Reed's Ruminations: A Blog by Dan Reed

A {Racing} Bicycle for the Mind: Helmet Recommended

The March 1973 issue of Scientific American contained an article entitled simply “Bicycle Technology,” written by S. S. Wilson.  As the title suggests, the article recounted the technological evolution of the bicycle and how those insights contributed to the development of early automobiles and airplanes.  After all, many of the mechanical techniques used by the Wright brothers in the Wright Flyer, the first heavier-than-air aircraft, were developed in their Dayton, Ohio bicycle shop and tested at windswept Kitty Hawk, North Carolina.

This Scientific American article would now be of only passing historical interest were it not for a single diagram (shown below), which plots the cost of transport (in calories per gram per kilometer) against body weight (in kilograms) for several organisms and devices, all on a log-log scale. As one would expect, flying organisms tend to be small in weight but expend energy at high levels to remain airborne. Land-based mammals, though much larger, consume less energy relative to their weight. Among the latter, bipedal humans are unremarkable, more efficient than dogs but less efficient than horses.  Similarly, helicopters are less efficient than airplanes or automobiles.  All of this matches our intuition and experience.

Ah, but here’s the story.  A human on a bicycle ranks first in energy efficiency among mobile animals and machines in energy consumed to move a certain distance as a function of body weight, and not by a little, but a lot! The chart shows that the rate of a bicyclist’s energy consumption is about one fifth that of a walking human. Anyone who has bicycled knows this is true.  A reasonably fit bicyclist can cover more than one hundred miles in a day, the so-called century ride.

By Geeks for Geeks

The fact that a human on a bicycle is quantitatively and qualitatively more efficient than anything nature has engineered via evolution caught the eye of an eager young reader, Steve Jobs.  More on that in just a minute.  First, let’s talk about the magazine, Scientific American.

Founded in 1845, like many magazines, Scientific American had its ups and downs until three partners reinvented it during the 1960s, a few years before I first encountered it.  For at least the next thirty years, it embodied everything the well-read – wait for it – scientific American might hope to know about cutting edge science and technology.   Unlike other popular science magazines, the articles were written by the scientists responsible for the discoveries they described, which gave them gravitas often lacking elsewhere. The Amateur Scientist column by Jearl Walker described experiments that could be conducted by savvy amateurs, and the Mathematical Games column by Martin Gardner enchanted many a budding mathematician and computer scientist.  

Scientific American was the kind of magazine geeks awaited eagerly each month, then stood by the mailbox reading cover-to-cover with undisguised joy, only to rush inside afterward to regale unsuspecting family members with their latest insights. I knew this firsthand, having had my own transformative experiences with the magazine, beginning with a stash of old copies I was given as a young teenager. There, I read about stellar nucleosynthesis of the elements, progress toward the Standard Model integrating three of the four fundamental forces, and new insights into DNA and gene actions, among many others. I have since been a loyal subscriber for over forty years, though I hope the magazine has been as successful in recruiting younger readers as it was with me.

I read many fascinating things in Scientific American over the years, but two things stand out most vividly. The first was Christopher Zeeman’s delightful exegesis on catastrophe theory, a mathematical model of non-linearity in complex systems, something that I wrote about recently in the context of COVID-19. (See On Catastrophes and Rebooting the Planet.) The second was Martin Gardner’s description of John Conway’s Game of Life, a beguilingly simple but powerful cellular automaton.  Sadly, John Conway, who spent many years at the Institute for Advanced Study, passed away during the COVID-19 pandemic.

The Game of Life became a staple in computer science circles, engaging both experimenters and theorists who derived self-replicating entities and ultimately proved the Game of Life is Turing complete. That rabbit hole is very deep, for via its three simple rules, the Game of Life is capable of computing all that is mechanically computable, bounded only by Gödel incompleteness and the Entscheidungsproblem. From there, it is just a hop-skip-and jump to the Chomsky hierarchy and Turing’s musings on what it means to think.

As an undergraduate, I burned through prodigious amounts of IBM S/370 computer time myself, running Game of Life simulations, enough so that I got called on the carpet by the computing center leadership.  Being fellow geeks themselves, after I showed them the code and explained what I was doing, I was given a friendly blessing and sent on my way. (See A Feeling for the Code.) Since then, I have used the Game of Life as an example in more than one parallel computing class, illustrating parallelism, irregular workloads, and synchronization constraints.

Amplifying Human Talent

Given the impact of Scientific American on the science and technology crowd, it is no surprise that a young Steve Jobs saw the article on bicycle technology and how it augmented human capabilities. It was an idea that became a passion, finding the intellectual analog of the bicycle, something that could augment the human mind in the same dramatic way a bicycle amplified locomotion. You can watch Jobs talk about it here and here, at two different periods in his life. His memory about the condor as the most efficient was incorrect; it was not on the list. However, he captured the essence of the story, when he talked about humans as tool builders and a computer being a bicycle for the mind, able to take us far beyond our human abilities. As Jobs put it,

… somebody at Scientific American had the insight to test the efficiency of locomotion for a man on a bicycle. And, a man on a bicycle, a human on a bicycle, blew the condor away, completely off the top of the charts.

And that’s what a computer is to me. What a computer is to me is it’s the most remarkable tool that we’ve ever come up with, and it’s the equivalent of a bicycle for our minds.

Jobs tried to name the then internally code-named Macintosh project the “Bicycle,” but it didn’t stick. Apple even used the bicycle for the mind analogy in its marketing, as this 1980 Wall Street Journal advertisement shows. (Alas, no copyeditors verified the condor allusion.)

A Universal Intellectual Amplifier

Over the years, I have crafted my own version of this analogy, emphasizing that computing is distinct from all other human tools in its universality. (See Intellectual Amplification via Computing.) Physical tools, from the simple inclined plane through a pulley to a screwdriver, rely on mechanical advantage, each specialized for a particular task. Put another way, a screwdriver will not help you lift a weight, nor will a pulley help you fasten two objects together. Each is useful, but each is specialized.

That specialization extends to scientific and intellectual endeavors as well.  Telescopes, such as the Vera Rubin Observatory or James Webb Space Telescope, expand our vision, reaching far beyond the electromagnetic range of visible light and the limits of the human retina. They let us see the faint, large, and distant, but do nothing to illuminate the spatiotemporal dynamics of a biochemical reaction. Nor can a cryo-EM’s amazing efficacy in understanding macromolecular structures measure the cosmic microwave background (CMB) or plumb the mysteries of dark matter and dark energy.

In contrast, computing is universally applicable to diverse human intellectual efforts. That bicycle for the mind can help write sonnets (or via AI, write them itself), explore the spread of viral disease, understand galactic evolution, or a thousand other things, limited only by human imagination.

In that spirit, my friend and former colleague, Fred Brooks, one of the fathers of IBM’s System/360 family of computers, once described himself as being in the “intelligence amplification” business. After leaving IBM and moving to the University of North Carolina at Chapel Hill, he met with a University of North Carolina at Chapel administrator, whom he then wryly asked who on campus most needed their intelligence amplified.  Beyond the twinkle in Fred’s eyes as he told the story, I’ve always loved the anecdote, because it speaks to the human nature of computing as a collaborative enabler for creativity.

The Supercomputing Racing Bike

If the personal computer – Mac or PC – is a bicycle for the mind, then a supercomputer is a carbon fiber composite racing bicycle with electronic shifters, hydraulic disc brakes, and tires engineered to minimize rolling resistance.  Though the principles are the same, a supercomputing bicycle is far different than the bike you rode as a child.

Back in May 2004, I testified to the U.S. Congress about the needs of racing bicycles for the mind, something I have done several times. At the time, I was serving as the Founding Director of the Renaissance Computing Institute (RENCI), itself based on the idea that computing was universally applicable to enriching the human experience.  It was an idea I learned from my NCSA colleague and friend, Donna Cox, an artist who has enriched the lives of so many via her Renaissance teams and scientific visualizations.

The other witnesses that day were Jack Marburger, then Director of the White House Office of Science and Technology Policy (OSTP); Irving Wladawsky-Berger, a Senior Vice President from IBM; and Rick Stevens from Argonne National Laboratory.  You can read the entire transcript of the hearing here. At that hearing of the House Science Committee, I offered these words about the universality of high-performance computing and its power to expand the frontiers of human knowledge, noting

The breadth of these examples highlights a unique aspect of high-performance computing that distinguishes it from other scientific instruments – its universality as an intellectual amplifier.  Powerful new telescopes advance astronomy, but not materials science.  Powerful new particle accelerators advance high energy physics, but not genetics.  In contrast, high-performance computing advances all of science and engineering, because all disciplines benefit from high-resolution model predictions, theoretical validations and experimental data analysis. As new scientific discoveries increasingly lie at the interstices of traditional disciplines, high-performance computing is the research integration enabler.

I went on to say something that I believed then but seems even more true now

Although this universality is the intellectual cornerstone of high-performance computing, it is also its political weakness.  Because all research domains benefit from high-performance computing, but none is solely defined by it, high-performance computing lacks the cohesive, well-organized scientific community of advocates found in other disciplines. In turn, this has led to over-dependence on market forces to shape the design and development of high-performance computing systems, to our current detriment.

