[Paleopsych] Edge: Marvin Minsky: The Emotion Universe (2002)

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Marvin Minsky: The Emotion Universe
http://www.edge.org/3rd_culture/minsky02/minsky02_print.html
2.7.11

    To say that the universe exists is silly, because it says that the
    universe is one of the things in the universe. So there's something
    wrong with questions like, "What caused the Universe to exist?

    MARVIN MINSKY, mathematician and computer scientist, is considered one
    of the fathers of Artificial Intelligence. He is Toshiba Professor of
    Media Arts and Sciences at the Massachusetts Institute of Technology;
    cofounder of MIT's Artificial Intelligence Laboratory; and the author
    of eight books, including The Society of Mind.

    [14]Marvin Minsky's Edge Bio Page
   14. http://www.edge.org/3rd_culture/bios/minsky.html
    _________________________________________________________________

    THE EMOTION UNIVERSE

    MARVIN MINSKY: I was listening to this group talking about universes,
    and it seems to me there's one possibility that's so simple that
    people don't discuss it. Certainly a question that occurs in all
    religions is, "Who created the universe, and why? And what's it for?"
    But something is wrong with such questions because they make extra
    hypotheses that don't make sense. When you say that X exists, you're
    saying that X is in the Universe. It's all right to say, "this glass
    of water exists" because that's the same as "This glass is in the
    Universe." But to say that the universe exists is silly, because it
    says that the universe is one of the things in the universe. So
    there's something wrong with questions like, "What caused the Universe
    to exist?"

    The only way I can see to make sense of this is to adopt the famous
    "many-worlds theory" which says that there are many "possible
    universes" and that there is nothing distinguished or unique about the
    one that we are in - except that it is the one we are in. In other
    words, there's no need to think that our world 'exists'; instead,
    think of it as like a computer game, and consider the following
    sequence of 'Theories of It":

    (1) Imagine that somewhere there is a computer that simulates a
    certain World, in which some simulated people evolve. Eventually, when
    these become smart, one of those persons asks the others, "What caused
    this particular World to exist, and why are we in it?" But of course
    that World doesn't 'really exist' because it is only a simulation.

    (2) Then it might occur to one of those people that, perhaps, they are
    part of a simulation. Then that person might go on to ask, "Who wrote
    the Program that simulates us, and who made the Computer that runs
    that Program?"

    (3) But then someone else could argue that, "Perhaps there is no
    Computer at all. Only the Program needs to exist - because once that
    Program is written, then this will determine everything that will
    happen in that simulation. After all, once the computer and program
    have been described (along with some set of initial conditions) this
    will explain the entire World, including all its inhabitants, and
    everything that will happen to them. So the only real question is what
    is that program and who wrote it, and why"

    (4) Finally another one of those 'people' observes, "No one needs to
    write it at all! It is just one of 'all possible computations!' No one
    has to write it down. No one even has to think of it! So long as it is
    'possible in principle,' then people in that Universe will think and
    believe that they exist!'

    So we have to conclude that it doesn't make sense to ask about why
    this world exists. However, there still remain other good questions to
    ask, about how this particular Universe works. For example, we know a
    lot about ourselves - in particular, about how we evolved - and we can
    see that, for this to occur, the 'program' that produced us must have
    certain kinds of properties. For example, there cannot be structures
    that evolve (that is, in the Darwinian way) unless there can be some
    structures that can make mutated copies of themselves; this means that
    some things must be stable enough to have some persistent properties.
    Something like molecules that last long enough, etc.

    So this, in turn, tells us something about Physics: a universe that
    has people like us must obey some conservation-like laws; otherwise
    nothing would last long enough to support a process of evolution. We
    couldn't 'exist' in a universe in which things are too frequently
    vanishing, blowing up, or being created in too many places. In other
    words, we couldn't exist in a universe that has the wrong kinds of
    laws. (To be sure, this leaves some disturbing questions about worlds
    that have no laws at all. This is related to what is sometimes called
    the Anthropic Principle." That's the idea that the only worlds in
    which physicists can ask about what created the universe are the
    worlds that can support such physicists.)

