If one were to rank a list of civilization’s greatest and most elusive intellectual challenges, the problem of “decoding” ourselves -- understanding the inner workings of our minds and our brains, and how the architecture of these elements is encoded in our genome -- would surely be at the top. Yet the diverse fields that took on this challenge, from philosophy and psychology to computer science and neuroscience, have been fraught with disagreement about the right approach.
In 1956, the computer scientist John McCarthy coined
the term “Artificial Intelligence” (AI) to describe the study of intelligence
by implementing its essential features on a computer. Instantiating an
intelligent system using man-made hardware, rather than our own “biological
hardware” of cells and tissues, would show ultimate understanding, and have
obvious practical applications in the creation of intelligent devices or even
robots.
Some of McCarthy’s colleagues in neighboring
departments, however, were more interested in how intelligence is implemented
in humans (and other animals) first. Noam Chomsky and others worked on what
became cognitive science, a field aimed at uncovering the mental
representations and rules that underlie our perceptual and cognitive abilities.
Chomsky and his colleagues had to overthrow the then-dominant paradigm of
behaviorism, championed by Harvard psychologist B.F. Skinner, where animal
behavior was reduced to a simple set of associations between an action and its
subsequent reward or punishment. The undoing of Skinner’s grip on psychology is
commonly marked by Chomsky’s 1959 critical review of Skinner’s book Verbal
Behavior, a book in which Skinner attempted to explain linguistic ability using
behaviorist principles.
Skinner’s approach stressed the historical
associations between a stimulus and the animal’s response -- an approach easily
framed as a kind of empirical statistical analysis, predicting the future as a
function of the past. Chomsky’s conception of language, on the other hand,
stressed the complexity of internal representations, encoded in the genome, and
their maturation in light of the right data into a sophisticated computational
system, one that cannot be usefully broken down into a set of associations.
Behaviorist principles of associations could not explain the richness of
linguistic knowledge, our endlessly creative use of it, or how quickly children
acquire it with only minimal and imperfect exposure to language presented by
their environment. The “language faculty,” as Chomsky referred to it, was part
of the organism’s genetic endowment, much like the visual system, the immune
system and the circulatory system, and we ought to approach it just as we
approach these other more down-to-earth biological systems.
David Marr, a neuroscientist colleague of Chomsky’s
at MIT, defined a general framework for studying complex biological systems
(like the brain) in his influential book Vision, one that Chomsky’s analysis of
the language capacity more or less fits into. According to Marr, a complex
biological system can be understood at three distinct levels. The first level (“computational
level”) describes the input and output to the system, which define the task the
system is performing. In the case of the visual system, the input might be the
image projected on our retina and the output might our brain’s identification
of the objects present in the image we had observed. The second level (“algorithmic
level”) describes the procedure by which an input is converted to an output,
i.e. how the image on our retina can be processed to achieve the task described
by the computational level. Finally, the third level (“implementation level”)
describes how our own biological hardware of cells implements the procedure
described by the algorithmic level.
The approach taken by Chomsky and Marr toward
understanding how our minds achieve what they do is as different as can be from
behaviorism. The emphasis here is on the internal structure of the system that
enables it to perform a task, rather than on external association between past
behavior of the system and the environment. The goal is to dig into the “black
box” that drives the system and describe its inner workings, much like how a
computer scientist would explain how a cleverly designed piece of software
works and how it can be executed on a desktop computer.
As written today, the history of cognitive science is
a story of the unequivocal triumph of an essentially Chomskyian approach over
Skinner’s behaviorist paradigm -- an achievement commonly referred to as the “cognitive
revolution,” though Chomsky himself rejects this term. While this may be a
relatively accurate depiction in cognitive science and psychology, behaviorist
thinking is far from dead in related disciplines. Behaviorist experimental
paradigms and associationist explanations for animal behavior are used
routinely by neuroscientists who aim to study the neurobiology of behavior in
laboratory animals such as rodents, where the systematic three-level framework
advocated by Marr is not applied.
In May of last year, during the 150th anniversary of
the Massachusetts Institute of Technology, a symposium on “Brains, Minds and Machines”
took place, where leading computer scientists, psychologists and
neuroscientists gathered to discuss the past and future of artificial
intelligence and its connection to the neurosciences.
The gathering was meant to inspire multidisciplinary
enthusiasm for the revival of the scientific question from which the field of
artificial intelligence originated: how does intelligence work? How does our
brain give rise to our cognitive abilities, and could this ever be implemented
in a machine?
Noam Chomsky, speaking in the symposium, wasn’t so
enthused. Chomsky critiqued the field of AI for adopting an approach
reminiscent of behaviorism, except in more modern, computationally
sophisticated form. Chomsky argued that the field’s heavy use of statistical
techniques to pick regularities in masses of data is unlikely to yield the
explanatory insight that science ought to offer. For Chomsky, the “new AI” --
focused on using statistical learning techniques to better mine and predict
data -- is unlikely to yield general principles about the nature of intelligent
beings or about cognition.
This critique sparked an elaborate reply to Chomsky
from Google’s director of research and noted AI researcher, Peter Norvig, who
defended the use of statistical models and argued that AI’s new methods and
definition of progress is not far off from what happens in the other sciences.
Chomsky acknowledged that the statistical approach
might have practical value, just as in the example of a useful search engine,
and is enabled by the advent of fast computers capable of processing massive
data. But as far as a science goes, Chomsky would argue it is inadequate, or
more harshly, kind of shallow. We wouldn’t have taught the computer much about
what the phrase “physicist Sir Isaac Newton” really means, even if we can build
a search engine that returns sensible hits to users who type the phrase in.
It turns out that related disagreements have been
pressing biologists who try to understand more traditional biological systems
of the sort Chomsky likened to the language faculty. Just as the computing
revolution enabled the massive data analysis that fuels the “new AI”, so has
the sequencing revolution in modern biology given rise to the blooming fields
of genomics and systems biology. High-throughput sequencing, a technique by
which millions of DNA molecules can be read quickly and cheaply, turned the
sequencing of a genome from a decade-long expensive venture to an affordable,
commonplace laboratory procedure. Rather than painstakingly studying genes in
isolation, we can now observe the behavior of a system of genes acting in cells
as a whole, in hundreds or thousands of different conditions.
