [LAUGHTER] The tragedy is, you probably
could have described it exactly in a paragraph. Because it's saying
that each structure has two parts, and each of
those has two parts. I don't remember
seeing any attempt to say something about this
programming language in-- yes. AUDIENCE: How do you
imagine the layers interact with each other? MARVIN MINSKY: Those? AUDIENCE: Yeah, those
and the six layers. MARVIN MINSKY: I think they're
layers of organization. Yes, because a trance frame
is made of two frames. But then, if there were
a neuroscientist who said, oh, maybe when you see
an apple and you're hungry, you reach out and eat
it, so you could think of that as a simple reflex-- if this, then that. Or you could say,
if I'm hungry now and I want to be not hungry,
what's a possible action-- could I do? And that action might
be, look for an apple. Yes. AUDIENCE: So I was teaching
my class on the day that McCarthy passed away,
and then I was explaining. And then I had some students
in the class who weren't even computer scientists,
so I was thinking about the same
problem, which is, how to explain what Lisp was? And so I said,
well, before that, there were languages
like Fortran that manipulated numbers. Lisp was the first language
to manipulate ideas. MARVIN MINSKY: Mm-hmm. Yes, you can manipulate
representations of-- yes, it's manipulating the
ideas that are represented by these expressions. And one thing that
interests me is, another analog
between psychology and modern cognitive
psychology and computer science or artificial
intelligence, is the idea of a goal. What does it mean
to have a goal ? And you could say, it's a piece
of machinery which says that, if there's a situation-- what you have now-- and if you have a representation
of some future thing-- what you want. So of course, at anytime,
you're in a situation that your brain is somehow
representing maybe five or ten ways, not just one. But what does it
mean to have a goal? And what it means is to
have two representations. One is a representation you've
made of some structure which says what things are like now. And the other is
some representation of what you want. I don't think I need that. And the important thing is,
what are the differences? And instead of saying,
what's the difference, maybe it's good to say,
what are the differences? So what does it
mean to have a goal. It means to have some piece
of machinery turned on. You can imagine a goal
that you don't have, right? Like I can say,
what's David's goal? It's to get people to
go to that meeting. So what you want is to minimize
the differences between what you have and what you want. And so there has to be a
machinery which does what? It picks out one of
these differences and tries to get rid of it. And how can you get rid of it? There's two ways. The good way is to
change the situation so that difference disappears. The other way is to say,
oh, that would take a year. I should give up that goal. I'm digressing. So you want something
that removes-- the feedback has to go this way. Let's change the world. And get rid of that difference. Well, how do you get
rid of a difference? That depends on-- maybe
you have a built-in reflex. Like if you have too
much CO2 in your blood, something senses it and
tells you to breathe. So you've got built-in things. And the question is,
how do you represent these sorts of things? And in fact, I think I
got a lot of this idea from the PhD thesis of
Patrick Winston, who was here a minute ago. [LAUGHTER] But the question is, how do
you represent what you want and how do you
represent what you have? And I think the big difference
between people and other primates and reptiles and
amphibians-- reptiles, fish, and going back to
plants and so forth-- is that we have these very
high-level powerful ways of representing
differences between things. And this enables us to develop
reflexes for getting rid of the differences. So this is what I think
might be a picture of how the brain is organized. And at every level, these
things are made of neurons. But you shouldn't be
looking at the neurons individually to see
how the brain works. It's like looking at
a computer and saying, oh, I understand that. All I have to do is
know a great deal about how each transistor works. The great thing
about a computer is that it doesn't matter
how the transistor works. The important thing is
to recognize, oh, look, they usually come in pairs-- or really four or 10 or
whatever-- called a flip-flop. Do you ever see a
neuroscientist saying, where are the flip-flops
in this or that? It's very strange. It's as though they're
trying to develop a very powerful computer
without using any concepts from computer science. It's a marvelous phenomenon. And you have to wonder,
where did they grow up and how did they stay isolated? AUDIENCE: In the biology
department, I think. MARVIN MINSKY: In
biology departments. AUDIENCE: Or
psychology departments. MARVIN MINSKY: Well, I started
out in biology, pretty much. And then I ran across these
early papers, one of which was by Lettvin and McCulloch
and Pitts and people like that. In the early 1940s, the idea
of symbolic neurons appeared. It had first appeared in
1895 or so with the paper that Sigmund Freud wrote
but couldn't get published. I think I mentioned that,
called Project for a Scientific Psychology. And it had the idea of
neurons with various levels of activation. And sometimes you would
have a pair of them. And one would be
inhibiting the other, and so that could
store some information. And he's not very explicit about
how these things might work. But as far as I know, it's
about the first attempt to have a biological theory of
information processing at all. And he was unable
to get it published. AUDIENCE: Marvin? MARVIN MINSKY: Yeah. AUDIENCE: Since
McCarthy did this, just, do you some reflections
on the stuff that he did or his contributions? MARVIN MINSKY: There are a
lot of things that he did. I noticed that none
of the obituaries actually had any background. What had happened is that
what Newell and Simon, they had struggled
to make programs that could do symbolic logic. And they made a
language called IPL. And IPL was a language of
very microscopic operations. Like you have several registers,
put a symbol in a register, perhaps do a piece of
arithmetic on two symbols if they're numbers. If not, link up-- you can sort of make
registers artificially in the memory of the computer. And you could take two
or three registers. It had instructions for
making lists or trees. So you could arrange
these in a way that-- so here are the three things. And this is just a
simple list, but I've drawn it as though these
two were subsidiary to that. Of course, that
depends on what program is going to look at this. And you could have a program
which can say, here are two arguments for a function. And it doesn't matter
what order they're in. It just depends how
you wrote the function. So Newell and Simon had
written a programming language which said, put
something in a register, link it to another
register, and then perform the usual arithmetic operations. In fact, what they were
doing is mostly performing logical operations, because
even the early computers had ANDs and ORs and XORs,
and things like that. So Newell and Simon had
written a program that could deal with
Boolean functions and prove little
theorems about, not A or B. Is this not A or not B? Is that wrong? I forget. It should have more nots. Anyway, they wrote this
beautiful but clumsy thing made of very simple
logical primitives. McCarthy, at the same time,
IBM had spent 400 man years writing a program called
Fortran 1, or Fortran. I'm not sure that people had
serial numbers on programming languages yet, because we're
talking about the middle 1950s. And McCarthy had been
thinking about, how would you make AI programs that
could do symbolic reasoning? And he was indeed particularly
interested in logic. Backtrack-- in fact,
Newell and Simon had got their program to find
