The Art of Doing Science and Engineering: Learning to Learn

by Richard Hamming

Education is what, when, and why to do things. Training is how to do it.

In science, if you know what you are doing, you should not be doing it. In engineering, if you do not know what you are doing, you should not be doing it.

No vision, not much of a future.

In forming your plan for your future you need to distinguish three different questions: What is possible? What is likely to happen? What is desirable to have happen?

Notice I leave it to you to pick your goals of excellence, but claim only a life without such a goal is not really living but merely existing—in my opinion. In ancient Greece, Socrates (469–399 bc) said: The unexamined life is not worth living.

It has rarely proved practical to produce exactly the same product by machines as we produced by hand.

now the door is open and the path made easy. Please remember, the inventor often has a very limited view of what he invented, and some others (you?) can see much more. But also remember this when you are the author of some brilliant new thing;

While each has some merit I have faith in only one, which is almost never mentioned—think before you write the program, it might be called. Before you start, think carefully about the whole thing, including what will be your acceptance test that it is right, as well as how later field maintenance will be done. Getting it right the first time is much better than fixing it up later!

From this I long ago concluded the best policy is to pay your good programmers very well but regularly fire the poorer ones—if you can get away with it!

In practice you may actually be better off to pay the worst to stay home and not get in the way of the more capable (and I am serious)!

You should always feel some excitement when you give a talk, since even the best actors and actresses usually have some stage fright. Your excitement tends to be communicated to the audience, and if you seem to be perfectly relaxed, then the audience also relaxes and may fall asleep!

routine, and, in the long run, more useful tasks. Any innovation is always against such a barrier, so do not get discouraged when you find your new idea is stoutly, and perhaps foolishly, resisted.

In such a rapidly changing field as computer software, if the payoff is not in the near future then it is doubtful it will ever pay off.

Can machines think?

“To what extent can machines think?”

“Which of all the things humans do can machines also do?,”

“Of all of life’s burdens, which are those machines can relieve, or significantly ease, for us?”

Perhaps “thinking” is not a yes/no thing, but maybe it is a matter of degree.

I must again digress, this time to point out why game playing has such a prominent role in ai research.

“The program learned from experience”?

decisions (hardly a mere branching of a program, though branch points are often called decision points to make the programmers feel more important),

You must struggle with your own beliefs if you are to make any progress in understanding the possibilities and limitations of computers in the intellectual area.

Perhaps that is all thinking really is! Perhaps it is not a separate thing, it is just an artifact of largeness. One cannot flatly deny this, as we have to admit we do not know what thinking really is.

But it is clear that on the average it is the lower-level jobs which are disappearing and the higher-level jobs which are appearing. Again, one would like to believe most people can be trained in the future to do the higher-level jobs—but that is a hope without any real evidence.

(to err is human).

There is an old claim that “free will” is a myth; in a given circumstance, you being you as you are at the moment, you can only do as you do.

As another example of the tacit belief in the lack of free will in others, consider that when there is a high rate of crime in some neighborhood of a city, many people believe the way to cure it is to change the environment—hence the people will have to change and the crime rate will go down!

Finally, perhaps thinking should be measured not by what you do but how you do it. When I watch a child learning how to multiply two, say, three-digit numbers, then I have the feeling the child is thinking; when I do the same multiplication I feel I am more doing “conditioned responses”; when a computer does the same multiplication I do not feel the machine is thinking at all. In the words of the old song, “It ain’t what you do, it’s the way that you do it.”

Two of the main sticky points are: (1) if a machine does it, then it must be an algorithm and cannot be thinking, and (2) on the other hand, how do we escape the molecule banging against molecule we apparently are—by what forces do our thinking, our self-awareness, and our self-consciousness affect the paths of the molecules?

Notice first this essential step happened only because there was a great deal of emotional stress on me at the moment, and this is characteristic of most great discoveries. Working calmly will let you elaborate and extend things, but the breakthroughs generally come only after great frustration and emotional involvement.

“Luck favors the prepared mind.”

It pays to know more than just what is needed at the moment!

Of course, as you go through life you do not know what you are preparing yourself for—only you want to do significant things and not spend the whole of your life being a “janitor of science,” or whatever your profession is. Of course luck plays a prominent role. But so far as I can see, life presents you with many, many opportunities for doing great things (define them as you will) and the prepared person usually hits one or more successes, and the unprepared person will miss almost every time.

I have both told and shown you how to be great; now you have no excuse for not doing so!

First, what is “information”? Shannon identified information with surprise. He chose the negative of the log of the probability of an event as the amount of information you get when the event of probability p happens. For example, if I tell you it is smoggy in Los Angeles, then p is near 1 and that is not much information, but if I tell you it is raining in Monterey in June, then that is surprising and represents more information. Because log 1 = 0, the certain event contains no information.

There is the famous story by Eddington about some people who went fishing in the sea with a net. Upon examining the size of the fish they had caught, they decided there was a minimum size to the fish in the sea! Their conclusion arose from the tool used and not from reality.

Get down to the basics every time!

Moral: when you know something cannot be done, also remember the essential reason why, so later, when the circumstances have changed, you will not say, “It can’t be done.”

When you decide something is not possible, don’t say at a later date it is still impossible without first reviewing all the details of why you originally were right in saying it couldn’t be done.

The experts were told something in class when they were students first learning things, and at the time they did not question it. It becomes an accepted fact, which they repeat and never really examine to see if what they are saying is true or not, especially in their current situation.

Moral: to the extent you can choose, work on problems you think will be important.

More than most people want to believe, what we see depends on how we approach the problem! Too often we see what we want to see, and therefore you need to consciously adopt a scientific attitude of doubting your own beliefs.

In closing, if you do not, now and then, doubt accepted rules, it is unlikely you will be a leader into new areas; if you doubt too much you will be paralyzed and will do nothing.

I strongly advise, when possible, to start with the simple simulation and evolve it to a more complete, more accurate, simulation later so the insights can arise early.

Jargon is both a necessity and a curse. You should realize you need to be active intellectually to gain the advantages of the jargon and to avoid the pitfalls, even in your own area of expertise!

In the insurance business the company is betting you will live a long time and you are betting you will die young.

Thus beware of any simulation of a situation which allows the human to use the output to alter their behavior patterns for their own benefit, since they will do so whenever they can.

You are responsible for your decisions, and you cannot blame them on those who do the simulations, much as you wish you could. Reliability is a central question with no easy answers.

“garbage in, garbage out,”

favorite trick of people in charge who do not want to bear the responsibility of their position. “Get more data,” they say.