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.
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.
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.)
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.
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.
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.
Deep in the soul of each human lurks an often unsated and unspoken desire – to be remembered. This is the blessing – and the curse – of sentience, awareness of our own mortality and an unquenched fear that our life will be little more than a flickering shadow, here, gone, and quickly forgotten. We all ache to be part of something greater than ourselves, to be remembered for having done something – anything – good and memorable, best of all, as part of a likeminded and committed group. Shakespeare eloquently captured that hope in the St. Crispin’s Day speech in Henry V:
This story shall the good man teach his son;
And Crispin Crispian shall ne'er go by,
From this day to the ending of the world,
But we in it shall be remember’d;
We few, we happy few, we band of brothers;
The opportunity for immortality is unexpectedly thrust upon some, though a few remarkable individuals seize it themselves. Sometimes the moment makes you; sometimes you make the moment.
An extraordinary few create it for those around them. (See On the Etiology of Heroes.) Who are these people? What makes them the subject of story and myth? More than masterful storytellers, they are the weavers of dreams, theirs and ours, into something connected, alluring, and compelling, making the rest of us believe in the seemingly impossible, so much so that we astound ourselves by joining them in making the impossible a reality.
Apple’s Steve Jobs was a master of the reality distortion field, with his insanely great ethos, but he was by no means the first nor the last. (As Jobs said when recruiting John Sculley from Pepsi, “Do you want to sell sugar water for the rest of your life or come with me and change the world?” Microsoft’s leaders made a similar pitch when recruiting me.) Shackleton, West (so wonderfully described in the Soul of a New Machine), Musk, they all knew the power of signing up.
The story of Ernest Shackleton’s famous 1914 Imperial Trans-Antarctic Expedition, an attempt to make the first land crossing of Antarctica, is of mythic proportions, memorialized in stories and in Frank Hurley’s extraordinary photographs.
Shackleton’s wooden ship, the Endurance, and his crew became trapped in the pack ice of the Weddell Sea. Eventually, the relentless ice pressure crushed the Endurance, forcing Shackleton and his men to abandon ship, then later, drag their lifeboats over the melting springtime ice to open water. Only then could the crew set sail for the desolate and uninhabited Elephant Island. (Explorers recently found the Endurance, intact except for the ice damage, resting nearly two miles deep on the frigid ocean floor.) This overland crossing was itself extraordinary, but it was but a prelude to what lay ahead.
Then, in a journey never before attempted, Shackleton and five others, took the sturdiest of their lifeboats, the James Caird, and set sail into almost certain death, hoping to cross 800 miles of treacherous waters in search of help for the remainder of the crew, who waited fearfully on South Georgia island. Fortune smiled upon them only a bit.
After seventeen torturous days at sea, Shackleton and the other five made landfall, albeit on the wrong side of the island. Leaving the weakest to wait, Shackleton and two others had no choice but to attempt the first land crossing of the South Georgia interior, crossing both a mountain range and a glacier. Only upon reaching a whaling station on the other side, was Shackleton able to mount a rescue for his remaining crew on Elephant Island.
With failure and death lurking at every turn, Shackleton’s 1914 expedition and the subsequent rescue of his men is one of the most extraordinary tales in exploration history. Lost in Antarctica for almost two years, fighting for their very survival, all were saved by their belief in one another and the undaunted determination of the expedition’s leader. As Shackleton put it, “Not a life lost, and we have been through hell.”
Since then, the story of Shackleton’s heroism, courage, perseverance, and an unwavering commitment to the rescue of his men has become the stuff of modern legend, de rigueur for any leadership or management training. Yet like all great stories, it reveals as much about us as it does the protagonists.
In the decades thereafter, stories began circulating about a London newspaper advertisement, where Shackleton had first sought the twenty-seven volunteers for his expedition. The advertisement is succinct in both its warning and its allure:
Men wanted for hazardous journey. Low wages, bitter cold, long hours of complete darkness. Safe return doubtful. Honour and recognition in event of success.
It was soon reported that five thousand men had responded to Shackleton’s call, and this pithy statement has been hailed as one of the greatest advertisements of all time.
Several groups have since offered rewards to the first person who found the advertisement in the newspaper archives. All these searches failed for a very simple reason; Shackleton never placed such an advertisement. That we so desire the story to be true speaks worlds about our own hopes and dreams and our deep desire to sign up and be part of something greater than ourselves.
As Shackleton himself later wrote in his book, South:
We had pierced the veneer of outside things. We had 'suffered, starved and triumphed, groveled down yet grasped at glory, grown bigger in the bigness of the whole.' We'd seen God in His splendours, heard the text that Nature renders. We had reached the naked soul of man.
Grasping at glory. Shackleton knew that the most arduous and rewarding journey was in finding one’s own self.
In his Pulitzer Prize winning book, The Soul of a New Machine, Tracy Kidder tells the story of Data General’s desperate bid to build a viable competitor to Digital Equipment Corporation’s (DEC) 32-bit superminicomputer, the VAX 11/780. (If you haven’t read Kidder’s book, stop reading this meandering essay immediately, order a copy, and savor the insights of a proper writer!)
I was a graduate student when Kidder came to Purdue to talk about his recently published book. Mind you, this was when computing was still viewed as arcane art, practiced by an obscure priesthood with its own rituals and incantations. Microcomputers were still mostly a hobbyist item, workstations were still birthing, and the IBM PC had yet to be announced. At the time, I was using a VAX 11/780 (and a CDC 6600) to complete my Ph.D. thesis, working on the then crazy idea of building supercomputers from lots of interconnected microprocessors.
During his presentation, Kidder discussed the Eclipse MV/8000 and the late-to-market challenges facing Data General, but more than that, he told a tale of extraordinary adventure, personal sacrifice, and shared dedication to an upstart cause. As the “Hardy Boys” built the hardware, the “Microkids” fused the microcode with the software to create a working machine; these were people like me! By the end of Kidder’s lecture, I knew I was part of something bigger than myself; the desire to build a beautiful machine was a secular, though holy thing.
The most compelling aspect of Kidder’s book, one Shackleton would have recognized instantly, was the notion of “signing up.” As Kidder explained it,
West [the team leader] invented the term, not the practice— “signing up.” By signing up for the project you agreed to do whatever was necessary for success.
I have signed up a few times myself, dedicating long nights, sleep-deprived days, and countless weekends to a cause and a team (e.g., the first NCSA Linux clusters and the TeraGrid, and most recently, to ensure the University of Utah continued to function and flourish during the COVID-19 pandemic). I suspect most of you reading this essay have signed up for something yourselves. (See Memos, Janitors, Teams, and Innovation.)
Stories from Microsoft's early days were legion of developers and team leaders taking a sleeping bag to work as the crunch time of product shipment neared. Eat, code, sleep a little, repeat. When the code pirouetted before you, embodying the logic of your imagination, and then the product shipped – that was the true reward. (See A Feeling for the Code.) The stock and the money were nice, but they weren’t why you did it. Money was just how you kept score; it was the idea, the adventure, and the team that mattered.
I am not suggesting that one must sacrifice one’s personal life for work; balance is critical for personal well-being ad family partnerships. There are times and circumstances, however, that require one's all.
Although the Age of Heroic Antarctic Exploration ended abruptly with the brutality of World War I, the desire still burns bright across the intervening century. It is perhaps evident no place more than in the ambitions of Elon Musk to make humans an interplanetary society.
Outspoken and controversial, but unquestionably committed to this vision, Musk has transformed the global space industry by attracting a team of talented engineers who believe passionately in that vision. All of them “signed up” for a high stakes’ disruption of space exploration.