proofs of most of the theorems in the first volume of Russell
and Whitehead's Principia Mathematica, which is a huge
two-volume book from about 1905, I think, which was
the first successful attempt to reduce mathematics to logic. And they managed to
get up to calculus and show that differentiation
and integration can be expressed as logical
functions and variables. It's a great tour de
force, because logic itself barely existed. Bool and a few others,
including Leibniz, had invented Boolean
algebra-like things and around-- Frege and others had got
predicate calculus with their [INAUDIBLE]. And that stuff was
just appearing. And Russell and Whitehead
wrote this huge book, which got all the
way up to describing continuous and differentiable
functions and so forth. The first volume was
huge and just did propositional calculus, which
Aristotle had done some of, also. And anyway, McCarthy
looked at that and said, why can't there
be a functional language like Fortran that can
do symbolic reasoning. And pretty much
all by himself, he got the basic ideas from Lisp. They're built on the Newell
and Simon experiment. But he basically converted the
symbolic system into something like Fortran, which was only
able to manipulate numbers into Lisp, which was able
to manipulate arbitrary symbols in various ways. And if you want to
know more about that, you can find McCarthy's
home page at Stanford. I think if you Google search
for McCarthy and SAIL-- SAIL is Stanford Artificial
Intelligence Laboratory-- and you'll get his home page. And there's a 1994 article
about how he invented Lisp. Yes. AUDIENCE: Right after you
started talking about Lisp, you jumped over and you said,
here's the now and the want, which is the current
state of desire. And I think you were going
for some kind of analogy between the symbolic
evaluation [INAUDIBLE] and what conscious entities do. However, one of the most
beautiful things about Lisp is the homo [INAUDIBLE],,
the fact that you have things such as macros. What is there in the
conscious entity that is equivalent to the macro? MARVIN MINSKY: Macros? AUDIENCE: Yeah. MARVIN MINSKY: Well,
each of these levels are ways of combining things
at the previous level. The way I drew it, at the
top, there are stories. What's a story? You mention some characters
in a typical story. Then you say, and
here's a problem these characters encountered. And here's what Joe did
to solve the problem, but it didn't work
and here are the bugs. The reason that didn't work
is that Mary was in the way, so he killed her. And then blah, blah, blah. So that's what stories are. If you just write a
series of sentences, it's not a story, even
though The New Yorker managed to make
those into stories for about a period of 20 years. But a typical story
introduces a scene and it introduces a problem,
and then it produces a solution. But the solution has a bug. And then the rest of it is
how you get around that bug and how you maybe change
the goals in the worst case. So a story is a
series of incidents. I wonder if I
brought my death ray. It looks like I didn't. Oh, but you don't
need it with this. You have to be here. Anyway, but what's
a story made of? Well, there's situations. And you do something and
you got a new situation. So what's that? That's what this thing I
called a trance frame, which is a pair of situations. OK, so what's an
individual situation? Well, it's a big network
of nodes and relationships. And I'm not sure why I have
semantic networks down here rather than here. Oh, well, a frame
is a collection of representations that's
sort of stereotyped or canned, that might have a
single word like-- oh, just about any word,
breaking something. If I say, John broke
the X, you immediately say, oh, that's a trance frame. Here is an object that had
certain properties, parts, and relationships. And it's been replaced
by this thing which has most of the same objects,
but different relationships, or one of the objects
is missing and it's been replaced by another
frame, and so forth. So one question is,
what's the relation? So this is a picture of
cognitive representations. Everything is made
of little parts. And in the society
of mind, I described the idea of K-line lines. What's a K-line line? It's an imaginary structure
in the nervous system. There's really two kinds. There's a sort of perceptual
K-line line, which is something that recognizes
that, say, 10 features of a situation are present. And if these are all
present, then a certain bunch of neurons or neural
activity goes on. And on the other hand, when
you think of something, like a word, suppose
I say microphone, then you're likely to think of
something that has a business end which collects sounds. And it has a stand or
maybe it has a handle. And if you're an
engineer, you know that probably it has a battery
or a transmitter or a wire. So those are the things that
I call frames or K-line lines, really. And anyway, chapter 8
of The Emotion Machine talks about that. And there's a lot of detail
in the old book, The Society of Mind, about what K-line
lines and things like that could be made of. Now, whatever those are,
and as far as I know, no matter how hard
you look, you won't find any published theories
of, what in the nervous system is used to represent the things
above that sort of midline there of cortical columns. I suspect that almost everything
that the human brain does, that fish and those lower
animals or earlier animals, I should say-- I'm not sure that higher
and lower makes any sense-- are probably symbolic
processes, where it probably does more harm than good to
have elaborate theories of, what's the physiological
chemistry of neurons. But at some point, we want
to know what, in fact, the brain is made of. The cortical column has a few
hundred brain cells arranged. And there are several projects
around the world trying to take electron microscopes
and pieces of mouse brain or cat brain and make a huge
connection matrix. The problem is that the electron
microscope, even the electron microscope pictures
still aren't good enough to show you at each synapse
what is probably going on. Eventually, people will
get theories of that and get slightly
better instruments. And I'm not sure that
the present diagrams that they're producing are
going to be much use to anyone. Yes-- some nice questions. AUDIENCE: This question might
be a little bit difficult. So I'm going to start
from the goal being a difference between the now
state and the desired state. MARVIN MINSKY: How do we
reduce the difference? Yeah. AUDIENCE: Yeah, how do
we get the difference? And now, I'm going
mean also take the fact of this, that the
animals, they're different. And I'd say that one of
the biggest big differences is that we, as people, we can
describe these differences. MARVIN MINSKY: It actually is
only good for the recording. AUDIENCE: OK, so we can
describe these differences and talk about them
with other people without having to act on those
goals or hypothetical goals. So if everything that
has to do with the goals is represented with a complex
structure like this one, and I want to implement
it in a computer, well, for every
hypothetical case that has to do with the solving
of a certain problem, I can just make more
copies in the memory. But if I have a
human brain, then I don't know how convenient
it is to postulate that there is like a huge memory
databank where you can make copies of everything
that's going on, or if you have to assume some
kind of a huge collection of pointers and acting
on those pointers. So this is just a
random point, and I'd like to hear if you
have any ideas on this. MARVIN MINSKY: That's
an important question. We know quite a bit
about the functions of some parts of the brain. I don't think I've ever
tried to draw a brain. But there's a structure
called the amygdala. How do you spell it? I believe that
means almond-shaped. Is that right? Anybody have a google handy? And that's down here somewhere. And it has the property