    The Certainty Principle

    In older times, when physicists tried to explain Quantum Theory, to
    the public what they call the uncertainty principle, they'd say that
    the world isn't the way Newton described it; instead it. They
    emphasized 'uncertainty' - that everything is probabilistic and
    indeterminate. However, they rarely mentioned the fact that it's
    really just the opposite: it is only because of quantization that we
    can depend on anything! For example in classical Newtonian physics,
    complex systems can't be stable for long. Jerry Sussman and John
    Wisdom once simulated our Solar System, and showed that the large
    outer planets would stable for billions of years. But they did not
    simulate the inner planets - so we have no assurance that our planet
    is stable. It might be that enough of the energy of the big planets
    might be transferred to throw our Earth out into space. (They did show
    that the orbit of Pluto must be chaotic.)

    Yes, quantum theory shows that things are uncertain: if you have a DNA
    molecule there's a possibility that one of its carbon atoms will
    suddenly tunnel out and appear in Arcturus. However, at room
    temperature a molecule of DNA is almost certain to stay in its place
    for billions of years, - because of quantum mechanics - and that is
    one of the reasons that evolution is possible! For quantum mechanics
    is the reason why most things don't usually jump around! So this
    suggests that we should take the anthropic principle seriously, by
    asking. "Which possible universes could have things that are stable
    enough to support our kind of evolution?" Apparently, the first cells
    appeared quickly after the earth got cool enough; I've heard estimate
    that this took less than a hundred million years. But then it took
    another three billion years to get to the kinds of cells that could
    evolve into animals and plants. This could only happen in possible
    worlds whose laws support stability. It could not happen in a
    Newtonian Universe. So this is why the world that we're in needs
    something like quantum mechanics - to keep things in place! (I
    discussed this "Certainty Principle" in my chapter in the book Feynman
    and Computation, A.J.G. Hey, editor, Perseus Books, 1999.)

    Intelligence

    Why don't we yet have good theories about what our minds are and how
    they work? In my view this is because we're only now beginning to have
    the concepts that we'll need for this. The brain is a very complex
    machine, far more advanced that today's computers, yet it was not
    until the 1950s that we began to acquire such simple ideas about (for
    example) memory - such as the concepts of data structures, cache
    memories, priority interrupt systems, and such representations of
    knowledge as 'semantic networks.' Computer science now has many
    hundreds of such concepts that were simply not available before the
    1960s.

    Psychology itself did not much develop before the twentieth century. A
    few thinkers like Aristotle had good ideas about psychology, but
    progress thereafter was slow; it seems to me that Aristotle's
    suggestions in the Rhetoric were about as good as those of other
    thinkers until around 1870. Then came the era of Galton, Wundt,
    William James and Freud - and we saw the first steps toward ideas
    about how minds work. But still, in my view, there was little more
    progress until the Cybernetics of the '40s, the Artificial
    Intelligence of the '50s and '60s, and the Cognitive Psychology that
    started to grow in the '70s and 80s.

    Why did psychology lag so far behind so many other sciences? In the
    late 1930s a botanist named Jean Piaget in Switzerland started to
    observe the behavior of his children. In the next ten years of
    watching these kids grow up he wrote down hundreds of little theories
    about the processes going on in their brains, and wrote about 20
    books, all based on observing three children carefully. Although some
    researchers still nitpick about his conclusions, the general structure
    seems to have held up, and many of the developments he described seem
    to happen at about the same rate and the same ages in all the cultures
    that have been studied. The question isn't, "Was Piaget right or
    wrong?" but "Why wasn't there someone like Piaget 2000 years ago?"
    What was it about all previous cultures that no one thought to observe
    children and try to figure out how they worked? It certainly was not
    from lack of technology: Piaget didn't need cyclotrons, but only
    glasses of water and pieces of candy.