In almost twenty years since, we have seen phenomenal computing advances.  We have broken the petascale and the exascale performance barriers.  Advanced AI delivers modern day miracles, capable of playing chess and Go at levels that humble even the world’s best human players, able to translate human languages with high and rapidly improving accuracy, adept at predicting how proteins fold, and able to compose prose with near human facility.

We have come far on the racing bicycle for the mind.  We need to keep pedaling, for we still have far to go to reach our destination, given both the manifest opportunities and the equally daunting challenges. 

I have written about the challenges of building ever faster computers, given economic and technical constraints, and I have called for new approaches to designing and building advanced computers that lessen our dependence on traditional market forces, recognizing that advanced computers are increasingly bespoke research instruments rather than commercial commodities. (See American Competitiveness: IT and HPC Futures – Follow the Money and the Talent and a related article.)

We also have deep technical and social issues surrounding AI ethics, digital equality, and digital communications. We are very far from the general AI (strong AI) so omnipresent in the movies, yet as our machines take on more tasks once viewed only as the province of humankind, we need to think about roles and responsibilities, and how we best imbue our machines with the best of humanity’s values. (See Reluctant Revolutionaries, the Trolley Paradox, and Ender’s Game.)

In addition to the handlebar streamers and the thrill of the wind in our hair, a helmet might be advised.

Coda

Make no mistake, Steve Jobs was absolutely right; the computer is arguably one of the most remarkable tools we humans have ever invented.

I have been proud and humbled to be a racing bicycle for the mind designer for these past forty years. Pedal on.

From Broadcast to Narrowcast: Finding Signal in the Noise

The arc of the 20^th century is interesting for many reasons, not the least of which is how it was shaped by dramatic and traumatic events, including the Great Depression and two world wars. However, the arc of its communication mechanisms is equally striking, though less discussed outside computing and business circles.  It began with Marconi and “wireless,” transitioned through commercial radio and broadcast television, and ended with the Internet and streaming media.

For millennials and those younger, it may now seem almost inconceivable, but there was a time when almost everyone in the United States heard the news from the same three television channels, watched the same television shows, and listened to the same music on a popular local radio station. Most cities had both morning and evening daily newspapers, filled with both the minutia of daily life – crimes, weddings, and funerals – with a smattering of national and international news, and local investigative journalism by the fourth estate, shining a bit of light into the darkness.  These were shared cultural experiences, woven throughout the fabric of American society.

All that has changed, some for the better, and arguably, some for the worse.

The Golden Age of Broadcast Television

In the first half of the 20^th century, radio was the primary mechanism for disseminating entertainment and news. Beginning in the 1950s, analog (NTSC) broadcast television on the VHF and UHF bands – the big three (CBS, NBC, and ABC) -- began dominating the airwaves, with PBS as a public television latecomer.  Led by CBS News and Walter Cronkite, the nightly news broadcasts from the big three defined the national conversation on U.S. and global events, albeit mostly from a white, middle American perspective.

Unlike today’s extended social commentaries on 24x7 cable news channels, the nightly news was largely delivered in a staccato “just the facts, ma’am” format.  As Cronkite put it at the end of each broadcast, “And that's the way it is.” Although the broadcast standards supported color, the high cost of color television meant broadcasts were mostly in black-and-white until the 1960s.

Nor were there live standups from international locations or from across the studio, rather international reports were often recorded on film, then shipped to news bureaus for subsequent development and editing; remote satellite broadcasts were yet to come. Breaking news interrupted normal network programming, as this iconic photograph of Walter Cronkite informing the nation of President Kennedy’s assassination shows. 

At thirty minutes, including commercials, the network news reports also offered little time for pontification and social commentary. That made Cronkite’s February 27, 1968 special report on the Vietnam War all the more telling, when after returning from Vietnam following the Tet Offensive, he concluded,

But it is increasingly clear to this reporter that the only rational way out then will be to negotiate, not as victors, but as an honorable people who lived up to their pledge to defend democracy, and did the best they could.

Such was the trust in Uncle Walter that unsubstantiated stories have long said President Johnson then remarked, “If I’ve lost Cronkite, I’ve lost Middle America.”

However, I did not see that, because for most of my grade school years, our aging black-and-white console TV worked only sporadically. When it did, to change channels, my father had to climb on the roof and manually rotate the antenna, while my mother leaned out the window to give him updates on the TV signal quality.  Needless to say, we picked a channel and stuck with it, in our case, the Springfield, Missouri NBC affiliate (KYTV), which aired the Huntley-Brinkley Report.

Consequently, I distinctly remember Huntley and Brinkley making their own antiwar statement in 1969 by playing Kenny Rogers’ version of Ruby, Don’t Take Your Love to Town. While Rogers sang haunting words about a “crazy Asian war,” a camera panned through various footage, highlighting the sacrifices of U.S. draftees and their families.  Huntley and Brinkley then ended the news with their distinctive signoff, “Good night, Chet. Good night, David. And good night, for NBC News.”  (Huntley broadcast from New York and Brinkley from Washington, D.C.)

When I was in the fifth grade, my parents bought a new Sears Silvertone black-and-white TV and splurged for a new cable TV connection, and for the first time, we received all four channels.  In later years, this gave me access to Star Trek reruns (See Star Trek@50: Inspiring Discovery and Innovation) and The Dick Cavett Show, which featured an interesting and eclectic set of guests unknown to a small town boy. (See Eudora, You Got the Love?)

Until the 1980s, the four channel cultural gestalt of broadcast television extended far beyond news.  In those days, people remembered where they were and those around them when they learned about the birth of Little Ricky, Who Shot J.R., and the ending of M*A*S*H. (For the latter, I was sitting on a hotel room bed in Madison, Wisconsin.) The morning-after water cooler bonding from discussing I Love Lucy, Bonanza, M*A*S*H. Dallas, and hundreds of other hit television shows has now disappeared.

Top 40 Radio

Top 40 AM radio had a similar cultural reach, where being in heavy rotation was a clear sign that a song was a national hit, known to most teenagers and adults.  Then each Saturday, Casey Kasem’s American Top 40, counted down “the biggest hits in the U.S.A.” and American Bandstand, with the ageless Dick Clark, highlighted a popular singer or band.  I heard and saw little of that, as I had neither a radio nor a television in my bedroom. Nor did I buy and play 45 RPM singles or 33 and 1/3 RPM albums. All of these were far beyond my financial reach.

My parents owned a small kitchen radio, which we only turned on as we prepared for work and school.  Lacking an external antenna, we received only clear channel AM stations and a few nearby FM stations. Like the television, it was an aging vacuum tube radio, which took a while to warm up after it was turned on.  When it failed, we would take the failing tube, which no longer lit up, to the local drugstore, use the tube tester, and buy a compatible replacement tube.

Tuned to the local AM station (KALM), we received news read from the Associated Press wires and a healthy dose of country music.  I became well acquainted with Conway Twitty, Loretta Lynn, Tammy Wynette, George Jones, and a variety of other country singers, but I didn’t like the affected twang and the frequent stories of pickup trucks and lost love, all in search of what David Allan Coe jokingly sang about as the perfect country song:

Well, I was drunk the day my mom got out of prison
And I went to pick her up in the rain
But before I could get to the station in my pickup truck
She got run over by a damned old train

The exception was Johnny Cash; I loved Cash from the very first time I heard him.  As a poet and a fellow Arkansas boy, he spoke to deeper things – social inequities, the war, and personal loss. There are few things more melancholy than his rendition of Sunday Morning Coming Down or more haunting than his late life cover of Hurt, a song by Nine Inch Nails. (Never underestimate the power of a great singer to make another’s song their own. Heart’s Kennedy Center Honors cover of Stairway to Heaven brought Led Zeppelin to tears.)

Surprisingly, we did have the radio on one afternoon on New Year’s Eve, when the local DJ cum newscaster said, with some mocking mirth, that he was going to play the most popular song in the country for that year, the implication being that this music was drug-crazed rock and roll. With that sardonic introduction, Don Mclean’s American Pie began to play, and I was immediately transfixed with wonder.  Here was another poet, as evocative as Cash, speaking to a different generation about sadness and loss.

Given our household’s one radio and shared TV, other than a few TV variety shows like The Hollywood Palace, my access to rock and roll was largely limited to our car, but only after I learned to drive in 1973 and could pick up a Memphis rock station.  As a result, I missed the British Invasion and almost all of the music of the 1960s and early 1970s. I do remember one of the older girls who lived nearby being devastated when the Beatles broke up, though I didn’t understand why it was such a big deal. 

Digital Transformation

The uniformity of radio and television content in the post-World War II era created common cultural referents, but they largely targeted white, middle class families. If you were black, Hispanic, Asian, immigrant, poor, rural, or in any other way not part of the white middle class, you were rarely heard or seen, except in caricature or stereotype. (For the record, the Clampetts on The Beverly Hillbillies were unlike anyone I knew in my small Ozarks town.)

Slowly and haltingly, things began to change for the better, as radio and television began to reflect the population’s social and economic diversity and its wide range of interests more accurately. Antiwar demonstrations and civil rights protests forced broadcast news to begin presenting important social issues in a more nuanced way, highlighting previously marginalized voices, and television shows began doing the same (e.g., see All in the Family).