Musk put his money where his mouth was, remarking,
My proceeds from the PayPal acquisition were $180 million. I put $100 million in SpaceX, $70m in Tesla, and $10m in Solar City. I had to borrow money for rent.
That’s called “all in.”
Against great odds and on its final attempt before likely bankruptcy, SpaceX became the first private company to reach low Earth orbit (LEO), with its two-stage-to-orbit Falcon 1 rocket. Successive iterations of the Falcon 9 have made much lower cost launches commonplace via reusable rockets, and for the first time since the space shuttle was retired, given the U.S. a human-rated launch vehicle with the Dragon 2.
The Falcon Heavy now has the largest payload capacity of any operating launch vehicle, trailing only the mighty Saturn V and Soviet Energia. All that, however, has been but a prelude to the future launch of Starship. Designed as a fully reusable, super heavy-lift launch vehicle, Starship is powered by new Raptor engines, and is the system designed by SpaceX to take humans back to the moon and to Mars.
Whatever one thinks of Musk, there is no doubt that he has transformed the thinking of all national space agencies, including NASA, about lower launch costs, rapid reusability, and the timelines of aspirational goals. Embodying a fail fast, iterative design process, failure IS an option, one from which you learn and continue innovating, even if you do sometimes experience what is euphemistically known as “rapid unscheduled disassembly (RUD).” It has also exposed the fallacy of the cost-plus Artemis program to return to the moon using expendable launch vehicles.
Equally importantly, Musk has rekindled the passion for spaceflight that galvanized the world in the 1960s and created new possibilities for what might be. That’s what dreamers do. (See That’s One Small Step for {a} Man.) Who can help but marvel at the Falcon Heavy side boosters landing at Cape Canaveral or the regular cadence of landings on SpaceX’s offshore barges.
As Musk put it once, “When something is important enough, you do it even if the odds are not in your favor.” Shackleton knew that, as does anyone who has “signed up.” It’s all about being part of something bigger than yourself and your personal risk-reward quotient, something I discussed in an essay entitled, A Question to Ponder.
The question I posed there was simple but revealing, “What probability of successful return would you accept to be the first human to set foot on Mars?” It’s the modern-day version of the opportunity posed by Shackleton’s apocryphal advertisement. There’s only one wrong answer, guaranteed success, for all adventures and explorations entail risk. Shackleton knew this well, and Musk does too.
As for me, I want to go.
As a member of the National Science Board, I have been privileged to visit Antarctica, and I recorded a few of my experiences in a video here. I have stood in the very places where Shackleton and Scott once looked out at the forbidding glaciers, the frozen ocean, and the windswept landscape. I have marveled at the still-preserved provisions in both Shackleton’s Nimrod Expedition Hut on Cape Royds (that’s it below) and Scott’s Discovery Hut on Ross Island. I have also felt the howling winds at the South Pole.
Even under its most benign conditions, Antarctica is a brutal and unforgiving place, still largely cut off from the world. It is easy to forget that Shackleton’s expedition also lacked both electricity and any means of long-distance communication, Marconi’s first transatlantic radio transmission having been less than fifteen years before. Even now, communication bandwidth at McMurdo Station is limited, as most satellites are low on the horizon, barely accessible at all.
A few years ago, I re-read the Soul of a New Machine. The technology has changed (RISC rather than CISC, CMOS rather than bipolar, microprocessors rather than multi-board designs), but the stories and the people remain deeply familiar. Although none of the protagonists risked life or limb as did Shackleton and his team, every one of the Data General designers felt the same call, to make a difference, to do something memorable, to be dissolved into something truly great.
Forty years ago, I had no idea that multiple characters in Kidder’s tale, notably Craig Mundie and Steve Wallach, would become deeply intertwined with my own life. At the University of Illinois, I worked on the Cedar project, led by David Kuck, which designed a shared memory multiprocessor. The hardware building blocks of the Cedar prototype were based on the Alliant FX/8, developed by a company co-founded by Mundie. Years later, our lives intersected again when I joined Microsoft and worked with Craig on a variety of multicore and cloud computing projects.
My connections with Steve Wallach are equally deep, as I used the Convex C1, a mini-supercomputer developed by a company co-founded by Steve. Later, we discussed the OSF/1 operating system performance challenges for the Exemplar, Convex’s shared memory multiprocessor, and while I was at Microsoft, we explored the potential cloud hardware applications of Steve’s later company, Convey. In between, we have served on more high-performance computing and government panels than I can count. I still have my first edition of Kidder’s book, and a few years ago I reached out to Steve, who graciously signed my copy one night at the SC conference.
Though the ravages of time inevitably bend our backs and palsy our hands, each of us yearns for a late life requiem, when even as we stare at the rheumy-eyed reflection of age, we can, just for a moment, stand tall, hold our head high, and say with confidence and pride, “My life mattered!”
Find your passion; sign up; take some risks; make a difference. Even if never lauded in the history books, you will know, and that is the most important knowledge of all. Your future self will thank you.
Richard Feynman, the late Caltech physicist and Nobel Laureate, perhaps best known for his work on quantum electrodynamics, was also a thoughtful explainer of science. Already a physics rock star, his 1961-1963 Caltech undergraduate lectures, designed to reinvigorate the undergraduate educational experience, arguably became his most famous work. Compiled as The Feynman Lectures on Physics, they have sold over 1.5 million paper copies and are now available online as well. Anyone reading the compiled lectures or viewing the recordings will immediately recognize the contents as brilliant, complex, and subtle, pitched perhaps even above the high intellectual level of Caltech undergraduates, where being “just an average genius” could well mark you as an underperforming student.
Alas, I never met Feynman, though I had hoped I would. As a young whippersnapper assistant professor at the University of Illinois in the 1980s, I was pursuing parallel computing projects related to the Caltech Cosmic Cube and ongoing work at the Jet Propulsion Laboratory (JPL). (My Ph.D. dissertation was on the then crazy idea that we might build supercomputers using large numbers of microprocessors.) I agreed to serve on a panel at JPL simply because Feynman had agreed to serve as well. I wanted to discover for myself if even a portion of the oft-repeated stories were true. Unfortunately, by then, Feynman was too sick to participate, and he died soon thereafter from the effects of cancer.
Just a few years before this, my friend and later Microsoft colleague, Tony Hey, had worked with Feynman to convert a second lecture series, on computation, into another book, the Feynman Lectures on Computation. However, this essay is about neither Feynman’s physics or computation insights, but about how to first learn and understand deeply, then explain simply and clearly.
Feynman was unparalleled in identifying the irreducible core of any scientific topic and elucidating it with clear and simple language. If you doubt me, remember his iconic ice water demonstration of O-ring failures during the Rogers Commission investigation of the space shuttle Challenger explosion. As he later opined on the dangers of go fever, “The first principle is that you must not fool yourself, and you are the easiest person to fool." (See Nature Cannot Be Fooled.)
Feynman’s simple, yet powerful four step approach to understanding a topic and sharing it with others has been codified in the now eponymously named, Feynman Technique:
It seems simple, and it is, but as Leonardo da Vinci reputedly said, “Simplicity is the ultimate sophistication.” Let’s deconstruct each of these four ideas, with a few personal detours along the way.
Entire forests have been decimated by pundits and experts, all pontificating on the art of learning. Yes, you learn faster and more efficiently if you have had a good night’s sleep, have eaten breakfast, and (hear me, college students) are not hungover. Nor is a yellow highlighter required, though it can help. Learning is not a black art, it simply requires patience, diligence, and some paper and pencils.
Pick a topic you want to understand, something not so broad as to be an abstraction. The Theory of Everything is not the place to start, unless you are a theologian or cosmologist. Instead, choose something clear and specific (e.g., Why is grass green?) that can be understood in terms of simpler concepts, then write down everything you know about the topic.