    Perhaps psychology lagged behind because it tried to imitate the more
    successful sciences. For example, in the early 20th century there were
    many attempts to make mathematical theories about psychological
    subjects - notable learning and pattern recognition. But there's a
    problem with mathematics. It works well for Physics, I think because
    fundamental physics has very few laws - and the kinds of mathematics
    that developed in the years before computers were good at describing
    systems based on just a few - say, 4, 5, or 6 laws - but doesn't work
    well for systems based on the order of a dozen laws. The physicist
    like Newton and Maxwell discovered ways to account for large classes
    of phenomena based on three or four laws; however, with 20
    assumptions, mathematical reasoning becomes impractical. The beautiful
    subject called Theory of Groups begins with only five assumptions -
    yet this leads to systems so complex that people have spent their
    lifetimes on them. Similarly, you can write a computer program with
    just a few lines of code that no one can thoroughly understand;
    however, at least we can run the computer to see how it behaves - and
    sometimes see enough then to make a good theory.

    However, there's more to computer science than that. Many people think
    of computer science as the science of what computers do, but I think
    of it quite differently: Computer Science is a new way collection of
    ways to describe and think about complicated systems. It comes with a
    huge library of new, useful concepts about how mental processes might
    work. For example, most of the ancient theories of memory envisioned
    knowledge like facts in a box. Later theories began to distinguish
    ideas about short and long-term memories, and conjectured that skills
    are stored in other ways.

    However, Computer Science suggests dozens of plausible ways to store
    knowledge away - as items in a database, or sets of "if-then" reaction
    rules, or in the forms of semantic networks (in which little fragments
    of information are connected by links that themselves have
    properties), or program-like procedural scripts, or neural networks,
    etc. You can store things in what are called neural networks - which
    are wonderful for learning certain things, but almost useless for
    other kinds of knowledge, because few higher-level processes can
    'reflect' on what's inside a neural network. This means that the rest
    of the brain cannot think and reason about what it's learned - that
    is, what was learned in that particular way. In artificial
    intelligence, we have learned many tricks that make programs faster -
    but in the long run lead to limitations because the results neural
    network type learning are too 'opaque' for other programs to
    understand.

    Yet even today, most brain scientists do not seem to know, for
    example, about cache-memory. If you buy a computer today you'll be
    told that it has a big memory on its slow hard disk, but it also has a
    much faster memory called cache, which remembers the last few things
    it did in case it needs them again, so it doesn't have to go and look
    somewhere else for them. And modern machines each use several such
    schemes - but I've not heard anyone talk about the hippocampus that
    way. All this suggests that brain scientists have been too
    conservative; they've not made enough hypotheses - and therefore, most
    experiments have been trying to distinguish between wrong
    alternatives.

    Reinforcement vs. Credit assignment.

    There have been several projects that were aimed toward making some
    sort of "Baby Machine" that would learn and develop by itself - to
    eventually become intelligent. However, all such projects, so far,
    have only progressed to a certain point, and then became weaker or
    even deteriorated. One problem has been finding adequate ways to
    represent the knowledge that they were acquiring. Another problem was
    not have good schemes for what we sometimes call 'credit assignment' -
    that us, how do you learning things that are relevant, that are
    essentials rather than accidents. For example, suppose that you find a
    new way to handle a screwdriver so that the screw remains in line and
    doesn't fall out. What is it that you learn? It certainly won't
    suffice merely to learn the exact sequence of motions (because the
    spatial relations will be different next time) - so you have to learn
    at some higher level of representation. How do you make the right
    abstractions? Also, when some experiment works, and you've done ten
    different things in that path toward success, which of those should
    you remember, and how should you represent them? How do you figure out
    which parts of your activity were relevant? Older psychology theories
    used the simple idea of 'reinforcing' what you did most recently. But
    that doesn't seem to work so well as the problems at hand get more
    complex. Clearly, one has to reinforce plans and not actions - which
    means that good Credit-Assignment has to involve some thinking about
    the things that you've done. But still, no one has designed and
    debugged a good architecture for doing such things.