Concurrently, legal and financial battles over cable television must carry rules, the rise of cable superstations such as WGN and WTBS, the emergence of FOX as a fourth television network, new pay-per-view services, movie networks such as HBO, and expanded cable capacity all diluted and diffused the market dominance of the big three commercial broadcast networks.  Suddenly, everyone could get their MTV.

In the midst of this, the transition to high definition, digital television began, which changed wireless spectrum needs.  Notably, as analog television stations vacated parts of the VHF and UHF bands, portions of this spectrum were repurposed.  While at Microsoft, I served as a member of the Federal Communications Commission (FCC) Technical Advisory Committee and worked with the International Telecommunication Union (ITU) on the use of the TV white spaces for unlicensed “super Wi-Fi” to address rural and underserved communities and regions in the U.S. and broadband access across the world. (See White Spaces: Celebrating the Cambridge Trial.)  Remember, analog broadcast television originally chose the VHF and UHF bands due to their long distance propagation characteristics, making them idea for communication across long distances.

Today, only live sporting events can deliver the viewership, advertising revenues, and shared experiences once common on broadcast television.  It’s why the bidding for U.S. television rights to the Olympics, the NFL and Super Bowl, and March Madness (NCAA men’s basketball) is now so intense.

Just as these brave new worlds of cable and digital television were reshaping mass media, the Internet and the World Wide Web appeared, something I was privileged to witness firsthand at the University of Illinois when the Mosaic web browser appeared.  As broadband availability and speeds increased, new access modes appeared, and creative market destruction soon followed.  (As the COVID-19 pandemic has again exposed, broadband inequity in the United States – availability, cost, and bandwidth – remains a serious problem. See Broadband: Oxygen for a Digital World.)

Although there were effective Internet search engines before Google search appeared (e.g., AltaVista), Google found a successful revenue model based on targeted advertising. After all, if you are searching for left-handed scissors, you might want to buy a pair. Combining this information with search history and web cookies could delivered a prospective customer hit rate rarely matched by the blanket advertising from newspapers, radio, and television. This devastated the traditional revenue models of daily newspapers, forcing some to merge with rivals and many others to close. Sadly, this has meant many towns and cities now lack local, independent investigative journalism.

Similarly, the convenience of Netflix movie streaming, with thousands of movies just a click away, destroyed the VHS and DVD rental market. Although Netflix initially distributed rental DVDs by mail, it was created in anticipation of higher broadband speeds that would enable streaming. Co-founder Reed Hastings once said this was why they didn’t name the company DVDflix. Coupled with inexpensive, large screen high-definition televisions, the locus of money and power soon shifted from the traditional movie studies, though few would have then imagined that Amazon would someday buy MGM to feed its Amazon Prime Video streaming service.

Paradoxically, Internet broadband gave birth to narrowcasting – websites, channels, and streaming media targeting very specific demographics. Enamored with reruns of The Gong Show (“We’ll be right back with more – stuff!”), The Lawrence Welk Show (my parents loved it) or The Midnight Special? Interested in collimating your Ritchey–Chrétien telescope, folding a paper airplane, or collecting vintage salt and pepper shakers?  Fear not, YouTube is there for you.  Love reggae/zydeco fusion or KISS tribute bands?  There’s a streaming music service for just your interests. Meanwhile, music CD sales (and even downloads) continue to plummet, following the same path to extinction as DVDs and Blu-ray discs.

The Triumph of Narrowcasting

In the early 21^st century, digital narrowcasting has clearly triumphed – whatever the interest, there’s now an app or a targeted steam for it. Every other form of media distribution, from print news organizations through movie studios to broadcast television networks, is now scrambling to adapt, with varying success.  Not surprisingly, any such change brings both new opportunities and associated challenges.

On the one hand, digital narrowcasting has been hugely democratizing, freeing individuals and organizations to communicate directly with their target demographic. This blog is an example, allowing me to ruminate on any topic of my choosing, then share my ideas using social media, something only possible because instant, global communication is now effectively free. Similarly, websites, streaming media, and other tools mean individuals and shared interest groups can quickly find likeminded partners and collaborators, however narrow or arcane their interests.

Historically, the small number of broadcast platforms – newspapers, radio, and television – each had a large audience. Today, a plethora of platforms compete for a much smaller slice of the global audience, some by choice and others by necessity. Although everyone can be a social media content creator, with a potentially global audience, in practice, only a few achieve any significant influence or visibility. As the late Nobel Laureate Herbert Simon astutely noted, “… a wealth of information creates a poverty of attention.”

The insatiable maw of digital channels must be filled, even if with endless and mind-numbing repetition. Just how many home renovation knockoffs of PBS’s This Old House can HGTV make? How many reruns of the Law and Order franchise can competing cable channels show simultaneously? Can the hosts of the cable news channels talk any more while saying even less, filling the minutes with personal opinions and creating venues that attract only the likeminded?

Digital Echo Chambers

As I look to Claude Shannon, the geek’s patron saint of information theory, I fear not all is well in the 21^st century world of digital narrowcasting; the signal-to-noise ratio of digital discourse is now near de minimis, with a cacophony of discordant voices vying for finite mindshare. Is it possible for us to imagine and create a digital world where we more clearly distinguish information (facts) from opinion, persuasion, and social consensus? Each has a role, when clearly demarcated and delineated, but they are not the same.

Facts are independently testable and verifiable, with truth independent of one’s own opinions or beliefs. It is raining at this place and this time is a testable statement, provably true or false. An opinion is not a fact. “I think it is raining” is an opinion; it may or may not be accurate. Fact-based persuasion is convincing someone else it is raining using clear, verifiable evidence that it is, in fact, raining; demagoguery is not. Social consensus is a widely held perspective, best supported by history and evidence.  It is bad if it rains on an outdoor baseball game is social consensus. (See Just the Facts, Ma’am: Reasoning is Not Dead, Jim.)

With that backdrop, the goal of any reputable information source should not be to tell you how to think; it should be to provide you with facts that allow you to develop your own hypotheses, test them against evidence, then revise them accordingly. Unbiased information – the facts – is the prelude to evidence-based reasoning and informed discourse. Such informed national discourse requires shared intellectual context, something in danger of being lost in a narrowcasting world.

All too often, the explosion of tailored communications and personal preferences can trap any one of us in a self-reinforcing echo chamber that separates and potentially demonizes those unlike us, confusing facts with opinions and beliefs. Put another way, if you have been sitting in your underwear for multiple days, binging on potato chips and beer, watching endless reruns of The Big Bang Theory, surfing the dark web, and reading extremist social media (either from the far right or the far left), it’s time to get dressed, open the curtains, seek information, and expose yourself to other perspectives, especially those that differ from your own. Fact-based understanding and multilateral communication are a messy, participatory process – one must listen, talk, and think – but from it, mutual understanding and shared perspectives, both grounded in facts, can emerge.

In higher education today, there is great focus on imparting knowledge and inculcating skills, and they are important, because employers prize them.  However, knowledge transmission pales in importance relative to teaching critical thinking – the ability to analyze evidence, test ideas, adjust one’s perspective, and form rational, thoughtful and nuanced insights. Critical thinking is the upgraded brain software that operates on the data we call knowledge. It is the hallmark of an educated citizenry.

To appreciate nuance, to understand discord, to feel empathy, one must glean the facts and also listen to diverse perspectives.  Read multiple newspapers, domestic and international; consider multiple websites from around the world; listen to news channels from the left, the middle, and the right; talk to people from different backgrounds.  Above all, think critically about what you learn; ask questions; seek your answers supported by facts.  Remember, not everyone who disagrees with you is ipso facto wrong; nor are you necessarily right if all your friends agree with you.

Cronkite knew that, as did Huntley and Brinkley. Each night, they and their compatriots did their best to bring the audience some unvarnished facts to ponder. They reserved editorial comment for rare and extraordinary occasions, and they labeled it explicitly as such, which gave it great power.  Today, the best news organizations and high-quality information sources strive to do the same.

Yes, broadcast news stories were and are filtered by mainstream cultural bias, but they are the mainstream. Though not always right, the mainstream, then and now, is more than a neologism.  It is a shared and evolving Weltanschauung created by the interplay of contrasting and often divergent perspectives, itself derived from broad communication and critical thinking. (See Shaping the Message, Using the Medium and Contemplative Reflection and Instantaneous Communication.)

And that’s the way it is, March 29^th, 2022.  Goodbye, Chet. Goodbye, David. And goodnight from HPCDan.

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!

Higher Education in the 21st Century

N.B. In rapidly changing, global knowledge economy, social, cultural, and economic advantage accrues to those societies that cultivate, nurture, and empower all of their citizens via advanced education, freeing each person to pursue their individual dreams, to the mutual betterment of all. 

On May 12, 1780, John Adams, then in France as part of a diplomatic delegation for the still nascent United States, penned a poignant letter to his wife, Abigail, in which he described the manifold and beguiling beauties of Paris.  Anyone who has strolled down the Champs-Élysées or visited the Palace of Versailles knows instinctively and immediately why Adams was so impressed.  (Mind you, that other modern wonder, the Louvre Museum, did not open until 1793, long after Adams had sailed home.) 

