Reed's Ruminations: A Blog by Dan Reed

Effervescent Clouds: Nimble Computing Infrastructure

N.B. A version of this essay appeared as a guest blog post for the EU Cloud Lightning project

What's a cloud? The word means many things to many people. In true Airplane! movie parody style, one is tempted to say, "It's a visible mass of condensed water vapor floating in the atmosphere. But, that's not important right now."

Some might say that our cloud confusion is indicative of a fog, a low hanging cloud. Indeed, in public perception, the cloud is a vaguely understood and amorphous capability that lurks behind our smartphones, enabling electronic communications, web searches, social networks, e-commerce, and 24x7 news and streaming media. It is a mysterious "something, somewhere" that is only noticed when it is inaccessible.

For those of us in computing, the cloud is more than a buzzword or meme, it is a set of technologies that are transforming the provisioning and delivery of computing cycles, storage and services, with profound implications for government services, research discovery and innovation, and businesses large and small. Given the public confusion and sometimes overhyped marketing, it seems worthwhile to dispel the fog and illuminate a few compelling features of clouds.

In a very real sense, public clouds – those hosted by cloud providers for on-demand access – have much in common with the timesharing systems of the 1960s and 1970s, albeit at unprecedented scale. They shift the capital costs of provisioning, along with the expertise required for operation, from users to providers, allowing users to focus on their core expertise (e.g., research, business, government) and pay for only what computing they need, when they need it.

In turn, cloud elasticity allows servers to be provisioned and de-provisioned dynamically in response to changing interest and demand. One of the few things worse than having your brilliant idea ignored is having it fail publicly from too much attention. Many an organization has first been thrilled to see their product or service be reported in the press and go viral, only to be horrified when on-premise servers collapse under the load of success; this is known as the slashdot effect, named after one of the popular "news for nerds" web sites. Without cloud elasticity, the global phenomenon of Pokémon Go would not be possible.

Although not required, many clouds also virtualize the underlying computing, allowing multiple services and software stacks to be co-resident on the same physical hardware, further reducing costs. With Infrastructure as a Service (IaaS), Platform as a Service (PaaS), Software as a Service (SaaS) and containerization, developers can choose the level of abstraction, from low level to high level, needed to support existing and new applications.

By allowing innovators to focus on their ideas rather than computing infrastructure, the pay-as-you-go cloud model has made it easier, faster and cheaper to bring ideas to life, unleashing a second dot.com boom of cloud-based services, unprecedented citizen access to government data, and new approaches to scientific discovery via big data.

The latter has sometimes been called the fourth paradigm, where new sensors, data analytics and deep learning have shifted the research approach from "what experiment should I conduct" to "what insights can I extract from available data." It has also stimulated interest in convergence architectures for high-performance computing (HPC) and data analytics that combine the best elements of cloud and HPC capabilities. (See my essay, Exascale Computing and Big Data: Time to Reunite.)

Implicit in this client-cloud model is ubiquitous, reliable and inexpensive wired and wireless broadband services. Without broadband access, clouds are simply isolated sets of computers and data, unconnected to the world of mobile devices and the nascent Internet of Things. To further ensure reliability, most public cloud providers geo-distribute their pools of servers. Even private clouds (i.e., those operated by governments and companies for their own use) often geo-distribute their servers at multiple data centers. Should disaster strike at one site, services can be shifted automatically to another site, perhaps even in another country or continent.

Finally, the unprecedented scale of cloud deployments, with commercial data centers each costing over $1B, has stimulated new approaches to energy efficiency and cooling, component failures and reliability, customized and open hardware, software defined networks (SDNs) and software defined storage (SDS), and system provisioning and management. In short, we are in a time of radical change in the computing ecosystem, all against a backdrop of Moore's law limitations.

What's a cloud? It's an elastic, on-demand, often geo-distributed, frequently virtualized, flexible hardware, software and services infrastructure that powers the 21^st century knowledge economy. And that is really important right now.

Related articles

Exascale Computing and Big Data: Time to Reunite

Names Matter: The False Dichotomy Between Big Data and HPC

N.B. An abridged version of this essay was submitted as a white paper to the BDEC Frankfurt workshop.

I am going to be a bit radical and suggest we must reason in new ways about the need to integrate high-performance computing (HPC) and big data analytics. That must begin with our thinking about how we label and discuss them. Any expert in journalism, communications or psychology will immediately and unhesitatingly affirm that names have extraordinary power. If you doubt that, look at the philosophy of proper names and the cultural history associated with true names. And if that's not convincing, ask any child who was taunted because his or her name rhymed with something unfortunate.

Names shape our thinking, define our discourse and reinforce our biases and perspectives. We use names and descriptors to categorize people, organizations and objects; we use names to define the positive and the negative; and we use names to market, reward and punish. Brand equity has real value. Sadly, as many studies have shown, names also play on stereotypes and our implicit bias. Take Harvard's Project Implicit bias test; you will be humbled and chastened as it exposes your own bias.

High-performance computing (HPC) and big data/machine learning are not exempt from the power of names and a "they could learn from us if they'd only listen" dichotomy. Done any low performance computing (LPC) lately? Analyzed any small data? Of course, you did, when you checked your email and text messages on your smartphone a few minutes ago, but you did not think about it in those terms. In fact, you ran a trans-petascale computation and staged data across a global set of fiber optically connected data caches (a content delivery network) when you searched for a coffee shop yesterday. But, none of us call it that – it's just a quick web search in the ill-defined and amorphous cloud to which we are connected via a global broadband (wired and wireless) infrastructure.