The sequencing revolution has just begun and a
staggering amount of data has already been obtained, bringing with it much
promise and hype for new therapeutics and diagnoses for human disease. For
example, when a conventional cancer drug fails to work for a group of patients,
the answer might lie in the genome of the patients, which might have a special
property that prevents the drug from acting. With enough data comparing the
relevant features of genomes from these cancer patients and the right control
groups, custom-made drugs might be discovered, leading to a kind of “personalized
medicine.” Implicit in this endeavor is the assumption that with enough
sophisticated statistical tools and a large enough collection of data, signals
of interest can be weeded it out from the noise in large and poorly understood
biological systems.
The success of fields like personalized medicine and
other offshoots of the sequencing revolution and the systems-biology approach
hinge upon our ability to deal with what Chomsky called “masses of unanalyzed
data” -- placing biology in the center of a debate similar to the one taking
place in psychology and artificial intelligence since the 1960s.
Systems biology did not rise without skepticism. The
great geneticist and Nobel-prize winning biologist Sydney Brenner once defined
the field as “low input, high throughput, no output science.” Brenner, a
contemporary of Chomsky who also participated in the same symposium on AI, was
equally skeptical about new systems approaches to understanding the brain. When
describing an up-and-coming systems approach to mapping brain circuits called
Connectomics, which seeks to map the wiring of all neurons in the brain (i.e.
diagramming which nerve cells are connected to others), Brenner called it a “form
of insanity.”
Brenner’s catch-phrase bite at systems biology and
related techniques in neuroscience is not far off from Chomsky’s criticism of
AI. An unlikely pair, systems biology and artificial intelligence both face the
same fundamental task of reverse-engineering a highly complex system whose
inner workings are largely a mystery. Yet, ever-improving technologies yield
massive data related to the system, only a fraction of which might be relevant.
Do we rely on powerful computing and statistical approaches to tease apart
signal from noise, or do we look for the more basic principles that underlie
the system and explain its essence? The urge to gather more data is
irresistible, though it’s not always clear what theoretical framework these
data might fit into. These debates raise an old and general question in the
philosophy of science: What makes a satisfying scientific theory or
explanation, and how ought success be defined for science?
I sat with Noam Chomsky on an April afternoon in a
somewhat disheveled conference room, tucked in a hidden corner of Frank Gehry’s
dazzling Stata Center at MIT. I wanted to better understand Chomsky’s critique
of artificial intelligence and why it may be headed in the wrong direction. I
also wanted to explore the implications of this critique for other branches of
science, such neuroscience and systems biology, which all face the challenge of
reverse-engineering complex systems -- and where researchers often find
themselves in an ever-expanding sea of massive data. The motivation for the
interview was in part that Chomsky is rarely asked about scientific topics
nowadays. Journalists are too occupied with getting his views on U.S. foreign
policy, the Middle East, the Obama administration and other standard topics. Another
reason was that Chomsky belongs to a rare and special breed of intellectuals,
one that is quickly becoming extinct. Ever since Isaiah Berlin’s famous essay,
it has become a favorite pastime of academics to place various thinkers and
scientists on the “Hedgehog-Fox” continuum: the Hedgehog, a meticulous and
specialized worker, driven by incremental progress in a clearly defined field
versus the Fox, a flashier, ideas-driven thinker who jumps from question to
question, ignoring field boundaries and applying his or her skills where they
seem applicable. Chomsky is special because he makes this distinction seem like
a tired old cliche. Chomsky’s depth doesn’t come at the expense of versatility
or breadth, yet for the most part, he devoted his entire scientific career to
the study of defined topics in linguistics and cognitive science. Chomsky’s
work has had tremendous influence on a variety of fields outside his own,
including computer science and philosophy, and he has not shied away from
discussing and critiquing the influence of these ideas, making him a
particularly interesting person to interview. Videos of the interview can be
found here.
1.
I want to start with a very basic question. At
the beginning of AI, people were extremely optimistic about the field’s
progress, but it hasn’t turned out that way. Why has it been so difficult? If
you ask neuroscientists why understanding the brain is so difficult, they give
you very intellectually unsatisfying answers, like that the brain has billions
of cells, and we can’t record from all of them, and so on.
2.
There’s something to that. If you take a look at
the progress of science, the sciences are kind of a continuum, but they’re
broken up into fields. The greatest progress is in the sciences that study the
simplest systems. So take, say physics -- greatest progress there. But one of
the reasons is that the physicists have an advantage that no other branch of
sciences has. If something gets too complicated, they hand it to someone else.
3.
Like the chemists?
4.
If a molecule is too big, you give it to the
chemists. The chemists, for them, if the molecule is too big or the system gets
too big, you give it to the biologists. And if it gets too big for them, they
give it to the psychologists, and finally it ends up in the hands of the
literary critic, and so on. So what the neuroscientists are saying is not
completely false. However, it could be -- and it has been argued in my view
rather plausibly, though neuroscientists don’t like it -- that neuroscience for
the last couple hundred years has been on the wrong track. There’s a fairly
recent book by a very good cognitive neuroscientist, Randy Gallistel and King,
arguing -- in my view, plausibly -- that neuroscience developed kind of
enthralled to associationism and related views of the way humans and animals
work. And as a result they’ve been looking for things that have the properties
of associationist psychology.
5.
Like Hebbian plasticity? [Editor’s note: A
theory, attributed to Donald Hebb, that associations between an environmental
stimulus and a response to the stimulus can be encoded by strengthening of
synaptic connections between neurons.]
6.
Well, like strengthening synaptic connections.
Gallistel has been arguing for years that if you want to study the brain
properly you should begin, kind of like Marr, by asking what tasks is it
performing. So he’s mostly interested in insects. So if you want to study, say,
the neurology of an ant, you ask what does the ant do? It turns out the ants do
pretty complicated things, like path integration, for example. If you look at
bees, bee navigation involves quite complicated computations, involving
position of the sun, and so on and so forth. But in general what he argues is
that if you take a look at animal cognition, human too, it’s computational
systems. Therefore, you want to look the units of computation. Think about a
Turing machine, say, which is the simplest form of computation, you have to
find units that have properties like “read”, “write” and “address.” That’s the
minimal computational unit, so you got to look in the brain for those. You’re
never going to find them if you look for strengthening of synaptic connections
or field properties, and so on. You’ve got to start by looking for what’s there
and what’s working and you see that from Marr’s highest level.