In a forward nod to Abraham Maslow’s hierarchy of needs, Adams resignedly acknowledges that his duty and commitment to an aborning country then struggling for its very existence must take precedence over his personal interests and desires. No matter how much he might yearn for different circumstances, immediate necessity must triumph over the desired:

I must study politics and war that my sons may have liberty to study mathematics and philosophy. My sons ought to study mathematics and philosophy, geography, natural history, naval architecture, navigation, commerce and agriculture, in order to give their children a right to study painting, poetry, music, architecture, statuary, tapestry and porcelain.

Across more than two centuries, Adams’ lament still rings true, albeit with amendments to embrace equity for all. The luxuries of time and resources, made possible by the successes of previous generations, afford their blessed progeny to pursue intellectually and emotionally rewarding endeavors of all kinds – art, music, literature, science, engineering, medicine, business – beyond those dedicated to immediate sustenance and survival.

Adams elegant characterization of the seeming dichotomy between present exigencies and an imagined, better future is now manifestly and explicitly visible in our debates about higher education as a public good accessible to all or a private benefit to only those willing and able to pay, as a birthing place for thoughtful and discriminating citizens of a democratic society or a workforce training and skills delivery system in a globally competitive world.

In a perfect world, each child would be educationally empowered and free to live a life defined by the content of their character, the scope of their talent, the reach of their dreams, and their willingness to work. Sadly, we do not live in that perfect world.  We must do better and do so now.

The circumstances of birth, gender, ethnicity, socioeconomic status, and a host of other factors all too often circumscribe a child’s future, with opportunity crushed and dreams stillborn. In theory, there is no difference between theory and practice. But in practice, there is.  We must do better and do so now.

Life’s Challenges and Exigencies

The students of our public research universities are neither socioeconomically, culturally, nor ethnically homogeneous. In that diversity lies both our strength – we all better when we both learn from others and respect and value our differences – and our educational challenge, for not all begin their educational journey on an equal footing.

For the sons and daughters of the rich and powerful, college is a place for maturation, exploration, and education. They have the freedom to stumble and recover, secure in the safety that societal status and money provide. The path for others is more narrow and difficult, even for the increasingly financially challenged middle class. Whether first generation, immigrant, underrepresented, or poor, college can be both a rare chance and a desperate struggle. As poor, first generation college student, on this I speak from some personal experience. (See Transforming Lives Via Public Higher Education and A Taste of Sherbet.)

Far greater than my own, students of color face compounded challenges alien to most and unimaginable to many. Biases – implicit and explicit – can and do make them feel marginalized and unwelcome, carrying social and cultural burdens that majority students neither see nor feel but sometimes themselves foster, whether intentionally or unintentionally. First generation students, regardless of background, often experience imposter syndrome and lack the social support networks common in families for whom college has been a multigenerational experience, with no familiar place to seek counsel and support when challenges arise.

Finally, life has a way of intruding in expected and unexpected ways. Despite our national angst over student debt and graduation rates, many students who fervently hope to finish a college degree never do, and an even larger fraction lack the opportunity to try. The reasons are myriad and diverse, including poor educational preparation from underfunded primary and secondary schools, lack of finances to pay tuition and housing, a multigenerational family needing support, feeling unwelcome and out of place, or simply not seeing curricular relevance to their personal objectives and needs. 

The American Dream was built on the shared principles of economic opportunity and upward mobility. The idea that everyone deserves a fair shot – not a guarantee, but a fair shot – to better their socioeconomic circumstances through hard work and determination is a central tenet of the American social contract.  Today, the lifeline of higher education and successful pursuit of the American Dream is increasingly out of reach for a growing fraction of the U.S. population.

Recognition of higher education’s importance to our nation’s future is not a partisan issue.  Multiple U.S. President’s from both parties have repeatedly convened august bodies to recommend ways to improve K-12 and post-secondary education, beginning with the 1946 Commission on Higher Education for American Democracy, commonly known as the Truman Report.  That report recommended expansion of federal financial aid for college students and the creation of a community college system, noting

If college opportunities are restricted to those in the higher income brackets, the way is open to the creation and perpetuation of a class society which has no place in the American way of life.

Despite these stentorian calls for action, a large fraction of children born in poverty still remain there, and less than 15 percent of students from the lowest socioeconomic bracket earn a bachelor’s degree by age twenty-four. No euphemistic intellectual legerdemain can hide the ugly truth of our current situation. It is neither unfortunate nor regrettable; it is morally and ethically wrong. Beyond educational opportunity as an ethical imperative, it has deep and profound justifications on economic and competitiveness grounds.  (See The Hopes of Parents and the Dreams of Children on the importance of giving every child a chance.)

Change in the Wind

Public research universities are in the business of helping people. This is especially true in times of accelerating and disruptive change.  Compounded by the consilience of globalization, each year now brings disruptions that once defined generations. In an earlier time of rapid change, the land-grant universities, authorized by the 1862 Morrill Act, were created to spread new agricultural and mechanical knowledge and “promote the liberal and practical education of the industrial classes in the several pursuits and professions in life.”  In modern parlance, the objective was to educate an underserved population, one whom extant higher education neither welcomed nor served.

Similarly, the Servicemen’s Readjustment Act of 1944 – the G.I. Bill – brought millions of veterans to college and university campuses, and it firmly rooted the graduates in the expanding middle class when many feared a post-war slide into recession. The G.I. Bill, along with post-World War II government investment in academic research, further expanded during the Cold War via the National Defense Education Act of 1958, reshaped academia and created the modern research university. Finally, the Civil Rights Act of 1964, the Higher Education Act of 1965, and Title IX of the Education Amendments of 1972 broke down further legal and economic barriers to educational access, though we have by no means eliminated those impediments.

Today, urbanization, global communications, artificial intelligence, talent mobility, and the global knowledge economy are reshaping social expectations. These changes are disrupting the historical web of interconnected economic sectors, while rendering some professions superfluous and creating demands for new skills in others.  A recent McKinsey report, The Future of Work in America, discusses regional economic stratification and the anticipated changes in workforce needs in the next decades.

The National Science Board, of which I am a member, recently issued two reports raising similar concerns.  The first of these, on The Skilled Technical Workforce, emphasizes the need for greater investment in community college technical training. The second, on The State of U.S. Science and Engineering, paints a worrisome picture of the position of the United States relative to its international competitors.

Our world is changing rapidly, and public higher education must adapt and engage as active and honest participants in renegotiating the societal compact.  Any compact reification begins with identifying the irreducible core – the essential values that define public research universities. Quite clearly, these are unfettered discovery (original scholarship and research), transference (student education and training) and fulfillment (societal engagement and services). Put another way, public research universities create new knowledge, transfer insights and ideas, and deliver solutions to societal problems. 

How universities do those things and in what measure are the subject of debate and negotiation. After all, the practices of a modern public research university span less than a single human lifetime. Some of the options will be new, and some will undoubtedly be philosophically challenging. (Remember that many feared the G.I. Bill would destroy U.S. higher education by bringing battle hardened veterans into academe.)

While protecting the irreducible core – the defining verities of academia – I believe we must be more organizationally nimble and flexible, more financially accountable and economically savvy, more societally engaged and responsive, more collaborative and transdisciplinary, and more team oriented. 

We face deep societal challenges – poverty, inequality, injustice, health and wellness, environmental sustainability, and economic uncertainty – and it is tempting to embrace a psychology of diminished expectations. But we also have unprecedented opportunities to unleash the creativity and talent of our citizens.  A university frees successive generations of human minds to realize their potential. We must not waiver in our commitment to the future of our students and our society.

Educational Futures: Beyond Degrees

The intellectual dichotomy between lifelong knowledge and near term skills is a canard, patently and demonstrably false, but the economic and social dilemma is very real. Our students arrive with manifestly different constraints and needs, and we must meet each of them where they are and respond accordingly. Put another way, one educational model and approach does not fit all.

I believe we must move beyond undergraduate degree completion as the sole, simplistic, and arbitrary metric of educational success. Degrees matter, but all education is valuable. It is neither a simple, linear process with a single terminus nor is it synonymous with an immediate continuation beyond secondary school. Degrees matter, but only insofar as they are talismans – social currency and indicators of our true objective, which is bettering the lives of our students.

If a student completes a single course and comes away with a deeper perspective on our world, we have added value to their life.  If another completes a certificate, gains a demonstrated competence, and achieves meaningful new employment, we have changed their economic circumstances for the better.  If we have informed and enlightened a community group, we have enriched our society, even if it were just a weekend seminar and discussion.

I believe we in academia must step back, and live up to our intellectual principals and ideals, remembering that no matter how high the academic ivory tower, its foundation and that of all public higher education, rest on a repeatedly renegotiated societal compact. The history of U.S. higher education is one of punctuated equilibria, with long periods of stability separated by brief interregna of extraordinary change, each coincident with shifting societal norms and expectations for broader access.

We must be more flexible in responding to the heterogeneity of student expectations and needs, while concurrently disintermediating knowledge delivery, certification of completion, and socialization across time and space. (See Pinball, War Games, and Universities.) I believe this means offering a smorgasbord of educational delivery modalities, content packages, and success models, each tailored to differing needs and constraints:

• Traditional degrees. The traditional undergraduate degree is the sine qua non of higher education, tested and proven, but adapting and adaptable to a rapidly changing world, preparing students for careers that do not yet exist, and educating them as critical thinkers and thoughtful communicators steeped in knowledge and wisdom.

