| Title | : | The Art of Doing Science and Engineering: Learning to Learn |
| Author | : | |
| Rating | : | |
| ISBN | : | 9056995014 |
| ISBN-10 | : | 9789056995010 |
| Language | : | English |
| Format Type | : | Paperback |
| Number of Pages | : | 376 |
| Publication | : | First published January 31, 1996 |
Provides the reader with a style of thinking that will enhance a person's ability to function as a problem-solver of complex technical issues. Consists of a collection of stories about the author's participation in significant discoveries, relating how those discoveries came about and, most importantly, provides analysis about the thought processes and reasoning that took place as the author and his associates progressed through engineering problems.
The Art of Doing Science and Engineering: Learning to Learn Reviews
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Q:
The unexamined life is not worth living. (c)
Q:
The use of FORTRAN, like the earlier symbolic programming, was very slow to be taken up by the
professionals. And this is typical of almost all professional groups. Doctors clearly do not follow the advice they give to others, and they also have a high proportion of drug addicts. Lawyers often do not leave decent wills when they die. Almost all professionals are slow to use their own expertise for their own work. The situation is nicely summarized by the old saying, “The shoe maker’s children go without shoes”. Consider how in the future, when you are a great expert, you will avoid this typical error! (c)
Q:
There is a clever proposed method whose effectiveness I do not know in practice. Suppose you want to measure the amount of murder which escapes detection. You interview people and tell them to toss a coin without anyone but themselves seeing the outcome, and then if it is heads they should claim they have committed a murder, while if tails they should tell the truth. In the arrangement there is no way anyone except themselves can know the outcome of the toss, hence no way they can be accused of murder if they say so. From a large sample the slight excess of murders above one half gives the measure you want. But that supposes the people asked, and given protection, will in fact respond accurately. Variations on this method have been discussed widely, but a serious study to find the effectiveness is still missing, so far as I know. (c)
Q:
In closing, you may have heard of the famous election where the newspapers announced the victory for President to one man when in fact the other won by a land slide. There is also the famous Literary Digest poll which was conducted via the telephone, and was amazingly wrong afterwards—so far wrong the Literary Digest folded soon after—some people say because of this faulty poll. It has been claimed at that time the ownership of a telephone was correlated with wealth and wealth with a political party—hence the error. (c) -
Original 2019 Review: Hamming invented a lot of cool stuff, but he is best known for sitting down and asking people why they weren't working on the most important problems in their domain. Presumably he didn't make a lot of friends with this strategy, but his is the name we remember, not theirs.
This book is excellent excellent excellent. The thesis is that a life lived without producing excellent work isn't one worth living. Hamming describes the book as a manual of style; while university is good at teaching technical skills, it's not very good at teaching the important stuff that falls /between/ the discrete subjects. Like how to choose important problems to work on, or where insight comes from, or how to stay ahead of the trend and not become obsolete.
To this extent, Hamming talks about his own successes and failures (though mostly his successes --- he says it's more important to study success than failure, since you'd like to replicate only the former.) He's obviously proud of his accomplishments, which is a refreshing note from most technical autobiographies, in which the authors present a cool, modest description of their work. Hamming provides commentary behind each of his wins, describing the circumstances that lead to it, and how having a "prepared mind" helped him jump on it before others did. He further notes how he could have done better, and gives explicit advice to the reader for how to do a better job than he did.
This is a wonderfully insightful book, and is chocked full inspiration and interesting technical topics. If you're in a technical field and you'd like to do great work, this is mandatory reading. -
Hamming's essay,
"The Unreasonable Effectiveness of Mathematics" (together with Eugene Wigner's precursor piece,
"The Unreasonable Effectiveness of Mathematics in the Natural Sciences", is one of the four or five most important papers I've ever read:Prologue. It is evident from the title that this is a philosophical discussion. I shall not apologize for the philosophy, though I am well aware that most scientists, engineers, and mathematicians have little regard for it; instead, I shall give this short prologue to justify the approach.
As G. H. Hardy said in
Man, so far as we know, has always wondered about himself, the world around him, and what life is all about. We have many myths from the past that tell how and why God, or the gods, made man and the universe. These I shall call theological explanations. They have one principal characteristic in common-there is little point in asking why things are the way they are, since we are given mainly a description of the creation as the gods chose to do it.
Philosophy started when man began to wonder about the world outside of this theological framework. An early example is the description by the philosophers that the world is made of earth, fire, water, and air. No doubt they were told at the time that the gods made things that way and to stop worrying about it.