that it contains the short-term memories. So anything that's happened
in the last couple of seconds is somehow represented
here in a trenchant way. And everything that's happened
in the last 20 minutes or so leaves traces in
the amygdala, so that if somebody is in
an automobile accident or is knocked out by
a real powerful boxing punch, then when
you wake up later, you can't remember anything that
happened in about 20 minutes or a half hour
before the trauma. So that's a very good
experiment to try. And there's a lot of
evidence that this happens because the memories
of the last half hour or so are stored in-- the conjecture is
in a dynamic form. Maybe there are huge numbers
of loops of neurons connected in circles, or circle. Anyway, nobody really knows. But if you have a bunch
of neurons connected in a big circle, maybe
20 or 30 of them, then you can probably put
10 or 20 bits of information in form of different
spaced pulses. And a great mystery would
be, how does the brain manage to maintain that particular
pattern of pulses for 10 or 20 minutes? Then no one knows where it
goes after the 20 minutes. But if a person gets
enough sleep that night, then it turns out that it's
no longer in the amygdala and it's somewhere
else in the brain. And so one question
is, if there's all this stuff stored here-- and you might think
of it as what's happened in the primary memory. Every computer starts out with-- the first computers had
just two or three registers. Then they got 16 and 32. And I imagine-- how many
fast registers are there in a modern computer? I haven't been paying
attention, maybe 64, whatever. Anyway, but the next day,
if you've gotten some sleep, you can retrieve them. Somehow they're
copied somewhere else. As far as I know,
there is no theory of how the brain decides
where to put them and how it records
it and so forth. There's something very strange
about this big science lacking theories, isn't there? What would you do if
you were in a profession where they're talking about
dozens and dozens of mechanisms which clearly
exist, and you say, how do you think that works, and
they say, blah, I don't know. None of my friends know
either, so I guess it's OK. [LAUGHTER] So we don't know how it
picks a place to put them. And we don't know how,
when you ask a question, it gets back there
so that you can reprocess it and talk about it. But anyway, so I made
up these theories that things are stored in
the form of K-line lines. And there's a lot of discussion
in The Society of Mind about how K-line lines probably
have to work and so forth. And if you look at The
Society of Mind in Amazon, you'll find this enraging
review by a neurologist named Robert [INAUDIBLE],,
who says he introduces these undefined terms called
K-line and paranomes and things like that, of which there's
whole chapters in the book. AUDIENCE: I've got a
question before we go on. MARVIN MINSKY: That's
my favorite review of explaining the
problem of getting neuroscience to grow up. Yes, who had a question? Yes. AUDIENCE: One thing that puzzles
me is, how does the the brain decide to do things? MARVIN MINSKY: How do they
decide what to do, did you say? AUDIENCE: Well, say you
want to relay a story, but like hundreds
of things happened. How do you select what
to tell and what not to? It seems like you need some sort
of intentionality behind it, but how do we learn to do that
in the first place, almost? Like when you
describe a goal, you describe what's going on now
and what is the thing you want. But then how do you decide which
few things to be considered? MARVIN MINSKY: Oh, you're
asking a wonderful question. Which is, after all,
if I were to talk to you for an hour
about your goals, you could tell me hundreds
of goals that you have. So the question is,
how do you pick the one that you're thinking about now. AUDIENCE: Yeah. Also, how do you represent
the priority of goals? They're almost described as
like [INAUDIBLE] machines, as turned on all the time. But how do you
resolve conflicts? How do you decide
which one to go first and which ones [INAUDIBLE]? MARVIN MINSKY: OK,
how do you represent the relations of your goals? The standard theory, for which
there's no evidence, is that-- if I could use the
word hierarchy. So if you ask a
naive person, they'll give you a pretty good theory-- completely wrong, but good. And the standard theory
says, well, there's one big goal at the top. And you say, what is it? And some people would say,
well, maybe it's to reproduce. Darwin could, but
didn't argue that. Or to survive or
something like that. OK, so if you take
that one, then it's obvious what the next goals
in the hierarchy would be. I'm not saying this
is how things work. I don't think it does. So there's food. And there's air. Air gets very high priority. If you put a pillow
over somebody, their first priority
is to breathe. And they don't even think
about eating for a long time. [LAUGHTER] So that's very nice. I don't know what
comes after that. If you're out in the cold
freezing, then there's temp. The nice thing
about air and temp is that, if you
have those goals, you can satisfy
them in parallel. Because in fact, you don't
need much of a hierarchy. The breathing thing has
this servo mechanism, where if there's a
higher level of CO2 than normal in your blood, then,
what is it, the vagus nerve? I don't know. They know a lot about
which part of the brain gets excited and
raises your breathing rate or your heartbeat rate. My favorite animal
is the swordfish. How many of you know
about brown fat? AUDIENCE: [INAUDIBLE] MARVIN MINSKY: Brown fat
is a particular thing found in invertebrates. And it's fat. It's brown, I guess. And it has the
property that it can be innervated by nerve fibers. And they cause it to
start burning calories. And the swordfish is
normally cold-blooded. But its carotid arteries
have a big organ of brown fat around them just as
the blood comes into the brain. And if you turn
on the brown fat, it warms the brain and the IQ
of the swordfish goes way up. [LAUGHTER] And it swims faster and
uses better evasive tactics. And there are a couple of other
cold-blooded animals that are known to have a warm-blooded
brain that they can-- isn't that a great feature? I wonder, does our brain
do a little bit of that? I got lost telling
funny stories. So anyway, this K-line
idea is very simple. It says that, perhaps
the way human memory works is that,
here and there you have big collections
of nerve fibers that go somewhere and
go to lots of cells. Let's say thousands
of these cells. And each one is connected to
some particular combination. Imagine there's 100 wires here. And each of these cells
is connected to, say, 10 of those wires. Then how many different
cells could you have for remembering
different features of what's on this big bus bar? How many ways are there
picking 10 things out of 100? It's about 100 to
the 10th power. So here's a simple
kind of memory. Of course, it'd be useless
unless these bits by themselves have some correlation
with some useful concept. And then at any particular
time on this particular bunch of fibers in the brain, maybe
20 of these are turned on. And if something very
important has happened, you send a signal to
all these cells and say, any of you cells who
are seeing more than 10 or 15 of these 20
fibers at this moment should remember that and set
themselves to do something next time you see that pattern. Something like
that, something has to decide which of these cells
is going to copy at this time and so forth. But so there is a
theory of how memory, kind of symbolic memory,
might work in the brain. And I got this
idea from a paper-- I don't remember what their
idea was-- but by David Waltz and Jordan Pollack. Pollack is a
theorist at Brandeis, I guess, who in recent
years has turned into some kind of
artist, and makes all sorts of beautiful
things and simple robots that do this and that. David Waltz, search
his web page sometime, because he was here for many