Semantic Cul-de-Sacs

I suspect we are trapped in a sematic cul-de-sac when we talk about high-performance computing and computational science. They are code words for physics-inspired numerical solution of partial differential questions; all too frequently, everything else is potentially suspect and inferior. The denotation of the word exascale says nothing about computing at all, but the connotation of FLOPS and batch-oriented HPC platforms is there in all of our minds. In turn, deep learning and big data conjure images of computer science types tuning neural nets and recommender systems in cloud data centers for targeted product marketing.

It's time to stop categorizing and name calling, time to end cultural and disciplinary silos. There is no hierarchy of needs or intellectual purity. There are just ideas and people who can learn from one other. Dare I say it – death to HPC; death to big data. It's time to stop the religious wars over HPC and big data cultures and technologies and focus on informatics-mediated discovery and innovation. The goal of an HPC center should not be self-perpetuation, either of infrastructure or organization. Nor should a cloud service be restricted to commercial domains. That means starting with intellectual outcomes and deriving approaches rather than defining approaches and searching for feasible intellectual outcomes.

From False Dichotomy to Unification

There are many technical ways to envision and realize the continuum and fusion of traditional HPC and computational science and big data and machine learning. These include integration of steam-based workflows and just-in-time schedulers, containerization and software stack packaging for application-specific configuration, fine-grained parallelization of learning packages, learning for algorithm and software adaptation and tuning, custom ASIC designs (see Google's recent TPU announcement), software defined networks and storage (SDN and SDS), and renewable and energy efficient hardware design and configuration. There are an equally large number of social and economic approaches, including denominating informatics costs in currency to illuminate investment priorities, spot pricing for priority access, economics-driven infrastructure selection and deployment, and institutional and funding agency policies for data management.

What we cannot do is allow ourselves to be bound by names, labels and cultures. Richard Hamming wisely noted, "The purpose of computing is insight, not numbers." HPC and machine learning are not goals, they are enablers. And that's the major consensus narrative.

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Exascale Computing and Big Data: Time to Reunite

Informatics, Sugar Water and Revolution

N.B. An abbreviated version of these remarks was delivered at the opening of the 2016 Informatics Showcase, a symposium hosted by the Iowa Informatics Initiative (UI3).

This morning, I'm going to do something perhaps unprecedented for a computing geek, especially one who is a former Microsoft executive. I'm going to speak without technical aids – no PowerPoint, no Prezi, no Google Docs. In response, some of you may feel compelled to use your smartphones to short selected IT stocks, as the NASDAQ may tremble as a result of my intemperance. I offer this as a safe harbor statement.

The late Steve Jobs recruited then Pepsi CEO John Sculley by asking, "Do you want to sell sugar water for the rest of your life, or do you want to come with me and change the world?" In retrospect, it was more than just a hyperbolic recruiting question; computing has changed our world. Via "your potential, our passion," it has been "insanely great."

If the Anthropocene defines human global impact on the Earth's geology and ecosystems, then the Information Age defines computing's universal impact on our own interactions. Like the questions of human triggered environmental and climate change, information technology mediated societal change requires sagacious and thoughtful response.

Computing Universality

When I was Head of Computer Science at Illinois during the first (1990s) dot.com boom, I somewhat impertinently told the Dean of Engineering that there were only two kinds of departments in the college: computing (computer science and computer engineering) and applications of computing (the remainder of engineering). The Dean was not particularly amused, but time has proven the basic truth of my assertion, even if it did take a decade for Internet pet food companies to finally flourish. (See Web and Service Futures.)

Computational modeling and data analytics, sometimes called the Third and Fourth Paradigm, now complement theory and experiment, and their use has profoundly transformed our approaches to scientific, biomedical and engineering research. Computing has catalyzed equally deep changes to humanities and social sciences scholarship, and it has unleashed new expressivity in digital media. To see this, one need look no further than the rise of computational science and engineering, machine learning and the explosion of digital scientific data, the growth of the digital humanities, and the expansion of digital music, art and sculpture.

Today, computing writ large – informatics – dominates our society, our culture, and our economy in ways that even the most imaginative dreamers of the 1990s would have found astounding. Unlike other human instruments, computing is a universal intellectual amplifier, augmenting human memory, senses, reasoning and actions in both discipline independent and dependent ways. (See Intellectual Amplification via Computing.) As such, it is lexicographically applicable to everything from aardvark monitoring to zyzzyva tracking.

A Tale of Two Futures

Charles Dickens opened A Tale of Two Cities with these iconic lines:

IT was the best of times, it was the worst of times, it was the age of wisdom, it was the age of foolishness, it was the epoch of belief, it was the epoch of incredulity, it was the season of Light, it was the season of Darkness, it was the spring of hope, it was the winter of despair, we had everything before us, we had nothing before us, we were all going direct to Heaven, we were all going direct the other way – in short, the period was so far like the present period, that some of its noisiest authorities insisted on its being received, for good or for evil, in the superlative degree of comparison only.

Dickens' introduction is a parable for our time. It is the best of times and (perhaps) also the worst of times in IT. The evolving interplay of enabling technology advances (hardware, software and algorithms) and new and diverse applications has simultaneously bedazzled and bewitched, excited and terrified, transformed and destroyed. It is made the planet a communicating global village, facilitated social revolution, and disintermediated entire industries via accelerated creative destruction. This is both the extraordinary promise and the often feared peril.

