Reed's Ruminations: A Blog by Dan Reed

Signing Up, Making A Difference: Shackleton, Musk, and Computing

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 Naked Soul

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.

Signing Up

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.

Mars Beckons

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.

Personal Connections

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.

Coda

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.

Understanding and Explaining: The Feynman Technique

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:

• Choose a topic, study it and the interrelations among its components
• Explain what you have learned, simply and clearly, to a child
• Identify and fill the knowledge gaps exposed by your explanation
• Refine and simplify your explanation and repeat as necessary

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.

Choosing and Learning a Topic

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.

Explain What You Have Learned To A Child

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.) 

Quantum Confusion and Uncertainty

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.

Identify and Fill Knowledge Gaps

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.)

Refine and Simplify

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.

Coda

There you have it – the Feynman technique for learning:

• Choose a topic, study it and the interrelations among its components
• Explain what you have learned, simply and clearly, to a child
• Identify and fill the knowledge gaps exposed by your explanation
• Refine and simplify your explanation and repeat as necessary

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!

Memos, Janitors, Teams, and Innovation

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.

Memo Filtering

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.

Including the Janitor

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.) 

Team Cultures

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.  

Feed the Mind, Thank the Janitor

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.

Stored Program Computing: Ideas and Individuals Do Change the World

Johnny von Neumann is a name often spoken with considerable reverence in scientific circles. The epitomy of prodigy and polymath – and even those words fail to do him justice – von Neumann had the kind of mind that made even the world’s most brilliant stare in slack jawed awe.  In idle musings on new topics, he often offered insights that would have been the crowning achievement and life’s work of other, lesser talents.

Indeed, based on his seminal contributions to mathematics, physics, economics, statistics, and yes, computing, one can make a rather compelling case that Johnny may well have been the smartest human being on the planet during his lifetime.  (Well, assuming he wasn’t a Martian, as Leo Szilard sometimes joked about the group of Hungarian expats.) As Edward Teller once put it, “von Neumann would carry on a conversation with my 3-year-old son, and the two of them would talk as equals, and I sometimes wondered if he used the same principle when he talked to the rest of us.” 

The “von Neumann” Design

Given von Neumann’s involvement in the Manhattan Project and its associated computing needs,  as well his deep familiarity with the work of Gödel and Turing, it is no surprise that he turned his attention to digital computing, writing the 101-page, “First Draft of a Report on the EDVAC,” while commuting by train from Princeton’s Institute for Advanced Study (IAS) to Los Alamos.  Released on June 30^th, 1945, the draft contained the first published description of the stored-program concept, now often called a von Neumann architecture, where both instructions and data were contained in the same memory, central arithmetic and control units coordinated instruction execution, and input/output devices serviced the computing infrastructure. It is no exaggeration to say the draft report transformed and codified modern digital computing. 

The report originated from von Neumann’s role as a consultant to the EDVAC (Electronic Discrete Variable Automatic Computer) design, and on its publication, the report generated some controversy. The EDVAC team, notably Mauchly and Eckert, felt von Neumann had simply summarized their design without listing them as authors, and via publication, prevented patents on the stored program ideas.  (In March 1946, both Eckert and Mauchly left the University of Pennsylvania over intellectual property disputes and formed the company that developed UNIVAC.)

Given his prodigious talent and contributions in other areas, one cannot help but believe that von Neumann generalized and codified several ideas he heard during the EDVAC conversations, but we are unlikely to ever know for sure. Regardless of the controversy, von Neumann’s report had a major influence on computer design and construction for the next decade, when each machine was a bespoke design.

Illinois Innovations

My first intellectual home, the University of Illinois, built two machines based on von Neumann’s IAS design, driven by Illinois faculty who were previously associated with the IAS. The first, ORDVAC, was delivered to the U.S Army Aberdeen Proving Grounds in 1951.  As part of that contract, Illinois was given funds to build a second copy in 1952, which became ILLIAC I (Illinois Automatic Computer), the first computer built and owned entirely by a U.S. educational institution.

It was the beginning of a long, illustrious, and continuing history of advanced computing innovation at Illinois, with the ILLIAC II (ECL, core memory, and pipelined operations), ILLIAC III (fine-grained SIMD pattern matching), ILLIAC IV (parallel processing), and Cedar (hierarchical shared memory) computing systems, as well as PLATO (Programmed Logic for Automatic Teaching Operations) computer-aided instruction, the Eudora e-mail client, and the Mosaic web browser, itself the progeny of the National Center for Supercomputing Applications (NCSA) ecosystem.

As an aside, just a few years before I arrived, the four color theorem, unresolved for nearly one hundred years, had been proven by Illinois mathematicians Appel and Haken, using a theoretical framework to test a provably finite number of cases by computer.  For many years, mail from the University of Illinois was embossed with the simple phrase “Four colors suffice.”  I loved that quiet and understated academic trash talking, a way of communicating, as Dizzy Dean once did, “It ain’t braggin’ if you can do it.”

Personal Reflections

I have written about several of these Illinois experiences over the years and how they have shaped my professional life, including the formation of the Renaissance Computing Institute (RENCI), application of high-performance computing ideas to energy efficient cloud data center design while at Microsoft, and my work as an academic leader:

• Luck Is a Fickle Friend (lessons from ILLIAC IV)
• Eudora, You Got the Love? (why passion matters)
• Mind to Mind: Building Innovation (reflections on PLATO and human empowerment)
• NCSA@30: Reflections on the Revolution (musings on rapid technological change)
• Renaissance Teams: Reifying the School at Athens (interdisciplinary collaboration insights)
• The Magic Behind the Curtain: Hardware and Software (the joy of teaching computing)

I have said elsewhere, and I will say again here, that I went to Illinois as a young assistant professor because it was as close to heaven as a young high-performance computing researcher could find here on Earth.  I was not disappointed!  I loved my time at Illinois because of the incredible people (Bardeen, Bitzer, Cox, Holonyak, Kuck, Lawrie, Smarr, Robertson, Slotnick, and so many others) and their repeated proof-by-demonstration that one should not just dream about the future, but that one can and must build it! (See Predicting Potentialities, Reifying Futures.)