years as a graduate student, and then developed beautiful
theories of vision. When did Dave move? Do you remember, Pat? Anyway-- AUDIENCE: '79? MARVIN MINSKY:
Something like that. But as far as I know-- so I made up this
theory, which is really copied from Waltz and Pollack,
but simplified and neatened up. Then I went on and made
other theories based on that. But without them,
I would have been stuck in some conditioned
reflex theory for a long time, I suspect. AUDIENCE: You mentioned
the role that the amygdala plays in storing the
short-term memory. And you mentioned
that [INAUDIBLE] the memories that are stored
in there are wiped out. MARVIN MINSKY: Well,
that's a question. Presumably, as you
grow up, your amygdala gets better at learning
what to recognize. But I've never seen any
discussion or theory of how much of your
short-term memory is-- how do you learn and
develop and get better at remembering things that
are worth remembering? Sorry, go ahead. AUDIENCE: My understanding is
that, so whatever memories are stored in the amygdala during-- whatever is stored
in the amygdala is wiped out after you sleep. What sort of
implications does this have about remembering dreams? Because my
understanding is that, after you have a lucid dream,
the memories of the dream are wiped out after a certain
point later on in the day. So what sort of
implications does it have on the ability of people to
remember something [INAUDIBLE]?? MARVIN MINSKY: Good question. There have been some theories. But I think I mentioned Freud's
theory, which is that you're not remembering anything. When you wake up, you
make up the dream. And I think that's
surely completely wrong. But Freud made it up
because he was mad at Jung. [LAUGHTER] Jung had a theory that people
have telepathic connections with other people. And so he had been Freud's
student or disciple for some years. And then he went mystical,
and Freud went up the wall, because Jung was obviously
very smart and imaginative. I'm trying to think if I've
had a very good student who turned mystical-- one or two, but not like that. Anyway, that's a great question. And when I said
things hang around in the amygdala for 20 minutes,
that's just some things. If it takes sleep
the next night, which is eight hours or 16 hours
later, to solidify it or to copy it in some way into
the other parts of the brain, it must still be in the
amygdala or somewhere. So in fact, maybe there's the
amygdala and some other parts of the brain that
haven't been identified that contain slightly different
copies of the memories for a longer time and so forth. So who knows where
and how they work. Maybe the language centers
remember paragraphs of things that you've heard or said
for some time, and so forth. I don't think anybody
really knows much about-- they're very sure
about the amygdala, because injuring the amygdala
or injecting Novocaine into a blood vessel
that goes there has such a dramatic effect on-- you just can't remember
anything for that short period or half hour or so. Maybe memories are stored
in 10 different ways in 10 different parts of the brain. Who knows? One problem that I
think I mentioned is that, although
a great deal has been learned about the
brain from modern scanning techniques, almost
every result that people talk about is obtained by
turning up the contrast so that most of the brain is
dark and nerve centers that are highly active show up in your-- you've all seen
these pictures which show three or four places
in the brain lighting up. Well, there's a good chance
that, for any particular event, there might be 10 or 20
places that have just increased a little bit. And when they turn
up the contrast, all that evidence is lost
because those regions all become black or whatever. AUDIENCE: Yeah, I don't
know the next part of that. But I would go as far as
to say that, probably they have a finding where there
are no specific areas, so you have a pretty
uniform picture of the changes in metabolism,
then you don't make theories or you don't
publish that result, because you don't have any
clear areas for [INAUDIBLE],, and that nobody knows that,
OK, that particular thing was actually exciting [INAUDIBLE]. That's just a guess. MARVIN MINSKY: Yeah, it could
be that some things involve very large amounts of brain. But I'm inclined to doubt it. Probably you want to turn a lot
of things off most of the time so they don't fill up
with random garbage. Who knows? Yes. AUDIENCE: And to
follow up with that, why do you think the
hierarchy of goals is naive? And what specific
features of goals do you think that
structure doesn't achieve? MARVIN MINSKY: Oh, I
didn't finish that, did I? She's asking-- I started to say,
what's the hierarchy of goals? But it looks like I got stuck
on the well-defined, instinctive goals that you
need to stay alive. And I guess my answer is, I
don't have any good theories of how you do that. At any time, when you're
talking to somebody, you usually have a couple
of very clear goals, like, I want to explain this,
I want this other person to understand this
for this reason. I'm having trouble. Maybe I have to get his
or her attention by-- and then you get a sub-goal
of doing some social thing to convince them to listen to
you and all sorts of things. But I just don't
have a nice picture. When you're writing
an AI program, you usually have
goals and sub-goals in a very clear arrangement. Like the theorem-proving
programs are wonderful, because you've proved
some kind of expression, but the particular theorem
you are trying to prove has another condition
which is different from this condition. And people have gone quite far
in making models of something like theorem-proving, where
the world is very simple. If you're proving something
in geometry or group theory or a little fragment
of mathematics, then there are only 5 or 10
assumptions hanging around. And so you could actually plan a
little bit of exhaustive search to go through your four levels. And then you would do something
like in a chess program of, over time, discovering
it never pays to explore a tree that has this
feature because of whatever. Yes. AUDIENCE: Are goals relevant? Like, we always have goals
where it's just like something where it's like, when I play
chess, maybe I have a goal, but why should I have a goal? Why isn't that like,
maybe, I don't know, that goal [INAUDIBLE] at
some points of my life. Why are goals important? MARVIN MINSKY: Well,
the survival goals are important
because if you cross the street without looking, you
could do that about 20 times before you're dead. AUDIENCE: So just really the
survival goals are important? MARVIN MINSKY: Well, if
you don't make a living, you'll starve. So now, if you've
committed yourself to being a
mathematician, now you have to be a good mathematician
or else you'll starve, and so forth. I was a pretty
good mathematician. Only my goal was, I had to
be the best mathematician, so I quit. You don't want to have a
goal you can't achieve. AUDIENCE: Yeah, but is
that part of [INAUDIBLE]?? MARVIN MINSKY: Well, a
lot of people do have one. So it eats up a lot of their
time and they're wasting it. I'm not sure what
the question is. I think the feature of
humans is that they're sort of general purpose. So there are a lot
of things people do, which are bad things to do. You can't justify them. You can think of people as
part of a huge search process. And as a species or
a genetic system, it pays to have a few crazy
ones every now and then. Because if the environment
suddenly changes, maybe they'll be the
only ones who survive. But William Calvin's
question, how come people evolved intelligence so
rapidly in five million years? And he attributes
it partly to five-- how many periods of
global cooling were there? It's about six or seven
ice ages in the last-- anybody know the
history of the Earth? Anyway, some evidence--
at least used to be, I haven't paid any attention-- is that the human
population's got down to maybe just tens of thousands several