We balance the convenience and utility of e-commerce against deep learning, recommender systems and targeted marketing. We value the power of inexpensive global communications and bemoan the 24x7, nearly instantaneous echo chamber that is the Internet. We hail the promise of personalized, precision medicine but fear genetic profiling and the ethical morass that is human germline editing. We prize smart devices and interconnectedness but are apprehensive about privacy loss, identity theft and cyberattacks. We yearn for autonomous vehicles and automated deliveries but fear loss of personal freedom and control. We value robotics and automation but suffer from income stratification and job elimination. Some wait for the singularity and others fear the emergence of strong AI.

Do you want to change the world? Whether technologist, scientist, humanist, or artist, you've come to the right place. The revolution is not only underway, it's accelerating.

Exascale Computing and Big Data: Time to Reunite

N.B. I also write for the Communications of the ACM (CACM). The following essay recently appeared on the CACM blog. In addition, you can find the video of an interview and the full text of the CACM article, co-authored with Jack Dongarra.

In other contexts, I have written at length about the cultural and technical divergence of the data analytics (aka machine learning and big data) and high-performance computing (aka big iron) communities. I have euphemistically called them "twins separated at both." (See HPC, Big Data and the Peloponnesian War and Scientific Clouds: Blowin' in the Wind.) Like all twins, they share technical DNA and innate behaviors, despite their superficial differences. After all, in a time long, long ago, they were once united by their use of BSD UNIX and SUN workstations for software development.

Since then, both communities have successfully built scalable infrastructures using high-performance, low cost x86 hardware and a rich suite of (mostly) open source software tools. Both have addressed ecosystem deficiencies by developing special-purpose software libraries and tools (e.g., SLURM and Zookeeper for resource management and MPI and Hadoop for parallelism), and both have optimized hardware for their problem domains (e.g., Open Compute hardware building block standardization, FPGAs for search and machine learning, and GPU accelerators for computational science).

Like many of you, I have seen this evolution firsthand, as a card-carrying geek in both the HPC and cloud computing worlds. One of the reasons I went to Microsoft was to bring HPC ideas and applications to the nascent world of cloud computing. While at Microsoft, I led a research team to explore energy-efficient cloud hardware designs and new programming models, and I launched a public-private partnership between Microsoft and the National Science Foundation on cloud applications. Now that I am back in academia, I am seeking to bring cloud computing ideas back to HPC.

In that spirit, Jack Dongarra and I recently co-authored an article for the Communications of the ACM on the twin ecosystems of HPC and big data and the challenges facing both. Entitled, Exascale Computing and Big Data, the article examines the commonalities and differences, and discusses many of the unresolved issues associated with resilience, programmability, scalability, and post-Dennard hardware futures. Most importantly, the article makes an impassioned plea for hardware and software integration and cultural convergence.

The possibilities for this convergence are legion. The algorithms underlying deep machine learning would benefit from the parallelization and data movement minimization techniques commonly used in HPC applications and libraries. Similarly, the approaches to failure tolerance and systemic resilience common in cloud software have broad applicability to high-performance computing. Both domains face growing energy constraints on the maximum size of feasible systems, necessitating shared focus on domain-specific architectural optimizations that maximize operations per joule.

Perhaps most important of all, there is increasing overlap of application domains. New generations of scientific instruments and sensors are producing unprecedented volumes of observational data, and intelligent, in situ algorithms are increasingly required to reduce raw data and identify important phenomena in real time. To see this, one need look no further than applications of machine learning to astronomy, which now include automated object identification. Conversely, client plus cloud services are increasingly model-based, with rich physics, imaging processing and context that depend on parallel algorithms to meet real-time needs; augmented reality applications are one such exemplar.

The explosive growth of Docker and containerized software management speaks to the need for lightweight, flexible software configuration management for increasingly complex and rich software environments. My hope is that we can develop a unified hardware/software ecosystem that leverages the technical and social strengths of each community. Each would benefit from the experiences and insights of the other. It is past time for the twins to have a family reunion.

Iowa and the Digital Lab for Manufacturing

Yesterday, I had the privilege to be at the White House as President Obama announced the Digital Lab for Manufacturing (the Digital Lab), a new partnership in the National Network for Manufacturing Innovation (NNMI). Led by the University of Illinois, the Digital Lab includes the University of Iowa, as well as Iowa State University and the University of Northern Iowa, as part of a national network of 40 industry partners, and 30+ academia, government, and community partners, and an additional 500+ supporting companies and organizations. The Digital Lab was awarded $70M from the U.S. Department of Defense and will be supported by an additional $250M in matching funds from industry and academia.

The focus of the NNMI and the Digital Lab is increasing the competitiveness of the U.S. manufacturing industry via industry-academic-government partnerships. This coalition will exploit advanced digital design and 3-D visualization, computational modeling and resilient design and manufacturing process optimization to maximize efficiency and minimize design and manufacturing costs.

To say that U.S manufacturing has been under great stress would be the understatement of the decade. Manufacturing plants have shuttered, middle class jobs have been lost, and communities have suffered. We cannot roll back the clock to the halcyon days of the past, but neither must we accept the inevitability of manufacturing declines and globalization. There is a way forward, one based on digital manufacturing technologies and industry-academic partnerships that can create new high-paying manufacturing jobs for Iowa and American workers. As I have written in other contexts, engaged public-private partnerships, where research universities and the private sector work in close partnership to address real-world problems via business and technology partnerships, are the keys to competitive success in the 21^st century global knowledge economy.