The act of writing will undoubtedly raise some questions; find the answers, and write them down too. Finally, organize and reorganize what you now know into a coherent story. Throughout, look for missing elements and contradictions. Ask yourself questions and see if you can derive the answers from what you now know.
I wasn’t joking about the pencils. Writing (or typing) is a powerful way to force oneself to think about ideas and to internalize their relationships as a coherent narrative. For these reasons, good teachers, whether in secondary schools or universities, always tell students to take their own notes, even if the textbook or course materials contain the same information.
Learning is not a passive process; it is an active one that requires questioning how and why the jigsaw puzzle pieces fit together, and if what you have been told is internally consistent. If you exhaust the pile of known pieces, and the puzzle is still incomplete, congratulations, you have identified the need for original research.
In his biography of Feynman, James Gleick relates a story about Feynman studying for his qualifying examinations, one of those life altering transition points for every putative PhD student. In Gleick’s telling, Feynman created a book of study notes:
On the title page he wrote: Notebook Of Things I Don’t Know About. For the first but not the last time he reorganized his knowledge. He worked for weeks at disassembling each branch of physics, oiling the parts, and putting them back together, looking all the while for the raw edges and inconsistencies. He tried to find the essential kernels of each subject.
As Feynman knew well, even as a graduate student, rather than rote memorization or blind acceptance of textbook statements, you must assemble the pieces yourself. Only by peering behind the wizard’s magic curtain, studying the theme park ride’s engineering, repeatedly asking yourself how and why, then reproducing what you have seen, can you truly be confident you understand.
If you cannot recreate an idea from its elementary components, you do not really understand it -- knowing something is not the same as knowing the name of something. It is why teachers always tell students not to memorize formulas, but to understand the principles. From that, one can derive the formulas. I agree, but it is best to do both.
Now about that green grass; it is a scientific and biological marvel. Understanding how it functions in the biological ecosystem and why it is green requires internalizing several key topics, among them the electromagnetic spectrum, differential absorption, photosynthesis, chlorophyll (itself worthy of a deep dive), quantum efficiency, ATP, optics, visual perception, and cognition. (For extra credit, consider whether grass would be green if the sun were not a G-type main-sequence star.)
There’s a lot to assemble and connect before one can thoughtfully and clearly answer a child’s question, “Mommy, why is the grass green?” Doing so while not hiding behind jargon is even harder.
Feynman himself said that the best way to know if you have well and truly understood a concept is to explain it to someone else. There is no better litmus test of one’s own understanding than explaining something to someone young. A child has a shorter attention span, a smaller vocabulary, and far fewer emotional filters than most adults. Moreover, a child will ask excited, impertinent, and repeated questions in ways that the social conditioning of adults would prevent.
Nothing can humble an adult more quickly than a child’s persistent questions. Therein lies the beauty of explaining ideas to children. They have neither the social filters to prevent themselves from asking uncomfortable questions, nor the shame in admitting ignorance. They expect clear, simple answers, all consistent with their own experience. Using unnecessarily complex technical jargon is also a sign of, at best, intellectual laziness, and at worse, one’s own shallow understanding.
Let me be clear. I believe every child is born a scientist, innately inquisitive and curious how the world works. They embody the essence of science, doggedly asking questions, being unsatisfied with answers that will not withstand scrutiny, and being utterly unimpressed by intellectual stature or pedigree. A resigned and frustrated appeal to authority – “because I said so” – is an indicator of elucidative failure. Though the incessant, “But why?” may seem like an infinite regress, fear not. It’s not turtles all the way down, and the Münchhausen trilemma lives to fight another day.
The classic xkcd Up Goer Five cartoon about the Saturn V rocket, which uses only the ten one hundred most common words (thousand not being among them), illustrates how challenging it can be to explain complexity via only simple words. If you doubt me, try explaining why the grass is green using a text editor that limits you to just those one thousand (uh, ten one hundred) most common words. (Grass is not among them; you’d best start with “green sticks that grow toward the sky” instead. This might also be a good time to think about explaining phototropism and the rabbit hole of multicellular biology.)
In a televised lecture, Feynman himself talked about the egalitarian nature of scientific exploration, noting, “It doesn't make any difference how beautiful your guess is, it doesn't matter how smart you are, who made the guess, or what his name is… If it disagrees with experiment, it's wrong. That's all there is to it.” (See The Epistemology of Science.)
Years ago, when I was a corporate vice-president at Microsoft, the company’s quantum computing project, Station Q, which was then located at UC Santa Barbara, reported to me as part of the eXtreme Computing Group. Station Q was headed by Michael Freedman, himself a Fields Medal winner, in recognition of his work on the generalized Poincaré conjecture.
As part of our computing research, we regularly brought leading experimentalists and theoreticians together for a multiday workshop, all of whom were working on topological approaches to quantum computing. The intellectual throw-weight of the invitees was a highly skewed sample of the smart kids club; everyone in the room had won their school’s fifth grade spelling bee.
Although I can impress unsuspecting undergraduates with a whiteboard discussion of quantum computing, superposition, Hilbert spaces, and Bloch spheres, I am by no means a quantum computing expert. Hence, I fully anticipated to become intellectually lost at some point during the workshop presentations, though I expected to understand the gist of each. As the first theoretician began speaking, I was dismayed, however, at just how quickly I was overwhelmed. After just a few minutes, as complex and subtle mathematics danced and whirled, juxtaposed with “as you can clearly see” phrasings, I realized I had no idea what the speaker was discussing, or even the point he was trying to make.
Just as I was about to sigh with resignation, open my laptop, and begin responding to my denumerable but enervating steam of backlogged email, an equally accomplished experimentalist spoke up, saying, “Excuse me, but I have no idea what you are trying to say! Can we go back to the beginning, please? I have some basic questions.” As I looked around the room, gratified that I was not the only lost soul, the puzzled but relieved expressions revealed everyone – theoreticians and experimentalists alike – felt the same way.
All of the very smart, highly educated people in the room were confused, and not in the way Niels Bohr meant when, in a mashup of a Zen koan, a physics superposition joke, and Dadaist wordplay, he had commented, ““If you are not confused by quantum mechanics, you do not understand it.” Rather, we were confused due to inadequate and opaque explanation.
The speaker responded quickly and with good humor, asking where people had become lost. What then followed was a lively give-and-take of questions and answers that led to mutual understanding, a pattern that continued throughout the day. None of that would have happened had one person not evinced the intellectual courage to profess total confusion and an utter lack of understanding.
Although I was just a bit player in this quantum play, regrettably, I had starred in my own production. As a senior graduate student, I had given a colloquium on my research, where I had excitedly derived all the queueing theory formulas underlying my work. It was a disaster, leaving everyone else confused and me flustered and disheartened. Afterward, my advisor pulled me aside and offered some sage advice, reminding me that the goal of a colloquium is to tell the audience what you have done, why it matters, and why they should care. As he put it, “If you excite people with clear rationales and explanations, they will want to read the paper later to understand the details.” Wisdom that.
Linguistic castles of idiosyncratic academic buzzwords frequently isolate individuals and disciplines, preventing collaboration and shared understanding. It is an unfortunate academic disease, and too few seek the Strunk and White treatment. (See Renaissance Teams: Reifying the School at Athens.) As anyone who has read (or tried to read) research papers or academic books knows all too well, we in academia often hide behind turgid, meandering prose. In the name of intellectual precision, we seek to impress and sometimes to obfuscate, rather than elucidate and explain. Perhaps driven by intellectual insecurity, we fear clear explanation might imply our ideas are simple and obvious – and sometimes they are. I have been guilty of this myself.