times in the last million years. And so only the really different
ones managed to pull through. It might be the one who had
all sorts of useless abilities. Yes. It was the ones who ate
the others, which would have been punished before that. Go ahead. AUDIENCE: You're talking
about representing the K-lines and everything like
[INAUDIBLE],, some lines and then activating
some of these features. So in the case of
learning, which have changed some of
these connections? And if this is the case, how
would this effect the higher order, like higher level, just
like frames and [INAUDIBLE]?? Like if you change
something that's really for low-level
representation, will this effect a lot-- like
the whole system will break because some
stop procedure wouldn't be able to return properly? MARVIN MINSKY: That's great. That's another question
I can't begin to answer. Namely, when you
learn something-- let's take the extreme form-- do you start a new
representation or you modify an old one? OK, that's a choice. If you modify an
old one, can you do that without losing
the previous version? So for example, if I grow up
monolingually, which I did-- so I learned English. And then I can't remember
why, but for some reason, around 4th grade, some teacher
tried to teach us German. And so for each
German word, I try very hard to find the
equivalent English word and figure out how to move
your mouth so that it comes out German, which
actually, for many words, works fine, because
English is a mixture of German and other things. And for many words, it doesn't. So that was a bad idea. [LAUGHTER] And if I had gotten
very good at it, I could have lost some English. Anyway, that's a great
set of questions. When do you make new memories? When do you modify old ones? And the hard one is, if you
can't modify an old one, how do you make a
copy that's different. And there's a section
called, in Society of Mind, about how we do that in
language by paraphrasing what someone said. Or you say something in your own
head and you misunderstand it. I'm doing that all the time. I say, such and such
is a this or a that. And then it's as though
somebody else had said that. And I have someone going, no,
that's wrong, he meant this. So when you're
talking to yourself, you're actually converting
some mysterious inarticulate representation to speech. And then you're running
it through your brain and listening to it, and
converting the speech back to a new representation. So I think the wonderful
thing about language is, it's not just
for expressing. How come the only
animal that thinks the way we do is the only
animal that talks the way we do? Who knows? Maybe whales, but
nobody has decoded them do something like that. But that's a question. How do you copy a memory
to make a new version? And the answer is, you can
ask anyone and they'll say, I don't know. Why aren't there five
theories of that? Yeah. AUDIENCE: I don't
know if that's true, but I believe there is sort of
an objective path between words in language-- like
for example, know, no. So for example,
if I have a table, I say [SPEAKING PORTUGUESE]
in Portuguese. There is sort of
an objective that-- even for colors, which is
sort of weird, because you're kind of dividing all colors
into [INAUDIBLE] of colors. And even, I believe, for
words, languages that were not formed from the same ancestry. I don't know. For me, it seems that there's
some sort of objective [INAUDIBLE] between words. AUDIENCE: Well, there
are certain things that are just sort of naturally
more useful to talk about, and so a lot of languages can
have words for the same thing. Like languages will have
a word that means table. But if there are
some cultures that don't have tables, that
they probably wouldn't have a single word for table. And regarding the
colors thing, there have actually been some
interesting studies done on that. Like they've done
color-naming surveys with lots of languages
in the world. And it turns out that
different languages partition the color space differently. MARVIN MINSKY: I was just
curious if anybody knows it. AUDIENCE: They tend
to do it similarly. The way that people perceive
colors, the way that they divide up the space based on the
number of words that they have is actually like maximizing
the similarity of things with the same name and the
difference between [INAUDIBLE].. AUDIENCE: [INAUDIBLE] did some
interesting studies on that. MARVIN MINSKY: Right. I'm trying to remember
when children use colors. Let's see. I had a daughter-- who I
have still, I must say-- and she suddenly
started color words, and had six or seven of them
in just a couple of days. And she sort of got
interested in that. And so suddenly, I
said, oh, my gosh, this is what Maria
Montessori calls a-- I forget what she called it. What's this moment when the
child is open to being taught? So I said, this is her chance to
learn a lot of new color names. So I said, and what about this? I said that's aqua. And she said, that's blue-green. [LAUGHTER] And I said, no, this is aqua. And she refused. So that was that. So some months later, she
learned some more color names, but it wasn't the same. AUDIENCE: When she
was saying blue-green, is that because somebody
had taught her blue-green, or it was because she was
combining those two colors? MARVIN MINSKY: I think
she was combining it. Do you remember? MARVIN MINSKY: Yeah, I I
think she was combining it, because it looked a little
blue and a little green. AUDIENCE: So she had
a concept that there were certain building
blocks of colors. MARVIN MINSKY: It looked like
she wouldn't accept a new one. [LAUGHTER] And I always wondered if
I was 15 minutes too late. When does the
Montessori door shut? AUDIENCE: Sort of
similarly, there are studies done on different
languages of how well people distinguish different colors. So in Russian, there's
a different word that means light
blue and dark blue, so Russians are better at
distinguishing different shades of blue than, say, Americans. And there's also
a lot of languages that don't have two distinct
words for blue and green, and native speakers
of those languages have trouble distinguishing
blues and greens. MARVIN MINSKY: Oh, that
gives me a great idea. If I could have found
some unfamiliar objects and colored them aqua,
that might have fooled her. [LAUGHTER] Is there a branch of
psychology that worries about-- of course, there must
be names for sounds too. And certainly, a lot's known
about ages at which children can't get new phonemes. Yes, Henry. AUDIENCE: I've got a story
in Memory and Language. So I'm bilingual. Maybe other bilingual people in
the audience can confirm this. MARVIN MINSKY: I
didn't know that. What's your other language? AUDIENCE: French. MARVIN MINSKY:
For heaven's sake. I've known Henry
for 20-odd years. So AUDIENCE: One think
that can happen is, you can be talking with
another bilingual person, so I can be talking to
someone who also both speaks French and English,
and then like a week later, I'll remember every detail about
a complicated conversation. We were working on
this project and we were going to meet at
this and all this stuff. I remember every detail,
except which language we had the conversation. AUDIENCE: Yeah, my entire
family is bilingual. We sort of generally speak
a mix of Russian and English all the time, so I can never
remember even what language I'm speaking at the time. [LAUGHTER] AUDIENCE: Yeah, and
that mystifies me. Because if we store
it in a language, how could I forget
what language? AUDIENCE: Well, I think
there is a very simple answer to that one. You have a language that's
neither English or French. And you just have a very
simple [INAUDIBLE] there. And then whenever you
want to express something in English or French,
you would just decode, encode,
whatever is the word, [INAUDIBLE] that
you were having. AUDIENCE: Well,
that's the question. What is that language? Do you have any
thoughts on that? MARVIN MINSKY: I don't know. You remind me-- I think
I mentioned this-- but I was once in a meeting