From these early attempts to explain things slowly came philosophy as well as our present science. Not that science explains "why" things are as they are-gravitation does not explain why things fall-but science gives so many details of "how" that we have the feeling we understand "why." Let us be clear about this point; it is by the sea of interrelated details that science seems to say "why" the universe is as it is.
Our main tool for carrying out the long chains of tight reasoning required by science is mathematics. Indeed, mathematics might be defined as being the mental tool designed for this purpose. Many people through the ages have asked the question I am effectively asking in the title, "Why is mathematics so unreasonably effective?" In asking this we are merely looking more at the logical side and less at the material side of what the universe is and how it works.
A Mathematician's Apology:What is the proper justification of a mathematician’s life? My answers will be, for the most part, such as are expected from a mathematician: I think that it is worthwhile, that there is ample justification. But I should say at once that my defense of mathematics will be a defense of myself, and that my apology is bound to be to some extent egotistical. I should not think it worthwhile to apologize for my subject if I regarded myself as one of its failures. Some egotism of this sort is inevitable, and I do not feel that it really needs justification. Good work is not done by "humble" men. It is one of the first duties of a professor, for example, in any subject, to exaggerate a little both the importance of his subject and his own importance in it. A man who is always asking "Is what I do worthwhile?" and "Am I the right person to do it?" will always be ineffective himself and a discouragement to others. He must shut his eyes a little and think a little more of his subject and himself than they deserve. This is not too difficult: it is harder not to make his subject and himself ridiculous by shutting his eyes too tightly.
Every schoolboy, of course, knows Hamming's Codes, without which tkis 2es7age woz7d 333 uNintel3siblke due to 434rror5 (or at least would have aarrrrrriiiiiivvvveeeedddd mooooooreeeeeeee sllllooooooooowwwwllllllllllyyyy). Van Roy highly recommends this slim volume in
Concepts Techniques and Models of Computer Programming, and who am I to reject a book by Hamming? Hoping for fun. -
A book full of wisdom from an engineer and scientist who spent his entire life in computing and research. Richard Hamming discusses why scientist do things they do, how leaders are different from followers, how to spot trends and focus on the core, what changes are going to take place in the near future and how do we adapt to them. "Luck favors the prepared", indeed a quote that is the main theme of this book.
Recommend to anyone in the search of the meaning of work, research and generally life. -
Richard Hamming was a leading computational scientist with significant contributions to Computer Science, Networks and many other fields.
This book is a collection of lectures given by him (titled Learning to Learn) at the US Naval Postgraduate College. The source videos (of low quality) are
available at YouTube. The videos are pretty excellent!
I skipped a few chapters on Electrical Engineering when they were too technical. But the rest of chapters were top notch! Hamming casually throws around profound quotes including but not limited to creativity, experts and research. My favorites chapters: History of Computers: Software, Artificial Intelligence-I and the chapters 25-30. The last 6 chapters should be required reading for all engineering graduates. This was long pending to be read and I'm so glad to have finally completed it.
This book is highly recommended. -
This was an inspiration read, it makes we want to brush up on my algebra and calculus. Despite not following all the mathematics, Hamming shares prophetic wisdom considering it was published in 1996. The book is a collections of essays where Hamming shares his experience as a scientist and researcher that guided his career at Bell Laboratories and work in computing creating error correction codes, among many other projects. Hamming offers advice on managing a career, focusing on doing high quality work solving problems that matter - and to anticipate you will have to constantly learn and reinvent yourself managing through compounding change and technical advancement in your career. I loved Hamming ideas on leadership, and to plan for ambiguity and change as inevitable in your field and career.
But be careful—the race is not to the one who works hardest! You need to work on the right problem at the right time and in the right way—what I have been calling “style.” -
Hamming's goal with this book is to teach style and creativity to people who do engineering or research. He primarily does this using a ton of anecdotes from his own research career. He'll give a story about doing something or other, then explain how it relates to the broader picture of being a top notch researcher.
The book itself is organized into separate chapters, each focusing on a technical area that Hamming was interested in. He gives enough information to understand the topic (assuming you know calculus) and then dives into various proofs. He does a lot of back of the envelope calculations, and they sometimes aren't motivated until afterwards. There were multiple times where I had no idea why he was doing some derivation until a while after it was done. The derivations are also sometimes not the most clear.
Interspersed in each chapter are the anecdotes about engineering style, and Hamming tries to use the technical content to illustrate his examples. This works pretty well, but it does mean that it's harder to read the book and just pick out his advice for being a good scientist.
Overall I thought the book was great. It's full of good advice and interesting histories about the discoveries of various theorems. -
"Man is not a rational animal, he is a rationalizing animal."