I am proud to have had a role in adding a few small pieces to that rich history via my own work on distributed memory parallel computing, leadership of the Illinois’ Department of Computer Science, recruitment of new faculty, construction of Siebel Center, and the conception, design, and construction of the NSF TeraGrid (later XSEDE) while NCSA director. In between, we also had some fun, playing with gigabit testbeds and I-WIRE (thanks, Charlie Catlett), building parallel computing clusters from Sony PlayStations (See the New York Times article), and visualizing web traffic in the CAVE, when NCSA’s webserver was the busiest in the world.

Coda

At a time when some question our continuing will and ability to mount big, audacious initiatives, it is worth pausing to draw a few intellectual lessons from the pioneers of digital computing, and remember the transformative power of dedicated individuals and compelling ideas.  (See On the Etiology of Heroes and That’s One Small Step for {a} Man.)  It has always been important to imagine the future, not as a dream, but as reality to be realized.

As the legendary Chicago architect, Daniel Burnham remarked,

Make no little plans; they have no magic to stir mens’ blood and probably themselves will not be realized. Make big plans; aim high in hope and work, remembering that a noble, logical diagram once recorded will never die, but long after we are gone will be a living thing, asserting itself with ever-growing insistency.

The logical diagram of the ”von Neumann” architecture has lived on and continued to transform our world. Today’s supercomputers, smartphones, and the Internet of Things (IoT) all rely on variants of the von Neumann idea.

Yet, like all great ideas, the “von Neumann” architecture has its limitations. (See Nothing Lasts Forever.) The bandwidth and latency bottlenecks of the separated execution and storage units continue to drive architectural and technological innovation, in the pursuit of higher performance via end-to-end co-design.  (See Advanced Computing: Integrative Thinking for the Future.) The end of Dennard scaling has led to renewed focus on parallelism, architectural specialization, and energy efficiency.  If he were alive today, I am confident Johnny would still be excited about computing and would be deeply involved in work on quantum computing and approximate computations.

Ask the deepest questions.  Run with the big dogs.  The company, the journey, and the destinations all matter.  It has always been and always will be where the action is. On that, Johnny would wholeheartedly agree!

The Simple Things Matter, Most of All Now

These are vexing times, with convolved public health and economic issues whose scope and impact are beyond the living memory of all but a handful of humans.  At best a complex dynamic system beset by chaos or at worst a stochastic process, the future is uncertain, shaped in part by our actions and inactions. History and those few who remember the influenza pandemic of 1918 have some important and sobering life lessons for us as we grapple with the COVID-19 pandemic.

In the midst of uncertainty, it may seem that we are powerless pawns in a great and complex game of biological systematics, population dynamics, and economics (both macro and micro) so eloquently described in Fitzgerald’s liberal translation of the Rubaiyat,

But helpless Pieces of the Game He plays

Upon this Chequer-board of Nights and Days,

Hither and thither moves, and checks, and slays,

And one by one back in the Closet lays.

Engage By Example

We may be but darkly wise and rudely great, but we are far from helpless. As individuals, academics, and public intellectuals, we have much to offer in our planet’s time of need. In domains as compelling as public health and biomedicine and as diverse as economics and finance, logistics and transportation, manufacturing and supply chain management, public policy and social services, and education and training, we can and must engage via a wide variety of mechanisms and modalities. Now is the time for each of us to be collaborative and engaged partners with our citizens and our society. (See Public Intellectuals: Seeing the Stars.)

First and foremost, the science matters, both its systematic execution and its public communication.  From molecular dynamics simulations of COVID-19 protein structures through study of viral DNA mutations and epidemiological models of viral spread to design of drugs that block infection, science is the path to understanding and treatment, as well as fact-based public policy.

But it is not enough to conduct science behind closed doors. Resolving the global crisis depends on a combination of sound science, thoughtful public policy, collaborative action, and clear communication. The latter means sharing tested and proven scientific ideas in plain English that informs and enlightens the public, while avoiding the turgid and elliptic prose so common in scientific discourse. (See Simplifying Communication.)

Manage Our Fears

Without doubt, there is reason for concern, but reason must also triumph over fear. Yes, I’m talking to those who descended on Costco and local grocery stores like a swarm of locusts to denude the shelves of hand sanitizer, toilet paper, canned goods, paper towels, and bread. I’m also talking to those who still congregate in devil may care groups or are afraid to help their fellow human beings in their time of need. To quote Frank Herbert from Dune, in the litany against fear:

I must not fear. Fear is the mind killer. Fear is the little death that brings total obliteration. I will face my fear. I will permit it to pass over me and through me. And when it has gone past I will turn the inner eye to see its path. Where the fear has gone there will be nothing. Only I will remain.

We must put aside fear, embrace reason, and act together, relying on both leading edge scientific research and common sense public health guidance, including social distancing and self-isolation to limit viral spread.  We will remain – together.

This is a time when each of us must temper our desire for personal freedoms in the cause of the common good.  Now is the time to be careful rather than frequenting public places and congregating, when calm separation and personal patience would lessen the frequency of personal interaction and the common good.