Remember, the primary purpose of communicating is not to impress others with one’s intellectual prowess and academic stature, it is to share ideas and invite discourse. A research colloquium, paper, or poster is a not a denouement, but a prelude to conversation, one where inevitable knowledge gaps are identified and rectified, the third step in the Feynman technique.
If you have ever spoken to a grade school class, whether as a practicing scientist or as a part of a “take a parent to school” day, you know the questions can be far more challenging than those experienced during either a venture capital startup pitch or a research colloquium. You need to know your stuff, because you will be asked, and you may discover that you are, in fact, not smarter than a fifth grader.
Humbled by such experiences, any teacher will readily confess that when teaching a class for the first time, even one in an intellectual area where they are an acknowledged expert, perhaps even the world’s leading authority, the teacher learns far more than any of the students. Whether from the unexpected questions that reveal poor explanations, or in an advanced graduate class, questions with no currently known answers, the act of explaining is a test of one’s own understanding. Explaining anything shows others what you truly know and do not know, exposing the gaps in your own knowledge, and it makes you ask yourself questions.
Occasionally during lectures, most often during a graduate class, I have had a student ask me a question that truly made me pause and think, “Why is that really so?” only to realize I was not sure myself. At point, honesty is the only approach, “I do not know, but I will answer your question in detail at the start of the next class.” At that point, it is time for some faculty homework, or maybe some original research.
Why is that so? How does this relate to that? I was told this, but why is it true? Is it actually true? Such questions are one of the purposes of PhD qualifying examinations, to assess a student’s knowledge base and readiness to conduct independent research. Do the student understand – at a deep level – or is the student merely parroting words, phrases, and concepts?
They may be using a CRISPR to edit genes and polymerase chain reactions (PCR) to amplify DNA, or deep neural networks to classify Seyfert galaxies, but can they explain mitosis, stellar evolution, and the convergence properties of stochastic gradient descent? Mastery of the known is a prerequisite to exploration of the unknown. (See Research: When There Are No Answers in the Back of the Book.)
Sadly, not all initial attempts at explanation succeed, as anyone who has taught or attempted to explain something to others will ruefully acknowledge. During their educational journey, each student develops a finely tuned sense of teacher quality and explanatory skill. Within minutes, seasoned students can separate experienced and effective explainers from those less adept. However, recognizing is not the same as doing, as more than one inexperienced teacher has learned to their chagrin. Explaining something clearly and accurately takes both skill and practice, though there are best practices.
Good teachers watch their students while speaking, seeking non-verbal cues for explanatory clarity. Confused faces trigger alternative explanations, additional decompositions, and new analogies. Recognizing that it takes courage to admit ignorance, good teachers encourage and welcome questions, for they provide additional educational opportunities. If one student is confused, others almost always are as well.
A teacher who is dismissive of questions is rarely an expert, but more often fears having their inadequacies exposed. Conversely, true experts have a deep reservoir of background knowledge on tap for interactions and clarifications. A willingness, even an eagerness, to entertain questions is a hallmark of someone who truly understands their subject and takes joy in sharing that understanding with others. Indeed, the joy of explaining something well is its own reward, reflected in enlightened faces.
There you have it – the Feynman technique for learning:
The next time a child asks why the sky is blue, do not send them to the encyclopedia. Embrace the curiosity and wide-eyed wonder, sit down and begin explaining, using words they understand. Share the passion and the joy! Few things in this world are more fun and exciting than science, if explained well and correctly. (See Science: It’s About the Wide-Eyed Wonder.) Under no circumstances should you consider saying – Rayleigh scattering. Though true, it is an obfuscation, not a clarification. Hiding behind big words and technical phrases is a “baffle them with B.S.” parlor trick to hid one’s own lack of understanding and intellectual depth.
Study the camber of the wings, twist the ailerons, move the rudder, work the pistons, spin the propeller, oil and assemble the parts. Build a model yourself. Then, when the intellectual airplane takes flight, you will understand how it flies, and you will not need fancy phrases like laminar flow or Bernoulli principle to explain how and why to others. (And for the record, that grade school demonstration, where the teacher blows over the paper, watches it rise, and exclaims, “This is how airplanes fly!” is not quite the correct explanation of lift.)
Finally, remember Feynman’s admonition, “Nearly everything is really interesting if you go into it deeply enough.” Go, learn, explore, explain, celebrate!
Memoranda (memos) are the detritus of organizational processes, frequently written only so the writer can point to the message as prima facia evidence of purported communication, concern, action, or principles: mistakes were made, committees have been appointed, or actions will be taken. Some are internal, revealing the jaded reality behind polished exteriors. Some are leaked, some are belatedly discovered in the archives of history, and still others are published and promulgated widely.
Not surprisingly, whether in academia, industry, or government, cynicism often runs high among any memo’s target audiences, for such memos can be turgid and meandering, written and edited by committees. Consequently, most are little read, and even fewer are remembered, much in the spirit of the old Communist adage, “They pretend to pay us, and we pretend to work.” The exceptions prove the rule, exposing existential concerns and broader truths, part of an authentic dialog.
Long ago, I was privileged to serve as head of the computer science department at the University of Illinois, one of the country’s great academic teams, famous for its work in high-performance computing. In those pre-Internet days, a veritable blizzard of paper memoranda came to me from the offices of the college dean and the university administration. After scanning them, I discarded the vast majority, forwarding only a few of the most relevant to the paper mailboxes of my colleagues, who still good-naturedly complained about unnecessary deforestation. Reminding them that I filtered the chaff and that they were only seeing the “good stuff,” I bided my time.
Eventually yet another memo from the campus reached my desk regarding campus pouring rights and the new campus contract for either Coca-Cola or Pepsi products. Though practically important to the campus for its financial contributions to scholarship, it was largely irrelevant to most. Seizing my opportunity for friendly parody, I wrote an extended, tongue-in-cheek cover memo encouraging my colleagues to engage in shared governance and participatory democracy by attending the upcoming campus town hall and expressing their views on one of the great and important issues of our day – the relative merits of Coca-Cola and Pepsi. I’d made my point, which elicited some laughter and friendly discussion at our next faculty meeting.
One of the most famous memos in all of computing, perhaps rivaled only by Bill Gates’ 1995 Internet Tidal Wave memo, was written by IBM President Thomas Watson Jr. on August 28, 1963. The entire memo, directed to the company’s senior leadership prior to an upcoming retreat at Jenny Lake, New York, is reproduced below:
August 28, 1963
Last week CDC had a press conference during which they officially announced their 6600 system. I understand that in the laboratory developing this system there are only 34 people, “including the janitor.” Of these, 14 are engineers and 4 are programmers, and only one has a Ph. D., a relatively junior programmer. To the outsider, the laboratory appeared to be cost conscious, hard working and highly motivated.
Contrasting this modest effort with our own vast development activities, I fail to understand why we have lost our industry leadership position by letting someone else offer the world’s most powerful computer. At Jenny Lake, I think top priority should be given to a discussion as to what we are doing wrong and how we should go about changing it immediately.
T. J. Watson, Jr.
Condensed to its essence, Watson asks how a pipsqueak little company could develop a world-leading technological marvel that eclipsed the efforts of a Fortune 500 corporation. After all, IBM’s slogan was and is simply THINK. I pondered similar things myself while at Microsoft, as we in the senior leadership looked at the Apple iPad and iPhone with similar dismay during our executive retreats.
At the time of Watson’s memo, IBM was the world’s largest computing company, deep in the development of the IBM System/360, a multibillion dollar, “bet the company” plan to develop an entire product line of instruction set compatible computers. The successful result reshaped the computing industry and ensured IBM’s dominance for the next twenty years. In the years prior, IBM had developed the IBM 7030 Stretch, the fastest computer in the world until the CDC 6600 appeared, a leadership role IBM would not reclaim until almost forty years later.