Iowa Implications

Manufacturing, both large and small, is an anchor of the Iowa economy. In fact, Iowa ranks very near the top of U.S. states in manufacturing. That is not a surprise to those of us in Iowa, but it may be a revelation to others. From Rockwell-Collins (high technology) through Lennox (furnaces and air conditioners) and Pella (windows and doors) to farm equipment (Kinze and Deere), manufacturing is one of Iowa's strengths – now and in the future.

It is essential we help these and other Iowa companies, large and small, remain successful and completive in a global economy. The Digital Lab, and Iowa's partnership with it, will give Iowa companies even greater access to engineering, information technologies, and computer-aided-design (CAD) expertise as part of regional and national collaborations.

In particular, Professor Karim Abdel-Malek, director of the Center for Computer Aided Design, is the Digital Labs project director for the Iowa team. He and his colleagues in the College of Engineering will bring their expertise in modeling and simulation of humans and of electromechanical systems, data visualization and virtual reality, and materials processing to the Digital Lab partnership. Examples of that expertise include:

• Virtual testing via simulation to understand equipment failure modes due to vibration and wear, enabling design for resilience, reducing repair costs and down time
• Software tools and simulations to minimize the number of design iterations and prototypes before manufacturing, reducing costs
• Additive manufacturing (3-D printing) of prototypes and parts, allowing efficient, low-cost, custom design of small volume parts
• Digital manufacturing for Product Lifecycle Management (PLM) of defense systems and extension to partner manufacturing companies
• Human-device interaction (human factors) for design and manufacturing
• Education and training opportunities for Iowa students and workers at all levels in science, technology, engineering, and mathematics (STEM) fields

Why I'm Excited

I am excited about the Digital Lab for many reasons. The most important is the opportunity it affords for the University of Iowa and Iowa companies to participate in one of the country's leading initiatives in manufacturing. Working together, we can strengthen Iowa businesses, create exciting and well-paying new manufacturing jobs, and attract and retain talent across the state.

The second is more personal. Many of my old friends and former colleagues from the University of Illinois lead the Digital Lab. As one of its former directors, I was also delighted that the National Center for Supercomputing Applications (NCSA) will play a leading role in applying advanced computing to manufacturing. This reflects both the capabilities of the center, host to one of the world's fastest computers, and its long history of industrial partnerships.

This is a great opportunity – for all of us.

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Celestial Mechanics: Why I Know Summer Will Come

My second floor office overlooks a delightful pedestrian mall lined with restaurants and shops. Strategically placed outdoor benches entice patrons and passersby to linger, converse and savor the ambience of a Midwestern college town. In the spring and fall, it is a venue filled with music, conversation and laughter.

Alas, as the continental United States remains in the ironfisted grip of a seemingly endless, harsh winter, the benches are snow covered and empty. The bundled pedestrians stride wearily forward, eyes squinted in a thousand yard stare as they seek solace from the prairie wind and cold. As Frost poetically observed, they have promises to keep, and miles to go before they sleep.

Trust the Science

Though the long past, languid days of northern latitude summer now seem like a waking dream, I have faith – grounded in science – that summer will come again. This oblate spheroid of revolution (aka a reference ellipsoid) we call planet Earth will again bring the warmth of summer to northern climes. (We can thank Isaac Newton's Principia for the proof that a rotating self-gravitating fluid body takes the form of an oblate spheroid of revolution.)

All of us are tempted to believe that seasonal variations are due to the varying distance of the Earth from the sun – its orbital eccentricity. It seems intuitive and obvious, but it is incorrect. The eccentricity of the Earth's orbit is quite small. Even more interestingly, the Earth is closest to the Sun on January 3, the periapsis of its orbit. The real cause of the seasons is the 23.5-degree axial tilt of the Earth relative to its orbital plane around the sun.

As the Earth orbits the sun, its tilted axis always points in the same direction. During part of the year, the northern hemisphere (that's us) is tilted toward the sun, yielding longer days and more direct sunlight; that is our summer. During our winter, the southern hemisphere tilts toward the sun, giving it summer and a now frigid winter to those of us in the north. As Newton showed and Einstein later expanded, in a few months, gravity and orbital mechanics will bring summer back again. A helpful NASA cartoon explains why this is the case.

STEM Education Really Matters

If you did not know the orbital cause of the seasons or had forgotten it, then you are in good company. In a famous, some might say infamous, 1987 study, a group of education researchers asked newly minted Harvard graduates to explain the cause of the seasons. As this brief video shows, most of them answered incorrectly, despite years of K-12 and college-level science and mathematics classes.

As my friend, Neil deGrasse Tyson, once wryly remarked, "The good thing about science is that it's true whether or not you believe in it." Natural processes proceed with or without our understanding, but they are discoverable via systematic application of the scientific method – formulating hypotheses and testing them via repeatable and verifiable experiments.

I am not suggesting that everyone should be a scientist or engineer. A detailed understanding of celestial mechanics will not affect your job prospects (unless you are an aspiring astronomer), but in a global economy, a working knowledge of basic science, engineering and mathematics and a systematic approach to problem solving certainly will. Equally importantly, public policy issues, whether in health care, transportation, communications or energy, increasingly rest on scientific principles and shared understanding that must inform our collective debate and decision-making.