The Simple Things

Now is also the time to embrace our shared humanity and the importance of the little things, even if separated by time and distance. Character is tempered and tested not in the good times, but in the difficult ones, not in the times of plenty but in the times of deprivation. When anyone suffers, we all suffer.

A nod and wave to a stranger across the street, a smile to the checkout clerk, a thank you to the delivery person – all from a safe distance – can bring comfort and a reminder that we are all united by common desires, whether hope for the health and safety of our families or concern for those who have lost their jobs and livelihoods.

Amidst our concerns, remember that life and nature continue apace. Take joy in a blue sky, a spring breeze, and a golden sunset. We will remain, buttressed by scientific reason, thoughtful action, and our shared values.  Blake was right, if you look, even in the darkest times, it is possible

To see a World in a Grain of Sand

And a Heaven in a Wild Flower

Hold Infinity in the palm of your hand

And Eternity in an hour

In these times of trouble, remember the basic truths; they are the lasting ones.  Stay calm. Trust the science. Help one another. Savor nature. Be kind.

This too shall pass.

Geek Gifts for the Holidays

The holiday season is here, with lights and festive celebrations of all kinds. Normally, I am loathe to offer holiday product endorsements, perhaps because, like you, I’ve been jaded by too many marketing campaigns and late night infomercials. (See The Ghosts of Holiday Shopping: Past, Present and Future.) Other than filing my taxes on time, acting before midnight tonight has never changed my life.  However, in a spirit of beneficence, I do make one exception to aid two connected, but suffering groups. I speak of course of geeks and their gift giving friends and family.

Let’s be honest; it’s hard to buy for geeks. Few would know that a signed copy of The Art of Computer Programming is a rare and precious thing, perhaps exceeded in value only by a Knuth reward check.  A copy of a Big Bang Theory DVD or a word of the day calendar just isn’t the same, though it helps if it is Klingon. 

So what’s a friend or family member to do when your geek smiles wanly and says thank you – for the socks? Fear not; even though the holidays are here, it’s not too late to trade those socks for a gift that will bring a delighted grin to your geek’s face, whatever their age.

Turing Tumble: Logic Is Child’s Play

Maybe you have a young geek, one interested in all things science related, and computing in particular. They may be too young for “the talk,” where you explain the dangers of unprotected ports, the subtleties of functors and futures, or the central dogma of the Church-Turing thesis, but they are still old enough to avoid specious logic, develop algorithms, and create working code.  (See Just the Facts, Ma’am: Reasoning is Not Dead, Jim.)

Turing Tumble ($69.95, ages 8+) is a game where players can build mechanical computers powered by marbles to solve logic puzzles.  In the spirit of LEGO-powered Turing machines, Turing Tumble exposes the logic of computers in a tangible way.  Developed by University of Minnesota professor and backed by a Kickstarter campaign, Turing Tumble is the real deal, combining marbles, levers, gears, and bits to teach logic and algorithms in an accessible way.  Just don’t let the adults play with it, or your child geek may never get it back.  And yes, I’m playing with mine over the holidays.

AWS DeepRacer: Start Your Engines

If you have an adult geek, particularly one who writes code for a living, this is the gift for you. AWS DeepRacer is a bit pricey at $399, but it’s the only gift your adult geek will want or need. AWS DeepRacer is a 1/18^th scale model car and Amazon Web Services (AWS) reinforcement learning toolkit to train your car for competition in either a real or a virtual racing league.

The DeepRacer can be also be driven manually via a joystick, and with an Intel Atom processor, HDMI port, and Ubuntu Linux, it can double as a serviceable, albeit somewhat slow, desktop computer. (Pro tip: Your GPU-accelerated Jetson Nano ($89 from Nvidia) is a great offline reinforcement learning training tool.)

If your geek is throwing around words and phrases like TensorFlow and generative adversarial network, while railing about deep fakes and social responsibility, relax.  Even if you think Sagemaker is the nom de plume of the family matriarch and her secret stuffing recipe, your geek knows that a reward function is more than a second helping of gravy and stuffing.  I will be racing mine in the living room tomorrow.

PDP-11 Emulator: When Nostalgia Reigns

If your geek’s native language is FORTRAN and they speak wistfully of the days when toggling absolute binary into the computer front panel was a rite of passage, you have an aging geek. They are likely to grow pensive when someone mentions the Great Editor War, modality, and meta keys. Viva Emacs; viva vi; those were heady and difficult days, when friends were forced to choose sides, and flames filled the nascent Internet. (See You Might Be a Computing Old Timer If …)

The PIDP-11 ($250) is a Raspberry Pi emulation of the iconic PDP-11/70, complete with hardware front panel lights and switches. If you want to impress your geek friends, comment on the orthogonality and beauty of the PDP-11 instruction set and smile knowingly. Even better, tell them the simh software supports all the old PDP-11 operating systems your geek knew and loved – RT-11, RSTS/E (my favorite), and UNIX V5, V6, and V7.

With the PIDP-11, you can toggle in code, or boot your operating system of choice, play with all the old system software tools, and even write or run old code. TECO lives!  (Warning: Some soldering is required to assemble the front panel, or you can skip the kit and do everything in software, albeit without that heady joy of blinking lights and toggle switches.)  Mine is on order.

Save the Detritus

Now it is possible you live in one of those increasingly rare places where the gray fog of nothingness lurks in the distance and same day or overnight package delivery is not possible. However, you still have the raw materials in your possession needed to satiate the emotional and intellectual yearnings of a nascent Time Lord, Dungeons and Dragons role player, Skywalker acolyte, or wannabe Gandalf.