"Learning a new subject is something
you will have to do many times in your career if you are to be a leader and not be left behind as a follower by newer developments."
"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.""
"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."
"What you learn from others you can use to follow;
What you learn for yourself you can use to lead."
"There is another trait of great people I must talk about-and it took me a long time to realize it. Great people can tolerate ambiguity; they can both believe and disbelieve at the same time. You must be able to believe your organization and field of research is the best there is, but also that there is much room for
improvement!" -
there's a lot of wisdom here.
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3.5 stars
Overall, really enjoyed this book but struggled immensely with Chapters 9 through 17, covering coding theory, n-dimensional space, error-correcting codes, digital filters.
The preface promised the book was about "learning to learn" but those above chapters got a little too into the weeds and I was having flashbacks from stuff I struggled with in my EE degree, despite Hamming's much better explanations than my former professors.
Good reading for current or future scientists, engineers, and/or technical leaders. I believe the book can still be enjoyed when speeding through the hard stuff and skipping over the math (not what Hamming wants of his readers, but too bad). -
This is a fairly niche book which presents itself as Hamming teaching a meta class on how to be successful in your scientific and engineering focused career. Taking a brief glance around the classroom, it seems that many people have pretty unqualified praise for this work. I’m not sure they are quite accurate. While all books are read by a self-selected group, BooksWithMath™are an even more hyper-selected group. Thus, there is going to be a propensity for them to inflate the overall score of the book just due to the nature of who they are. Enough about the other reviewers, let’s look at the book.
Hamming starts out by presenting what he is going to teach you in this “class”. He purports to teach them the idea of creativity and the style of doing engineering.“There really isn't this course any technical content, although I'm going to talk about digital fillers and all kinds of things. There are things you presumably know. I am concerned about style.
I have studied great scientists, ever since I was at Los Alamos during the war. What is different between those who do and those who do not do significant things? Mainly, it's a manner of style.”
As you’ll come to learn later, this isn’t explicitly true. Hamming spends four whole chapters discussing relatively useless information concerning Digital Fillers—by far the worst chapters in my opinion—and only one on the error correcting codes which he is most known for. In these chapters, which thoroughly dissuade you of the notion that there will be little mathematics in this book, there seems to be much more bragging than there is actionable information. Interesting if you want an analysis of what it was like to work around the best scientists of that era, but not quite relevant to the thesis.
Personally, I didn’t feel like the book started revealing secrets until Hamming was able to write through all of his personal triumphs. Sure, there are important sentences scattered around, but nothing really compares to the chapters from 25 on.“A long gestation period of intense thinking about the problem may result in a solution, or else the temporary abandonment of the problem. This temporary abandonment is a common feature of many great creative acts. The monomaniacal pursuit often does not work: the temporary dropping of the idea sometimes seems to be essential to let the subconscious find a new approach.”
He drops a few short lines, like an engineering poet:“Society will not stand still for you,...”
Longer asides, which aid in the orienteering of a career when times are fruitless:“If, on the average campus, you asked a sample of professors what they were going to do the next class hour, you would hear they were going to: “teach partial fractions”, “show how to find the moments of a normal distribution, “explain Young’s modulus and how to measure it”, etc. I doubt you would often hear a professor say, “I am going to educate the students and prepare them for their future careers.”
You may claim in both cases the larger aim was so well understood there was no need to mention it, but I doubt you really believe it. Most of the time each person is immersed in the details of one special part of the whole and does not think of how what they are doing relates to the larger picture. It is characteristic of most people that they keep a myopic view of their work and seldom, if ever, connect it with the larger aims they will admit, when pressed hard, are the true goals of the system. This myopic view is the chief characteristic of a bureaucrat. To rise to the top you should have the larger view—at least when you get there.”
And then a restatement of his true purpose throughout the book: to create better scientists. To introduce and cement in his students the notion that the biggest ideas do not come to the smallest thinkers.“I strongly recommend this taking the time, on a regular basis, to ask the larger questions and not stay immersed in the sea of detail where almost every one stays almost all of the time. These chapters have regularly stressed the bigger picture, and if you are to be a leader into the future, rather than to be a follower of others, I am now saying it seems to me to be necessary for you to look at the bigger picture on a regular, frequent basis for many years.”
I could probably share another 5+ quotes from the final chapters, but I’m afraid that you would be better off reading those final chapters in full. However, despite my praise, I’m not falling into the trap of rating something on the strength of its ending vs. the strength of the whole. Overall, the work was chaotic, messy, and without cogent direction. I suppose one could make an interesting analogy between the construction of the book and the often winding path of an engineering career, but I may be veering into the cliche with that comparison. I can only hope that I do work that is good enough to have people complaining about my lack of direction one day on some random review on the internet.