On being told of Watson’s memo, CDC’s Seymour Cray, the architect of the CDC 6600 and widely known in computing for eschewing bureaucracy, reportedly quipped, “It seems like Mr. Watson has answered his own question.” Cray, universally lauded as the father of supercomputing, later designed the eponymously named Cray-1, once again the world’s fastest computer. However, this is not a story about supercomputers, but about culture, shared responsibility, and respect.
As Watson noted, IBM had vast development activities and a large cadre of visionary and talented people, luminaries like Fran Allen, John Backus, Fred Brooks, John Cocke, and Gene Amdahl, several of whom, along with Seymour Cray, I have known over the years, and others I was blessed to count as colleagues. Where IBM struggled, by its own admission, was not talent; it was bureaucracy and culture. Some of its best minds left the company to pursue their own ideas, and others were relieved of responsibility when they challenged the orthodoxy.
As an example, John Cocke’s own, legendary work on the IBM 801, which long predated the reduced instruction set computer (RISC) revolution, took years to see the light of day. In a story that has been told and retold, and embellished with retelling until it has become the stuff of legend, John returned from a meeting with management, frustrated and disgruntled by their lack of vision. He gathered his team and reputedly said, “<EXPLETIVE> them! We are going to build the best <EXPLETIVE> computer the world has ever seen.”
With IBM’s tolerance and unencumbered by market niches and product lines, they did just that, though the reality was more nuanced and complicated than this story might suggest. It was a powerful combination -- John’s vision and the quality of his team, each of whom knew this was the intellectual opportunity of a lifetime. Because the project lacked the blessing and imprimatur of any product line, they named it the 801, after the offices where they worked. (The New York Times has a great story about John Cocke and his maverick ways.)
Mr. Watson’s deprecating allusion to the janitor is telling. As Tolstoy remarked in Anna Karenina, “Happy families are all alike; every unhappy family is unhappy in its own way.” Dysfunctional teams can and do fail in a variety of ways. Conversely, great teams, regardless of the mission and objective, share several common attributes.
First, the team members believe passionately in a greater goal and mission, as something worthy of shared commitment and mutual sacrifice. (“We are going to build the fastest computer the world has ever seen.”) Individually and collectively, they are united in their desire to do something that will be remembered, something others may believe improbable or impossible; they want to make a difference. Whether that vision originates from the team’s leader or from one of its members matters not.
Second, a successful team is greater than its parts. Each team member recognizes and understands they each have a critical role, and if they fail individually, the team will fail collectively. Hence, each depends on the others to not only meet their commitments but to exceed them. They will disappoint or fail their partners. Everything and everyone matter, and everyone knows it, including the janitor.
It is also why being “lean and mean” is often so critical to success. With no distractions, no tangential activities, leadership shielding them from bureaucratic intrusions, and just enough resources to complete the task – but no more – everyone is highly motivated and keenly focused on the goal and the competition. The fierce urgency of now motivates one and all. Venture capitalists know this well; overfunding a startup is as deadly as critically underfunding.
Third, leaders know the buck stops with them. They bear the ultimate responsibility for the team’s overall performance, and they will accept blame if necessary to protect the team. Steve Jobs often talked about the difference between the janitor’s responsibility and that of a leader. As he put it, if the janitor cannot empty the trash because the locks have been changed and the janitor lacks a key, that is understandable. Conversely, a leader must anticipate such problems and clear the path for the team. Locked doors cannot be a barrier.
The Spider-Man rule applies to leaders: With great power comes great responsibility. Sometimes, that responsibility means providing the vision and strategy that animates the team, other times it means holding the bureaucracy at bay. Or, it can mean getting new keys made over the weekend, so the janitor can empty the trash without interruption. In other cases, it can mean buying and personally delivering pizza to fuel the inevitable late nights, or sweeping the floors if the janitor is ill. Good leaders do what needs to be done, instinctively and unhesitatingly, without drama.
Finally, and most importantly, in a great team, everyone is respected for their differential skills and responsibilities. The janitor matters, just as does the Ph.D., the engineer, and the software developer, albeit each with different roles and expectations. As M. L. King, Jr. once said, “Whatever your life’s work is, do it well. A man should do his job so well that the living, the dead, and the unborn could do it no better.” There is virtue and honor in being a great janitor, just as there is in being a computer architect.
Never forget, the way leaders treat each individual sets the organization’s tone. In the cultures of great teams, the members feel empowered to generate ideas, offer suggestions, question authority, and take the initiative. If the leader respects and values the janitor, treating him or her as an equal and valued partner, the other team members will know, intuitively and immediately, that they too are valued, and they will respond accordingly. This is the working definition of servant leadership.
Inertia is endemic to large organizations, where bureaucratic processes can hamper or even strangle innovation. In response, wise leaders often create alternative cultures, research institutes, and skunkworks that let radical innovation flourish unimpeded by memoranda, processes, and bureaucracy. It is precisely this dichotomy – those who work within established paradigms and those who are compelled to create new ones – that Thomas Kuhn explored in his book, The Structure of Scientific Revolutions. Both are needed, and effective cultures support and nourish both.
Unfettered exploration by passionate individuals and committed teams is the fundamental premise of academic freedom and exploratory research, whether in universities or in industry. As Vannevar Bush perspicuously noted in his famous memo to President Roosevelt at the end of World War II, “Scientific progress on a broad front results from the free play of free intellects, working on subjects of their own choice, in the manner dictated by their curiosity for exploration of the unknown.” Such individuals and teams rarely read memos; trust me on this.
And the next time you see a janitor, or anyone else engaged in a seemingly thankless but important task, stop, smile, and say thank you; they too make a difference. The word will spread, far faster and far more effectively than via any memo.
Growing businesses and economies depend critically on a pool of educated people, leading edge ideas and technologies, robust investment, and growing market opportunities. Whether advanced manufacturing, information technology, energy, medicine and healthcare, or a host of domains, in an increasingly competitive global economy, continued innovation, fueled by a judicious combination of private and public sector actions, is key to success. Implicit in this model is an educational system that attracts and trains the talented individuals needed by businesses and a research and development environment that continually catalyzes new products and services.
Every two years, the National Science Board (NSB) assesses the state of the U.S. research and development enterprise, issuing a Congressionally mandated report on science, technology, engineering, and mathematics (STEM) education and U.S. investment in research and development. How is the U.S. faring in education and innovation relative to the rest of the world? Simply put, not nearly as well as in the past.
The NSB is a group of twenty-four scientific experts, appointed by the U.S. President for six-year terms. The NSB has two statutory responsibilities. First, it provides strategic, policy, and budgetary oversight for the National Science Foundation. In its second role, most relevant here, it provides advice to the White House and Congress on science and technology issues, STEM education, and workforce concerns. I am honored to serve as a member of the NSB.
This year’s NSB report, The State of U.S. Science and Engineering 2022, shows that other countries have learned from the historical successes of the U.S., and they are dramatically increasing their investment in STEM education and research and development. Even as U.S. intellectual output has grown, China has surpassed the U.S. on several key metrics, including the number of science and engineering research publications and the number of issued patents, and it is at near parity on the number of science and engineering doctoral degrees awarded. Overall, the U.S. still leads, but not by nearly as large a margin as in the past.
Let’s begin by examining STEM workers in the U.S., which now comprise 23% of the total U.S. workforce. These STEM workers include a wide spectrum of occupations, ranging from scientists, engineers, and healthcare workers through technicians and workers in production and construction. About 16 million of these STEM workers hold a bachelor’s degree or higher, with the remaining 20 million comprising the skilled technical workforce (i.e., with associate degrees, certificates, and other technical training). Reflecting their criticality to the U.S. economy, throughout the COVID-19 pandemic, these high paying STEM jobs were resilient, with STEM workers employed at a higher rate than their non-STEM counterparts – at all education levels.