7.
Right, but most neuroscientists do not sit down
and describe the inputs and outputs to the problem that they’re studying. They’re
more driven by say, putting a mouse in a learning task and recording as many
neurons possible, or asking if Gene X is required for the learning task, and so
on. These are the kinds of statements that their experiments generate.
8.
That’s right.
9.
Is that conceptually flawed?
10.
Well, you know, you may get useful information
from it. But if what’s actually going on is some kind of computation involving
computational units, you’re not going to find them that way. It’s kind of,
looking at the wrong lamp post, sort of. It’s a debate... I don’t think
Gallistel’s position is very widely accepted among neuroscientists, but it’s
not an implausible position, and it’s basically in the spirit of Marr’s
analysis. So when you’re studying vision, he argues, you first ask what kind of
computational tasks is the visual system carrying out. And then you look for an
algorithm that might carry out those computations and finally you search for
mechanisms of the kind that would make the algorithm work. Otherwise, you may
never find anything. There are many examples of this, even in the hard
sciences, but certainly in the soft sciences. People tend to study what you
know how to study, I mean that makes sense. You have certain experimental
techniques, you have certain level of understanding, you try to push the
envelope -- which is okay, I mean, it’s not a criticism, but people do what you
can do. On the other hand, it’s worth thinking whether you’re aiming in the
right direction. And it could be that if you take roughly the Marr-Gallistel
point of view, which personally I’m sympathetic to, you would work differently,
look for different kind of experiments.
11.
Right, so I think a key idea in Marr is, like
you said, finding the right units to describing the problem, sort of the right “level
of abstraction” if you will. So if we take a concrete example of a new field in
neuroscience, called Connectomics, where the goal is to find the wiring diagram
of very complex organisms, find the connectivity of all the neurons in say
human cerebral cortex, or mouse cortex. This approach was criticized by Sidney
Brenner, who in many ways is [historically] one of the originators of the
approach. Advocates of this field don’t stop to ask if the wiring diagram is
the right level of abstraction -- maybe it’s not, so what is your view on that?
12.
Well, there are much simpler questions. Like
here at MIT, there’s been an interdisciplinary program on the nematode C.
elegans for decades, and as far as I understand, even with this miniscule
animal, where you know the wiring diagram, I think there’s 800 neurons or
something.
13.
I think 300.
14.
Still, you can’t predict what the thing [C.
elegans nematode] is going to do. Maybe because you’re looking in the wrong
place.
15.
I’d like to shift the topic to different
methodologies that were used in AI. So “Good Old Fashioned AI,” as it’s labeled
now, made strong use of formalisms in the tradition of Gottlob Frege and
Bertrand Russell, mathematical logic for example, or derivatives of it, like
nonmonotonic reasoning and so on. It’s interesting from a history of science
perspective that even very recently, these approaches have been almost wiped
out from the mainstream and have been largely replaced -- in the field that
calls itself AI now -- by probabilistic and statistical models. My question is,
what do you think explains that shift and is it a step in the right direction?
16.
I heard Pat Winston give a talk about this years
ago. One of the points he made was that AI and robotics got to the point where
you could actually do things that were useful, so it turned to the practical
applications and somewhat, maybe not abandoned, but put to the side, the more
fundamental scientific questions, just caught up in the success of the
technology and achieving specific goals.
17.
So it shifted to engineering.
18.
It became... well, which is understandable, but
would of course direct people away from the original questions. I have to say,
myself, that I was very skeptical about the original work. I thought it was
first of all way too optimistic, it was assuming you could achieve things that
required real understanding of systems that were barely understood, and you just
can’t get to that understanding by throwing a complicated machine at it. If you
try to do that you are led to a conception of success, which is
self-reinforcing, because you do get success in terms of this conception, but
it’s very different from what’s done in the sciences. So for example, take an
extreme case, suppose that somebody says he wants to eliminate the physics
department and do it the right way. The “right” way is to take endless numbers
of videotapes of what’s happening outside the video, and feed them into the
biggest and fastest computer, gigabytes of data, and do complex statistical
analysis -- you know, Bayesian this and that [Editor’s note: A modern approach
to analysis of data which makes heavy use of probability theory.] -- and you’ll
get some kind of prediction about what’s gonna happen outside the window next.
In fact, you get a much better prediction than the physics department will ever
give. Well, if success is defined as getting a fair approximation to a mass of
chaotic unanalyzed data, then it’s way better to do it this way than to do it
the way the physicists do, you know, no thought experiments about frictionless
planes and so on and so forth. But you won’t get the kind of understanding that
the sciences have always been aimed at -- what you’ll get at is an
approximation to what’s happening. And that’s done all over the place. Suppose
you want to predict tomorrow’s weather. One way to do it is okay I’ll get my
statistical priors, if you like, there’s a high probability that tomorrow’s
weather here will be the same as it was yesterday in Cleveland, so I’ll stick
that in, and where the sun is will have some effect, so I’ll stick that in, and
you get a bunch of assumptions like that, you run the experiment, you look at
it over and over again, you correct it by Bayesian methods, you get better
priors. You get a pretty good approximation of what tomorrow’s weather is going
to be. That’s not what meteorologists do -- they want to understand how it’s
working. And these are just two different concepts of what success means, of
what achievement is. In my own field, language fields, it’s all over the place.
Like computational cognitive science applied to language, the concept of
success that’s used is virtually always this. So if you get more and more data,
and better and better statistics, you can get a better and better approximation
to some immense corpus of text, like everything in The Wall Street Journal
archives -- but you learn nothing about the language. A very different
approach, which I think is the right approach, is to try to see if you can
understand what the fundamental principles are that deal with the core
properties, and recognize that in the actual usage, there’s going to be a
thousand other variables intervening -- kind of like what’s happening outside
the window, and you’ll sort of tack those on later on if you want better
approximations, that’s a different approach. These are just two different
concepts of science. The second one is what science has been since Galileo,
that’s modern science. The approximating unanalyzed data kind is sort of a new
approach, not totally, there’s things like it in the past. It’s basically a new
approach that has been accelerated by the existence of massive memories, very
rapid processing, which enables you to do things like this that you couldn’t
have done by hand. But I think, myself, that it is leading subjects like
computational cognitive science into a direction of maybe some practical
applicability.