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"Is programming closer to novel writing than it is to classical engineering?" I am amazed how he posed the question in the 90's when the primary language in business was still C and FORTRAN!
This book was basically a peek into Hamming's remarkable work ethic. If you are interested in his approach towards science in general I recommend his famous talk: You and Your Research, which is how I found this book. -
Sort of a weird mix of general thoughts and highly technical information. I have no idea what the chapters on digital filters were even about, and was starting to wonder if the rest of the book would be a waste of time due to lacking the right background knowledge, but fortunately it goes back to higher-level discussions after that.
Some of the most interesting points to me:
- Consciously try to predict the future of your field; Hamming set aside a specific time each week to think about the future of computing
- Ruminate on new knowledge from many angles as soon as you encounter it
- Bad data is everywhere; don't trust without taking a careful look
- What worked well in your past often hinders you in the future; don't become overconfident in one way of thinking simply because your own experience has validated it
- The relevance of n-dimensional geometry for approaching any kind of design decision that involves n parameters*
* An example he gives is that, for a large n, most of the volume of an n-dimensional sphere actually resides very near the surface. From this he draws the conclusion, if I understand correctly, that when you're trying to find the optimal design when you have a large number of parameters to make decisions about, you should expect that at least one of the parameters will have an extreme value, rather than all of them being sort of in the middle. I don't know if I buy this - it seems to assume the optimal design lies somewhere at random within the volume, but what if having balanced values of all parameters is part of what makes a design optimal for a particular problem? - but it's an interesting perspective. -
recommendation: Patrick Collison
https://www.stitcher.com/podcast/the-... -
A sort of a good leadership book, and I keep feeling I'm not fond of leadership books.
The author is a respected person with lots of experience and wisdom. He explains how computing changed science and engineering, how such changes may continue, how new paradigma in science may replace the old one and what obstacles it gets on the way. It was quite interesting about the role of experts, systems engineering, work with data and measuring, some good notes on creativity and focus in your career.
The author also focuses on "your own style", but it did not feel explained well through the book. What I also missed is how to make author's advices usable for myself. Obviously, I should have my own style, anyway. :)
In the end, a very good book worth reading! -
Chapter 9: n-dimensional spaces is a good chapter to decide whether to give this one a read. Engineering or science background helps a great deal in reading. Turns out it will be one of my favorite books.
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Incredibly interesting and inspiring reflections on effective “styles” of thinking. Lost one star because Dick Hamming could easily read through it once and clean up the prose, repetitions, and chaotic organization a little. Don’t let that deter you, though!
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330-The Art of Doing Science and Engineering-Richard Hamming-Science-1996
Barack
2021/05/09
" The Art of Doing Science and Engineering ", first edition in 1996. It explores efficient thinking methods. The author proposes ways of enhancing people's thinking to solve complex technical problems by thinking about how failure affects the thinking process.
Richard Hamming was born in Chicago, Illinois, US in 1915 and died in 1998. Studied at the University of Chicago, University of Nebraska, the University of Illinois at Urbana - Champaign. He is a mathematician whose research work has had a profound impact on computer engineering and telecommunications engineering. He won the Turing Award in 1968. Representative works: " The Art of Doing Science and Engineering: Learning to Learn ", " Numerical Methods for Scientists and Engineers ", etc.
Table of Contents
1 Orientation
2 Foundations of the Digital (Discrete) Revolution
3 History of Computer—Hardware
4 History of Computer—Software
5 History of Computer Applications
6 Limits of Computer Applications—AI—I
7 Limits of Computer Applications—AI—II
8 Limits of Computer Applications—AI—III
9 n -Dimensional Space
10 Coding Theory—I
" After more thought I decided that since I was trying to teach "style" of thinking in science and engineering, and "style" is an art, I should therefore copy the methods of teaching used for the other arts—once the fundamentals have been learned. How to be a great painter cannot be taught in words; one learns by trying many different approaches that seem to surround the subject. ”
People between people, the biggest difference, I think that what the naked eye can not see, but the difference lies in unseen ways of thinking. The difference in the way of thinking of each person is the essential difference between each person and others.
“ I have used the "story" approach, often emphasizing the initial part of the discovery, because I firmly believe in Pasteur's remark, "Luck favors the prepared mind." ”
The so-called conspiracy depends on people, and success depends on heaven. Luck is hard to find. The greatest significance of human effort in what we do is that if good things happen, we can seize opportunities; if bad things happen, we can minimize losses. This is the greatest significance of preparation.