Sadly, U.S. STEM education leaves many students behind in our K-12 schools. U.S. 15-year-olds rank between Hungary and Turkey in national mathematics proficiency, behind the large majority of industrialized economies. However, the challenges begin even earlier. On average, U.S. 8th grade students of all ethnicities who are eligible for free or reduced school lunches fail to achieve mathematics proficiency, but the disparity is most pronounced for Black, Hispanic, Native Hawaiian or Pacific Islander, and American Indian or Alaska Native students. If a student is fortunate to be in a higher socioeconomic status (SES), the mathematics news is not much better.
Moreover, K-12 STEM teachers with fewer than 3 years of experience are more prevalent at schools with high-minority or high-poverty populations, perpetuating generational inequities. Nor does this inequitable state of affairs improve in college and post-graduate studies, where women and individuals of color are substantially underrepresented in STEM fields.
Why is it culturally acceptable in the U.S. to say, “I’m bad at math and science” when few would willingly confess to semi-literacy by saying “I don’t read very well.” Unless we reward and remunerate STEM teachers, celebrate student STEM accomplishments, and value scientific and mathematics literacy, the U.S. is likely to continue falling further behind its global competitors.
In a previous life, I was a Microsoft corporate officer, and met I often with government leaders from around the world. In my travels, I consistently emphasized the importance of a country’s people as its only truly renewable resource. I also reminded leaders that talent comes from everywhere – from all ethnic groups and socioeconomic strata. Wise leaders nurture and cultivate talent everywhere, without exception. The U.S. needs to heed that lesson.
If the U.S. STEM workforce were truly representative of the U.S. population, millions of additional skilled workers would be inventing the future and supercharging the U.S. economy. These “missing millions” are a huge lost opportunity for the U.S., all at a time when an increasing fraction of jobs require critical thinking, technical knowledge, and mathematical reasoning.
To meet a portion of this shortfall, the U.S., has historically imported talent, particularly at advanced levels. In my own discipline, computer science, over 60 percent of Ph.D.’s. working in the U.S. are foreign born. Many of them came here because U.S. research universities remain the envy of the world, attracting some of the world’s best and brightest.
However, the numbers of immigrant graduate students have declined recently due to COVID-19, shifting U.S. immigration policies, and rising opportunities in their home countries. U.S. academic preeminence is also increasingly at risk, as state support for public higher education declines, tuition rises, and competition for limited research funding intensifies, making it harder for domestic students to attend college, continue graduate studies, and pursue faculty careers.
Continued U.S. scientific and engineering leadership, with its manifest economic benefits, is not a birthright. Every business person instinctively knows the truth of the aphorism from Intel’s Andrew Grove:
Success breeds complacency. Complacency breeds failure. Only the paranoid survive.
Leadership is built on sustained and strategic investment in education and critical workforce skills, as well as a collaborative industry and government research and development investment engine that fuels new businesses. The U.S. is now at a critical inflection point. It is time to up our game by investing in our people and infrastructure to compete more effectively in the 21st century.
First principles, ab initio computational modeling of biological processes is one of those grand visions, like artificial general intelligence, that both inspires the imagination and conjures images of mad scientists exclaiming, “It’s alive!” At present, we are a long way from either, but real progress is being made. At times generative adversarial networks and systems biology may seem like magic, but they are not, Arthur C. Clarke’s observation (“Any sufficiently advanced technology is indistinguishable from magic.”) notwithstanding. There is no magic, simply a plethora of scientific and technological advances by global teams of researchers.
The dream of predictive biological modeling is an old one, with many proponents, dating to some of the earliest days of digital computing. Since then, progress has been slow, but steady, dependent on both greater understanding of biological processes and increased computational power. To be clear, ab initio biological modeling is distinct from artificial life, which examines systems and emergent behavior similar to, though different from biology, though there are important connections.
Why do we want to build such models? The first and foremost reason is to validate our understanding of biological processes, comparing computational predictions with experimental results. As Richard Feynman once remarked, “What I cannot create, I do not understand.” If we cannot build models that accurately duplicate experiment, we do not fully understand the biology. The second, of course, is to allow us to understand how changes to genes and gene expression can affect biological processes, with profound implications for the environment, and for health care and treatments. A corollary of this is the ability to predict the effects of gene editing and other modifications, all within critical ethical boundaries.
Twenty years ago, when I was director of NCSA, the NSF Director, Rita Colwell, and I used to discuss the possibility of predictive whole cell modeling, the limitations in our biological knowledge and the computing infrastructure that might be required to make such a thing possible. A brilliant biologist, she knew for more about the biological aspects than I ever have, though I’d like to believe I brought a bit of computing game to the discussion. Mind you, at that time, the Human Genome Project was still underway, and we were really excited to have just deployed terascale Linux clusters and the NSF TeraGrid as national resources. Since then, we have come a long way in both biology and computing.
Experimentalists have long targeted certain, simple lifeforms (e.g., Caenorhabditis elegans, a transparent nematode; Arabidopsis thaliana, a small flowering plant with a short lifecycle; Drosophila melanogaster, the fruit fly; and Saccharomyces cerevisiae, a yeast) as model organisms. The simplicity or specific morphology of those organisms simplify study. Similarly, those building computational biological models have long chosen to start with the simplest living cells.
This raises the obvious question: what is the minimal genome required for life (i.e., the cell cycle of birth and death), and concomitantly, can we either find or construct such a minimal genome. Minimal is a subtle thing, as the set of genes needed for a cell to grow and reproduce in a perfect medium is different from the larger and interrelated set that allows it to thrive in adverse or changing conductions. Finally, the interrelation is key; the sum (the gene network) is more than the parts (the genes themselves).
In 2010, researchers at the J. Craig Venter Institute (JCVI) first created a chemically synthesized genome (JCVI-syn1.0), then in 2016 succeeded in creating a synthetic bacterial cell with a minimal number of genes, but capable of reproducing in a controlled environment. JCVI-syn3A consists of fewer than 500 genes on a single circular chromosome. (See the Science articles in 2010 and 2016 for the details.) Not surprisingly, JCVI-syn3A has since been the target of whole cell modeling, of increasing sophistication.
I wrote about the excitement surrounding the 2010 synthetic genome work in the Communications of the ACM, in a piece entitled In Vivo, In Vitro, In Silico. (I also posted on this blog.) As I noted then, the work was a fascinating example of the interplay of biology and computing, but it also highlighted that we did not fully understand the function of all the genetic material in even this simple genome. Indeed, when the 2016 article was published, the precise biological functions of roughly 30 percent of the JCVI-syn3.0 genes were unknown; the function of more, though not all, is now understood.
In 2019, a University of Illinois team (Luthey-Schulten and colleagues) constructed a computational genome scale metabolic model (GSMM), a flux balance model, of the metabolic network (the metabolism) of JCVI-syn3A, showing how DNA specifications drive molecular processes. That work, published in eLife, also raised questions about the minimal genome, offered some suggestions for further gene removal, and raised questions about some essential genes whose function was unclear.
I was beyond thrilled to read the latest whole cell modeling results from this team, which has continued expanding in silico models of JCVI-syn3A. Remember, a detailed whole cell model (WCM) includes the cell’s size, shape, components, reactions within the cell, and environmental interactions, all along the cell lifecycle. Published in Cell earlier this year, these simulations of a group of roughly 200 cells are a fully dynamical kinetic model capable of demonstrating emergent behavior. (The University of Illinois press release can be read here.)