in Yerevan, Armenia. And there was a translator
who was practically real-time. And Francis Crick was talking. And at some point, the
translator switched and he started translating
from English to English. So Crick would say
something and the translator would translate it into
English, very well, I thought. It wasn't quite the same words. And after a while,
somebody asked him why he was doing
that, and he said he didn't realize he had switched. Do you think that
could happen to you? AUDIENCE: I suppose. AUDIENCE: Do you
think that there are other ways of translating
ideas and learning them, maybe like art or music,
besides language that can be really helpful? MARVIN MINSKY: I
don't think there's anything nearly like language. Art is pretty good,
but it's so ambiguous. Cartoons, they're awfully good. AUDIENCE: How about Lisp? [LAUGHTER] MARVIN MINSKY: What? AUDIENCE: How about Lisp? Yeah, that was a joke. MARVIN MINSKY: Oh, right,
programming languages. Yes, why is mathematics so hard? I wonder if the habit
of using a single letter for every variable might
make it easy and hard. Who knows. Yes. You have great questions. AUDIENCE: Can you
you talk about, last year you mentioned
that mathematics is hard. I thought about it. MARVIN MINSKY: Say it again. AUDIENCE: Last
year, you mentioned that mathematics is hard. I thought about
it, and I do feel like there's an extreme lack
of representations of ideas. Solving a problem, we need
to identify so many things and there's so many
processes that you apply to them without having
a name for any of them, or like classification. Well, you have induction and
deduction, that's about it-- and contradiction. MARVIN MINSKY: Yes. One feature of mathematics
is completely unredundant representations. I wonder if there's some way
to fix that or change it. What other activity
do we have where there's absolutely
no redundancy at all in the mathematical expression? So for some people it's
delightful, and other people it's very hard. I mentioned Licklider, who
in programming, he would have very long variable names. Sometimes they'd
even be sentences, like the register in
which I put the result of. And the great thing was, you
could read those programs. They looked sort of stupid,
but he didn't have to-- what do you call notes? He didn't have to have,
exclamation point, this means that. Comments, comments. AUDIENCE: When I sit in
like an [INAUDIBLE] class, I just can't make myself accept
the concepts unless I can understand them algebraically
and [INAUDIBLE] geometric equation. MARVIN MINSKY: What kind
of math do you like? Do you do topology? AUDIENCE: I like topology. I like [INAUDIBLE]. MARVIN MINSKY: I love topology. I once was tutoring
a high school student who couldn't do algebra. I don't know if
I mentioned this. And it turned out he didn't
know how to use parentheses. So he would have an
expression, stuff like that. But if there were
something in there, he didn't know what that meant. He didn't know
how to match them. So I couldn't figure out why. And I ask, how come? And he said, maybe
I was sick the day they explained parentheses. And so I gave him
little exercises like, make them into eggs. And you see, if you
make this into an egg, then this egg won't work. [LAUGHTER] And the funny thing was, he
got that in five minutes, and then he didn't have any
trouble with algebra the rest-- can you imagine? I've never done much
tutoring but, if you can find the bug like
that, it must be great fun. But I bet it doesn't
happen very often. That was so funny. I couldn't imagine not-- you know? So if you don't you have
language, there must be-- well, why are some people
so much better at math than others? Is it just that they've not
understood about five things? AUDIENCE: I feel like
they have a set of things they know to go to when
they face a problem. Well, that's kind of similar
to your [INAUDIBLE] story. But I feel like
they know exactly. They have names for concepts of
like ways of solving problems. So they look like I'm
trying this approach and then if it doesn't work. I know this other approach. I just try it. Instead of looking at the
problem and you think, OK, so what possible thing,
what possible method, can I think of using? MARVIN MINSKY: Oh,
names for methods. So do you have public names
or are they secret names? AUDIENCE: I feel like
they are secret names. They're just like stores--
because they can't explain it to other people. They can't be like,
this problem-- like very few of them-- and like
this problem and that problem, they have the general
same method [INAUDIBLE].. MARVIN MINSKY: I had a
friend who was a composer. And she had all sorts of sounds. And they were filed
away on tapes. And the tapes had
little symbols on them. And if she needed a sound,
she would go to the closet and pull out the right tape. And it had more symbols
written on the reel. And she'd get this thing,
which might be a thunderstorm or a bird or something. So I asked her what was her
notation for these sounds. And she giggled and
said, I can't tell you. It's too embarrassing. I never found out. [LAUGHTER] But she had developed
some code for sounds. Yeah. AUDIENCE: So I would say that
they have some [INAUDIBLE] representation of things that
require [INAUDIBLE] symbols or patterns of solutions. And their representation
is optimal. And if you're good
at math, it doesn't mean that you're good
at playing music. Because when you
play music well, you have maybe a good
representation of the sounds. And so it's just [INAUDIBLE]. And so you cannot access all-- so I have these
solutions, these patterns of solutions that I need to-- I don't know. I need to solve this math
problem, by deduction or by contradiction. And in his brain,
or somebody that knows a lot, like
the person can access very faster, their
representation itself is very well-defined. So you can access [INAUDIBLE]. MARVIN MINSKY: That's
an interesting whole-- let me interrupt. I was once in some
class, math class. And it was about n
dimensional vector spaces. And some student asked,
well, how do you imagine the n dimensional vector space? Its two stories. And the instructor, who I
forget who, thought for a while. Then he said, oh,
it's very simple. Just pick a particular n So
that was completely useless. [LAUGHTER] And I was a disciple
of a mathematician at Harvard named Andrew Gleason,
who was a wonderful man. Only a couple of
years older than me, but he had won the Putnam
three times, first prize. And I said what would
you tell a student who wanted to understand
an n dimensional vector space, what it means? And he said, well, you should
give him five or six ways. I don't know. Like imagine a bunch of arrows. And remember that each of
them is at right angles to all the others, just like that. Then he added, of if
there's an infinite number, you should have the sum of
the squares of their lengths converge to a finite value. And then he said, or you should
think of it as a Fourier series with things of
different frequencies. And then he said,
or you should think of an object in a
topological space, and each dimension is finding
the boundary of the last one. And he went on for
about six or seven, and that was a great idea. Well, have seven
completely different ways. And I remember I once had the
same conversation with Richard Feynman. And I said, well,
how did you do that? And he said, well,
when I grew up, whatever it was, I always
thought of three or four representations. So if one of them didn't
work, another one would. What? AUDIENCE: So my
idea for somebody, if you ask them about how to
understand multiple dimension space, is I'd say,
read Flatland. [LAUGHTER] Because that would
give you the analogy. Once you had that
analogy, then it would be easy to extend
it to other dimensions. MARVIN MINSKY: Oh, good. Has anybody written
a 4D Flatland, where you make fun
of the 3D people. They can't get out
of a paper bag. [LAUGHTER] AUDIENCE: And so there
will be some events that maybe will prove that. So for example,