Wired’s 2011 review of the All Time Best Toy Ideas ($0) exposes the truth: the detritus of your gift wrapping, along with some yard debris, holds the key to happiness.  Stick (Glamdring), box (TARDIS), string, cardboard tube (lightsaber), and dirt, these are the enduring toys for geeks and the rest of you, though the purist might insist on the addition of rock, hole, and mud puddle.

I’d explain more, but a launch window has been posted for my LEGO Saturn V.  I need to finish the LEGO launch gantry before the Mobile Launcher Platform can roll out of the Vehicle Assembly Building to pad 39A. If you were a geek you would understand why that is important.

Predicting Potentialities, Reifying Futures

N.B.  This is a rather long post, motivated by my recent discussions about the art of predicting the future.

Baseball player and everyman philosopher, Yogi Berra, is justifiably famous for his malapropisms (e.g., “Nobody goes there anymore, it’s too crowded.”), some actual, some simply attributed.  Now enshrined in the popular vernacular is his amusingly true and philosophically vacuous observation, “It's tough to make predictions, especially about the future.”

Alas, navigating the darkling plain of history is as murky as predicting the pristine future, for variants of Yogi’s bon mot on the uncertain future have been attributed to such diverse figures as physicist and Nobel Laureate, Niels Bohr, and filmmaker, Samuel Goldwyn.  I speak from personal experience on uncertain exegesis.

At various times, in op-eds, columns, articles, interviews, debates, and speeches, I have pontificated on the murky future, speculating on possible and likely outcomes in education, innovation, science, medicine, technology, and society. While doing so, I have also been privileged to serve on a diverse set of advisory groups, among them, the U.S. President’s Council of Advisors on Science and Technology (PCAST), the FCC’s Technical Advisory Council, the Department of Energy’s Advanced Scientific Computing Advisory Committee, and the National Science Board (NSB). I have also participated in or led various World Economic Forum and B20 events.  

None of these experiences made me an oracle, far from it, but they have given me a sense that, as Morpheus promised in The Matrix, the rabbit hole of the future goes very deep indeed. Have a cookie and free your mind.

From musings on Jevon’s paradox though innovation friction and educational futures to the effects of accelerating change, I have plunged in fearlessly – and some might say foolishly – to reflect on the future, with the uncertain chronicle of my dubious musings documented at http://www.hpcdan.org.

In one memorable exchange, I once debated the European Union’s science adviser, Anne Glover, before EU policymakers. It quickly became clear that she (a biologist) and I (a computer geek) were in complete agreement, though both of us puzzled at the audience’s repeated and insistent questioning about slowing the pace of change in microbiology and computing.  Whether concerning digital privacy, genetically modified organisms, cloud computing, or genetic engineering, we emphasized that scientific insights could only be managed wisely, not forestalled. 

I remain convinced this was and is the correct answer, but it was not the desired one. It is also why I believe passionately that an educated populace is an essential prerequisite to thoughtful and informed debate on the appropriate use of new technologies. Absent that, we are but on a darkling plain, swept with confused alarms of struggle and flight.

In a less serious but equally thoughtful vein, I interviewed and debated futurist and science fiction writer, Bruce Sterling. To the amusement of our live audience, we sought to establish the major consensus narrative (aka the truth) about future technology and society, as we rambled across post-genomic biology and medicine, nanotechnology and personalized manufacturing, cognitive radio and the Internet of Things, autonomous vehicles and grand AI, and privacy and information flow in the digital age, while discussing the cultural aspects of Clayton Christensen's innovator's dilemma.  (See A Conversation on Designing the Future.)

Several times, I have been mostly right (e.g., the criticality of digital privacy) about the future.  Other times, I have been right about the technology and wrong about the socioeconomics (e.g., online higher education and MOOCs) or wrong about the technology but right about the socioeconomics.  In many cases, I have just been unambiguously and laughably wrong.  (Bought any Itanium computers lately?) However, embarrassment has never stopped me from thinking or asking others what the future might become.  As writer Neal Stephenson provocatively noted, “… if we want to create a better future, we need to start with better dreams.”

Those waking dreams begin by identifying which aspects of the present are malleable – and the associated costs (economic, political, social, and technical) and timescales (months, years, or decades) – as well as those aspects that are truly immutable.  The goal, however, is not just to understand and adapt to the future, but to shape and reify it, for dreams without plans are simply illusions.

Recognize, though, that each envisioned change must be pursued thoughtfully and wisely, for change has both expected and unexpected benefits and consequences.  Foresight is always uncertain, as Ursula K. Le Guin so poignantly portrayed in the Lathe of Heaven.

Futurist Imagining

Technical innovation – predicting the qualitative effects of quantitative change – is often easier than social and legal/policy change, though I recognize my geek bias may be evident in so saying. Even within a specific technical domain, the locus of innovation can shift with time and technology. Consider commercial aviation as one salutatory illustration.

Though the cruising speed of commercial aircraft rose throughout the first half of the 20^th century, today’s Boeing and Airbus commercial airplanes fly no faster than the vaunted Boeing 707 of the late 1950s. Higher speeds were and are technically possible, as military aircraft and the Concorde demonstrated, but supersonic commercial transport remains economically non-viable. Nevertheless, aircraft innovation continues apace, with more reliable, fuel efficient jet engines, carbon fiber designs, glass cockpits and fly by wire. (See Nothing Lasts Forever.)