In technology-driven world, effective Science, Technology, Engineering and Mathematics (STEM) education is crucial for all of our students. We must do a better job of inculcating fundamental scientific principles and their practical applications, moving science and mathematics education beyond dry facts and formulas to vibrant and predictive experiential learning. Science education, when done right, is fun, filled with joy and passion for discovery and understanding. That's one of the great beauties of science; anyone can apply the scientific method and independently verify scientific claims.

Spring will come again, as surely as the Earth orbits the sun. Meanwhile, I am counting the days until the vernal equinox brings the sound of laughter back to the pedestrian mall.

Deferred Maintenance on the Future

N.B. I also write for the Communications of the ACM (CACM). The following essay recently appeared on the CACM blog.

In straitened financial times, time horizons shrink. This observation is self-similar across scales, applying equally to individuals and families, small businesses and corporations, and countries and economic blocs. If you find yourself struggling to pay bills, even after eliminating luxuries, then you defer some purchases, often painfully. Indeed, if you are homeless, cold and hungry, physical needs shrink time to the here and now – the next meal and a warm place to sleep trump all else. That is something worth remembering about those less fortunate as we face the post-holiday winter and a near-record cold across much of the continental United States.

When times are difficult, as an individual, you keep driving that aging car or truck, even as its reliability declines and the risks of major failure increase. As a small business owner, you defer that infrastructure upgrade, making do with what you have. As a CEO, you avoid risks, focusing on expense reduction and weathering the financial storm. As a country, you collectively focus on the short term, avoiding or militating the effects of recession, prioritizing short-term expenditures over long-term investments.

These sacrifices are natural and rational – in the short term. If continued too long, however, they ultimately lead to calamity and loss, as individuals suffer, infrastructure fails and the future becomes shrouded in a miasma of unfulfilled dreams. For all our physical wants and needs, we are creatures of dreams.

Make no mistake; balancing the immediate, pressing and real needs of the here and now against the uncertain and ill-defined future is a difficult task, made no easier by a cacophony of competing petitioners, each with compelling arguments and considerable needs. Yet is precisely such a time when wisdom and foresight are required; it is the very definition of leadership. Although the needs of the present are real, dreams of the future must not be sacrificed on the altar of exigency.

Telling the Future the Past

Today, in the U.S. we face difficult challenges, with a growing backlog of deferred intellectual maintenance. Over-worked and sleep-deprived drivers are steering many of our vehicles of discovery on balding tires across potholed roads. Stripping away the metaphor, we are struggling to sustain appropriate investments in basic research infrastructure and facilities operations, and our dispirited researchers face ever-diminishing odds of research funding as they work to keep laboratories operational and students and post-doctoral research associates funded.

It is worth remembering that the Computer Science and Telecommunications Board (CSTB) of the U.S. National Academies released and updated the famous "tire tracks" diagram, illustrating the path from basic computing research ideas to major industries. In almost every case, the time from discovery to major societal impact was a decade or more, yet few could have imagined that impact at the time of discovery.

Today's ubiquitous smartphone or tablet has its roots in Engelbart's 1960s "mother of all demos," and a host of other advances in microprocessors, memory and storage systems, web services, and wired and wireless broadband communications. The same is true of cloud computing, advanced robotics, streaming multimedia, global positioning systems, and supercomputing. Each capability is the evolving outgrowth of decades of basic and applied research by tens of thousands of dedicated and passionate researchers. Their dreams of what might be became the computing and communications infrastructure that underpins today's society.

The impact of basic research is no less profound in a host of other domains, and today's choices have long-term implications for national and international competitiveness. As history has repeatedly shown, investment in the future – basic research – is integral to economic recovery and long-term growth. Yet by its very definition, the intellectual and pragmatic outcomes of specific research projects and directions are unpredictable. It is only in retrospect that we see the clear and unmistakable benefits – in medicine and public health, in design and manufacturing, in energy production and efficiency and yes, in computing and communications.

The Road Ahead

The past speaks urgently to the present about the future. It whispers about what could be, about dreams deferred and opportunities lost, about innovation and economic success, and about creativity.

It is time to put some new tires on the vehicle of scientific discovery and head out to the future. As Kerouac noted in On the Road, there's "Nothing behind me, everything ahead of me, as is ever so on the road." We do not know what we will find, but the journey itself is the destination. It leads to the future and a better world.

Understanding the World through Genetics and New Technologies

N.B.: An abbreviated version of this perspective is scheduled to appear in the Iowa City Press-Citizen. On February 15, 2013, I will be participating in a televised and webcast discussion of personalized medicine as part of the University of Iowa's WorldCanvass series.

DNA (deoxyribonucleic acid) – it is literally the stuff of life. Three billion instances of four nucleotides (abbreviated GATC) (in the haploid genome) define our humanity, and slight variations across those three billion instances are responsible for all our differences, including our susceptibility and predisposition to diseases. Thus, understanding how DNA regulates biological processes is key to the mechanics of life and to treating disease at its most fundamental levels.

In 2003, after multiple years of painstaking work, two groups, one public and one private, each succeeded in sequencing the DNA of one individual – a human genome – at a cost of roughly three billion dollars. This technological tour de force required collaborations among research laboratories across the country, vast arrays of robotic machines to identify DNA snippets and massive amounts of computing power to assemble the snippets into a complete genome sequence via a technique known as shotgun sequencing.

In the intervening ten years, the cost to sequence a genome has dropped below ten thousand dollars. In other words, for the price of a minivan, you could have your family's DNA sequenced today. More importantly, technological advances will soon push that price below $1000, with $100 sequencing soon to follow. Very soon, having your DNA sequenced is likely to cost less than what most of us spend on gas for our cars each month.

