The March 1973 issue of Scientific American contained an article entitled simply “Bicycle Technology,” written by S. S. Wilson. As the title suggests, the article recounted the technological evolution of the bicycle and how those insights contributed to the development of early automobiles and airplanes. After all, many of the mechanical techniques used by the Wright brothers in the Wright Flyer, the first heavier-than-air aircraft, were developed in their Dayton, Ohio bicycle shop and tested at windswept Kitty Hawk, North Carolina.
This Scientific American article would now be of only passing historical interest were it not for a single diagram (shown below), which plots the cost of transport (in calories per gram per kilometer) against body weight (in kilograms) for several organisms and devices, all on a log-log scale. As one would expect, flying organisms tend to be small in weight but expend energy at high levels to remain airborne. Land-based mammals, though much larger, consume less energy relative to their weight. Among the latter, bipedal humans are unremarkable, more efficient than dogs but less efficient than horses. Similarly, helicopters are less efficient than airplanes or automobiles. All of this matches our intuition and experience.
Ah, but here’s the story. A human on a bicycle ranks first in energy efficiency among mobile animals and machines in energy consumed to move a certain distance as a function of body weight, and not by a little, but a lot! The chart shows that the rate of a bicyclist’s energy consumption is about one fifth that of a walking human. Anyone who has bicycled knows this is true. A reasonably fit bicyclist can cover more than one hundred miles in a day, the so-called century ride.
By Geeks for Geeks
The fact that a human on a bicycle is quantitatively and qualitatively more efficient than anything nature has engineered via evolution caught the eye of an eager young reader, Steve Jobs. More on that in just a minute. First, let’s talk about the magazine, Scientific American.
Founded in 1845, like many magazines, Scientific American had its ups and downs until three partners reinvented it during the 1960s, a few years before I first encountered it. For at least the next thirty years, it embodied everything the well-read – wait for it – scientific American might hope to know about cutting edge science and technology. Unlike other popular science magazines, the articles were written by the scientists responsible for the discoveries they described, which gave them gravitas often lacking elsewhere. The Amateur Scientist column by Jearl Walker described experiments that could be conducted by savvy amateurs, and the Mathematical Games column by Martin Gardner enchanted many a budding mathematician and computer scientist.
Scientific American was the kind of magazine geeks awaited eagerly each month, then stood by the mailbox reading cover-to-cover with undisguised joy, only to rush inside afterward to regale unsuspecting family members with their latest insights. I knew this firsthand, having had my own transformative experiences with the magazine, beginning with a stash of old copies I was given as a young teenager. There, I read about stellar nucleosynthesis of the elements, progress toward the Standard Model integrating three of the four fundamental forces, and new insights into DNA and gene actions, among many others. I have since been a loyal subscriber for over forty years, though I hope the magazine has been as successful in recruiting younger readers as it was with me.
I read many fascinating things in Scientific American over the years, but two things stand out most vividly. The first was Christopher Zeeman’s delightful exegesis on catastrophe theory, a mathematical model of non-linearity in complex systems, something that I wrote about recently in the context of COVID-19. (See On Catastrophes and Rebooting the Planet.) The second was Martin Gardner’s description of John Conway’s Game of Life, a beguilingly simple but powerful cellular automaton. Sadly, John Conway, who spent many years at the Institute for Advanced Study, passed away during the COVID-19 pandemic.
The Game of Life became a staple in computer science circles, engaging both experimenters and theorists who derived self-replicating entities and ultimately proved the Game of Life is Turing complete. That rabbit hole is very deep, for via its three simple rules, the Game of Life is capable of computing all that is mechanically computable, bounded only by Gödel incompleteness and the Entscheidungsproblem. From there, it is just a hop-skip-and jump to the Chomsky hierarchy and Turing’s musings on what it means to think.
As an undergraduate, I burned through prodigious amounts of IBM S/370 computer time myself, running Game of Life simulations, enough so that I got called on the carpet by the computing center leadership. Being fellow geeks themselves, after I showed them the code and explained what I was doing, I was given a friendly blessing and sent on my way. (See A Feeling for the Code.) Since then, I have used the Game of Life as an example in more than one parallel computing class, illustrating parallelism, irregular workloads, and synchronization constraints.
Amplifying Human Talent
Given the impact of Scientific American on the science and technology crowd, it is no surprise that a young Steve Jobs saw the article on bicycle technology and how it augmented human capabilities. It was an idea that became a passion, finding the intellectual analog of the bicycle, something that could augment the human mind in the same dramatic way a bicycle amplified locomotion. You can watch Jobs talk about it here and here, at two different periods in his life. His memory about the condor as the most efficient was incorrect; it was not on the list. However, he captured the essence of the story, when he talked about humans as tool builders and a computer being a bicycle for the mind, able to take us far beyond our human abilities. As Jobs put it,
… somebody at Scientific American had the insight to test the efficiency of locomotion for a man on a bicycle. And, a man on a bicycle, a human on a bicycle, blew the condor away, completely off the top of the charts.
And that’s what a computer is to me. What a computer is to me is it’s the most remarkable tool that we’ve ever come up with, and it’s the equivalent of a bicycle for our minds.
Jobs tried to name the then internally code-named Macintosh project the “Bicycle,” but it didn’t stick. Apple even used the bicycle for the mind analogy in its marketing, as this 1980 Wall Street Journal advertisement shows. (Alas, no copyeditors verified the condor allusion.)