19.
In engineering?
20.
But away from understanding. Yeah, maybe some
effective engineering. And it’s kind of interesting to see what happened to
engineering. So like when I got to MIT, it was 1950s, this was an engineering
school. There was a very good math department, physics department, but they
were service departments. They were teaching the engineers tricks they could
use. The electrical engineering department, you learned how to build a circuit.
Well if you went to MIT in the 1960s, or now, it’s completely different. No
matter what engineering field you’re in, you learn the same basic science and
mathematics. And then maybe you learn a little bit about how to apply it. But
that’s a very different approach. And it resulted maybe from the fact that
really for the first time in history, the basic sciences, like physics, had
something really to tell engineers. And besides, technologies began to change
very fast, so not very much point in learning the technologies of today if it’s
going to be different 10 years from now. So you have to learn the fundamental
science that’s going to be applicable to whatever comes along next. And the
same thing pretty much happened in medicine. So in the past century, again for
the first time, biology had something serious to tell to the practice of
medicine, so you had to understand biology if you want to be a doctor, and
technologies again will change. Well, I think that’s the kind of transition
from something like an art, that you learn how to practice -- an analog would
be trying to match some data that you don’t understand, in some fashion, maybe
building something that will work -- to science, what happened in the modern
period, roughly Galilean science.
21.
I see. Returning to the point about Bayesian
statistics in models of language and cognition. You’ve argued famously that
speaking of the probability of a sentence is unintelligible on its own.
22.
Well you can get a number if you want, but it
doesn’t mean anything.
23.
It doesn’t mean anything. But it seems like
there’s almost a trivial way to unify the probabilistic method with acknowledging
that there are very rich internal mental representations, comprised of rules
and other symbolic structures, and the goal of probability theory is just to
link noisy sparse data in the world with these internal symbolic structures.
And that doesn’t commit you to saying anything about how these structures were
acquired -- they could have been there all along, or there partially with some
parameters being tuned, whatever your conception is. But probability theory
just serves as a kind of glue between noisy data and very rich mental
representations.
24.
Well... there’s nothing wrong with probability
theory, there’s nothing wrong with statistics.
25.
But does it have a role?
26.
If you can use it, fine. But the question is
what are you using it for? First of all, first question is, is there any point
in understanding noisy data? Is there some point to understanding what’s going
on outside the window?
27.
Well, we are bombarded with it [noisy data], it’s
one of Marr’s examples, we are faced with noisy data all the time, from our
retina to.
28.
That’s true. But what he says is: Let’s ask
ourselves how the biological system is picking out of that noise things that
are significant. The retina is not trying to duplicate the noise that comes in.
It’s saying I’m going to look for this, that and the other thing. And it’s the
same with say, language acquisition. The newborn infant is confronted with
massive noise, what William James called “a blooming, buzzing confusion,” just
a mess. If say, an ape or a kitten or a bird or whatever is presented with that
noise, that’s where it ends. However, the human infants, somehow,
instantaneously and reflexively, picks out of the noise some scattered subpart
which is language-related. That’s the first step. Well, how is it doing that?
It’s not doing it by statistical analysis, because the ape can do roughly the
same probabilistic analysis. It’s looking for particular things. So
psycholinguists, neurolinguists, and others are trying to discover the
particular parts of the computational system and of the neurophysiology that
are somehow tuned to particular aspects of the environment. Well, it turns out
that there actually are neural circuits which are reacting to particular kinds
of rhythm, which happen to show up in language, like syllable length and so on.
And there’s some evidence that that’s one of the first things that the infant
brain is seeking -- rhythmic structures. And going back to Gallistel and Marr,
its got some computational system inside which is saying “okay, here’s what I
do with these things” and say, by nine months, the typical infant has rejected
-- eliminated from its repertoire -- the phonetic distinctions that aren’t used
in its own language. So initially of course, any infant is tuned to any
language. But say, a Japanese kid at nine months won’t react to the R-L
distinction anymore, that’s kind of weeded out. So the system seems to sort out
lots of possibilities and restrict it to just ones that are part of the
language, and there’s a narrow set of those. You can make up a non-language in
which the infant could never do it, and then you’re looking for other things.
For example, to get into a more abstract kind of language, there’s substantial
evidence by now that such a simple thing as linear order, what precedes what,
doesn’t enter into the syntactic and semantic computational systems, they’re
just not designed to look for linear order. So you find overwhelmingly that
more abstract notions of distance are computed and not linear distance, and you
can find some neurophysiological evidence for this, too. Like if artificial
languages are invented and taught to people, which use linear order, like you
negate a sentence by doing something to the third word. People can solve the
puzzle, but apparently the standard language areas of the brain are not
activated -- other areas are activated, so they’re treating it as a puzzle not
as a language problem. You need more work, but...
29.
You take that as convincing evidence that
activation or lack of activation for the brain area ...
30.