For those playing the futurist game at home, consider just a few other disruptive examples, past and present. What if long distance communications were effectively free, rather than costing dollars per minute as in the days of Ma Bell? (Answer: the Internet, the web, and e-commerce). What if computers were small and cheap and could store the digital equivalent of the Library of Congress in the palm of your hand, rather than costing millions of dollars and filling massive rooms?  (Answer: laptops, smartphones, and the Internet of Things)

What if the DNA you inherited at birth were just a starting blueprint, rather than a lifelong constant? (Answer: CRISPR and personalized medicine). What if each decade the summers got just a little hotter and the winters got just a little colder? (Answer: climate change, economic loss, and global migration) What if autonomous AI systems were empowered to identify individuals and make life and death decisions? (Answer: stay tuned, this experiment is still underway)

Now slip on your futurist goggles and engage in a brief, though humorous, gedanken experiment.  Might the triumvirate of 5G cellular, talking shoes (no, not the ones with flapping soles), and drone pizza delivery be in our near future? (Stop, right now! How dare you laugh at talking shoes; they’re just a lateral from talking thermostats. Where’s your George Jetson ethos?) In a world where a dystopian Max Headroom is a quaint memory, all three depend on advanced computing, deep learning, and edge AI, but each has divergent social and economic drivers and probabilities. (See Come to the Supercomputing Big Data Edge.)

Despite trade wars, deployment costs now measured in the hundreds of billions of dollars, and massive picocell requirements, the early rollout of 5G cellular – or at least its marketing twin – is already underway, driven by revenue-salivating carriers and insatiable consumer demand for wireless connectivity and cat videos.  Conversely, marketing talking shoes would be cheap, a dubious fashion statement realizable by any wannabe teenaged designer of chic couture, no more or less likely than any of a dozen other bizarre trends.  Finally, pizza delivery by autonomous drone is now technically feasible, and Dominos has already delivered pizza by drone in New Zealand. Drone delivery is currently illegal in the United States, though legislation to satisfy a national craving for pepperoni might be in our future.

Multivariate Uncertainty

Make no mistake, predicting the future is not for the faint of heart, even with a 5G smartphone, a full belly, and talking shoes; the future is not a simple convolution of linearly independent possibilities. Each possibility depends on a host of uncertainties, with different constraints and interdependences – some are technical, others are cultural; some are economic, others are social; and still others are legal or ethical. More tellingly, each of these uncertainties evolves with varying speeds and scales.  The future is many things, but it is not an unconstrained n-dimensional random walk; even if it does occasionally feel like a Wiener process; it is far more Bayesian, as any inveterate Let’s Make a Deal knows all too well. (The Monty Hall problem is a classic veridical paradox; choose wisely lest you lose the Big Deal behind door number three.)

A few of my geekier friends might say the future is explained by Everett’s many worlds interpretation of quantum mechanics, where all possible outcomes attain, and we live in but one of many possible universes. Yes, this might mean there is a world where polyester leisure suits and platform shoes are part of a man’s professional wardrobe, disco reigns as a national art form, Inktomi dominates web search and digital advertising, high-speed maglev trains crisscross the U.S. hourly, and adult obesity and diabetes are rare.  This is not our world, though I might reluctantly accept disco, but not leisure suits, in exchange for high-speed trains and diabetes’ elimination.

Despair not, there is hope in understanding the Copenhagen interpretation of the wave function’s collapse – when the superposition of eigenstates reduces to a single state consistent with observation.  Put another way, observation tells us the status of Schrödinger's cat, the future of flying cars, and talking shoes.  Flying cars, though technically possible, are economically and statistically unlikely. However, the jury is still out on talking shoes. (For an anecdote about uncertainty and faculty recruiting, see Schrödinger’s Gershwin.)

Lessons for Futurists

Enough philosophy and raconteuring – herewith are five things I have learned about predicting and shaping the future.

1. n the near term, we tend to overestimate change, but in the long term, we underestimate it.

The Gartner hype cycle is real.  Shiny objects readily capture our attention, mesmerizing and beguiling us with pregnant possibilities.  We see a world of inflated options, infected with go fever, ignoring the challenges posed by a still nascent idea. After the trough of disillusionment, most ideas descend quickly into the valley of death.  In a few cases, after innovation and much hard work, the slope of enlightenment appears, with subsequent success.  By then, we have forgotten how much change the innovation has wrought, now comfortable in an Orwellian sense that the past is invariant, as we willingly and enthusiastically embrace double think. Yes, we have always had smartphones, and Google has always been the go to source for homework questions.

2. Linear extrapolation is easy; exponential extrapolation is extraordinarily difficult.

Tomorrow is generally an incremental variant of today, and small changes rarely have dramatic effects.  Yet there are situations where change truly is exponential.  The cadence of transistor shrinkage, popularly known as Moore’s law, along with its more subtle technical enabler, Dennard scaling, has birthed exponentially more powerful and inexpensive computing systems over the past thirty years. That cadence is now ending, with profound implications for a future where an exponential has reached its fuzzy terminus. The cost of whole genome sequencing has plunged similarly, with concomitant disruptions in the science and finances of health care. (DNA sequencing at near consumer prices is here, albeit for smaller sequences – look at the nanopore-based SmidgION.)  Neither of these exponential changes was guaranteed; rather, they were the product of continued, incremental innovation.

3. Quantitative change leads to qualitative change, but this but one type of disruptive change.

Sufficient change in speed, size, capacity, capability, and price almost always lead to qualitative change. If unit prices drop dramatically and volumes rise commensurately, new opportunities and markets arise. Contrarily, continuing design improvements in gas lamps would have never led to the electric light bulb, whose disruptive technical innovation triggered an ecosystem revolution, as did the automobile and the airplane. Anticipating these disruptive innovations is by definition, difficult, but understanding context and tipping points can lessen surprise; these are the trigger points of the hype cycle.

