Marvin Minsky Quotes

An ethicist is someone who sees something wrong with whatever you have in mind.

For avoiding nonsense in general, we might accumulate millions of censors. For all we know, this "negative meta-knowledge" — about patterns of thought and inference that have been found defective or harmful — may be a large portion of all we know.

Most adults have some childlike fascination for making and arranging larger structures out of smaller ones.

Computer languages of the future will be more concerned with goals and less with procedures specified by the programmer.

You don't understand anything until you learn it more than one way.

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?

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.

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.

The way the mathematics game is played, most variations lie outside the rules, while music can insist on perfect canon or tolerate a casual accompaniment.

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.

Most of our future attempts to build large, growing Artificial Intelligences will be subject to all sorts of mental disorders.

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.

When no idea seems right, the right one must seem wrong.

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

I cannot articulate enough to express my dislike to people who think that understanding spoils your experience… How would they know?