My Goodreads Review
rating: 4 of 5 stars
There’s a reason Richard Feynman is the most famous physics lecturer of all time. No, it’s not because he held his office hours in a strip club (though he did) or that he helped develop the atomic bomb (though he did) or that he openly abused drugs, attended nudist gatherings, and played the bongos (though he did). Surely these have contributed to his legend but, most importantly, RPF was a master of the analogy.
Warning: Impending Tangent on Science Education and Modeling
Science education lends itself very well to the analogy. Consider the following. It seems most intuitive that a student wanting to learn a topic should want the most straightforward and realistic explanation possible. The student might say, “Tell me exactly how it really works.” Unfortunately for the student, that’s not always the best approach. First of all, science does not tell us exactly how things work. Science gives us models that act similarly enough to what we’re interested in that the models make useful and accurate predictions. And where do we get inspiration from these models? From our everyday experience and intuition! Fortunately for us, the universe seems to have a beautiful mathematical structure to it and many different systems in nature seem to follow roughly the same models. What does this mean for the student? It means that often the best explanation of a physical phenomenon will, instead of focusing solely on that phenomenon, touch on related yet more familiar phenomenon that follow similar dynamics. In other words, the student should say, “Tell me how something similar but more familiar to me works, then connect them.” The ability to appropriately cite these related phenomenon is the mark of a truly great teacher and was a staple of RPF’s lecture style.
Tangent Over
Undergraduate “computer science” education in the US has unfortunately come to mean “database manager training.” The Feynman Lectures on Computation are the perfect flotation device for any disheartened, theory-loving, future mathematician or computer scientist drowning in the overwhelming sea of code that is the path to a B.S. in computer science.
Feynman begins with the question “Exactly what does a computer do?” and offers a wonderful analogy of simpleton file clerks shuttling papers back and forth. From there, he takes the reader on a tour through basic gates and operations, reversible computing, the theory of computation, “Mr. Turing’s machines”, computability and the halting problem, coding and information theory, thermodynamics, exotic forms of computation, the physics of transistors and other components, and the physical limits of computation. Though many books play it safe and treat only the most established theories and ideas, Feynman isn’t afraid to pose current (circa 1983) research questions and his work-in-progress solutions. Feynman’s primary interests are in exploring just how far we can push computers given the laws of physics: how fast can they go, what can or cannot be computed, and how much energy must we use?
Despite the quality of the lectures, this book’s finest feature is the exercises. Feynman frequently preaches the “pleasure of discovery” and embeds his lectures with creative, fun, and instructive exercises. In fact, the most memorable lesson I drew from this book was that an hour of thinking and playing with an idea is often worth more than 24 of reading about it. To those who don’t see the point of solving problems that were solved decades or centuries before by others, Feynman offers the following wonderful characterization of science (paraphrased):
The life of a young scientist is spent rederiving old results, gradually rediscovering more and more recent ideas, until one day, he discovers something that no one else has ever discovered before. They key point is that without all that practice on “old” problems, it’s insanely difficult to develop the skill and confidence to work on “new” ones.
The major weakness of this book is that parts of it are quite dated. Feynman gave his lectures in the early 80s and even by the time this book was published in 1996, much of the hardware physics was already archaic. However, the parts of the book on theory (the bulk) are still quite relevant. Even the dated bits are quite useful to simply get the flavor of how the laws of physics can be exploited to do useful computations. Most importantly, however, dated or not, this book is just plain fun to read.
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