4. Disciplinary experts are often blinded by knowledge of certain constraints; outsiders see differently.

If you have ever worked on any new idea or approach, you are intimately aware of its limitations and the associated risks, and occasionally terrified when others embrace it with blithe obliviousness.  At one level, this cautionary awareness is a critical damper on unbridled enthusiasm.  (After all, few things really are better than sliced bread, though some might opine that canned beer has been a close competitor.) Building an inclusive and thoughtful circle can lessen surprise; non-domain experts, including many science fiction writers, often better anticipate the broader implications and uptake (or rejection) of new ideas.

5. The interplay of technology evolution and social dynamics is both complex and subtle.

Predicting social reaction to technical change is challenging.  I have personally seen a single technical idea met with unbridled enthusiasm and unqualified horror when applied to slightly different human domains. None of the scientists and technologists, and I count myself among that community, would have predicted that response divergence. Again, diverse teams with disparate experiences and knowledge can lessen surprise and increase predictive accuracy.

Coda

The future is a tabula rasa, albeit with social, economic, political, and technical constraints.  We are not simply passive voyagers on an uncertain sea; we can and should shape our future. Doing so requires both contextual awareness and thoughtful shaping of alternatives. 

Remember, the best way to predict the future is to create it.

Yogi was right; the futurist mantra is pity and cautionary.

Predict, reify; rinse, repeat.

Business, AI, and the Future

N.B. You can watch a video of the panel here, along with another summary.

A few days ago, I had the opportunity and pleasure to chair a panel on AI as part of the David Eccles School of Business (DESB) Alumni forum at the University of Utah. The panel members were local innovators and entrepreneurs Bassam Salem (CEO, AtlasRTX), Glenn Colby (Founder, Invarius), and Keelan Johns (Senior Director of Engineering, Clearlink).  My sincere thanks to each of them for participating.

I began the panel by observing that AI researchers have long bemoaned that AI is often defined as what they do not know how to do, rather than what they have accomplished. By that definition, AI has always been an unsolved problem.

I also noted that despite the prominence of AI in the popular consciousness, whether because of its much discussed influence on social networks or its renderings in movies, we are very far from creating a general-purpose AI, a goal-directed entity capable of independent activity and reasoning.  Put another way, HAL in 2001: A Space Odyssey, Skynet in the Terminator movies, or, more recently, the Turing Test and Ex Machina and love and relationships in Her, are far from reality.

 I did note, however, that the human faces created via generative adversarial networks (GANs) were now indistinguishable from photographs.  This proliferation of deepfakes is raising a host of social, ethical and legal issues, as well as political and social sentiment influence.

Lest we sink into a miasma of AI ennui, I then turned to the panelists, and asked each of them what the term AI meant to them and how it was being used in each of their companies, stimulating a lively discussion. Bassam discussed the value and importance of realistic chatbots for improving the quality of human services management, allowing computer systems to adapt to the questions asked by humans, rather than forcing humans through a fixed decision tree.

As an aside, he remarked on how far they have come from the early, simplistic days of Eliza. Seeing a few puzzled faces, I quickly polled the audience to see how many people remembered Eliza.  When precisely zero people raised their hands, I realized Bassam and I, both with computer science PhDs, had unmistakably labeled ourselves as geeks.

Rather than press on deeper into the big muddy, I quickly pivoted to Glenn and Keelan, who highlighted the power of big data and deep learning systems to identify solutions and approaches that might not be obvious to humans, even domain experts. While discussing the AI advantages to businesses, we also touched on the importance of fairness and ethics in AI systems, and the risk that algorithms and learned behaviors might discriminate against particular individuals and certain groups.

Perhaps the liveliest panel discussion was around the utility, usability, and cost of AI for small businesses.   All of the panelists agreed that the AI water is great, and most small businesses could benefit from cloud-based AI services.

Near the end, audience questions turned to the future, with one questioner asking when AI systems would be more proficient than humans.  I took the opportunity to mention AlphaZero and the fact that it was both a much more powerful Go player than any human but that it also played Go in strikingly different ways than the approaches used by even the best human players.  

The last pensive question concerned our future in a world of powerful AI. I opined that the history of our society was replete with technology disruptions, each shaped by our sense of ethics, justice and law. Incumbent in that evolution is our need for informed debate and collaborative shaping of appropriate and acceptable uses.

 

The Endless Frontier and the National Science Board

N.B. I am pleased that the White House has announced that I am being appointed to the National Science Board (NSB), which provides advice and oversight for the National Science Foundation (NSF). In recognition of this honor and responsibility, I have been reflecting on how much the NSF has shaped my career over the past thirty-five years, and how much I owe the Foundation in return.

Anxious Proposal Writing

As a new, assistant professor in the fall of 1983, armed with a thin NSF proposal guide, I labored to write my first proposal. Like all young and inexperienced proposal writers, I struggled, searching for a lede that was both novel and compelling.  I wrote and rewrote, agonizing over each paragraph, late into the nights.  Sleep deprived, with the submission window at hand, I decamped to the local post office with a postage meter, where I mailed a set of proposal copies to NSF’s old Washington, DC. address at 1800 G St. NW, near the International Monetary Fund. 

Mind you, this was before electronic proposal routing, with far fewer certifications and checklists.  Paper forms were simply duplicated and stapled for mailing. The World Wide Web did not exist, the NSF supercomputer centers (including the National Center for Supercomputing Applications (NCSA)) had not been created, and NCSA’s Mosaic web browser not yet been released.  It was a time when a daily dialup CSNET connection was the state of the art among most computer science departments, and I knew a non-trivial percentage of the electronic world’s denizens blessed with an email address. (See Remembering Internet Dogs.) 