In many ways, the dramatic reductions in DNA sequencing cost are due to advances in some of the same technologies that have given us powerful, yet inexpensive mobile telephones and other electronic devices. Automated DNA sequencers rely on robotics for sample management, advanced computing for coordination and data management, and miniaturization and nanotechnology for biological process and sample analysis.

Beyond the potential for scientific insight, these dramatic declines in DNA sequence costs have been in part due to perceived business and healthcare opportunities. Many companies, including ones created by faculty and students at the University of , see personalized medicine as a new frontier, much in the way that advanced imaging – x-ray computerized tomography (CT), positron emission tomography (PET) and magnetic resonance imaging (MRI) – transformed assessment and diagnosis in the 1970s and 1980s. To spur research and innovation, the X Prize Foundation has offered 10 million dollars to the first group to sequence 100 human genomes highly accurately at a cost of $1,000 or less.

Toward Personalized Medicine

What are the implications of inexpensive DNA sequencing for each of us? We can read the letters in each of our personal books of life. However, we do not yet understand fully how those letters collectively define the operating manual for our cells and our bodies, but biomedical research is bringing us closer to commonplace medical treatments.

Today, if you visit your primary care physician, he or she compares your current health to that of a typical human of your age and gender. Therein is the problem. There is no mass production of typical humans; each of us is custom made and slightly different, unique among the roughly seven billion people on this planet. We celebrate those differences, for they define our humanity. In that sense, every child's mother is right when she calls her child special and precious, for we are, in so many wondrous ways.

Biologically, DNA variations and the genes expressed lead to our differing appearance, behavior, physiology and metabolic processes. When combined with our varied lifestyles, environments, exercise patterns and food preferences, it is no surprise that we have different physical reactions to the same drugs and medical treatments. None of us is typical, yet today's medicine treats us as if we are.

When you visit your physician in a few years, to what might he or she compare your current health? Ideally, it would be you at your very best, perhaps at age 25 when you were in peak physical and mental condition, at your optimum weight, and living a healthy lifestyle. More to the point, your physician would then tailor your treatment based on a deep understanding of your unique genetic characteristics, your current condition, physical environment, and your body's particular reactions to those treatments. This is the promise of personalized medicine – earlier and more effective treatment tailored specifically for you.

Our DNA is the personal operating manual that directs our cells and physiology. Understanding that is essential to personalized medicine, but it is not enough. We also need inexpensive and routine diagnostics that can compare the "current you" and the "healthiest possible you" to determine what is wrong.

All of this is analogous to how we now diagnose automobile problems. In addition to inspecting the vehicle, all mechanics read the data captured by the vehicle's onboard monitoring electronics. That data include the vehicle's history of operation and all deviations from the factory-defined standard. While you drive, the vehicle continually monitors itself, raising alarms if there are problems.

Just as the best car repair is the one you never need, the best health care is treatment you never need because you are well. The next best case is early intervention that alerts you and your caregivers before serious issues develop. Late intervention when you are very sick is both damaging to you and expensive for all of us.

As with DNA sequencing, new technologies are bringing the medical diagnostics version of personal monitoring ever closer, allowing each of us to track our physiology and lifestyle. There are already smartphone apps that can measure heart rate and lung function, wearable devices that monitor exercise and sleep functions, and wireless meters for glucose monitoring. Some individuals in the quantified self community are now measuring their bodies in ways that were heretofore only possible in a research environment. Via microfluidics, nanotechnology, robotics, advanced computing and other technologies, the Star Trek tricorder is on the horizon.

Societal Implications

Like all new technologies, genetic medicine brings a new set of societal questions. If DNA sequencing uncovers an untreatable genetic defect, do you want to know? It is not a hypothetical question; we are already facing this ethical dilemma for selected diseases. Because you are genetically similar to your siblings, what are the implications for them if you fit a particular disease profile? What is the appropriate ethical and economic balance between personalized health care treatment and cost, particularly if you choose a lifestyle that worsens your health, given a genetic predisposition to a disease? How do we protect individual privacy in a world of "big data" and inexpensive health monitoring devices?

As a comprehensive research university that combines the sciences, engineering and medicine with the liberal arts and humanities, the University of Iowa (UI) brings insights and expertise to all aspects of genetics-based personalized medicine. The UI is a major participant in scientific and biomedical research, as well as the transfer of research ideas into practice via new companies created by its faculty, research staff and students. It is also engaged actively in helping shape the ethical, social, legal and economic frameworks that will govern this transformation.

This exciting new world of personalized medicine is ripe with the promise of improved health for our citizens, by helping our children remain healthy, by allowing our seniors to live independently for longer periods, and by ensuring our citizens in rural areas can monitor their health in detail.

I believe the future is bright. Via personalized medicine, we can improve the quality of life and reduce health care costs for everyone, while respecting and protecting our individuality.

Remember, we are all special. Our DNA tells us so.

Related articles

A human genome in minutes, and what it will mean to you

Could DNA databases herald a new era of medicine?

The personalized medicine revolution is almost here

DNA test analyzes genes known to contribute to cancer

Online Higher Education

N.B. I also write for the Communications of the ACM (CACM). The following essay recently appeared on the CACM blog.

Update:  Today, Coursera and a consortium of twelve major universities announced that they will offer online courses together.