As the authors note, metabolism, genetic information processes, and growth can, at present, only be modeled via hybrid stochastic and deterministic simulations. Specifically, they model the metabolic network via deterministic ordinary differential equations (ODEs) and the kinetics of genetic processes via stochastic models on a cubic lattice of the cell interior. Given their computational requirements, the latter are dependent on GPUs for acceleration. The additional details on the mathematics of the models are found in the paper, and the code is available on github (here and here), a substantial fraction of which was written in Python.
Multiple groups are continuing to build ever more detailed cellular models, leveraging both growing biological understanding and greater computing capability. Modeling the cellular behavior of a cell with a minimal genome is another step into an exciting future.
N.B. In rapidly changing, global knowledge economy, social, cultural, and economic advantage accrues to those societies that cultivate, nurture, and empower all of their citizens via advanced education, freeing each person to pursue their individual dreams, to the mutual betterment of all.
On May 12, 1780, John Adams, then in France as part of a diplomatic delegation for the still nascent United States, penned a poignant letter to his wife, Abigail, in which he described the manifold and beguiling beauties of Paris. Anyone who has strolled down the Champs-Élysées or visited the Palace of Versailles knows instinctively and immediately why Adams was so impressed. (Mind you, that other modern wonder, the Louvre Museum, did not open until 1793, long after Adams had sailed home.)
In a forward nod to Abraham Maslow’s hierarchy of needs, Adams resignedly acknowledges that his duty and commitment to an aborning country then struggling for its very existence must take precedence over his personal interests and desires. No matter how much he might yearn for different circumstances, immediate necessity must triumph over the desired:
I must study politics and war that my sons may have liberty to study mathematics and philosophy. My sons ought to study mathematics and philosophy, geography, natural history, naval architecture, navigation, commerce and agriculture, in order to give their children a right to study painting, poetry, music, architecture, statuary, tapestry and porcelain.
Across more than two centuries, Adams’ lament still rings true, albeit with amendments to embrace equity for all. The luxuries of time and resources, made possible by the successes of previous generations, afford their blessed progeny to pursue intellectually and emotionally rewarding endeavors of all kinds – art, music, literature, science, engineering, medicine, business – beyond those dedicated to immediate sustenance and survival.
Adams elegant characterization of the seeming dichotomy between present exigencies and an imagined, better future is now manifestly and explicitly visible in our debates about higher education as a public good accessible to all or a private benefit to only those willing and able to pay, as a birthing place for thoughtful and discriminating citizens of a democratic society or a workforce training and skills delivery system in a globally competitive world.
In a perfect world, each child would be educationally empowered and free to live a life defined by the content of their character, the scope of their talent, the reach of their dreams, and their willingness to work. Sadly, we do not live in that perfect world. We must do better and do so now.
The circumstances of birth, gender, ethnicity, socioeconomic status, and a host of other factors all too often circumscribe a child’s future, with opportunity crushed and dreams stillborn. In theory, there is no difference between theory and practice. But in practice, there is. We must do better and do so now.
The students of our public research universities are neither socioeconomically, culturally, nor ethnically homogeneous. In that diversity lies both our strength – we all better when we both learn from others and respect and value our differences – and our educational challenge, for not all begin their educational journey on an equal footing.
For the sons and daughters of the rich and powerful, college is a place for maturation, exploration, and education. They have the freedom to stumble and recover, secure in the safety that societal status and money provide. The path for others is more narrow and difficult, even for the increasingly financially challenged middle class. Whether first generation, immigrant, underrepresented, or poor, college can be both a rare chance and a desperate struggle. As poor, first generation college student, on this I speak from some personal experience. (See Transforming Lives Via Public Higher Education and A Taste of Sherbet.)
Far greater than my own, students of color face compounded challenges alien to most and unimaginable to many. Biases – implicit and explicit – can and do make them feel marginalized and unwelcome, carrying social and cultural burdens that majority students neither see nor feel but sometimes themselves foster, whether intentionally or unintentionally. First generation students, regardless of background, often experience imposter syndrome and lack the social support networks common in families for whom college has been a multigenerational experience, with no familiar place to seek counsel and support when challenges arise.
Finally, life has a way of intruding in expected and unexpected ways. Despite our national angst over student debt and graduation rates, many students who fervently hope to finish a college degree never do, and an even larger fraction lack the opportunity to try. The reasons are myriad and diverse, including poor educational preparation from underfunded primary and secondary schools, lack of finances to pay tuition and housing, a multigenerational family needing support, feeling unwelcome and out of place, or simply not seeing curricular relevance to their personal objectives and needs.
The American Dream was built on the shared principles of economic opportunity and upward mobility. The idea that everyone deserves a fair shot – not a guarantee, but a fair shot – to better their socioeconomic circumstances through hard work and determination is a central tenet of the American social contract. Today, the lifeline of higher education and successful pursuit of the American Dream is increasingly out of reach for a growing fraction of the U.S. population.
Recognition of higher education’s importance to our nation’s future is not a partisan issue. Multiple U.S. President’s from both parties have repeatedly convened august bodies to recommend ways to improve K-12 and post-secondary education, beginning with the 1946 Commission on Higher Education for American Democracy, commonly known as the Truman Report. That report recommended expansion of federal financial aid for college students and the creation of a community college system, noting
If college opportunities are restricted to those in the higher income brackets, the way is open to the creation and perpetuation of a class society which has no place in the American way of life.
Despite these stentorian calls for action, a large fraction of children born in poverty still remain there, and less than 15 percent of students from the lowest socioeconomic bracket earn a bachelor’s degree by age twenty-four. No euphemistic intellectual legerdemain can hide the ugly truth of our current situation. It is neither unfortunate nor regrettable; it is morally and ethically wrong. Beyond educational opportunity as an ethical imperative, it has deep and profound justifications on economic and competitiveness grounds. (See The Hopes of Parents and the Dreams of Children on the importance of giving every child a chance.)
Public research universities are in the business of helping people. This is especially true in times of accelerating and disruptive change. Compounded by the consilience of globalization, each year now brings disruptions that once defined generations. In an earlier time of rapid change, the land-grant universities, authorized by the 1862 Morrill Act, were created to spread new agricultural and mechanical knowledge and “promote the liberal and practical education of the industrial classes in the several pursuits and professions in life.” In modern parlance, the objective was to educate an underserved population, one whom extant higher education neither welcomed nor served.
Similarly, the Servicemen’s Readjustment Act of 1944 – the G.I. Bill – brought millions of veterans to college and university campuses, and it firmly rooted the graduates in the expanding middle class when many feared a post-war slide into recession. The G.I. Bill, along with post-World War II government investment in academic research, further expanded during the Cold War via the National Defense Education Act of 1958, reshaped academia and created the modern research university. Finally, the Civil Rights Act of 1964, the Higher Education Act of 1965, and Title IX of the Education Amendments of 1972 broke down further legal and economic barriers to educational access, though we have by no means eliminated those impediments.
Today, urbanization, global communications, artificial intelligence, talent mobility, and the global knowledge economy are reshaping social expectations. These changes are disrupting the historical web of interconnected economic sectors, while rendering some professions superfluous and creating demands for new skills in others. A recent McKinsey report, The Future of Work in America, discusses regional economic stratification and the anticipated changes in workforce needs in the next decades.
The National Science Board, of which I am a member, recently issued two reports raising similar concerns. The first of these, on The Skilled Technical Workforce, emphasizes the need for greater investment in community college technical training. The second, on The State of U.S. Science and Engineering, paints a worrisome picture of the position of the United States relative to its international competitors.