My first NSF proposal was about a then radical idea, one I had explored in my Purdue Ph.D. thesis, that supercomputers could and should be built from large collections of microprocessors. When I began that work, the Intel 8086 was still a new and novel product, and the IBM PC had not been released. Doubling down on this crazy idea, my friend and colleague, Richard Fujimoto, and I later wrote an MIT Press book about this approach. Fortunately, time has shown that we – and others – were right about microprocessor clusters.

A few months after submitting my first proposal, I mustered my courage and called the NSF program officer, hoping for good news. To my great joy, he kindly related that the proposal, entitled Performance Directed Design of Multimicrocomputer Systems, would be funded in the fall of 1984, with the munificent sum of $55,825. Although many tens of millions of research dollars have since followed, I will always be grateful for NSF’s initial investment in my future.

A Lifetime of NSF Connections

At the time of that first NSF proposal, I had no idea what arc my career might follow, nor how intimately I would be involved in computing’s future and its myriad connections to NSF.  These have included serving as computer science department head at Illinois, as NCSA director, as founding director of the Renaissance Computing Institute (RENCI) at North Carolina, and as the leader of the eXtreme Computing Group (XCG) at Microsoft. Throughout, I have been fortunate to be involved in building advanced computing infrastructure for scientific research (clusters, GPUs, and the NSF TeraGrid, now XSEDE), advancing high-speed networking, ensuring access to cloud services for science, and engaging in research collaborations of many forms.

When I wrote that first proposal, neither could I have then foreseen how much the world would change due to computing’s now ubiquitous influence. For a few musings on these effects, see NCSA@30: The Revolution Continues, Microsoft/NSF Cloud Research Partnership, and Reluctant Revolutionaries, the Trolley Paradox, and Ender’s Game. 

The one constant in all of my experiences has been the National Science Foundation. Over the years, I have been fortunate to engage NSF on many levels:

• As a researcher and an early Presidential Young Investigator (PYI) awardee, which made possible long-standing industry collaborations with Intel, IBM, and other companies
• As a member of many multi-institution research projects (e.g., on scalable I/O, virtual grids, and mesoscale weather forecasting)
• As a member of the Computer and Information Science and Engineering (CISE) advisory committee
• As a proposal reviewer, workshop organizer, report writer, informal advisor, distinguished lecturer, member of National Academy studies, and member of both the President’s Information Technology Advisory Committee (PITAC) and the President’s Council of Advisers on Science and Technology (PCAST)
• As a witness in Congressional hearings about computing, science, and technology futures (e.g., this House Science Committee hearing)
• As a leader of a Major Research Equipment and Facility Construction (MREFC) project – the Distributed Terascale Facility (DTF) and Extended Terascale Facility (ETF) that were the TeraGrid
• As an industry partner – the Microsoft/NSF Cloud Research Partnership

All of that built on a long legacy of NSF innovation.

The Endless Frontier: Looking Forward

In July 1945, Vannevar Bush responded to a request from President Roosevelt with a prescient and insightful report, one whose significance and import echo across the decades.  In that report, entitled, Science: The Endless Frontier, Bush noted, “…without scientific progress no amount of achievement in other directions can insure our health, prosperity, and security as a nation in the modern world.” He further notes, 

… since health, well-being, and security are proper concerns of Government, scientific progress is, and must be, of vital interest to Government. Without scientific progress the national health would deteriorate; without scientific progress we could not hope for improvement in our standard of living or for an increased number of jobs for our citizens; and without scientific progress we could not have maintained our liberties against tyranny.

Bush’s seminal report, still worth reading, resulted in what we now know as the National Science Foundation, charged to both develop new knowledge and to train new generations of scholars.

I, along with many of you, have been blessed beneficiaries of that wisdom and foresight. I have also been fortunate to count many CISE leaders and NSF directors as friends and advisers.   It is in that spirit, that I am now pleased that I am being appointed to the National Science Board, where I hope to help the NSF continue to support and sustain scholars – young and old – and to advance scientific research and ensure national competitiveness.

The Magic behind the Curtain: Hardware and Software

The Wizard of Oz is a beloved and widely watched movie that brought us Technicolor, several memorable songs, and lasting cultural memes – “We’re not in Kansas anymore,” “There’s no place like home,” and “Somewhere over the rainbow.” In the movie, Dorothy’s dog, Toto, pulls aside the curtain, revealing the great and mighty Wizard as a mere mortal projecting the trappings of power through indirection and abstraction.  Attempting to continue the charade, Oz proclaims, “Pay no attention to that man behind the curtain!”

Deception once revealed, however well intentioned, is mere artifice.  Accordingly, magicians guard the secrets of their tricks assiduously. After all, it is how they earn a living. More importantly, a trick’s mystique makes it both alluring and intriguing. The legerdemain in a coin’s disappearance may still bring a smile to one’s face, but familiarity with the French drop lessens its mystery.

In my experience, a distinguishing feature of researchers and scholars is their interest in not just in the “trick,” but how the trick is created and performed. These are the people peering around the corner, dissecting the art and engineering beneath each Disney World^TM theme park ride

Hence, some seek the truth, whereas others treasure illusion and, sometimes, their own self-deception. As Morpheus says in The Matrix, “You take the blue pill - the story ends, you wake up in your bed and believe whatever you want to believe. You take the red pill – you stay in Wonderland and I show you how deep the rabbit hole goes.”

Segue alert! Every teacher wants his or her students to take the metaphorical red pill and explore the rabbit hole. It is about challenging beliefs, perspectives, and illusions with new ideas and approaches.  I have reflected on this dichotomy since my very first teaching experience, when I looked back at aspiring young students with both anticipation and trepidation.

As a teacher, there is no greater joy than seeing understanding in the eyes of students, when seeming magic is at last replaced by holistic insight. In short, it is about illumination and understanding, pulling back the curtain on apparent magic to reveal an unbroken chain of insights.