    We need better programming languages and architectures.

    I find it strange how little progress we've seen in the design of
    problem solving programs - or languages for describing them, or
    machines for implementing those designs. The first experiments to get
    programs to simulate human problem-solving started in the early 1950s,
    just before computers became available to the general public; for
    example, the work of Newell, Simon, and Shaw using the early machine
    designed by John von Neumann's group. To do this, they developed the
    list-processing language IPL. Around 1960, John McCarthy developed a
    higher-level language LISP, which made it easier to do such things;
    now one could write programs that could modify themselves in real
    time. Unfortunately, the rest of the programming community did not
    recognize the importance of this, so the world is now dominated by
    clumsy languages like Fortran, C, and their successors - which
    describe programs that cannot change themselves. Modern operating
    systems suffered the same fate, so we see the industry turning to the
    35-year-old system called Unix, a fossil retrieved from the ancient
    past because its competitors became so filled with stuff that no one
    cold understand and modify them. So now we're starting over again,
    most likely to make the same mistakes again. What's wrong with the
    computing community?

    Expertise vs. Common Sense

    In the early days of artificial intelligence, we wrote programs to do
    things that were very advanced. One of the first such programs was
    able to prove theorems in Euclidean geometry. This was easy because
    geometry depends only upon a few assumptions: Two points determine a
    unique line. If there are two lines then they are either parallel or
    they intersect min just one place. Or, two triangles are the same in
    all respects if the two sides and the angle between them are
    equivalent. This is a wonderful subject because you're in a world
    where assumptions are very simple, there are only a small number of
    them, and you use a logic that is very clear. It's a beautiful place,
    and you can discover wonderful things there.

    However, I think that, in retrospect, it may have been a mistake to do
    so much work on task that were so 'advanced.' The result was that -
    until today - no one paid much attention to the kinds of problems that
    any child can solve. That geometry program did about as well as a
    superior high school student could do. Then one of our graduate
    students wrote a program that solved symbolic problems in integral
    calculus. Jim Slagle's program did this well enough to get a grade of
    A in MIT's first-year calculus course. (However, it could only solve
    symbolic problems, and not the kinds that were expressed in words.
    Eventually, the descendants of that program evolved to be better than
    any human in the world, and this led to the successful commercial
    mathematical assistant programs called MACSYMA and Mathematica. It's
    an exciting story - but those programs could still not solve "word
    problems." However in the mid 1960s, graduate student Daniel Bobrow
    wrote a program that could solve problems like "Bill's father's uncle
    is twice as old as Bill's father. 2 years from now Bill's father will
    be three times as old as Bill. The sum of their ages is 92. Find
    Bill's age." Most high school students have considerable trouble with
    that. Bobrow's program was able to take convert those English
    sentences into linear equations, and then solve those equations - but
    it could not do anything at all with sentences that had other kinds of
    meanings. We tried to improve that kind of program, but this did not
    lead to anything good because those programs did not know enough about
    how people use commonsense language.

    By 1980 we had thousands of programs, each good at solving some
    specialized problems - but none of those program that could do the
    kinds of things that a typical five-year-old can do. A five-year-old
    can beat you in an argument if you're wrong enough and the kid is
    right enough. To make a long story short, we've regressed from
    calculus and geometry and high school algebra and so forth. Now, only
    in the past few years have a few researchers in AI started to work on
    the kinds of common sense problems that every normal child can solve.
    But although there are perhaps a hundred thousand people writing
    expert specialized programs, I've found only about a dozen people in
    the world who aim toward finding ways to make programs deal with the
    kinds of everyday, commonsense jobs of the sort that almost every
    child can do.