A Universal Intellectual Amplifier
Over the years, I have crafted my own version of this analogy, emphasizing that computing is distinct from all other human tools in its universality. (See Intellectual Amplification via Computing.) Physical tools, from the simple inclined plane through a pulley to a screwdriver, rely on mechanical advantage, each specialized for a particular task. Put another way, a screwdriver will not help you lift a weight, nor will a pulley help you fasten two objects together. Each is useful, but each is specialized.
That specialization extends to scientific and intellectual endeavors as well. Telescopes, such as the Vera Rubin Observatory or James Webb Space Telescope, expand our vision, reaching far beyond the electromagnetic range of visible light and the limits of the human retina. They let us see the faint, large, and distant, but do nothing to illuminate the spatiotemporal dynamics of a biochemical reaction. Nor can a cryo-EM’s amazing efficacy in understanding macromolecular structures measure the cosmic microwave background (CMB) or plumb the mysteries of dark matter and dark energy.
In contrast, computing is universally applicable to diverse human intellectual efforts. That bicycle for the mind can help write sonnets (or via AI, write them itself), explore the spread of viral disease, understand galactic evolution, or a thousand other things, limited only by human imagination.
In that spirit, my friend and former colleague, Fred Brooks, one of the fathers of IBM’s System/360 family of computers, once described himself as being in the “intelligence amplification” business. After leaving IBM and moving to the University of North Carolina at Chapel Hill, he met with a University of North Carolina at Chapel administrator, whom he then wryly asked who on campus most needed their intelligence amplified. Beyond the twinkle in Fred’s eyes as he told the story, I’ve always loved the anecdote, because it speaks to the human nature of computing as a collaborative enabler for creativity.
The Supercomputing Racing Bike
If the personal computer – Mac or PC – is a bicycle for the mind, then a supercomputer is a carbon fiber composite racing bicycle with electronic shifters, hydraulic disc brakes, and tires engineered to minimize rolling resistance. Though the principles are the same, a supercomputing bicycle is far different than the bike you rode as a child.
Back in May 2004, I testified to the U.S. Congress about the needs of racing bicycles for the mind, something I have done several times. At the time, I was serving as the Founding Director of the Renaissance Computing Institute (RENCI), itself based on the idea that computing was universally applicable to enriching the human experience. It was an idea I learned from my NCSA colleague and friend, Donna Cox, an artist who has enriched the lives of so many via her Renaissance teams and scientific visualizations.
The other witnesses that day were Jack Marburger, then Director of the White House Office of Science and Technology Policy (OSTP); Irving Wladawsky-Berger, a Senior Vice President from IBM; and Rick Stevens from Argonne National Laboratory. You can read the entire transcript of the hearing here. At that hearing of the House Science Committee, I offered these words about the universality of high-performance computing and its power to expand the frontiers of human knowledge, noting
The breadth of these examples highlights a unique aspect of high-performance computing that distinguishes it from other scientific instruments – its universality as an intellectual amplifier. Powerful new telescopes advance astronomy, but not materials science. Powerful new particle accelerators advance high energy physics, but not genetics. In contrast, high-performance computing advances all of science and engineering, because all disciplines benefit from high-resolution model predictions, theoretical validations and experimental data analysis. As new scientific discoveries increasingly lie at the interstices of traditional disciplines, high-performance computing is the research integration enabler.
I went on to say something that I believed then but seems even more true now
Although this universality is the intellectual cornerstone of high-performance computing, it is also its political weakness. Because all research domains benefit from high-performance computing, but none is solely defined by it, high-performance computing lacks the cohesive, well-organized scientific community of advocates found in other disciplines. In turn, this has led to over-dependence on market forces to shape the design and development of high-performance computing systems, to our current detriment.
In almost twenty years since, we have seen phenomenal computing advances. We have broken the petascale and the exascale performance barriers. Advanced AI delivers modern day miracles, capable of playing chess and Go at levels that humble even the world’s best human players, able to translate human languages with high and rapidly improving accuracy, adept at predicting how proteins fold, and able to compose prose with near human facility.
We have come far on the racing bicycle for the mind. We need to keep pedaling, for we still have far to go to reach our destination, given both the manifest opportunities and the equally daunting challenges.
I have written about the challenges of building ever faster computers, given economic and technical constraints, and I have called for new approaches to designing and building advanced computers that lessen our dependence on traditional market forces, recognizing that advanced computers are increasingly bespoke research instruments rather than commercial commodities. (See American Competitiveness: IT and HPC Futures – Follow the Money and the Talent and a related article.)
We also have deep technical and social issues surrounding AI ethics, digital equality, and digital communications. We are very far from the general AI (strong AI) so omnipresent in the movies, yet as our machines take on more tasks once viewed only as the province of humankind, we need to think about roles and responsibilities, and how we best imbue our machines with the best of humanity’s values. (See Reluctant Revolutionaries, the Trolley Paradox, and Ender’s Game.)
In addition to the handlebar streamers and the thrill of the wind in our hair, a helmet might be advised.
Coda
Make no mistake, Steve Jobs was absolutely right; the computer is arguably one of the most remarkable tools we humans have ever invented.
I have been proud and humbled to be a racing bicycle for the mind designer for these past forty years. Pedal on.
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