...It’s evidence, you’d want more of course. But
this is the kind of evidence, both on the linguistics side you look at how
languages work -- they don’t use things like third word in sentence. Take a
simple sentence like “Instinctively, Eagles that fly swim”, well, “instinctively”
goes with swim, it doesn’t go with fly, even though it doesn’t make sense. And
that’s reflexive. “Instinctively”, the adverb, isn’t looking for the nearest
verb, it’s looking for the structurally most prominent one. That’s a much
harder computation. But that’s the only computation which is ever used. Linear
order is a very easy computation, but it’s never used. There’s a ton of
evidence like this, and a little neurolinguistic evidence, but they point in
the same direction. And as you go to more complex structures, that’s where you
find more and more of that. That’s, in my view at least, the way to try to
discover how the system is actually working, just like in vision, in Marr’s
lab, people like Shimon Ullman discovered some pretty remarkable things like
the rigidity principle. You’re not going to find that by statistical analysis
of data. But he did find it by carefully designed experiments. Then you look
for the neurophysiology, and see if you can find something there that carries
out these computations. I think it’s the same in language, the same in studying
our arithmetical capacity, planning, almost anything you look at. Just trying
to deal with the unanalyzed chaotic data is unlikely to get you anywhere, just
like as it wouldn’t have gotten Galileo anywhere. In fact, if you go back to
this, in the 17th century, it wasn’t easy for people like Galileo and other
major scientists to convince the NSF [National Science Foundation] of the day
-- namely, the aristocrats -- that any of this made any sense. I mean, why
study balls rolling down frictionless planes, which don’t exist. Why not study
the growth of flowers? Well, if you tried to study the growth of flowers at
that time, you would get maybe a statistical analysis of what things looked
like. It’s worth remembering that with regard to cognitive science, we’re kind
of pre-Galilean, just beginning to open up the subject. And I think you can
learn something from the way science worked [back then]. In fact, one of the
founding experiments in history of chemistry, was about 1640 or so, when
somebody proved to the satisfaction of the scientific world, all the way up to
Newton, that water can be turned into living matter. The way they did it was --
of course, nobody knew anything about photosynthesis -- so what you do is you
take a pile of earth, you heat it so all the water escapes. You weigh it, and
put it in a branch of a willow tree, and pour water on it, and measure you the
amount of water you put in. When you’re done, you the willow tree is grown, you
again take the earth and heat it so all the water is gone -- same as before.
Therefore, you’ve shown that water can turn into an oak tree or something. It
is an experiment, it’s sort of right, but it’s just that you don’t know what
things you ought to be looking for. And they weren’t known until Priestly found
that air is a component of the world, it’s got nitrogen, and so on, and you
learn about photosynthesis and so on. Then you can redo the experiment and find
out what’s going on. But you can easily be misled by experiments that seem to
work because you don’t know enough about what to look for. And you can be
misled even more if you try to study the growth of trees by just taking a lot
of data about how trees growing, feeding it into a massive computer, doing some
statistics and getting an approximation of what happened.
31.
In the domain of biology, would you consider the
work of Mendel, as a successful case, where you take this noisy data --
essentially counts -- and you leap to postulate this theoretical object...
32.
...Well, throwing out a lot of the data that
didn’t work.
33.
...But seeing the ratio that made sense, given
the theory.
34.
Yeah, he did the right thing. He let the theory
guide the data. There was counter data which was more or less dismissed, you
know you don’t put it in your papers. And he was of course talking about things
that nobody could find, like you couldn’t find the units that he was
postulating. But that’s, sure, that’s the way science works. Same with
chemistry. Chemistry, until my childhood, not that long ago, was regarded as a
calculating device. Because you couldn’t reduce to physics. So it’s just some
way of calculating the result of experiments. The Bohr atom was treated that
way. It’s the way of calculating the results of experiments but it can’t be
real science, because you can’t reduce it to physics, which incidentally turned
out to be true, you couldn’t reduce it to physics because physics was wrong.
When quantum physics came along, you could unify it with virtually unchanged
chemistry. So the project of reduction was just the wrong project. The right
project was to see how these two ways of looking at the world could be unified.
And it turned out to be a surprise -- they were unified by radically changing
the underlying science. That could very well be the case with say, psychology
and neuroscience. I mean, neuroscience is nowhere near as advanced as physics
was a century ago.
35.
That would go against the reductionist approach
of looking for molecules that are correlates of...
36.
Yeah. In fact, the reductionist approach has
often been shown to be wrong. The unification approach makes sense. But
unification might not turn out to be reduction, because the core science might
be misconceived as in the physics-chemistry case and I suspect very likely in
the neuroscience-psychology case. If Gallistel is right, that would be a case
in point that yeah, they can be unified, but with a different approach to the
neurosciences.
37.
So is that a worthy goal of unification or the
fields should proceed in parallel?
38.
Well, unification is kind of an intuitive ideal,
part of the scientific mystique, if you like. It’s that you’re trying to find a
unified theory of the world. Now maybe there isn’t one, maybe different parts
work in different ways, but your assumption is until I’m proven wrong
definitively, I’ll assume that there’s a unified account of the world, and it’s
my task to try to find it. And the unification may not come out by reduction --
it often doesn’t. And that’s kind of the guiding logic of David Marr’s
approach: what you discover at the computational level ought to be unified with
what you’ll some day find out at the mechanism level, but maybe not in terms of
the way we now understand the mechanisms.
39.
And implicit in Marr it seems that you can’t work
on all three in parallel [computational, algorithmic, implementation levels],
it has to proceed top-down, which is a very stringent requirement, given that
science usually doesn’t work that way.
40.
Well, he wouldn’t have said it has to be rigid.
Like for example, discovering more about the mechanisms might lead you to
change your concept of computation. But there’s kind of a logical precedence,
which isn’t necessarily the research precedence, since in research everything
goes on at the same time. But I think that the rough picture is okay. Though I
should mention that Marr’s conception was designed for input systems...
41.
information-processing systems...
42.
Yeah, like vision. There’s some data out there
-- it’s a processing system -- and something goes on inside. It isn’t very well
designed for cognitive systems. Like take your capacity to take out
arithmetical operations..
43.
It’s very poor, but yeah...
44.
Okay [laughs]. But it’s an internal capacity,
you know your brain is a controlling unit of some kind of Turing machine, and
it has access to some external data, like memory, time and so on. And in
principle, you could multiply anything, but of course not in practice. If you
try to find out what that internal system is of yours, the Marr hierarchy doesn’t
really work very well. You can talk about the computational level -- maybe the
rules I have are Peano’s axioms [Editor’s note: a mathematical theory (named
after Italian mathematician Giuseppe Peano) that describes a core set of basic
rules of arithmetic and natural numbers, from which many useful facts about
arithmetic can be deduced], or something, whatever they are -- that’s the
computational level. In theory, though we don’t know how, you can talk about
the neurophysiological level, nobody knows how, but there’s no real algorithmic
level. Because there’s no calculation of knowledge, it’s just a system of
knowledge. To find out the nature of the system of knowledge, there is no
algorithm, because there is no process. Using the system of knowledge, that’ll
have a process, but that’s something different.