Architectural Magic

As a computer and computational scientist, I long ago realized that introductory computer architecture is the transformative place where computing prestidigitation becomes intellectual engineering. It is where varied computing insights unite in the big reveal – how an idea and the abstraction of an algorithm embodied in code becomes an instantiation in logic, hardware, and device physics.

Make no mistake, the rabbit hole runs unbroken from quantum mechanics through undecidability, the Church-Turing thesis and Turing machines to computability theory and formal language theory (Chomsky hierarchy). It touches the very nature of what it means to think.

First, let’s step back and remember how the intellectual puzzle pieces form the big reveal.

By the time most undergraduate students study basic computer architecture, they have often received an intellectual dose of electricity and magnetism (E&M), perhaps via the latest edition of the same Fundamentals of Physics by Halliday, Resnick (and now Walker) that I studied as an undergraduate over forty years ago. Conductance, capacitance, inductance, wave/particle duality, and quantum tunneling; it is all there. (I so treasured the joy of my E&M course that I have saved my lecture notes and worked problem sets across multiple moves and several decades.)

An introductory class in device physics, transistors, and digital logic often follows E&M. There, the rudiments of semiconductor devices and VLSI design include field effect transistors (FET) (See gate, source, and drain), MOSFET NAND gates, and flip-flops. From there, it is just a hop, skip, and jump to circuit design and logic minimization. At this point, students have architectural Lego^TM blocks and some vague notion these are intellectually sufficient, but microprocessor architecture remains as aspirational and unattainable as a Lego Empire State Building.

Meanwhile, most students have also been learning new programming language(s), efficient algorithms (quicksort), and associated data structures (balanced binary trees). Years ago, the programming languages of choice would have been C or FORTRAN. (I first learned FORTRAN in the 1970s, using WATFIV; see A Feeling for the Code). Today, most introductory computing courses teach Python, with its large sets of libraries, tools, and IDEs.

What really happens when one writes a loop or invokes a procedure? Is it just an incantation, an intellectual sacrifice offered to the machine? Therein lies the reification magic of compilers, which translate code in a high-level language such as C or FORTRAN into equivalent sequences of machine instructions. (The nuance of interpreted languages such as Python is a topic for another day.)

I have found manually translating a simple piece of code (e.g., a loop that sums a set of numbers) into a sequence of assembly language instructions small enough to fit on a single piece of paper is by far the best pedagogical technique. Those symbolic assembly language instructions then map directly and intuitively to individual machine language instructions, each defining a specific hardware behavior (add, read, write, branch), but whose collective describe the intent of the high-level language code.

Trust me; we are now nearing the French drop, but there is nothing up my sleeve …

Computer architecture is where code meets hardware, and the seemingly disparate Lego pieces become a dazzling whole. (It is not turtles all the way down.) Constructed using only those elementary logic gates (aka Lego blocks), the conceptual execution of machine instructions for a simple, non-pipelined stored program computer (superscalar, out-of-order, and parallel are topics for another day) can be traced easily using only pencil and paper. (Computer Organization and Design by my friends Dave Patterson and John Hennessy is as legendary in introductory computer architecture as Halliday and Resnick in physics.)

Simple, yet elegant, these designs embody the core ideas of the von Neumann architecture and its fetch-decode-execute instruction cycle. To test their designs, many a budding computer architect has also trekked to Radio Shack (RIP) or Fry’s (one stop shopping for potato and DRAM chips), or let their fingers do the walking online, for parts to breadboard simple computer components. The more ambitious and adventuresome also stuffed circuit boards and sported solder burns as a point of pride.

The great reveal, the denouement is now at hand. From electrons through transistors and gates to architecture, and from algorithms and code to machine instructions on architecture, it all comes together.  It is all there, the magic of ideas – the abstraction that is code – is now manifest in architecture, hardware, and physics. That is the “Aha!” moment, when students truly appreciate the intellectual construction, like Chartes, that both dazzles and inspires. The true wonder is that one can explain it all on a single whiteboard!

Deep and Open Questions: Not All Is Yet Revealed

The amazing construction that is modern computing rests on many scientific and engineering insights, a few of which are now feeling the strain of their age. The von Neumann model, for all of its elegance and simplicity, increasingly limits future innovation (See Nothing Lasts Forever), and the future of semiconductor technology – 7 nanometers and below – is economically and technologically uncertain. The Landauer principle and low energy (operations/joule) or even reversible computing beckon.  Quantum computing labors in an uncertain aborning, brain-inspired neuromorphic computing is in its infancy, and DNA-based storage and computing are nascent.

Perhaps, the most important intellectual question of all, one deeply related to the nature of computational complexity and the partial recursive functions lies open. Does P=NP? (i.e., is the class of problems where verifying a solution requires non-deterministic polynomial time (NP) the same as the class of problems where computing the solution requires polynomial time (P))? Put another way, if the solution to a problem can be verified in polynomial time, can it also always be found in polynomial time? Most think not, but resolving the question has bedeviled the some of the best mathematical minds of the last century. (If you think you have a proof, sketch it in the margin and claim mathematical immortality.)

Make no mistake, the rabbit hole goes deep, connecting two of the greatest intellectual edifices of the last hundred years – decidability, computability, and complexity united with quantum mechanics and the standard model – via algorithms, software, and computer architecture. The rabbit hole reaches to the very foundational question of what it means to think and share information. There is undeniable magic inside, and that magic continues to transform our perceptions and our world.

Given a choice, always follow the rabbit hole. Scrutinize the man behind the curtain. Seek the big reveal. See the whole whiteboard. Therein lies magic.