• Stackable certificates. Not all students learn the same way, nor do they have similar motivations and objectives. Certificates, both credit and non-credit, organize a coherent body of knowledge with a demonstrable applicability and outcome, ranging from perspective on one of the liberal arts to concrete technical skills. If organized wisely, a combination of credit-bearing certificates can lead to a degree, though the certificates need not be completed sequentially, and they may be delivered either online or via traditional classroom modalities.

• Credit, non-credit, and convertible credit. The goal of educational delivery is the transfer of knowledge and recognized certification of that knowledge. Traditional credit with a transcript grade, earned on completion of an academic course, is the internationally recognized metric of accomplishment. It need not be and increasingly is not the only one. What matters is trusted validation of knowledge and competence. Non-credit certificates and badges are rising in importance as alternative validations.  Today, non-credit programs are often viewed as ancillary, second class activities on most campus; this must change. More importantly, students who acquire skills via non-credit mechanisms and later decide to pursue certificates or degrees may want to convert those accomplishments into academic credit.  We need more flexible mechanisms to establish knowledge and recognize that knowledge as credit.

• Competency certification. Not all knowledge is acquired in the virtual or physical classroom, nor is course completion fully synonymous with demonstrated competence. Life and work are themselves valued teachers. There are a variety of ways to prove and accept competence in lieu of course credit, ranging from academic and professional service testing through probationary credit to work experience

• Mixed online and placed-based education. Synchronous cohort learning on academic campuses will remain part of the educational ecosystem, both for student socialization and access to specific facilities and instrumentation, but it will be complemented by and in some cases replaced by online content delivery that is asynchronous from academic calendars. Online, asynchronous content delivery both allows students to participate based on personal schedules and also allows them to begin classes when the need arises. (See Online Higher Education for some lessons from the history of technology mediated education.)

• Lifelong/lifewide and just-in-time education. The time has long past when the knowledge and skills acquired before age twenty-five could serve one for a lifetime. Today’s worker can expect technological and economic shifts to render many of their skills obsolete every few years. The persistent skills are reasoning and communication, coupled with a desire to learn and relearn throughout life. We must move from a model of “education then life” to lifelong and just-in-time education that delivers new knowledge and skills as needed, via a variety of modalities and mechanisms, recognizing that many will be unable study in situ on an academic campus. For a few, this education will result in new degrees, but for most it will be by securing new credentials, badges, and certificates.

Some might say many of these educational roles and responsibilities are the proper function of community colleges and comprehensive universities.  That is true, in part.  Higher education is a system, with differentiated and overlapping, but complementary roles for community and technical colleges, comprehensive universities, and public research universities. 

However, in a rapidly changing world, public research universities are the critical incubators of intellectual discovery and translation, with national and international stature and partnerships.  They also attract a faculty and student talent base in part distinct from all others. As such, they bear unique responsibility as educational thought leaders and innovators, with the resources and capability to conduct educational experiments at scale. They are also charged with ensuring the economic competitiveness of our citizens and industry in a global knowledge economy.

University of Utah Analysis and Actions

As a flagship public research university and a member of the Association of American Universities (AAU), the University of Utah is a leader in basic research, knowledge creation, and scholarship.  It is also unwaveringly committed to educational access, broad-based liberal arts education, workforce skills training, and economic development.  

A University of Utah Student Success and Educational Futures Taskforce, composed of seventy five students, faculty, and staff members, assessed the current state and future needs of higher education in Utah, based on a charge to consider our changing world and the needs of Utah’s citizens.  Composed of six working groups – facilities and infrastructure; financial analysis; enrollment management, marketing and communications; student success and services; educational delivery and partnerships; and graduate student success – the taskforce outlined a series of recommendations intended to reduce student costs, expand educational access, and deliver targeted, just-in-time training. Common themes from the recommendations include:

• Diversifying pathways to student success by developing alternative credentialing options, improved pathways for transfer students, expanding undergraduate student experience roadmaps, improving support for graduate students and programs, and developing strategic partnerships with employers.
• Expanding online offerings and, where appropriate, using technology to increase educational accessibility, affordability, and inclusiveness.
• Utilizing the University’s physical infrastructure to its fullest by increasing student and mixed-use housing, improving transportation options, and optimizing the allocation of classrooms and teaching facilities.
• Ensuring students have a sense of belonging through individualized attention and support, deeply engaged learning experiences, a robust and inclusive campus community, on-campus career opportunities, and coordinated health and wellness programs.
• Continually revising and adapting initiatives to ensure consistency with the University’s core values: student success and engagement, research and teaching excellence, diversity, sustainability, global vision and strategy, community, and leadership.

In response to the taskforce recommendations, the University is launching several initiatives, including:

• For Utah The For Utah Scholarship creates a fully funded path to a University of Utah degree by covering four years of tuition and fees for Pell Grant-eligible Utah residents. As a result, the fall 2020 entering class was the most diverse in the University’s history, though we still have far to go to ensure broad access.

• Inexpensive all online degree completion. The University of Utah offers lower cost tuition for students taking all classes online as part of a qualifying online undergraduate degree program. This special all-online tuition rate help students who have started college finish their degrees and graduate, even if they cannot return to the campus. (Sadly, we were forced to temporarily defer this program during the COVID-19 pandemic.)

• Expanded credit and non-credit certificates. The University is rapidly expanding its suite of both non-credit and for credit certificates, targeting domains with where there are critical job shortages across Utah. Examples include software development, data science, and entrepreneurship.

• Integrating continuing education and online learning. Reflecting the continuum of learning objectives and the need for fluid transition between non-credit and for credit learning processes, this integration is enabling coordinated management of all aspects of non-traditional education, under the supervision of an Assistant Vice President/Dean.

• Predictive intervention and support. The best time to help students is before they fail, allowing time for recovery from early academic missteps. While protecting and respecting privacy, data can support powerful analytic and predictive interventions, allowing advisors and instructors to work with students while there is still time to recover, both in individual courses and in certificates and majors. This will rely on a combination of commercial software (e.g., Civitas) and local software and data.

• Storefront and electronic services. A large and growing fraction of students are “non-traditional” – they have lives, jobs, and families outside academe, and many lack the flexibility to visit academic support offices during normal business officers. Moreover, the born digital generation expects anywhere, anytime access.  The University of Utah plans to offer advising and consulting services at multiple sites.

• Skill and aptitude assessment. Accurate assessment of student knowledge and skills, via leading edge measurement tools can help students understand their aptitudes and preparations for study emphases and reduce the time needed to complete degrees and certificates by validating extant competencies. The University seeks to adopt and adapt assessment tools that can maximize student success.

• Enhanced student support. A sense of community and belonging is key to student success, enabling students persevere in the face the inevitable challenges students face.  Among many other programs, the University of Utah has created Student Ambassadors who share their experiences with prospective students and parents, Student Success Advocates who serve as peer mentors and advisors, Living Learning and Theme Communities where students share common courses and deeply engaged learning experiences that offer practical and collaborative opportunities to engage communities and the world.

• Faculty mentoring and inclusion. Expanded faculty onboarding and training are essential to preparing new faculty and new administrators to respond effectively to shifting student needs.  These services include (a) training search committees in best practices for recruiting and hiring a diverse faculty, (b) sharing best practices for student mentoring and advising, (c) and support and training for new faculty and new administrators in culture and processes – leadership, education, research and scholarship, and service.

• Corporate and business partnerships. Regularly upgrading the skills and degrees of employees is in the best interests of employers, as it provides the insights, training, and perspectives that allows employees to grow and adapt to changing business circumstances. The University seeks to partner with Utah and national companies on certificate and degree programs for employee and corporate benefit.

Coda

In the Christian tradition, the New Testament (Acts 16:9-10) says a vision appeared to the Apostle Paul  in the night, “A man of Macedonia stood and pleaded with him, saying, ‘Come over to Macedonia and help us.’ Now after he had seen the vision, immediately we sought to go to Macedonia, concluding that the Lord had called us to preach the gospel to them.”

The world gospel is an Old English translation of the original Greek word, εὐαγγέλιον, meaning “good news.” The preservation, creation, and transmission of knowledge are defining attributes of our civilization, and our educational system is an embodiment of the ever-expanding 21^st century “good news.” Education transforms lives, lifts generations out of poverty, nurtures our democracy, fuels innovation, and creates our future.

Our citizens are pleading; we must come help them, without judgment and in partnership. All education, regardless of scale or scope, is valuable. There is no dichotomy between knowledge and skills, but the guilt of our dilemma and our failure to act are very real.

My Balm in Gilead

COVID-19 has disrupted our lives in so many ways – commerce and trade, personal livelihoods and economic vitality, family and social interactions, health and wellness (obviously), mobility and travel, and education, to name just a few. This global pandemic has also exposed deep social inequities in new ways, convolved with political disarray and often dysfunctional government processes.  (See On the Etiology of Heroes for a few thoughts on a way forward that calls on our best to address the challenges before us.)