" Teachers should prepare the student for the student's future, not for the teacher's past. Most teachers rarely discuss the important topic of the future of their field, and when this is pointed out they usually reply: "No one can know the future." "
When we study history and look back on the past, the greatest significance is not to change history, nor can we change history. The greatest significance is that we learn from history and apply it to future behaviors.
“ The year 2020 seems a convenient date to center the preparation for their future—a sort of 20/20 foresight, as it were. As graduate students working toward a master's degree, they have the basics well in hand. ”
At that time, 2020 seemed to be very far away. Who can predict what will happen more than two decades later? But now, 2020 all year last year, it is already a thing. Standing at the time node in the future, it is very interesting to see the predecessors looking at the future.
" The subtitle of this book, Learning to Learn, is the main solution I offer to help students cope with the rapid changes they will have to endure in their fields. The course centers around how to look at and think about knowledge, and it supplies some historical perspectives that might be useful. ”
Everyone can think and learn. But learning how to think about itself is sometimes easily overlooked. Thinking is a natural ability that we will be born with. But different people thinking methods and techniques affect it as differently as day. If the important things that we did often ignored to the day after learning their importance. How easy it is for people to ignore what their eyes do not see.
" Apparently an "art"—which almost by definition cannot be put into words—is probably best communicated by approaching it from many sides and doing so repeatedly, hoping thereby students will finally master enough of the art, or if you wish, style, to significantly increase their future contributions to society. ”
The so-called Tao is very Tao. Art is something beyond words. Those who have attained the Tao describe the way to those who have not attained the Tao, it is probably the same as the description of the sun to a blind person by a visible person. It can only be described in a profile, but it cannot be displayed directly.
“ The course is concerned with "style", and almost by definition style cannot be taught in the normal manner by using words. ”
In fact, many things humans do are to predict the future as accurately as possible. Of course, we can not be 100% of the forecast is correct, but if we increase the accuracy of some, we will be able to gain an advantage, it is to predict what the future is all about.
" The belief anything can be "talked about" in words was certainly held by the early Greek philosophers, Socrates (469–399), Plato (427–347), and Aristotle (384–322). This attitude ignored the current mystery cults of the time who asserted you had to "experience" some things which could not be communicated in words. Examples might be the gods, truth, justice, the arts, beauty, and love. ”
In fact, at this point, human beings are divided. Some of the greatest of thinkers, road or truth is there is no way to use language, but there are some thinkers considered. All things should be able to be expressed in words, they can be discussed, and they are not unknowable.
It is difficult to know which statement is of or to say, each is to say just under certain circumstances. In any case, based on different premises and assumptions, a very great system of thought has been derived.
“ Vicarious learning from the experiences of others saves making errors yourself, but I regard the study of successes as being basically more important than the study of failures. ”
Some people think we go to learn someone else's failure cases more meaningful, some people are considered, from the success of others Stories to learn more sense. It is difficult to say which of these two views has its own merits, and which one is more reasonable.
" Again, you will get out of this course only as much as you put in, and if you put in little effort beyond sitting in the class or reading the book, then it is simply a waste of your time. You must also mull things over, compare what I say with your own experiences, talk with others, and make some of the points part of your way of doing things. ”
With the same learning materials, different people will have different learning effects, there will naturally be differences in innate talents, and some people are more efficient in learning and learning. But talent is a difficult thing to change, after all, so we should not pay too much attention to it. We should try our best to improve the efficiency of learning through acquired efforts when learning. -
The Art of Doing Science and Engineering is a lecture series from the great Richard Hamming on the qualia of scientific research and progress. This is clearly something that Hamming is more than qualified to speak on – during his time at Bell Labs, he was a pioneer of coding theory and several of his inventions, like Hamming codes, underly much of our modern communication technology.
The lectures were addressed to graduate engineering students; this makes much of the book somewhat inaccessible for those lacking STEM training. The other side of this coin is that the book provides a nice high-level-but-somewhat-technical introduction to coding theory and signal processing, which is an underappreciated and unexpectedly fascinating area.
Nevertheless, I think that the last few chapters are a valuable read for anyone whose job involves research (which should be true for most knowledge workers!). Hamming addresses difficult topics like creativity, experts, and research organisations with adroit wisdom.The two main problems of dealing with the experts. They are: (1) the expert is certain they are right, and (2) they do not consider the basis for their beliefs and the extent to which they apply to new situations
My highlights
here.
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Richard W Hamming discusses the importance of staying ahead of the curve in science and engineering. He uses experiences from his life to talk about how to achieve and succeed in these fields. The central theme is weathering the future. Hamming recommends investing in yourself and focusing on the most significant issues your profession has.