45.
But since we make mistakes, isn’t that evidence
of a process gone wrong?
46.
That’s the process of using the internal system.
But the internal system itself is not a process, because it doesn’t have an
algorithm. Take, say, ordinary mathematics. If you take Peano’s axioms and
rules of inference, they determine all arithmetical computations, but there’s
no algorithm. If you ask how does a number theoretician applies these, well all
kinds of ways. Maybe you don’t start with the axioms and start with the rules
of inference. You take the theorem, and see if I can establish a lemma, and if
it works, then see if I can try to ground this lemma in something, and finally
you get a proof which is a geometrical object.
47.
But that’s a fundamentally different activity
from me adding up small numbers in my head, which surely does have some kind of
algorithm.
48.
Not necessarily. There’s an algorithm for the
process in both cases. But there’s no algorithm for the system itself, it’s
kind of a category mistake. You don’t ask the question what’s the process
defined by Peano’s axioms and the rules of inference, there’s no process. There
can be a process of using them. And it could be a complicated process, and the
same is true of your calculating. The internal system that you have -- for
that, the question of process doesn’t arise. But for your using that internal
system, it arises, and you may carry out multiplications all kinds of ways.
Like maybe when you add 7 and 6, let’s say, one algorithm is to say “I’ll see
how much it takes to get to 10” -- it takes 3, and now I’ve got 4 left, so I
gotta go from 10 and add 4, I get 14. That’s an algorithm for adding -- it’s
actually one I was taught in kindergarten. That’s one way to add. But there are
other ways to add -- there’s no kind of right algorithm. These are algorithms
for carrying out the process the cognitive system that’s in your head. And for
that system, you don’t ask about algorithms. You can ask about the
computational level, you can ask about the mechanism level. But the algorithm
level doesn’t exist for that system. It’s the same with language. Language is
kind of like the arithmetical capacity. There’s some system in there that
determines the sound and meaning of an infinite array of possible sentences.
But there’s no question about what the algorithm is. Like there’s no question
about what a formal system of arithmetic tells you about proving theorems. The
use of the system is a process and you can study it in terms of Marr’s level.
But it’s important to be conceptually clear about these distinctions.
49.
It just seems like an astounding task to go from
a computational level theory, like Peano axioms, to Marr level 3 of the...
50.
mechanisms...
51.
...mechanisms and implementations...
52.
Oh yeah. Well..
53.
..without an algorithm at least.
54.
Well, I don’t think that’s true. Maybe
information about how it’s used, that’ll tell you something about the
mechanisms. But some higher intelligence -- maybe higher than ours -- would see
that there’s an internal system, its got a physiological basis, and I can study
the physiological basis of that internal system. Not even looking at the
process by which it’s used. Maybe looking at the process by which it’s used
maybe gives you helpful information about how to proceed. But it’s conceptually
a different problem. That’s the question of what’s the best way to study
something. So maybe the best way to study the relation between Peano’s axioms
and neurons is by watching mathematicians prove theorems. But that’s just
because it’ll give you information that may be helpful. The actual end result
of that will be an account of the system in the brain, the physiological basis
for it, with no reference to any algorithm. The algorithms are about a process
of using it, which may help you get answers. Maybe like incline planes tell you
something about the rate of fall, but if you take a look at Newton’s laws, they
don’t say anything about incline planes.
55.
Right. So the logic for studying cognitive and
language systems using this kind of Marr approach makes sense, but since you’ve
argued that language capacity is part of the genetic endowment, you could apply
it to other biological systems, like the immune system, the circulatory
system....
56.
Certainly, I think it’s very similar. You can
say the same thing about study of the immune system.
57.
It might even be simpler, in fact, to do it for
those systems than for cognition.
58.
Though you’d expect different answers. You can
do it for the digestive system. Suppose somebody’s studying the digestive
system. Well, they’re not going to study what happens when you have a stomach
flu, or when you’ve just eaten a big Mac, or something. Let’s go back to taking
pictures outside the window. One way of studying the digestive system is just
to take all data you can find about what digestive systems do under any
circumstances, toss the data into a computer, do statistical analysis -- you
get something. But it’s not gonna be what any biologist would do. They want to
abstract away, at the very beginning, from what are presumed -- maybe wrongly,
you can always be wrong -- irrelevant variables, like do you have stomach flu.
59.
But that’s precisely what the biologists are
doing, they are taking the sick people with the sick digestive system,
comparing them to the normals, and measuring these molecular properties.
60.
They’re doing it in an advanced stage. They
already understand a lot about the study of the digestive system before we
compare them, otherwise you wouldn’t know what to compare, and why is one sick
and one isn’t.
61.
Well, they’re relying on statistical analysis to
pick out the features that discriminate. It’s a highly fundable approach,
because you’re claiming to study sick people.
62.
It may be the way to fund things. Like maybe the
way to fund study of language is to say, maybe help cure autism. That’s a
different question [laughs]. But the logic of the search is to begin by
studying the system abstracted from what you, plausibly, take to be irrelevant
intrusions, see if you can find its basic nature -- then ask, well, what happens
when I bring in some of this other stuff, like stomach flu.
63.
It still seems like there’s a difficulty in
applying Marr’s levels to these kinds of systems. If you ask, what is the
computational problem that the brain is solving, we have kind of an answer, it’s
sort of like a computer. But if you ask, what is the computational problem that’s
being solved by the lung, that’s very difficult to even think -- it’s not
obviously an information-processing kind of problem.
64.
No, but there’s no reason to assume that all of
biology is computational. There may be reasons to assume that cognition is. And
in fact Gallistel is not saying that everything is in the body ought to be
studied by finding read/write/address units.
65.
It just seems contrary to any evolutionary intuition.
These systems evolved together, reusing many of the same parts, same molecules,
pathways. Cells are computing things.
66.
You don’t study the lung by asking what cells
compute. You study the immune system and the visual system, but you’re not
going to expect to find the same answers. An organism is a highly modular
system, has a lot of complex subsystems, which are more or less internally
integrated. They operate by different principles. The biology is highly
modular. You don’t assume it’s all just one big mess, all acting the same way.