Meanwhile, cue theme music over a video montage of transformed academia – grassy quadrangles devoid of human activity, sparsely populated classrooms denuded of desks, and research laboratories accessible only by those in personal protective equipment (PPE).  As the music fades, the final cut shows me sitting in my home office, dressed in stereotypical Zoom attire – a business shirt and tie, visible on camera, complemented by off-camera sweatpants and slippers.

In this morality play, my character is a balding, myopic, astigmatic university provost navigating the ship of academe through the iceberg filled waters of these uncertain times, seeking to ensure fair and equal access, facilitate student academic success, promote faculty research and scholarship productivity, and safeguard staff wellness. It’s not an easy thing, and I have lain awake many nights, agonizing over decisions and their unexpected consequences.

About that balding thing; it’s neither an appellation nor a sobriquet, simply an attribute. A few years ago, I sent a saliva sample to 23andMe, curious what I might learn from my SNPs and the company’s database of expressed traits. Like Captain Renault, I was shocked, shocked, I say, when the report showed there was some ever so slightly elevated probability that my genetic profile might be consistent with male pattern baldness.  LOL, who knew?  It’s a cogent warning to consider carefully insights a modern GWAS can sometimes reveal. But I digress, lest I also pontificate on my childhood surprise, when glasses first let me see leaves on trees, rather than just a green blur atop a gray one.  Now back to the scene of the balding provost …

In my provost role, I regularly counsel faculty, staff, and students that successfully navigating this pandemic requires constant vigilance, determined collaboration, committed partnerships, and mutual support. A yearlong pandemic is an emotional and logistical marathon, not a sprint, and in the midst of unremitting stress, each of us must allocate some time and space to focus on our individual mental health and engage in activities that bring happiness and serenity.  In the spirit of “physician, heal thyself” where have I sought my own balm in Gilead?

So Many Books, So Little Time

In differing times and places, I have, unlike Poe, found books to provide a surcease for sorrow. Books can teleport one into the mind of another’s memories and knowledge (non-fiction) or imagination and fancy (fiction). That a string of stylized symbols can record the thoughts of another, sometimes long dead, whose insights and ideas reach across time and space to illuminate and inspire, is itself an extraordinary thing, one that still fills me with unabashed wonder.

Arguably among humanity’s greatest inventions, these things we call language and writing are perhaps rivaled only by fire, agriculture, and science in their importance. In their embodiment via books and libraries, ideas and knowledge accumulate and set ready for transmission and uptake by successive generations of willing and malleable minds.

It’s no surprise then, that books were one of my first and most joyous childhood discoveries, a precursor to that other abiding love, science. (See Libraries: Arms Too Short to Feed the Mind.) With a good book, I could, then and now, sit transfixed for six, twelve, or even eighteen hours, oblivious to my surroundings, transported into worlds unknown and challenged by ideas beyond the reach of my experience and current understanding, each page crackling with enlightenment.

Emboldened, seeking meaning in an oft-confusing world, I sometimes try my own, feeble hand at the mystic art of writing, finding comfort and refuge in the feel of words and their juxtaposition to ruminate and communicate ideas. (Yes, I was a geeky kid who read dictionaries for pleasure, savoring both the etymology and the denotation. No surprise – myopia and astigmatism came early; baldness reared its sunburned head somewhat later.)

In a world of eight second sound bites and enervating ad hominem social media attacks, when Warhol’s fifteen minutes of fame seems like the imprimatur of immortality, meditative reflection and long-form written discourse now seem like lost arts. (See Contemplative Reflection and Instantaneous Communication and Just the Facts, Ma’am: Reasoning is Not Dead, Jim.)

At its best, writing is a contemplative Zen state where words and phrases spring unbidden from the recesses of your mind, bound by a set of unspoken rules and imagined mores and shaped by a lifetime of intellectual and emotional milieu. Part puppeteer, part curious observer, you are a transcriber of events in real and imagined worlds seen only through the uncertain lens of your mind. It is an exhilarating and sometimes frustrating art, at its best, informed by experience, wisdom, and passions. (See Eudora, You Got the Love?)

Some of my writing samples are archived on this website (www.hpcdan.org), and some others are shared only with select friends. You now hold one such weak musing in your digital hands. Those writers with the greatest gifts can touch our naked souls, but none of us can ever truly master the form. I know I never will, though I continue to try, and perhaps that is, in the end, enough. 

Pure Thought Stuff

Of late, I have returned to one of my other teenaged and lasting loves, the art of software.  (See A Feeling for the Code.)  As in any creative medium, there are hacks, artisans, and artists of the highest gift. Hence, I use the word art with thoughtful intent, for well-written software has an austere and ethereal beauty all its own, and like writing, it can bring joys unknown to all but the most committed and talented acolytes.

In his wonderful book, The Mythical Man Month, my friend and former colleague, Fred Brooks, writes elegantly of the joy of software and why it is so enthralling:

The Programmer, like the poet, works only slightly removed from pure thought-stuff. He builds his castles in the air, from air, creating by exertion of the imagination. ... The magic of myth and legend has come true in our time. One types the correct incantation on a keyboard, and a display screen comes to life, showing things that never were nor could be.

When fully immersed in the code, you hold in your mind an abstract world of your own creation, defined by the rules of logic, but mutable and capable of evolving in complex and unexpected ways, just as an author’s fully formed characters can take on lives of their own, sometimes surprising even their creator. In these moments, pure thought-stuff becomes incarnate and nested causal chains glisten with beauty like raindrops on a spring flower.

Reconciling the perfect world as imagined and the inevitably flawed creation then becomes an exhilarating and encompassing quest. Why did this happen?  How can that be true?  Inspired and chagrined by unexpected behaviors and sometimes subtle clues, the creator becomes detective, seeking insights and remediations that return the code to a state of grace.  The magic of myth and legend, indeed.

It can take hours to load one’s working memory with such a virtual world of software, and doing so necessarily pushes all other concerns from one’s mind. As Morpheus tells Neo in The Matrix, you must “free your mind” from all other thoughts, perspectives, and assumptions and follow the code. During that oneness, there are no other actors, no other concerns, and one can abide in the bliss of total immersion.  It is intellectual joy that unexpectedly changed our world. (See Reluctant Revolutionaries, the Trolley Paradox, and Ender’s Game.)

Such creativity and full cognitive loading brooks no exceptions and tolerates no distractions.  It can be broken by such mundane events as an unexpected telephone call, a colleague’s doorway interrogative, or an unwelcomed text message. In that momentary interruption, connections can disappear, insights can fade, and perspectives can be lost. That too is a fall from grace. (See On Cognitive Loading and Intellectual Hierophany.)

In the past few months, I have sought solace in the beauty and logic of such software, incarnated as the end-to-end development of a wireless environmental sensor network. As a potential extension to the SAGE research project of which I am a part, it offers a path to citizen science empowerment and real-time response to environmental change. The terms of art – ESP32, Raspberry Pi, Jetson, JSON, MQTT, InfluxDB, Grafana, LoRaWAN, TensorFlow – matter only to those in the discipline.  For me, the beauty and the cognitive load can, for just a few hours of the nights and weekends, free my mind, and in the midst of the COVID-19 pandemic, that too brings me peace.

Coda

Our culture and our future rest on mutual respect, sharing insights, ideas, and knowledge, and the empowerment of human minds and their ingenuity.  In the crucible of the pandemic, we must support and draw strength from one another. As I have noted before, these simple things matter most of all. (See The Simple Things Matter, Most of All Now.) Ultimately, that is humanity’s sustainable balm in Gilead.

On Catastrophes and Rebooting the Planet

As all those who can self-isolate to help reduce the spread of COVID-19 and all but essential services are closed in many locations, local, regional, national, and international commerce has declined precipitously, with calamitous effects for many workers, businesses – large and small – and even governments and their instrumentalities.  There is fear, pain, and suffering in many places and in many forms, and this reality supersedes any claims to the contrary. (See Nature Cannot Be Fooled.)

This is a wicked problem, more complex in many ways than the Great Recession of 2008 because it convolves two complex issues – public health and economic vitality. The same actions that encourage commerce endanger human lives. Conversely, the actions that protect lives have profound, negative economic consequences.  Our socioeconomic equilibrium has been disrupted, with an uncertain path to its restoration.  It is, to use a common phrase, a global catastrophe -- an abrupt and unexpected event causing great and relatively sudden damage and suffering.

There’s a branch of mathematics that studies such abrupt and unexpected changes; it’s aptly named catastrophe theory.  As Wikipedia explains, “catastrophe theory is a branch of bifurcation theory in the study of dynamical systems,” but a simpler description is that catastrophe theory characterizes small changes in the parameters of a nonlinear system that cause equilibria to appear or disappear and a system to jump from one macrostate to another. In that sense, it is a formalization of the colloquial meaning of a disruptive event.

My Geek Childhood

When I was a young teenager, the catastrophe theory work of René Thom had a brief run in the popular consciousness, as the country grappled with economic shocks engendered by the energy crisis of the 1970s. It was even featured in the print edition of Newsweek, due in part to its popularization by Christopher Zeeman. Catastrophe theory was also featured in that other must read periodical of my childhood, Scientific American, alas after I was already in college. (See Zeeman’s delightful April 1976 Scientific American article, entitled simply, Catastrophe Theory.)