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Very systematic view of doing science and engineering. Very inspiring book for researchers in more principled way to do research and self-development.
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For professor Hamming the whole world was an equation and he was solving it one part at a time. A unique perspective on few of the engineering problems.
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Just some snippets:
Added to the problem of the growth of new knowledge is the obsolescence of old knowledge. It is
claimed by many the half-life of the technical knowledge you just learned in school is about 15 years—in 15
years half of it will be obsolete (either we have gone in other directions or have replaced it with new
material). For example, having taught myself a bit about vacuum tubes (because at Bell Telephone
Laboratories they were at that time obviously important) I soon found myself helping, in the form of
computing, the development of transistors—which obsoleted my just learned knowledge!
To bring the meaning of this doubling down to your own life, suppose you have a child when you are x
years old. That child will face, when it is in college, about y times the amount you faced
This doubling is not just in theorems of mathematics and technical results, but in musical recordings of
Beethoven’s Ninth, of where to go skiing, of TV programs to watch or not to watch. If you were at times awed
by the mass of knowledge you faced when you went to college, or even now, think of your children’s
troubles when they are there! The technical knowledge involved in your life will quadruple in 34 years, and
many of you will then be near the high point of your career. Pick your estimated years to retirement and
then look in the left-hand column for the probable factor of increase over the present current knowledge
when you finally quit!
What is my answer to this dilemma? One answer is you must concentrate on fundamentals, at least what
you think at the time are fundamentals, and also develop the ability to learn new fields of knowledge when
4 CHAPTER 1
they arise so you will not be left behind, as so many good engineers are in the long run.
One of the main tasks of this course is to start you on the path of creating in some detail your vision of
your future. If I fail in this I fail in the whole course. You will probably object that if you try to get a vision
now it is likely to be wrong—and my reply is from observation I have seen the accuracy of the vision
matters less than you might suppose, getting anywhere is better than drifting, there are potentially many
paths to greatness for you, and just which path you go on, so long as it takes you to greatness, is none of my
business. You must, as in the case of forging your personal style, find your vision of your future career, and
then follow it as best you can.
Lastly, in a sense, this is a religious course—I am preaching the message that, with apparently only one
life to live on this earth, you ought to try to make significant contributions to humanity rather than just get
along through life comfortably—that the life of trying to achieve excellence in some area is in itself a
worthy goal for your life. It has often been observed the true gain is in the struggle and not in the
achievement—a life without a struggle on your part to make yourself excellent is hardly a life worth living.
This, it must be observed, is an opinion and not a fact, but it is based on observing many people’s lives and
speculating on their total happiness rather than the moment to moment pleasures they enjoyed. Again, this
opinion of their happiness must be my own interpretation as no one can know another’s life. Many reports
by people who have written about the “good life” agree with the above opinion. 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 it is merely
existing—in my opinion. In ancient Greece Socrates (469–399) said:
The unexamined life is not worth living.
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Since, as I have been repeatedly said, technical progress is going on at an increasing rate, it follows
technological obsolescence will be much more rapid in the future than it is now. You will hardly get a
system installed and working before there are significant improvements which you can adapt by mere
program changes If you have used general purpose chips and good programming methods rather than
your special purpose chip which will almost certainly tie you down to your first design.
Hence beware of special purpose chips!
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However, let me observe in all honesty to the Department Head, it was remarks by him which made me
realize it was not the number of operations done that mattered, it was, as it were, the number of micro#Nobel prizes I computed that mattered. Thus the motto of a book I published in 1961:
The purpose of computing is insight, not numbers.
A good friend of mine revised it to:
The purpose of computing numbers is not yet in sight.
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But before we leave the topic, recall in ancient Greece Democritus (460?–362?) observed; “All is atoms and void”. He thus expressed the view of many physicists today, the world, including you and me, is made of molecules, and we exist in a radiant energy field. There is nothing more! Are we machines? Many of you do not wish to settle for this, but feel there is more to you than just a lot of molecules banging against one another mindlessly, which we see is one view of a computer. We will examine this point in Chapters 6–8 under the title of Artificial Intelligence (AI). There is value in the machine view of a computer, that it is just a collection of storage devices and gates processing bits, and nothing more. This view is useful, at times, when debugging (finding errors) in a program; indeed is what you must assume when you try to debug. You assume the machine obeys the instructions one at a time, and does nothing more—it has no “free will” or any of the other attributes such as the self-awareness and self-consciousness we often associate with humans. How different are we in practice from the machines? We would all like to think we are different from machines, but are we essentially? It is a touchy point for most people, and the emotional and religious aspects tend to dominate most arguments. We will return to this point in the Chapters 6–8 on AI when we have more background to discuss it reasonably.