67.
No, sure, but I’m saying you would apply the
same approach to study each of the modules.
68.
Not necessarily, not if the modules are
different. Some of the modules may be computational, others may not be.
69.
So what would you think would be an adequate
theory that is explanatory, rather than just predicting data, the statistical
way, what would be an adequate theory of these systems that are not computing
systems -- can we even understand them?
70.
Sure. You can understand a lot about say, what
makes an embryo turn into a chicken rather than a mouse, let’s say. It’s a very
intricate system, involves all kinds of chemical interactions, all sorts of
other things. Even the nematode, it’s by no means obviously -- in fact there
are reports from the study here -- that it’s all just a matter of a neural net.
You have to look into complex chemical interactions that take place in the
brain, in the nervous system. You have to look into each system on its own.
These chemical interactions might not be related to how your arithmetical
capacity works -- probably aren’t. But they might very well be related to
whether you decide to raise your arm or lower it.
71.
Though if you study the chemical interactions it
might lead you into what you’ve called just a redescription of the phenomena.
72.
Or an explanation. Because maybe that’s
directly, crucially, involved.
73.
But if you explain it in terms of chemical X has
to be turned on, or gene X has to be turned on, you’ve not really explained how
organism-determination is done. You’ve simply found a switch, and hit that
switch.
74.
But then you look further, and find out what
makes this gene do such and such under these circumstances, and do something
else under different circumstances.
75.
But if genes are the wrong level of abstraction,
you’d be screwed.
76.
Then you won’t get the right answer. And maybe
they’re not. For example, it’s notoriously difficult to account for how an
organism arises from a genome. There’s all kinds of production going on in the
cell. If you just look at gene action, you may not be in the right level of
abstraction. You never know, that’s what you try to study. I don’t think there’s
any algorithm for answering those questions, you try.
77.
So I want to shift gears more toward evolution.
You’ve criticized a very interesting position you’ve called “phylogenetic
empiricism.” You’ve criticized this position for not having explanatory power.
It simply states that: well, the mind is the way it because of adaptations to
the environment that were selected for. And these were selected for by natural
selection. You’ve argued that this doesn’t explain anything because you can
always appeal to these two principles of mutation and selection.
78.
Well you can wave your hands at them, but they
might be right. It could be that, say, the development of your arithmetical
capacity, arose from random mutation and selection. If it turned out to be
true, fine.
79.
It seems like a truism.
80.
Well, I mean, doesn’t mean it’s false. Truisms
are true. [laughs].
81.
But they don’t explain much.
82.
Maybe that’s the highest level of explanation
you can get. You can invent a world -- I don’t think it’s our world -- but you
can invent a world in which nothing happens except random changes in objects
and selection on the basis of external forces. I don’t think that’s the way our
world works, I don’t think it’s the way any biologist thinks it is. There are
all kind of ways in which natural law imposes channels within which selection
can take place, and some things can happen and other things don’t happen.
Plenty of things that go on in the biology in organisms aren’t like this. So
take the first step, meiosis. Why do cells split into spheres and not cubes? It’s
not random mutation and natural selection; it’s a law of physics. There’s no
reason to think that laws of physics stop there, they work all the way through.
83.
Well, they constrain the biology, sure.
84.
Okay, well then it’s not just random mutation
and selection. It’s random mutation, selection, and everything that matters,
like laws of physics.
85.
So is there room for these approaches which are
now labeled “comparative genomics”, like the Broad Institute here [at
MIT/Harvard] is generating massive amounts of data, of different genomes,
different animals, different cells under different conditions and sequencing
any molecule that is sequenceable. Is there anything that can be gleaned about
these high-level cognitive tasks from these comparative evolutionary studies or
is it premature?
86.
I am not saying it’s the wrong approach, but I
don’t know anything that can be drawn from it. Nor would you expect to.
87.
You don’t have any examples where this
evolutionary analysis has informed something? Like Foxp2 mutations? [Editor’s
note: A gene that is thought be implicated in speech or language capacities. A
family with a stereotyped speech disorder was found to have genetic mutations
that disrupt this gene. This gene evolved to have several mutations unique to
the human evolutionary lineage.]
88.
Foxp2 is kind of interesting, but it doesn’t
have anything to do with language. It has to do with fine motor coordinations
and things like that. Which takes place in the use of language, like when you
speak you control your lips and so on, but all that’s very peripheral to
language, and we know that. So for example, whether you use the articulatory
organs or sign, you know hand motions, it’s the same language. In fact, it’s
even being analyzed and produced in the same parts of the brain, even though
one of them is moving your hands and the other is moving your lips. So whatever
the externalization is, it seems quite peripheral. I think they’re too
complicated to talk about, but I think if you look closely at the design
features of language, you get evidence for that. There are interesting cases in
the study of language where you find conflicts between computational efficiency
and communicative efficiency. Take this case I even mentioned of linear order.
If you want to know which verb the adverb attaches to, the infant reflexively
using minimal structural distance, not minimal linear distance. Well, it’s
using minimal linear distances, computationally easy, but it requires having
linear order available. And if linear order is only a reflex of the
sensory-motor system, which makes sense, it won’t be available. That’s evidence
that the mapping of the internal system to the sensory-motor system is
peripheral to the workings of the computational system.
89.
But it might constrain it like physics
constrains meiosis?
90.