That I both read Newsweek and Scientific American avidly and ravaged multiple libraries for books on the underlying mathematics of catastrophe theory perhaps says more about my young teenaged years in rural Arkansas than about catastrophe theory’s grip on the popular imagination.  (See Libraries: Arms Too Short to Feed the Mind.) Let us now bow our heads in a brief acknowledgement of the self-evident reality that I was and am a geek or nerd. (I accept either appellation – but I take umbrage at dweeb or dork.) Needless to say, I had plenty of time for reading on Saturday nights.  Having explicated the limitations of my teenaged social life, let us plunge deeper into catastrophe theory.

Catastrophe Theory 101

This simple diagram on the right illustrates one of the most commonly used models, a cusp catastrophe. Here, two control variables (a, b) perturb a simple function y = u⁴/4 by the addition of a quadratic

yielding a potential function of the form

(Note that the constants are purely for mathematically simplicity.  Any desired scaling factor can be applied to the control or internal variables.)

When the control variables (a, b) have fixed values, the system settles into an equilibrium state where the internal variable u minimizes the potential function.  However, as the control variables change, a local minimum can disappear, and the internal variable can jump suddenly to a different equilibrium, the cusp in the diagram.

A little calculus – setting the first derivative to zero – shows that the equilibrium surface is

The line that marks the edges of the bend in the surface from the upper sheet to the lower sheet is called the fold curve. Finally, the bifurcation set (cusp) is the critical image of the projection (a, b, u) -> (a, b) from the equilibrium surface onto the control space.

Intuitively, small changes in the (a, b) control plane define situations where a stable solution (for values of u) will suddenly jump to an alternate stable solution. As the diagram also shows, there are hysteresis loops where crossing the cusp curve in one direction may cause a catastrophe, but immediately reversing direction does not restore the previous state.

The astute reader will have noted that the cusp catastrophe describes a deterministic system (i.e., the first derivative is time invariant), and most domains include random, time-dependent elements.  The simple cusp catastrophe can be readily extended to stochastic environments (e.g., by adding a Wiener process, W(t)), resulting in a stochastic differential equation.  This means the equilibrium plane and transition cusp vary over time, based on the underlying stochastic process.

For those of you playing the game at home, a Weiner process is a real valued continuous time stochastic process that is the scaling limit of a random walk.  This is yet another example of the interconnectedness of things, spanning topology, dynamic systems, random walks, probability theory, and diffusion processes.

Rebooting the Planet

Now that I have totally buried the lede of the current public health and economic crisis under childhood geekdom and the mathematics of catastrophe theory, here is the relevance and intuition. Think of our control variables (a, b) as social distancing and government economic policy, respectively, and our outcome as socioeconomic health and stability.  Small changes in social distancing or government policy can trigger abrupt transitions in socioeconomic stability due to large declines in public health or economic function (e.g., a pandemic and recession). This is the important point: no simple reversal of those changes can immediately return society to its prior, healthy and prosperous state. All of which brings us to hysteresis and a restart of global society.

Shutting down a planet, a country, a city, or even a university is neither a small nor a simple thing.  Critical infrastructure must be maintained and essential services must remain operational.  More importantly, each of those has collateral dependences.  Infrastructure and services are fractal and self-similar at multiple levels. They are also globally interconnected, with dependences unimagined even a generation ago. (See The World Is Small and Globally Connected, Globally Worried.)

How does one reboot a planet?  First, an immediate return to normal is simply not possible; as the cusp catastrophe model shows, there is hysteresis in any individual and collective action set.  Jobs and income have disappeared; businesses and industries have been damaged and destroyed; tax revenue has been lost; and families and communities have been devastated by illness and death.

Second, an overly rapid and aggressive restart – an “all is back to normal” ethos – risks a rebound pandemic, given inadequate herd immunity or a time to develop a future vaccine.  Conversely, a timid, overly cautious resumption of business and social activity risks unnecessary economic loss and prolonged pain and suffering for hundreds of millions of individuals.

Resumption is not a binary process. Today, we are self-isolating and social distancing, alternating between sitting alone in sweatpants while anxiously watching talking heads on cable television and venturing out with trepidation for food and supplies, warily eyeing the person across the street, fearful they will not keep their distance. We cannot immediately shift to large group activities, attending mass sporting events and glad-handing colleagues, friends, and family with abandon. Rather, resumption must be phased, with careful monitoring of public health and economic activity and recognition that there may be multiple missteps and necessary backtracking. It is a thoughtful, careful process that must be driven by reason and data, not emotion and intuition. (See The Simple Things Matter, Most of All Now and Just the Facts, Ma’am: Reasoning is Not Dead, Jim.)

Nor are universities exempt, with their tens of thousands of students, faculty, and staff drawn from around the world, living, working, and eating in close proximity.  As a practical matter, universities are small cities, with logistics and transportation systems, museums and performing arts centers, hospitals and healthcare systems, housing, dining, and recreation facilities, all interconnected with research, education, and community engagement.

These are complex dynamic systems with many co-moving stochastic components, self-similar at many, many scales.  Individuals adapt continuously, but public policy is necessarily discrete. As my colleague, Michael Good noted, it is wise to view the post-pandemic reboot as consisting of red, orange, yellow, and green phases, each representing incremental resumption of social and economic activities, albeit with potential reversion to earlier phases based on observed outcomes.

Remember the key lesson of the stochastic cusp catastrophe. Disruptions cannot be immediately inverted; complex dynamic systems involve hysteresis. We will persevere and the planet will reboot, but it is more complex than a simple Ctrl+Alt+Del. The future will not look like the past for quite a while, but it can be both different and better if we work together in a collaborative spirit.  (See Renaissance Teams: Reifying the School at Athens.)  

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.

Big Bata, Big Challenges, Big Opportunities, and the Reification of Consilience

N.B.  This is a modified version of an essay to appear in the 2019 Merrill Advanced Studies Center Workshop

Each individual’s Weltanschauung is shaped by the totality of their life experiences and it defines their perspectives, philosophy, and understanding of the cultural, economic, and scientific milieu. (The phrase “world view,” the nearest English equivalent, seems rather prosaic by comparison.) Each perspective is necessarily constrained, bringing biases, both explicit and implicit.  If in doubt, spend a bit of time looking at a simple doorway – any example will do – and think about what you truly see. 

Beyond the superficial, a humble doorway, like many human objects, embodies large fractions of our culture, history, and innovation: protection and the rule of law; security, privacy and mathematics (locks); metallurgy and materials science; environmental systems and fluid dynamics; trade and economic specialization; manufacturing and replication; microbiology and cellular structure (wood); human social dynamics and structures; art, design and aesthetics; paint, chemistry and polymers; and mechanical advantage and physics, to name just a view.  On any cursory examination, each of us typically sees but a few of these things, lost in the minutiae of daily life, but they remain there to see despite our obliviousness.

The explosive growth of knowledge has had similar, deleterious effects on our ability to see the integrative whole.  Intellectual consilience is increasingly obscured by increasing specialization and the seeming triumph of reductionism over holistic perspective.  Is there any academic anywhere who has not heard or repeated the old joke, “You learn more and more about less and less, until you know everything about nothing, then they give you a Ph.D.?” (See Simplifying Communication and Shaping the Message, Using the Medium.)

Humor aside, the original seven liberal arts, the Trivium (grammar, logic, and rhetoric) and Quadrivium (arithmetic, geometry, music, and astronomy), have given way to the repeated speciation of disciplines, each with their own arcane argot, incomprehensible to all but the speciated initiate. Yet the three big and enduring questions about matter and the universe, life and its processes, and the human condition are deeply intertwined. How did it all begin?  How does it work? How will it end? What does it mean? Philosophy, ethics, mathematics, the physical and biological sciences are all elements of our doorway, Plato’s Cave manifest in new ways. (See Eudora, You Got the Love?)

As academics, we ardently seek to be the embodiment of Raphael’s Causarum Cognitio (The School of Athens) when disciplinary isolation means Pieter Bruegel the Elder’s The Tower of Babel may often be more apt. The concomitant loss of a lingua franca, an ontology of shared discourse, and a deep and binding epistemology of knowledge endanger our ability and our deep need for convergent conversation and reflection.

CRISPR and gene editing, climate change and the Anthropocene, technological revolutions, and socioeconomic disruption all cry out for disciplinary, interdisciplinary, and transdisciplinary collaboration and shared insights. Across this cacophony, the emergence of big data and machine learning is a potential Diogenean lantern, illuminating a mechanism to reunify divergent domains in holistic ways, an enabler for collaborative Renaissance teams.  (See Renaissance Teams: Reifying the School at Athens.)   

As with any new approach, the combination of big data and machine learning brings both great opportunities and equally grave risks. Data from disparate academic and social sources can be used to predict when students may struggle, but must be used wisely lest privacy be compromised or bias be introduced.  Similarly, social and e-commerce data can be used for targeted advertising and product marketing, but must never be used to discriminate against certain groups.  Finally, data from multiple scientific domains can be used to glean insights into complex interdependent phenomena such as the effects of human behavior on climate change.