in my case, when I do a lot of math, when I try to
talk to people, it's very hard. And like maybe [INAUDIBLE]
my representation of solving problems in math. And people tend to get better in
math if they practice it a lot, because they are optimizing
their representation of math, and that would be the case. MARVIN MINSKY: I think I
understand the problem, but I don't think I
have any friends left who are not mathematicians. [LAUGHTER] That's what happens if you
live in this place long enough. Yeah. AUDIENCE: So that's one
way they're doing better. I feel like the other
way they're doing better is, they objectify things
that we don't objectify. It's like the learning
how to learn better idea, learning how to learn
better to learn better. So it's one thing to know what
the right representation is for a particular problem,
like the right method is. It's another thing to
optimize that process. So like, what process did I use
in finding that representation? And then they make
that into a concept. And then they have a lot
of these kinds of concepts. MARVIN MINSKY: Have you
ever helped somebody to learn better. AUDIENCE: Yeah, [INAUDIBLE]. MARVIN MINSKY: What
did you tell them. AUDIENCE: So she had
trouble with, basically, two truth tables,
something like that, like AND, OR and
stuff like that. So her way of seeing a
problem is to make a chart. I forget what I told her. I told her to kind of
like map simple problems. MARVIN MINSKY: You
know, maybe most people don't have the
word representation in their language. Is there any place in grade
school where you actually talk about, what's your
representation of acceleration? Do we teach that word
as part of any subject? AUDIENCE: [INAUDIBLE]
If you're drawing some base or
something like that, it'll ask you to represent it. MARVIN MINSKY: Yes. But it's hard to get out of-- yeah, OK. So they're radically
different representations of-- AUDIENCE: Maybe
that's [INAUDIBLE] MARVIN MINSKY:
Yes, I don't know. AUDIENCE: [INAUDIBLE] MARVIN MINSKY: Tinker Toys. Tinker Toy. AUDIENCE: What's that? MARVIN MINSKY: Yes, to
represent physical structures as Tinker Toys. Yeah, I wrote a little
article complaining about the popularity of LEGO
as opposed to Tinker Toy. Because the children
who grow up with LEGO can't understand how
to make something strong by making a triangle. So I sort of had the conjecture
that although those people could build all sorts of
wonderful houses and things, they ended up deficient in
having the most important of all architectural concepts. A triangle is infinitely
strong, because you can't alter a triangle
without breaking it, whereas, I don't know what. That's a run-on
sentence I can't finish. AUDIENCE: This explains
the deterioration of society, Marvin. We don't have Tinker Toys and
we don't have chemistry sets with chemicals that
make explosives anymore. [LAUGHTER] You have to go to terrorist
school to get a good education. [LAUGHTER] AUDIENCE: So actually
in this conversation about being good at things and
learning how to learn better, I think that a point
that sort of relates to this idea of Tinker sets
and playing around with things, I think that it's not enough
to simply come up with the best representations of a concept. In order to actually
be good at something, whether it's music or
speaking a new language, you have to not only
understand it conceptually, but you actually have to gain
a certain amount of fluency. And to gain fluency, you
do have to play around with the thing a
lot, whether it's turning it around in your mind
or practicing it physically. So in the case of math,
it's like, yeah, you can come up with all these
different representations of it. And that's the first
step, understanding it. And it's great once
you understand it. But just because you
understand the concept, like on a conceptual
level, doesn't mean that you can
actually know when to use it or know how to use it
when you're solving a problem. And similarly, for music-- I guess I'm mostly
talking in the case of improvisational music when
I'm trying to speak something with the music. So I have something
that I want to say. And maybe it's something
that sort of low level. I'm trying to resolve one chord
to get to some sort of cadence. Now, I can have multiple
ways of resolving the chord. And in order to do this,
I have a vocabulary of the different ways. And if one way doesn't occur to
me when I'm playing the piece, I can try another way. But the important
thing is that I have some way that I can
resolve it in real-time, or else my piece is never
going to come out. And then same thing about
learning different languages or speaking different languages. In order to be able to speak
or to express ourselves, we have to have not only
understanding of the language, of the structure,
but the immediacy of being able to access it. And that comes with practice,
with fingering, what have you. MARVIN MINSKY: Well, that
goes in several directions. Where in our educational
system do we-- in grade school,
is there a place where you emphasize having
several representations? Because I can't think
clearly right now. But it seems to me that
you're usually trying to tell them the one best way. AUDIENCE: It's like, A, there's
the idea of the one best way, and B, there's the idea of
reinforcing the same process over and over again. So when you learn math, it's
like, you learn this technique, and you reinforce the technique
by doing a bunch of homework problems that are
essentially like repetitions of the same thing. Whereas, I think a better
way of doing it-- well, two better ways-- A, you have multiple
representations. And 2, you create problems where
you make people traverse paths differently. And different people may
have different solutions. And each time you
solve the problem you may have a
different solution. But the idea is, you lay
out a whole network of paths in your head to solve any
given type of problem. MARVIN MINSKY: OK, so
where in grade school do you ask children to solve
the same problem three ways? Can anybody think of-- is that part of education. AUDIENCE: [INAUDIBLE] fractions. MARVIN MINSKY: What? AUDIENCE: That's the the
closest thing I think of-- fractions. AUDIENCE: What about literature
class, where they ask you for interpretations of novels. MARVIN MINSKY: Yes. I bet there are
things that happen in literature that
don't happen anywhere else in the curriculum. But most children
don't transfer it. AUDIENCE: Another thing,
in China, in learning math, when we try to find areas
of certain geometric shapes, we always do it multiple
times, multiple ways. MARVIN MINSKY: In
topology, whatever it is, you just make it into
triangles and simplexes. [LAUGHTER] So that's a very
strange subject. AUDIENCE: Maybe
[INAUDIBLE] so nice is because we can think about
it as logical concepts, like [INAUDIBLE] sets,
[INAUDIBLE] points, stuff like that. And then you [INAUDIBLE] MARVIN MINSKY: Co-sets. Where in real life
do you have duality? That's a nice feature
of a lot of mathematics. Whatever you're
doing in some fields, there's a dual
way, where you look at the space of the functions
on the objects rather than the objects. Where is that in-- is there anything like
that in real life? Because in mathematics,
a lot of problems suddenly become much
easier in their dual form. It would just change everything. AUDIENCE: There's
a question, Marvin. MARVIN MINSKY: I've
been facing one way. AUDIENCE: I guess a
couple of points-- you said, why is it difficult? Whenever I've struggled, I think
it's because it's constructive, and you have to code
a lot in your head. You have to code the entire
structure of [INAUDIBLE] field. Because if you're
learning algebra topology, you're holding all
of algebra and all of that structure in your head. And so sometimes it
becomes difficult if you have it constructed
at the right level. And what I found, I think, my
advisor was just really good. Many times, he basically
said two things. Really good mathematicians are