It might, but there’s very little evidence of
that. So for example the left end -- left in the sense of early -- of a
sentence has different properties from the right end. If you want to ask a
question, let’s say “Who did you see?” You put the “Who” infront, not in the
end. In fact, in every language in which a wh-phrase -- like who, or which
book, or something -- moves to somewhere else, it moves to the left, not to the
right. That’s very likely a processing constraint. The sentence opens by
telling you, the hearer, here’s what kind of a sentence it is. If it’s at the
end, you have to have the whole declarative sentence, and at the end you get
the information I’m asking about. If you spell it out, it could be a processing
constraint. So that’s a case, if true, in which the processing constraint,
externalization, do affect the computational character of the syntax and
semantics. There are cases where you find clear conflicts between computational
efficiency and communicative efficiency. Take a simple case, structural
ambiguity. If I say, “Visiting relatives can be a nuisance” -- that’s
ambiguous. Relatives that visit, or going to visit relatives. It turns out in
every such case that’s known, the ambiguity is derived by simply allowing the
rules to function freely, with no constraints, and that sometimes yields
ambiguities. So it’s computationally efficient, but it’s inefficient for
communication, because it leads to unresolvable ambiguity. Or take what are
called garden-path sentences, sentences like “The horse raced past the barn
fell”. People presented with that don’t understand it, because the way it’s
put, they’re led down a garden path. “The horse raced past the barn” sounds
like a sentence, and then you ask what’s “fell” doing there at the end. On the
other hand, if you think about it, it’s a perfectly well formed sentence. It
means the horse that was raced past the barn, by someone, fell. But the rules
of the language when they just function happen to give you a sentence which is
unintelligible because of the garden-path phenomena. And there are lots of
cases like that. There are things you just can’t say, for some reason. So if I
say, “The mechanics fixed the cars”. And you say, “They wondered if the
mechanics fixed the cars.” You can ask questions about the cars, “How many cars
did they wonder if the mechanics fixed?” More or less okay. Suppose you want to
ask a question about the mechanics. “How many mechanics did they wonder if
fixed the cars?” Somehow it doesn’t work, can’t say that. It’s a fine thought,
but you can’t say it. Well, if you look into it in detail, the most efficient
computational rules prevent you from saying it. But for expressing thought, for
communication, it’d be better if you could say it -- so that’s a conflict. And
in fact, every case of a conflict that’s known, computational efficiency wins.
The externalization is yielding all kinds of ambiguities but for simple
computational reasons, it seems that the system internally is just computing
efficiently, it doesn’t care about the externalization. Well, I haven’t made
that a very plausible, but if you spell it out it can be made quite a
convincing argument I think. That tells something about evolution. What it
strongly suggests is that in the evolution of language, a computational system
developed, and later on it was externalized. And if you think about how a
language might have evolved, you’re almost driven to that position. At some
point in human evolution, and it’s apparently pretty recent given the
archeological record -- maybe last hundred thousand years, which is nothing --
at some point a computational system emerged with had new properties, that
other organisms don’t have, that has kind of arithmetical type properties...
91.
It enabled better thought before
externalization?
92.
It gives you thought. Some rewiring of the
brain, that happens in a single person, not in a group. So that person had the
capacity for thought -- the group didn’t. So there isn’t any point in
externalization. Later on, if this genetic change proliferates, maybe a lot of
people have it, okay then there’s a point in figuring out a way to map it to
the sensory-motor system and that’s externalization but it’s a secondary
process.
93.
Unless the externalization and the internal
thought system are coupled in ways we just don’t predict.
94.
We don’t predict, and they don’t make a lot of
sense. Why should it be connected to the external system? In fact, say your
arithmetical capacity isn’t. And there are other animals, like songbirds, which
have internal computational systems, bird song. It’s not the same system but it’s
some kind of internal computational system. And it is externalized, but
sometimes it’s not. A chick in some species acquires the song of that species
but doesn’t produce it until maturity. During that early period it has the
song, but it doesn’t have the externalization system. Actually that’s true of
humans too, like a human infant understands a lot more than it can produce --
plenty of experimental evidence for this, meaning it’s got the internal system
somehow, but it can’t externalize it. Maybe it doesn’t have enough memory, or
whatever it may be.
95.
I’d like to close with one question about the
philosophy of science. In a recent interview, you said that part of the problem
is that scientists don’t think enough about what they’re up to. You mentioned
that you taught a philosophy of science course at MIT and people would read,
say, Willard van Orman Quine, and it would go in one ear out the other, and
people would go back doing the same kind of science that they were doing. What
are the insights that have been obtained in philosophy of science that are most
relevant to scientists who are trying to let’s say, explain biology, and give
an explanatory theory rather than redescription of the phenomena? What do you
expect from such a theory, and what are the insights that help guide science in
that way? Rather than guiding it towards behaviorism which seems to be an
intuition that many, say, neuroscientists have?
96.
Philosophy of science is a very interesting
field, but I don’t think it really contribute to science, it learns from
science. It tries to understand what the sciences do, why do they achieve
things, what are the wrong paths, see if we can codify that and come to
understand. What I think is valuable is the history of science. I think we
learn a lot of things from the history of science that can be very valuable to
the emerging sciences. Particularly when we realize that in say, the emerging
cognitive sciences, we really are in a kind of pre-Galilean stage. We don’t
know what we’re looking for anymore than Galileo did, and there’s a lot to
learn from that. So for example one striking fact about early science, not just
Galileo, but the Galilean breakthrough, was the recognition that simple things
are puzzling. Take say, if I’m holding this here [cup of water], and say the
water is boiling [putting hand over water], the steam will rise, but if I take
my hand away the cup will fall. Well why does the cup fall and the steam rise?
Well for millennia there was a satisfactory answer to that: they’re seeking
their natural place.
97.
Like in Aristotelian physics?
98.
That’s the Aristotelian physics. The best and
greatest scientists thought that was answer. Galileo allowed himself to be
puzzled by it. As soon as you allow yourself to be puzzled by it, you
immediately find that all your intuitions are wrong. Like the fall of a big mass
and a small mass, and so on. All your intuitions are wrong -- there are puzzles
everywhere you look. That’s something to learn from the history of science.
Take the one example that I gave to you, “Instinctively eagles that fly swim.”
Nobody ever thought that was puzzling -- yeah, why not. But if you think about
it, it’s very puzzling, you’re using a complex computation instead of a simple
one. Well, if you allow yourself to be puzzled by that, like the fall of a cup,
you ask “Why?” and then you’re led down a path to some pretty interesting
answers. Like maybe linear order just isn’t part of the computational system,
which is a strong claim about the architecture of the mind -- it says it’s just
part of the externalization system, secondary, you know. And that opens up all
sorts of other paths, same with everything else. Take another case: the
difference between reduction and unification. History of science gives some
very interesting illustrations of that, like chemistry and physics, and I think
they’re quite relevant to the state of the cognitive and neurosciences today.