The Rise of Big Data

One of the enduring lessons of computing is that quantitative change begets qualitative change, with viability determined by the ratios of speeds, capacities, costs, and market scale. The smartphone of today embodies the same principles as the mainframe computers of the 1960s.  Dramatic shifts in both component capacity and performance made what were then room-sized, multimillion dollar systems available now for hundreds of dollars to billions of people, with data volumes dwarfing those heretofore available. (See The Zeros Matter: Bigger Is Different.)

Put another way, today’s smartphone is more powerful and more interconnected than the supercomputers of yesteryear. The iconic Cray X-MP supercomputer of 1985 cost roughly $18M in 2018 U.S. dollars, with a peak performance of 800 million floating point operations per second (800 megaflops) and a 56 kilobit/second network connection. By comparison, a 2017-era Apple iPhone costs roughly $700 (U.S.), has a performance of roughly 3000 million floating point operations per second (3000 megaflops), and a broadband network speed of roughly 5-10 megabits/second that can access the vast network that is the Internet. (See HPC, Big Data and the Peloponnesian Wars.)

Similarly, big data denotes data of a volume and scale that dwarfs government and enterprise data scales of prior years, made possible by the same quantitative technological changes. Commercial terabyte data stores were once nation-scale resources; today, they are consumer storage devices. This explosive data growth rests on three socioeconomic and technical developments. First, ubiquitous, interconnected, mobile computing devices, and the associated growth of social media and e-commerce, have created enormous volumes of consumer behavioral data, whose large economic value have been unlocked by predictive machine learning. Every major corporation and many governments and universities now leverage this data to tailor marketing messages and to shape products and services.

Second, new scientific instruments, themselves enabled by quantitative computing changes, are transforming the nature of academic research. As an example, the Large Synoptic Survey Telescope (LSST), is designed to survey the southern sky and help understand dark matter and dark energy and the formation and structure of the Milky Way and will produce tens of terabytes of sky survey data each night and petabytes per year. Analogously, National Ecological Observing Network (NEON) is a continental-scale observation facility designed to collect long-term open access ecological data to better understand how U.S. ecosystems are changing.

In science, the rise of big data has profound social and potentially democratizing implications. For most of scientific history, scientific data has been both difficult and challenging to obtain.  Indeed, the experimental method – hypothesis, experiment, theory – is rooted in the capture of new data to validate ideas. As Richard Feynman once described science, a researcher guesses at a law that would explain the currently inexplicable, they then derive the consequences of the putative law, then they make further observations to see if the consequences predicted match the reality now found.  (See The Epistemology of Science.)  With large volumes of scientific data now readily available, hypothesis-driven experimentation is now being complemented by an abduction inversion – what interesting things might the existing data reveal?

Third, concurrently with the deployment of a modest number of large-scale scientific instruments, very large numbers of small, inexpensive sensors are being deployed worldwide.  This Internet of Things (IoT) now includes billions of consumer health devices, environmental monitors, and smart and connected household objects (doorbells, cameras, and thermostats), each a rich source of data for understanding human behavior and interactions.

The opportunity posed by heterogeneous big data is obvious – statistically rare events are manifest at scale, and the fusion of data from multiple sensors and domains offers opportunity for correlation and holistic understanding. Yet big data’s very scale brings challenges, for humans are rarely either accurate or effective in repetitive, manual analysis. Technology for producing and recording data is of little value unless there are effective ways to extract insights from it. As the late Nobel Laureate, Herbert Simon wisely noted,

What information consumes is rather obvious: it consumes the attention of its recipients. Hence a wealth of information creates a poverty of attention, and a need to allocate that attention efficiently among the overabundance of information sources that might consume it.  

The goal of machine learning is to focus limited human attention on salient data attributes, while automating the laborious and error-prone attributes of data processing. From experiment and theory to computational modeling, this new model of data-driven exploratory discovery has been called the fourth paradigm of scientific discovery.

The Machine Learning Revolution

Machine learning – the use of computing technology to glean insights from data, identify patterns, and make decisions with little or no human intervention – is an idea that dates to the very beginning of modern computing. Its recent rise depends on the confluence of rich sources of big data, inexpensive, high-performance computing, and deep learning. Though the latter is but one of a wide range of learning techniques, deep neural networks have transformed many notions of practical machine learning. 

Though a detailed description of machine learning techniques is beyond the scope of this brief review, it is instructive to view deep learning as a subset of machine learning, which is itself a subset of artificial intelligence. As the name suggests, deep neural networks (DNNs) were inspired by biological neural networks and consist of many levels of simple algorithmic neurons that progressively identify extract higher level features from their inputs. Thus, each level of a network trained to recognize faces might move from pixels to edges, then features, then faces.

There are many variants of DNNs, each with strengths and weaknesses and varying applicability to specific domains (e.g., handwriting, speech recognition, image and feature identification, drug discovery, advertising, fraud detection, or computer vision).  As noted above, deep learning’s recent success depends on large volumes of training data (big data) and powerful computing systems, particularly GPUs and targeted hardware such as TPUs to support DNN configuration and training. Once trained, DNNs can then be deployed on much more modest hardware with new data, yielding highly accurate predictions and identifications.

More recently, the recent appearance of Generative Adversarial Networks (GANs) has led to breakthroughs in both competitive games and in automated digital object creation.  As the name suggests, GANs consist of two neural networks in competition; a generative network creates candidates, and a discriminator network evaluates them.  As an example, one might use a GAN to create artificial images of human faces, where the generator creates the facial images and the discriminator accesses them for validity. Using GANs, Google’s AlphaZero system has automatically learned winning strategies for games such as chess and Go.

The automation of many tasks long considered solely in the human cognitive domain raises many important social, economic, and ethical questions.  The data used to train DNNs can introduce bias (e.g., by training facial recognition systems with images lacking a wide range of ethnic backgrounds). Likewise, the creation of “deep fakes” (news, images, or videos combining algorithmically generated attributes with actual images or videos) can be used to sway social or political sentiment or to facilitate fraud.

Challenges and Opportunities

As with any change, the explosive growth of big data has brought a new set of challenges. What data should be retained and for how long? Who pays for the data retention and for how long? How does one ensure data accessibility for long periods, particularly as storage and retrieval technologies continue to change rapidly? Industry, government, and academia all struggle with this balance, as do consumers. Magnetic tapes, floppy disks, and tape cartridges all had their day; few working readers remain. Unlike books and papers, digital data must be repeatedly transferred to new media to be preserved.

In reality, there are few economic or social incentives to retain data for long periods, particularly when in many domains, the costs to acquire new data are so low.  The exceptions of course, are when the data is the record of a rare or non-reproducible event or when the costs of data reproduction are exorbitant. Once the disciplinary value of data dissipates, creators often have little incentive to retain the data.

More perniciously, the long-term value of data may accrue to those other than the creators or maintainers, particularly when insights are gleaned from transdisciplinary data fusion. Maintaining metadata is equally important, as it defines the provenance and content for data capture.  In many cases, the metadata is as valuable, and sometimes more valuable, than the data itself, as it shapes the context for data fusion and integration.

New government policies for data preservation, particularly for experimental reproducibility and validation, together with rising data volumes are placing unprecedented financial and regulatory pressures on academic institutions for data management. (See Research Data Sustainability and Access.) Historically, one of the fundamental functions of libraries has been triage – deciding what materials should be discarded, which should be preserved, and which should be monitored closely for future evaluation.

It is imperative that the analogs of such policies in the digital age be defined based on thoughtful experiment and assessment. Simply put, we need an interoperable research and data marketplace that exposes and sustains true costs and benefits, recognizing that the costs of data preservation are not self-similar across temporal and spatial scales.

Finally, security, privacy, and bias loom large in any discussion of big data and machine learning, particularly data associated with individuals.  (See Information Privacy: Changing Norms and Expectations.) Who is liable when data breaches inevitably occur? How are disparate international laws reconciled with transnational data flows?  When and where do you have the “right to be forgotten?” Who controls use of an individual’s data and when is consent required? How can we best determine the bias or lack of bias in machine learning predictions?  How are these policies tested and validated?  

Final Thoughts

The big data and machine learning revolution is accelerating. Beyond its institutional effects, the consumerization of artificial intelligence, with deep neural networks now embedded in Internet-connected consumer devices – edge AI – is reshaping the nature of computing and society. (See Come to the Supercomputing Big Data Edge.)

Targeted face recognition systems such as Amazon’s DeepLens are now available for ~$250 (US), and any technically savvy hobbyist can build an equivalent device for ~$100 (US) using a Raspberry Pi computer and open source face recognition software, with concomitant privacy risks. The same technology, however, is enabling improved cancer detection via feature identification and urban environmental monitoring and smart cities. 

As with any new technology, we must choose wisely regarding acceptable use, recognizing that there are always expected benefits and unexpected consequences. Only engaged and thoughtful debate, one dependent on a diverse, educated and engaged citizenry, can balance benefits and risks to define both a social consensus and acceptable legal and ethical frameworks.  (See Public Intellectuals: Seeing the Stars.)

Our future depends on an inclusive Weltanschauung, a reunification of speciated perspectives, one where we all see our doorway to the future as a holistic opportunity and cautionary future. There are no easy answers; there never have been.

The future awaits.  Come, let us reason together.

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.