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In about 1962 LISP language began. Various rumors floated around as to how actually it came about-the probable truth is something like this: John McCarthy suggested the elements of the language for theoretical purposes, the suggestion was taken up and significantly elaborated others, and when some student observed he could write a compiler for it in LISP, using the simple trick of self-compiling, all were astounded, including, apparently, McCarthy himself. But he urged the student to try, and magically almost overnight they moved from theory to a real operating LISP compiler!
Let me digress, and discuss my experiences with the IBM 650. It was a two address drum machine, and operated in fixed decimal point. I knew from my past experiences in research floating point was necessary (von Neumann to the contrary) and I needed index registers which were not in the machine as delivered. IBM would some day supply the floating point subroutines, so they said, but that was not enough for me. I had reviewed for a Journal the EDSAC book on programming, and there in Appendix D was a peculiar program written to get a large program into a small storage. It was an interpreter. But if it was in Appendix D did they see the importance? I doubt it! Furthermore, in the second edition it was still in Appendix D apparently unrecognized by them for what it was.
This raises, as I wished to, the ugly point of when is something understood? Yes, they wrote one, and used it, but did they understand the generality of interpreters and compilers? I believe not. Similarly, when around that time a number of us realized computers were actually symbol manipulators and not just number crunchers, we went around giving talks, and I saw people nod their heads sagely when I said it, but I also realized most of them did not understand. Of course you can say Turing’s original paper (1937) clearly showed computers were symbol manipulating machines, but on carefully rereading the von Neumann reports you would not guess the authors did-though there is one combinatorial program and a sorting routine.
History tends to be charitable in this matter. It gives credit for understanding what something means when we first to do it. But there is a wise saying, “Almost everyone who opens up a new field does not really understand it the way the followers do”. The evidence for this is, unfortunately, all too good. It has been said in physics no creator of any significant thing ever understood what he had done. I never found Einstein on the special relativity theory as clear as some later commentators. And at least one friend of mine has said, behind my back, “Hamming doesn’t seem to understand error correcting codes!” He is probably right; I do not understand what I invented as clearly as he does. The reason this happens so often is the creators have to fight through so many dark difficulties, and wade through so much misunderstanding and confusion, they cannot see the light as others can, 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 27 see much more. But also remember this when you are the author of some brilliant new thing; in time the same will probably be true of you. It has been said Newton was the last of the ancients and not the first of the moderns, though he was very significant in making our modern world.
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It would be absurd to call this book a work of genius if not for the fact that Richard Hamming was one. A key part of Bell Labs’ heyday, Hamming was a mathematician’s mathematician: the guy called in to help a researcher punch up their equations or, as his department came to employ and manage computers, the guy who could translate your problem into one the machine could ponder (he also seems to have been the guy who could allocate you the machine time to have the pondering performed).
This put Hamming in a position to collaborate with a huge range of insanely talented luminaries, and he made numerous contributions along the way. The most famous is “Hamming Codes,” a method for reliable communication across noisy channels. Imagine a staticky telephone connection: you can still manage to get your message across by yelling it repeatedly, at the cost of taking up transmitting time that, absent the noise, could have been used for sending other messages. Hamming’s approach is a mathematically pristine version of this, by which digital signals can trade some content for error-correcting codes, allowing messages to be reliably transmitted across arbitrarily noisy channels. It’s a critical accompaniment to the Information Theory insights of Claude Shannon (with whom Hamming shared an office), and a fundamental feature of how digital communications actually work in circumstances ranging from your home wifi to interplanetary probes.
This book is a collection of adapted talks from a course Hamming taught to select students at the U.S. Naval Postgraduate School. It’s a bit of a victory lap. The idea seemed to have been: here’s a impeccably accomplished titan of the field. Let’s stick our brightest students in a room with him and see what happens.
The results are a bit uneven. Although he spares you its full fury, one can’t read this book without developing a sense of the mathematical genius that must lurk inside its author. But that’s not what these essays are about. They’re general observations about scientific progress and professional life, made by a mind that is clearly intelligent, but not unreachably so. He modestly recounts some triumphs; he speculates; he pats himself on the back; he grinds some long-rusty axes (he really thinks Shannon should have picked a different, less grandiose name for his theory). And he makes a bunch of mildly cynical observations about human organizations and bureaucracies that, while not exactly revelatory, are still nice to hear coming from someone who achieved so much.
