I first read The Art and Science of Doing Engineering by Richard Hamming in my sophomore year of college. It came recommended to me, and so I picked it up. I’d just changed my major to electrical engineering and didn’t really know who Hamming was. I was excited by the quality of the Stripe Press printing, though.
Now that I have a few years of work experience under my belt, I wanted to reread the book, and got an opportunity to do so and discuss the ideas in it with my office’s book club.
This book has been nothing short of formative in how I think about my work and career. We look back on the highlights and milestones, forgetting that a career is really forged in the day-to-day work and relationships. Hamming emphasizes that achieving excellence is a constant pursuit, and no one really gets “lucky” - it’s a matter of having the right skills in the right place at the right time. I enjoyed revisiting many of Hamming’s ideas now that I have a real understanding of just how formative his work has been to my field.
Can anyone truly do science and engineering?
I often meet people who, when I tell them I am an engineer, say some variation of “wow, you must be really smart! I could never do that.” My reply is generally along the lines of “thank you, I really enjoy my work,” because I don’t think it’s useful or interesting to focus on smartness or lack thereof. I do think some people are better disposed to thinking analytically or picking up concepts quickly and therefore tend to succeed in engineering fields (and I think I am one of those people).
Hamming writes that there is “no royal road to geometry” — that is, wealth, power and influence have no impact on your understanding of geometry. You actually have to roll up your sleeves and learn. On one hand, wealth and power can give a student access to better tutors and more opportunities, and fancy credentials do hold a lot of weight. That said, I’ve found, in my academic, professional, and athletic careers, that there’s no substitute for actually applying yourself and doing the thing when it comes to “go time”. One of my colleagues, who tutored for many years, found that some students struggled so much with technical material that they never gained an understanding — there was simply no level of applying oneself that could solve their issues. Honestly, it’s probably true that one must have a baseline level of technical capability to succeed in engineering, and that that capability can grow with hard work.
How does one develop knowledge?
In the tech industry, I feel there is a fixation on youth, particularly in the entrepreneurial space. I am excited about young people bringing new ideas to the field, and I also have benefited from the lessons gained from years of experience that senior engineers have shared with me. I feel that I have a base of knowledge from college and past experience, but I do feel that developing an intuition for how circuits work is a constant process. Take transistors for instance — they’re nonlinear devices, and while I do know the equations that describe a BJT’s current response for example, understanding the theory and understanding why you might use a particular transistor in a particular configuration to produce a particular effect are different things. When talking about this attitude towards knowledge production, another colleague called this “the skeleton” — the idea that you have some sort of framework of understanding that you fill in as you see applications and learn things. I can’t exactly give a percentage of how full my skeleton is — I don’t know what I don’t know — but I’m glad that I am starting to shade in big pieces.
Relatedly, you need to just try things to learn. Reshma Saujani, the founder of Girls Who Code, talks about this in her Teach Girls Bravery, Not Perfection TED talk. Women tend to be afraid of making mistakes, because if we do, it would reflect badly on all women engineers, not just us as individuals (see: stereotype threat and this XKCD comic). Many of the engineers whose technical opinions I trust (of all genders) learned a lot by simply making things, messing up, thinking “oops,” and moving on. As I’ve become more tolerant of making mistakes and asking for help, my learnings have increased.
I find the chapters on AI particularly interesting with regards to knowledge acquisition. They are titled “Limits of computer applications — AI,” and in them Hamming discusses what computers can or cannot do, with a focus on what it means to think. When Hamming discusses AI, he talks about all calculations a computer does, not only the ones we understand today as “AI” like ChatGPT and the like.
In understanding the difference between humans and computers, it is not enough to say that we humans can think and computers cannot. Hamming writes that “perhaps thinking should be measured not by what you do but how you do it.” In other words, the real thing that distinguishes human thought from AI thought is an understanding of the greater context and the ability to use the results to make strategic decisions. I think there is a lot of potential in AI, so long as a great amount of skepticism, fact-checking, and strategic thinking is applied to the output — and this is true for both AI in today’s hype and more classical computations. You have to understand what you’re asking a computer to do so that the result is actually useful for your goals. “This is the type of AI that I am interested in — what can the human and machine do together.”
What about luck?
I’m always impressed by how humble Hamming is about his contributions to the field. Like, this guy was all over my electrical engineering curriculum in college and has an IEEE achievement medal named after him. And yet, he attributes a lot of his accomplishments to luck. He does say that “luck favors the prepared mind,” and so one should be prepared by having a good understanding of the fundamentals that can be applied to whatever the problem happens to be. This makes a lot of sense — the opportunities I’ve learned the most from have come about by happenstance and because I’ve made myself available as a resource to others.
I’m also impressed by just how many of his technical innovations came from helping other people out with their problems as a computing expert. Supposedly, error-correcting codes came about because he would come in on Monday morning and found that his computing experiments he was running over the weekends for another scientist had failed due to errors. He wanted to be able to detect and correct errors and so devised a system for that.
This also goes to show that you learn the most through collaboration with other disciplines. One of the things I enjoy most in my work in consulting is being able to drill down on a project’s requirements and figure out what the real problem we’re solving is. Sometimes, clients will have a particular platform they want to use, and we need to figure out whether their needs are best met by that platform or whether something completely different would be better for cost / deployability / timeline / performance / et cetera. I find engineering can have the biggest impact when relating to other fields, and this helps advance engineering for its sake too.
Are these ideas still relevant today?
I really love this book, but I do occasionally find its ideals to be lofty and perhaps aspirational. These days, we’re in a very different political environment than Hamming was when he worked at Bell Labs. For one, the number of science and engineering workers in the United States has increased sevenfold from 1960 to 2017 (see stats). That science and engineering workforce has also gotten more diverse, with more women and underrepresented minorities joining the field. Bell Labs itself has a kind of mythological status among engineers, since it’s a place that did so much of the research that we now understand as fundamental for electrical engineering. The centers of excellence do seem to be more distributed now, since talented engineers no longer spend the entirety of their careers at one organization. I say these are different political conditions not because of politicians and their decisions (although funding for STEM remains political), but because the people who are doing the work of science and engineering are different, and the conditions under which they perform their work are also different.
Though Hamming speaks eloquently about the beauty of mathematics, it is no longer enough to do engineering for engineering’s sake. We need to turn more of a critical eye towards how we perform our jobs and produce knowledge, and what impact that knowledge has on the world around us. Most of us, I’d even argue all of us, don’t work on pure research, even if we think we do — we produce knowledge that should ultimately contribute to turning a profit.
I deeply want us to be able to do engineering for the love of it without worrying too much about a profit motive, and I think my current role strikes a good balance here. We do interesting work (never for destructive military or financial applications), and make money doing it. I count myself fortunate that I’m able to live my values in my professional life.
On some level, so what if Hamming writes from what we can understand to be the pinnacle of engineering? He does step off the “Great Man” pedestal with his writing, and I do think there’s a place for aspirations and striving to be the best engineers we can. As such, I truly recommend this book to anyone in the STEM fields. I find it a good blueprint for finding meaning in the minutia of everyday engineering and doing work you can be proud of, regardless of the results.
Comments