transcript

I'm currently a second year graduate student in computer science and I found this talk to be very interesting in how it changes my view of research and made notes. Here is a summary of his most important points, but I recommend going to watch the actual video.

Summary

“The particular thing you do is luck, but that you do something is not.” - Hamming

• be prepared and you will eventually find important work to do.
• work hard, if you worked as hard as great scientist do, you would get similar results. Assuming effort is applied correctly.
• have courage, have confidence
• do not be afraid to work on small problems instead of great problems. The Nobel Prize Effect may be attributed to how, “When you are famous it is hard to work on small problems.”
• YOLO - you have one life, what do you want to dedicate it to? Working on great science? Fighting the system?
• working conditions are not important. What is important is that you can turn these problems, defects into something that works for you. ideal working conditions are very strange. The ones you want aren’t always the best ones for you.
• work: productivity and knowledge → compound interest.
• be emotionally committed to producing good work. Let your subconscious work for you.
• write down every piece of info that contradicts your theories. Keep track of the flaws so you can improve your theories.
• important work: problems for which you have a reasonable ’attack’ for. Problems which are significant (Nobel prize worthy) that people will invest money in.
• reflect: what is important work. What is the impact of X on science. What is the role of Y, How will X change science… How things are, how they should be.
• Pursue opportunities, drop everything else. Inevitably you will neglect some parts of your life.
• be open minded, keep in touch with current research. This is more important than working hard without distraction. (Keep your door open) Talk to people. Brainstorm with others.
• Make work that is reproducible that other people can work on. Don’t make it so people have to duplicate what you did. It should be something they can build off on.
• Lessons from Math: we make problems generalizable and apply existing solutions. **Abst

Hey everyone. I read this transcript from time to time when I need it. Just wanted to share it with you all in case you haven't seen it.

If you don't have time to read through the whole thing I have the most salient points (to me) TLDR style on my blog.

Hope you find it helpful.

Hi everyone,

I am a rising junior majoring in computer science and recently decide to switch to math. I recently read “You and Your Research” written by Richard Hamming and was inspired by many points he stated in his speech. It will help you a lot even if he talked about only scientific research in general.

However, I think I still have some questions and appreciate any inputs from fellow mathematicians, especially Ph.D. students in grad school:

1. He talked about importance of working hard. I tried to study new math materials outside classroom, and when I did so, I usually would worry that I would not complete other homework, such as programming assignments, so at the end of semester, I did not achieve a lot. So I wonder that how productive people allocate their time on different things?
2. Talking to other people and communicating with people from different fields is also important. I was a relatively shy and introvert student, even if the situation improves in recent years, I usually still do not talk to other students in class (most people do not do so as well, I am not sure if it’s something in my school since it’s a state school in Utah). Is it only a thing in my school, or it happened to some of you as well?

I think that is all the questions I could think of now. Thanks for any help!

> Most mathematics books are filled with finished theorems and polished proofs, and to a surprising extent they ignore the methods used to create mathematics. It is as if you merely walked through a picture gallery and never told how to mix paints, how to compose pictures, or all the other 'tricks of the trade.'

-- "Methods of Mathematics Applied to Calculus, Probability, and Statistics" (1985)

"The purpose of computing is insight, not numbers."

-Richard Hamming

Source: Preface of Numerical Methods for Scientists and Engineers, 1962

From a speech titled "You and Your Research."

>Now for the matter of drive. You observe that most great scientists have tremendous drive. I worked for ten years with John Tukey at Bell Labs. He had tremendous drive. One day about three or four years after I joined, I discovered that John Tukey was slightly younger than I was. John was a genius and I clearly was not. Well I went storming into Bode's office and said, "How can anybody my age know as much as John Tukey does?" He leaned back in his chair, put his hands behind his head, grinned slightly, and said, "You would be surprised Hamming, how much you would know if you worked as hard as he did that many years." I simply slunk out of the office!

>What Bode was saying was this: "Knowledge and productivity are like compound interest." Given two people of approximately the same ability and one person who works ten percent more than the other, the latter will more than twice outproduce the former. The more you know, the more you learn; the more you learn, the more you can do; the more you can do, the more the opportunity - it is very much like compound interest. I don't want to give you a rate, but it is a very high rate. Given two people with exactly the same ability, the one person who manages day in and day out to get in one more hour of thinking will be tremendously more productive over a lifetime. I took Bode's remark to heart; I spent a good deal more of my time for some years trying to work a bit harder and I found, in fact, I could get more work done. I don't like to say it in front of my wife, but I did sort of neglect her sometimes; I needed to study. You have to neglect things if you intend to get what you want done. There's no question about this.

Hey everyone. I know this transcript makes its rounds from time to time on the science subreddits but I thought it is worth posting here as well since it is relevant to computer science.

I read this transcript from time to time when I need it. Just wanted to share it with you all in case you haven't seen it.

If you don't have time to read through the whole transcript I have the most salient points (to me) TLDR style on my blog.

Hope you find it helpful.