There are two aspects of this that are genuinely useful. First is Hamming’s work ethic. I don’t think the man was a genius at the kinds of sociological observations that fill much of this book. But he is diligent. He clearly spent a lot of time thinking about creativity and research, considering them methodically, and with a firm sense of his own talents and shortcomings. He thinks carefully about his work and why he’s doing it. This is the primary lesson that he’s trying to convey, and I think he does a pretty good job of it.
The second aspect is perhaps more interesting. We often hear about mathematics as a language. But it’s rare for non-practitioners to see how this really works. If you get your hands on this book, skip to the chapter on n-dimensional space. Hamming elegantly explains a few simple results, their application to generalized problems, and what they reveal about the limits of human cognition and intuition. It’s a way of thinking about problems that is very different--and powerful--compared to the economic and legal frameworks for reasoning that most of us tend to absorb involuntarily through professional culture. I wish I had a better grasp on it; I wish I saw a better way into it. I am left convinced that we should find ways to admit more people who can do it into our wider discourse.
For me, that’s the highlight (the less said about his chapters on theory of mind, the better; also, it's mildly amusing to see him predict so much about the digital age but miss packet-switching). This book is a tour of a great mind, and if the book itself does not achieve that same greatness, it still provides a useful journey. -
A very interesting book when you take into account the context and year it was written, even though its style feels outdated nowadays. The lessons in here are timeless, it’s just that so many after Hamming have repeated them ad nauseam that a few of the takeaways are not so surprising anymore.
The book is very recommended to students and researchers everywhere. The author wants to inspire readers that work is only worth when you’re trying to produce excellent work, and he wants to help you with the directions to do so. This is done not only with first principles thinking but also through stories of his rich professional life. Come on, this guy watched scientific history unfold through his participation in the Manhattan Project and his years at the legendary Bell Labs. This includes the most important developments in Computer Science just before Computer Science became one of main forces in the Modern world.
Of course I have a few favorite quotes from the book. Here’s one about errors in short term and long term predictions:
“There is a saying,"Short term predictions are always optimistic and long term predictions are always pessimistic". The reason, so it is claimed, the second part is true is for most people the geometric growth due to the compounding of knowledge is hard to grasp.”
On how new technologies should win over skeptics:
“Yes, we did some of the hardest problems on the most primitive equipment—it was necessary to do this in order to prove machines could do things which could not be done otherwise. Then, and only then, could we turn to the economical solutions of problems which could be done only laboriously by hand! And to do this we needed to develop the basic theories of numerical analysis and practical computing suitable for machines rather than for hand calculations.
This is typical of many situations. It is first necessary to prove beyond any doubt the new thing, device, method, or whatever it is, can cope with heroic tasks before it can get into the system to do the more 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.”
Or on the limitations of the answers sought by Science:
“You should realize in all of science there are only descriptions of how things happen and nothing about why they happen.”
Hamming is such an important figure in the History of Science that having an unusual text such as this one coming from him feels like a real privilege. -
Richard Hamming is an outstanding figure in the history of technology. I think that programmers may recognize his name from the Hamming codes, which allow you to detect and correct errors in bits of information.
But his achievements are not limited to this. He inspired many people to discover new things by teaching them the “style of thinking” through which innovative ideas are born.
Working with Feynman, Fermi, and Oppenheimer on a nuclear bomb, Hamming sought to understand the qualities of these great scientists, which allowed them to achieve outstanding results in their fields. After joining Bell Labs and contributing to almost everything that the organization produced, he decided to pass on his knowledge through teaching in the U.S. Naval Postgraduate School. Therefore, this book is a textual adaptation of his course that bore the same name.
In his remarkable work, Hamming often speaks of the importance of predicting what the future would look like. These predictions are paramount since they help us discover novel ideas. The irony is that he makes his assumptions on what the world would look like in 2020. Last year, Stripe Press also republished the book. And it turns out that Hamming managed to predict many developments taking place in our time, except for the global pandemic 🙂
The topics that resonated with me the most were:
- The difference between strong will and stubbornness with the example of Albert Einstein and his Unified Field Theory;
- The importance of people who stimulate your thinking, even if they are challenging to work with;
- The impact of a new environment on how others perceive you or you perceive yourself;
- Techniques of the famous mathematician John Tukey that helped him expand the application context of new knowledge;
- Expertise vs. broad specialization;
- The influence of age and experience on discovering new concepts in various fields – from science to politics.
Hamming explores these topics through practical examples from the history of hardware, software, or fiber optics. There is a big chunk of math in the book, but you can safely skip it. Sometimes, if read in bed, it even helps you fall asleep faster 🙂
If you'd like to see a selection of books that teach you how to discover novel ideas, check out
the post I've written.