The higher education community is abuzz with debates regarding massive open online courses (MOOCs) and distance education, fueled in no small part by the stunning response to Sebastian Thrun and Peter Norvig's online AI course at Stanford. (This and other courses have since become part of Udacity.) In turn, MIT's Open Courseware and online learning initiative, MITx, were the progenitors of edX, a joint venture between MIT and Harvard. The Khan Academy has adopted a different model, emphasizing micro-lectures on thousands of topics. Since then, other public, private and non-profit institutions, not to mention pundits and commentators, have joined the fray.

Many of us have seen parts of this movie before, when new e-learning technologies were projected to transform higher education. Although no one died in a climactic car crash, a series of these technologies failed to deliver on their early promise.

At one time, CATV lectures for continuing education were common in the U.S. At Illinois, I remember PLATO, a much more ambitious experiment, which brought plasma displays, speech synthesis, touch screens and computer-aided instruction (CAI) to thousands of students in the 1970s and 1980s. This amazing set of technologies reshaped the lives of both students and developers, but it faded away. (See Mind to Mind: Building Innovation). Roughly twenty years later, we tried again, when a group of us at Illinois launched an Internet computer science master's program that targeted India. Alas, broadband limitations then constrained delivery to batch mode; a revised version of it continues today.

Each of these technologies and many others had their place, but none fundamentally reshaped the active lecturer, passive listener model that is the hallmark of most university courses. Today and tomorrow may be different, with nearly ubiquitous global broadband now available for delivery, a plethora of inexpensive consumer computing devices for consumption, and rich cloud services to host that content. In addition, the rise of social networks and crowdsourcing connecting geographically distributed groups has created cohesive intellectual communities.

Yet the other social, political and economic dynamics may be even more important. There is a growing public backlash against rising college tuition, there are persistent questions about the economic value of college degrees, and there is continuing demand for lifelong skills refresh as economic shifts eliminate job classes.

Like technological and cultural shifts, the online education issues are subtle and intertwined. Thus, it is instructive (how apt) to learn from the past. Heeding Santayana's dictum, there are lessons to be gleaned from another labor intensive, active-passive endeavor – musical performance – when a transformative technology was introduced, the lowly phonograph record. Let us begin with a bit of history.

Lessons from the Shellac 78s and Vinyl 45s

The year 1877 was an important demarcation line in the history of musical experiences and delivery. Thomas Edison perfected the cylindrical phonograph, later more widely popularized by Emile Berliner in the form of the gramophone record. In a testimony to the longevity of a successful technology, that format defined recorded music delivery for most of the 20^th century.

Today, the time when music could only be heard live and locally seems like an era lost in the mists of pre-history, yet it was little more than a century ago. In short order, the widespread recording of individual musicians, groups and orchestras, and the subsequent rise of record contracts and distribution systems transformed the sociology and economics of music. For the first time, these recordings brought musical performances to diverse national and international audiences. A single performance could now be heard and reheard by millions of individuals at the times and places of their own choosing.

As a personal aside, I still remember the Victrola and 78 RPM records that belonged to my aunt. The Victrola was spring driven and powered by a crank on the side. The smell of the record sleeves, the heft and thickness of the shellac 78s, the scratchy sound, and sheen of the oil polish on the wooden cabinet are deeply imprinted.

Widespread record distribution and radio made some performers, later called recording artists, wealthy, international stars. Meanwhile, other musicians of equal or near equal skill, who did not secure recording contracts, were relegated to an economic and cultural netherworld of local performances and limited fame. During my teen years, the apotheosis of this highly bimodal distribution was AM Top 40 radio and Casey Kasem's American Top 40, along with the LP concept album.

Since then, musical genres have become ever more diverse and specialized, as new technology has allowed recording labels to target likeminded audiences, even if small in size. In turn, independent musicians have taken to the web, sometimes bypassing record labels and delivering music directly to their fans, all the while balancing income against size of fan base. Against this backdrop, digital content pirates and intellectual property advocates are locked in a technological and cultural battle around verification and access, payment, sharing and portability.

Educational Commodities

What are the parallels with the music industry and higher education? First, both musical performance and education began as "local and live." To experience a performance, one traditionally had little choice but to buy tickets (pay tuition) to see the musician (instructor) perform live. In both cases, the quality of the performance might vary widely with the skill of the performer, but the approbation and fame, or derision and complaints, were geographically bounded. Each city and region had its local performers and acolytes.

As I noted earlier, records and distribution systems changed this music production and consumption model profoundly. Truly successful musicians, defined by popularity and revenues, now have international reach and global distribution networks. They are megabrands with millions of followers. Via the web, even performers in esoteric musical genres have geographically distributed, albeit much smaller, audiences.

Online education has the same potential to transform a small number of instructors and institutions into megabrands, with hundreds of thousands of globally distributed students. It could also allow teachers of more esoteric material to reach global audiences. The question is culturally simple. As a student, would you rather take a required general education or specialty elective course from one of several internationally rated instructors and/or lauded scholars, or be constrained to the pedagogical skill and intellectual acumen of the professors at a single university?

Second, by making courses marketable commodities that can be purchased from multiple, non-local purveyors, the spatio-temporal constraints of only local delivery and consumption disappear, allowing students (customers) to purchase from a much wider palette. This has several important corollaries, some good and some not. We may be able to deliver higher quality content to more students and disseminate new knowledge more rapidly, including lifelong skills refresh. There is also the risk that diversity of pedagogical approaches and experimentation could be lost, that cross-disciplinary experiences could be more difficult to facilitate, or that linking research and education could more challenging.