Our world is changing rapidly, and public higher education must adapt and engage as active and honest participants in renegotiating the societal compact. Any compact reification begins with identifying the irreducible core – the essential values that define public research universities. Quite clearly, these are unfettered discovery (original scholarship and research), transference (student education and training) and fulfillment (societal engagement and services). Put another way, public research universities create new knowledge, transfer insights and ideas, and deliver solutions to societal problems.
How universities do those things and in what measure are the subject of debate and negotiation. After all, the practices of a modern public research university span less than a single human lifetime. Some of the options will be new, and some will undoubtedly be philosophically challenging. (Remember that many feared the G.I. Bill would destroy U.S. higher education by bringing battle hardened veterans into academe.)
While protecting the irreducible core – the defining verities of academia – I believe we must be more organizationally nimble and flexible, more financially accountable and economically savvy, more societally engaged and responsive, more collaborative and transdisciplinary, and more team oriented.
We face deep societal challenges – poverty, inequality, injustice, health and wellness, environmental sustainability, and economic uncertainty – and it is tempting to embrace a psychology of diminished expectations. But we also have unprecedented opportunities to unleash the creativity and talent of our citizens. A university frees successive generations of human minds to realize their potential. We must not waiver in our commitment to the future of our students and our society.
The intellectual dichotomy between lifelong knowledge and near term skills is a canard, patently and demonstrably false, but the economic and social dilemma is very real. Our students arrive with manifestly different constraints and needs, and we must meet each of them where they are and respond accordingly. Put another way, one educational model and approach does not fit all.
I believe we must move beyond undergraduate degree completion as the sole, simplistic, and arbitrary metric of educational success. Degrees matter, but all education is valuable. It is neither a simple, linear process with a single terminus nor is it synonymous with an immediate continuation beyond secondary school. Degrees matter, but only insofar as they are talismans – social currency and indicators of our true objective, which is bettering the lives of our students.
If a student completes a single course and comes away with a deeper perspective on our world, we have added value to their life. If another completes a certificate, gains a demonstrated competence, and achieves meaningful new employment, we have changed their economic circumstances for the better. If we have informed and enlightened a community group, we have enriched our society, even if it were just a weekend seminar and discussion.
I believe we in academia must step back, and live up to our intellectual principals and ideals, remembering that no matter how high the academic ivory tower, its foundation and that of all public higher education, rest on a repeatedly renegotiated societal compact. The history of U.S. higher education is one of punctuated equilibria, with long periods of stability separated by brief interregna of extraordinary change, each coincident with shifting societal norms and expectations for broader access.
We must be more flexible in responding to the heterogeneity of student expectations and needs, while concurrently disintermediating knowledge delivery, certification of completion, and socialization across time and space. (See Pinball, War Games, and Universities.) I believe this means offering a smorgasbord of educational delivery modalities, content packages, and success models, each tailored to differing needs and constraints:
Some might say many of these educational roles and responsibilities are the proper function of community colleges and comprehensive universities. That is true, in part. Higher education is a system, with differentiated and overlapping, but complementary roles for community and technical colleges, comprehensive universities, and public research universities.
However, in a rapidly changing world, public research universities are the critical incubators of intellectual discovery and translation, with national and international stature and partnerships. They also attract a faculty and student talent base in part distinct from all others. As such, they bear unique responsibility as educational thought leaders and innovators, with the resources and capability to conduct educational experiments at scale. They are also charged with ensuring the economic competitiveness of our citizens and industry in a global knowledge economy.
As a flagship public research university and a member of the Association of American Universities (AAU), the University of Utah is a leader in basic research, knowledge creation, and scholarship. It is also unwaveringly committed to educational access, broad-based liberal arts education, workforce skills training, and economic development.
A University of Utah Student Success and Educational Futures Taskforce, composed of seventy five students, faculty, and staff members, assessed the current state and future needs of higher education in Utah, based on a charge to consider our changing world and the needs of Utah’s citizens. Composed of six working groups – facilities and infrastructure; financial analysis; enrollment management, marketing and communications; student success and services; educational delivery and partnerships; and graduate student success – the taskforce outlined a series of recommendations intended to reduce student costs, expand educational access, and deliver targeted, just-in-time training. Common themes from the recommendations include:
In response to the taskforce recommendations, the University is launching several initiatives, including:
In the Christian tradition, the New Testament (Acts 16:9-10) says a vision appeared to the Apostle Paul in the night, “A man of Macedonia stood and pleaded with him, saying, ‘Come over to Macedonia and help us.’ Now after he had seen the vision, immediately we sought to go to Macedonia, concluding that the Lord had called us to preach the gospel to them.”
The world gospel is an Old English translation of the original Greek word, εὐαγγέλιον, meaning “good news.” The preservation, creation, and transmission of knowledge are defining attributes of our civilization, and our educational system is an embodiment of the ever-expanding 21st century “good news.” Education transforms lives, lifts generations out of poverty, nurtures our democracy, fuels innovation, and creates our future.
Our citizens are pleading; we must come help them, without judgment and in partnership. All education, regardless of scale or scope, is valuable. There is no dichotomy between knowledge and skills, but the guilt of our dilemma and our failure to act are very real.
SC19, the annual gathering of the advanced computing cognoscenti, many sporting shirts festooned with corporate and institutional logos and geek jokes, is now underway in Denver, Colorado. After arriving from the outskirts of western Nebraska and Kansas (i.e., Denver International Airport), high-performance computing fans are descending on the Colorado Convention Center in downtown Denver, eager to discuss new server design, interconnect advances, and software, while surfing the artificial intelligence tsunami that now consumes all things computing, whether exascale or edge, bare metal or containerized.
I will soon be at SC19 myself, visiting with all my geeky friends, after being at the National Science Board and having dinner with the new PCAST members, but this essay is about none of that. Rather, I want to highlight two recent reports, both of which outline the need for a rethinking of our approaches. Put another way, Feynman’s 1959 “go small” clarion call in There’s Plenty of Room at the Bottom has been answered, and there’s not much more room at the inn, for CMOS FinFETs are down to small numbers of atoms.
Concurrently, we are encountering fabrication and economic challenges below 7 nanometers, though there is still progress to be made. As I told Nature in 2016, I think we will run out of money before we run out of physics. (See The Chips Are Down for Moore’s Law.) Regardless, innovation will continue, albeit along other axes. (See Nothing Lasts Forever for musings on how aircraft innovation has morphed from a focus on speed.)
Last week, the U.S. Department of Energy released the Basic Research Needs for Microelectronics report, which many of us have been editing and writing for the past year. I was pleased to co-chair the effort with Cherry Murray (Harvard/Arizona) and Supratik Guha (Argonne National Laboratory). The report, a collaboration among three Department of Energy offices – Advanced Scientific Computing Research (ASCR), Basic Energy Sciences (BES), and High Energy Physics (HEP) – highlights five critical needs:
Last week, the Fast Track Action Committee on Strategic Computing, part of the U.S. National Science and Technology Council (NSTC), released the National Strategic Computing Initiative Update: Pioneering the Future of Computing. The report’s opening sentence echoes many of the points in the Department of Energy study, noting,
The national computing landscape is undergoing rapid evolution along multiple dimensions due to the introduction of new and potentially disruptive technologies and the demands of new classes of data intensive applications. Computer architectures and systems are more heterogeneous and complex, and the challenges associated with the complexity and sustainability of software are significant.
The NSTC strategic computing report recommendations include
There’s trouble in River City. The free ride from Dennard scaling is over; the von Neumann architecture is showing its age; and the hierarchical abstractions we have so cherished are now an intellectual box from which we must escape.
It’s time for ab initio, integrative, transdisciplinary thinking, as I discussed in Renaissance Teams: Reifying the School at Athens.
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