really good at making analogies in mathematics. And [INAUDIBLE] geometry. And he says, and really
good algebraic geometers can boil everything
down to linear algebra. And he said you can
only do that if you abstract at the right level. And he never gave
techniques of doing that. But I think the difficulty
with that [INAUDIBLE].. AUDIENCE: An analogy
is the relationship between two objects. And if you're good at making
analogies, you're at a level beyond, a level above
just looking at objects. You're looking at
relationships of objects. And regarding the
practicing thing, I mean, it's still related
to representations, because at each practice
you're learning something new about these type
of problems that might make you better at
identifying them in the future. And it's not like
numerous practices. The number of practices
doesn't matter to your ability of solving problems. It's like, what you
learn from each practice, if you can do a thing once-- I have a friend who basically
told me how to do math. He's like, you look at a
problem, solve it once, you go back and
you think about how you solved it, like what's the
process you used to solve it. MARVIN MINSKY: So a
good problem is, make up another problem like this. AUDIENCE: That's essentially
what you are learning when you are practicing. MARVIN MINSKY: It's
probably too hard to great. You can't teach things you
can't grade in the modern-- Yes. AUDIENCE: So I believe
that math is too abstract. And so it's difficult to go
from one to representation to another, and that would
be the whole problem. I can't learn a new
concept without having a concept that's very near that
concept, that's very similar. So it's just that, if I don't
have good representations of a lot of things, it's
difficult to continue representation. So when I learned, I
don't know, topology, I should know analogies. And then I can go
from there to there, because the representation
is very close. And so people that
are good at math, maybe they have a lot
of representations so it's easier to add a new
representation of a thing, because it's close. MARVIN MINSKY: It certainly
would be nice to know. AUDIENCE: Like in the
example of vectors, I already have the
concept of, I don't know, the perpendicular lines. And so just adding
more lines it's easy. But I don't know, the
n dimensional thing, it's very abstract. I don't have any
other representation that's close by that concept. It's just that I need a lot of
concepts and representations. And I need one that's close by. MARVIN MINSKY: Yes, between
the vectors and the Fourier, they're so different. What would be in
between those two? AUDIENCE: The Fourier is the
[INAUDIBLE] kind of concepts. MARVIN MINSKY:
Actually square waves-- probably square waves
are easier to understand than sines and cosines. But they're not continuous-- I mean, not differential. Who has a problem to solve? AUDIENCE: I can comment on,
I think, [INAUDIBLE] comment on having lots of practice. And I don't think
that actually is so much out of the
representational view. I don't know what's
going on neurologically. But if you have new
representations, if you assume that they are
symbolic representations, in this sense, they are
quite generative things. You can combine them and
you can make a lot of stuff. But usually when you
will learn something new, like if you learn the rule,
there's going to be exceptions. So when you repeat things,
one of the reasons for that might be to find that box. You might not [INAUDIBLE] MARVIN MINSKY: Well
now, when you practice a little piece of
music by repeating, do you change your
representation or do you just repeat
and hope it gets better? AUDIENCE: So I think there's
a difference between-- OK, so there's the
practice in this sense of traditional classical
music practice. And then there's the idea of
tinker practice, if you will. So I think that the type of
practice that I'm advocating is the type of practice
where you're actually sort of turning around
the concept or the thing, the object in your
head, so that you're looking at it from many
different perspectives and connecting it to many
different means, which is actually conducive to
expanding your representation, connecting it to
different concepts, like all the good things that
help us remember it and better use it. This is very different from
the classical music practice. Having been a classical
pianist for 18 years, it's not a really good
way of doing things. AUDIENCE: I was
wondering, has there been any studies of
children, or have there been any children who have just
played piano or some keyboard instrument [INAUDIBLE]
over their lives, and then they suddenly
have something where, when you press it, where
you can control the volume? MARVIN MINSKY: A theremin. AUDIENCE: Yeah, yeah, yeah. But if a child
very suddenly could learn a completely new
dimension, that's the dynamics and how would it react to that. So I don't know if there
are any such cases. But I would suspect
that it would go to [INAUDIBLE]
backward [INAUDIBLE] and then find some
children [INAUDIBLE].. MARVIN MINSKY: There was a
nice period in the Media Lab when we were building
three-dimensional theremin for Penn of Penn and Teller. He's quite a good musician. We were making gadgets so
he could wave his hands. But I wonder, in
classical, there ought to be some very
short pieces that come in 10 variations. Because we make children
learn fairly long pieces where it's just repeating. AUDIENCE: There's the
Diabelli Variations, which is like 32 or 33
really short pieces. MARVIN MINSKY: Well, but only
eight people in the world can play it. AUDIENCE: But I guess, the
thing with classical music is that, it kind of just makes
you learn this one thing. And you learn it by repeating
it over and over again. Whereas in something like jazz
that's more improvisational, it's like you have a template. And each time you
go through it, you can traverse a different
path through it. But even more like
classical, classical music, where you're playing the
same thing each time, I think that there's still
like a good way of doing it and a not so good
way, the good way being, each time you practice
it, you subtly vary it somehow. Like you change the
expression of it, you play it faster or
slower, things like that. And I guess this goes
back to the whole-- it's like, each time
you reinforce an idea, like simply repeating it,
well, in the beginning, it might help you familiarize
yourself with the idea. But if you repeat it and
vary it slightly each time to look at different
dimensions of it, then you learn it better. MARVIN MINSKY: How many of you
know that piece, Beethoven's Diabelli? Well, you should google
it up and listen to it. It has 32. Is it 32? AUDIENCE: I think it's
32, and then there's the original theme. So it's like 33
[INAUDIBLE] maybe. MARVIN MINSKY: The one I
like is the next to last, which is [WORDLESS SINGING]. AUDIENCE: Oh, the fugue, yeah. MARVIN MINSKY: The fugue. So that'll give you another
view of classical music, because the pieces
are fairly short and they all have
some ideas in common. And it's sort of like poetry. What do you call those poems
where there are many verses and each verse ends
with the same line, but it means something
different each time? AUDIENCE: What's an example? MARVIN MINSKY: What? AUDIENCE: What's
an example of it? Can you think of a poem? MARVIN MINSKY: I
couldn't hear you. AUDIENCE: Well, there's
like the villanelle-- MARVIN MINSKY:
It's like a rondo, except that it changes
its meaning each time. And it's the same words. They're pretty hard
to make, I guess. AUDIENCE: There's
the villanelle, which has a bit of that. There's the famous one that's,
what, like, do not go gentle into the night or something. Rage, rage against the
coming of something. MARVIN MINSKY: Anyway, I'll
email you the Diabelli. I have a friend,
Manfred Clynes, who wrote this book
called Sentics, who used to play that
particular Beethoven thing. Well, last important question. Thanks for coming. [LAUGHTER]
