Marvin Minsky Quotes

Perhaps it is no accident that one meaning of the word express is "to squeeze"—for when you try to "express yourself," your language resources will have to pick and choose among the descriptions your other resources construct—and then attempt to squeeze a few of these through your tiny channels of phrases and gestures.

The "laws of thought" depend not only on the property of brain cells, but also on how they are connected. And these connections are established not by the basic, "general" laws of physics... To be sure, "general" laws apply to everything. But, for that very reason, they can rarely explain anything in particular. ...Each higher level of description must add to our knowledge about lower levels.

How many processes are going on, to keep that teacup level in your grasp? There must be a hundred of them.

If explaining minds seems harder than explaining songs, we should remember that sometimes enlarging problems makes them simpler! The theory of the roots of equations seemed hard for centuries within its little world of real numbers, but it suddenly seemed simple once Gauss exposed the larger world of so-called complex numbers. Similarly, music should make more sense once seen through listeners' minds.

We could extend them either by scaling up small connection­ist models or by combining small-scale networks into some larger organization. In the first case, we would expect to encounter theoretical obstacles to maintaining [generalized delta rule]’s effectiveness on larger, deeper nets. And despite the reputed efficacy of other alleged remedies for the deficiencies of hill-climbing, such as “annealing,” we stay with our research conjecture that no such procedures will work very well on large-scale nets, except in the case of problems that turn out to be of low order in some appropriate sense. The second alternative is to employ a variety of smaller networks rather than try to scale up a single one... No single-method learning scheme can operate efficiently for every possible task; we cannot expect any one type of machine to account for any large portion of human psychology.

When David Marr at MIT moved into computer vision, he generated a lot of excitement, but he hit up against the problem of knowledge representation; he had no good representations for knowledge in his vision systems.

More concretely, we would call the student's atten­tion to the following considerations: 1. Multilayer machines with loops clearly open all the questions of the general theory of automata. 2. A system with no loops but with an order restriction at each layer can compute only predicates of finite order. 3. On the other hand, if there is no restriction except for the absence of loops, the monster of vacuous generality once more raises its head. The perceptron has shown itself worthy of study despite (and even because of!) its severe limitations. It has many features to attract attention: its linearity; its intriguing learning theorem; its clear paradigmatic simplicity as a kind of parallel computation. There is no reason to suppose that any of these virtues carry over to the many-layered version. Nevertheless, we consider it to be an important research problem to elucidate (or reject) our intuitive judgment that the extension is sterile. Per­haps some powerful convergence theorem will be discovered, or some profound reason for the failure to produce an interesting “learning theorem” for the multilayered machine will be found.

Our eyes are always flashing sudden flicks of different pictures to our brains, yet none of that saccadic action leads to any sense of change or motion in the world; each thing reposes calmly in its "place"! ...What makes us such innate Copernicans?

What is the difference between merely knowing (or remembering, or memorizing) and understanding? ...A thing or idea seems meaningful only when we have several different ways to represent it — different perspectives and different associations. ...Then we can turn it around in our minds, so to speak: however it seems at the moment, we can see it another way and we never come to a full stop. In other words, we can 'think' about it. If there were only one way to represent this thing or idea, we would not call this representation thinking.

Of what use is musical knowledge? Here is one idea. Each child spends endless days in curious ways; we call this play. A child stacks and packs all kinds of blocks and boxes, lines them up, and knocks them down. … Clearly, the child is learning about space! ...how on earth does one learn about time? Can one time fit inside another? Can two of them go side by side? In music, we find out!

One popular version is that the publication of our book so discouraged research on learning in network machines that a promising line of research was interrupted. Our version is that progress had already come to a virtual halt because of the lack of adequate basic theories... Most theorists had tried to focus only on the mathematical structure of what was common to all learning, and the theories to which this had led were too general and too weak to explain which patterns perceptrons could learn to recognize... The trouble appeared when perceptrons had no way to represent the knowledge required for solving certain problems. The moral was that one simply cannot learn enough by studying learning by itself; one also has to understand the nature of what one wants to learn.

This book... too, is a society — of many small ideas. Each by itself is only common sense, yet when we join enough of them we explain the strangest mysteries of mind.

We rarely recognize how wonderful it is that a person can traverse an entire lifetime without making a single really serious mistake — like putting a fork in one's eye or using a window instead of a door.

One's present personality cannot share all the thoughts of one's older personalities — and yet it has some sense that they exist. This is one reason why we feel that we possess an inner Self — a sort of ever-present person-friend, inside the mind, whom we can always ask for help.

I had the naive idea that if one could build a big enough network, with enough memory loops, it might get lucky and acquire the ability to envision things in its head. This became a field of study later. It was called self-organizing random networks. Even today, I still get letters from young students who say, 'Why are you people trying to program intelligence? Why don't you try to find a way to build a nervous system that will just spontaneously create it?' Finally, I decided that either this was a bad idea or it would take thousands or millions of neurons to make it work, and I couldn't afford to try to build a machine like that.