It is likely, even possible, however, that many generic courses will become true commodities, with much less local value differentiation. After all, introductory courses such as chemistry 101 are similar at all institutions across the country. Disciplinary curriculum standards and accreditation bodies help assure this. Should such courses be replicated everywhere, or should a marketplace of consumer (student) choice determine which instances of these courses succeed?

This is not just a question for general education, but one that applies to computing as well. Introductory programming, algorithms and data structures and many of our other core curriculum courses are amenable to this approach. The advanced courses and seminars offered by highly respected faculty have potentially different dynamics, as Thrun and Norvig's AI course showed, but there are commonalities.

Without doubt, there are some "third rails" in this uncertain future, a few of which challenge the very nature of today's higher education model. These include certification and verification, branding and economics. Today, there are more questions than answers, which is why university leaders and trustees are so concerned and engaged.

Quo Vadis?

In addition to delivering content, our existing higher education structures and processes triage applicants for quality, validate student identities and certify (via testing) that graduates possess the knowledge and skills asserted. Yes, the true degree of competency varies widely across individual graduates, but the brand equity of the institutions is a qualitative standard (i.e., one knows what an MIT or Michigan degree connotes).

We have long passed the point when no one on the Internet knows if you are a dog. (See Remembering Internet Dogs for a bit of history.) Nevertheless, online certification and verification remain difficult issues, with some analogs to the ongoing debate around digital content piracy and free versus paid content access. How do we best verify online student capabilities and identities and confirm that the asserted identity corresponds to the individual completing the online coursework? In the old days of CATV education, onsite, proctored examinations were the solution.

On the economic front, what content should be freely available and what should be accessible only via fee? Perhaps more importantly, what are the economic implications for educational institutions and faculty? The true production costs of courses (e.g., history and engineering) vary substantially, and one often implicitly subsidizes the other via tuition. The national commoditization of some courses could eliminate many of these cross-disciplinary subsidies and even eliminate faculty positions or even entire departments at some universities.

Online education could also drive states to reduce support for public colleges and universities even more. They might opt to offer course vouchers or financial support directly to students rather than institutions. In aggregate, the effects of such changes could also have profound research implications, with colleges and universities unable to sustain the economic base needed to subsidize research.

Finally, what are the branding implications? If a student has taken online courses from ten different institutions, from whom do they receive a degree? Does the degree even matter, or is the only substantive question one of verified competency? In this brave new world, I suspect the institutions with the most to fear are those with lesser brands. One attends MIT, Harvard, or Stanford for brand equity, not just knowledge. The music analogy is strong here, relegating some institutions to the status of local cover bands, performing for secondary audiences.

Parting Thoughts

As a technologist, I am always somewhat chary when anyone proclaims a new technology as radical and game changing. Sometimes the technology is revolutionary, as the phonograph proved, but we have all seen technologies introduced with great fanfare, only to watch them fade into obscurity. In addition, our higher education institutions have proven resilient and effective in socializing and educating young adults for hundreds of years. Make no mistake, our colleges and universities are not without faults, and they are often slow to embrace change and respond to shifting social expectations.

Nevertheless, I think this is time of profound change, of punctuated equilibrium when the confluence of technologies, social expectations and economic pressures will necessitate substantive change in our university structures. We must move carefully and thoughtfully, embracing opportunity while diligently preserving that which we cherish and value.

Lux et veritas.

A Question to Ponder

I speak to groups large and small on an almost weekly basis. Some of these are technical presentations; others address global policy issues; and still others are public events for a popular audience. If you were to see me speak on related topics in semi-contiguous weeks, you might assume that the presentations never change. It is true that I have a series of standard "stump speeches," but I also add new content to each of these, allowing them to evolve as I develop new ideas and understand what topics resonate with the audience.

For popular audiences, I occasionally open by jokingly opining that some members of the audience, for reasons both baffling and befuddling, may find my erudite and insightful presentation, though delivered with elegance, verve and panache, rather turgid and opaque. In that rare and extraordinary event, I humbly offer an alternate question for them to ponder while the dismal proceedings that are my prepared remarks meander interminably toward their much-anticipated conclusion.

That self-deprecating opening usually makes people smile, albeit weakly. What follows, though, is a very simple, though serious question. I simply ask, "What probability of successful return would you accept to be the first human to set foot on Mars?"

Walking on Mars

Superficially, this is the quintessential geek question, conjuring images of mighty rockets like the old Saturn V, complex orbital dynamics, spacesuits and EVAs, and ultimately, the an adventure on the red planet itself. On reflection, though, the question is more subtle, morphing into something deeper that makes the audience think carefully about themselves and the nature of the human condition.

Let us begin with the geek part, probability and statistics. There are many possible answers to the question, only one of which is categorically wrong. Anyone who says, "Why probability one, of course!" has failed to consider that nothing in life – other than death – is certain. Notwithstanding the popular claim, even taxes can be changed. One need only recall the old astronaut joke about sitting atop millions of pounds of fuel and thousands of parts, all produced by the lowest bidder. Hence, unless one's answer is, "I have no interest in going," any answer involves accepting some degree of risk.

The technically inclined among the audience inevitably then begin considering possible failure modes and probabilities, including mechanical, electronic, biological, chemical, environmental and social ones. In the mind's eye, one sees rockets exploding on the launch pad, solar storms creating lethal radiation during the long flight, failed entry in the thin Martian atmosphere, and a host of other deadly scenarios. The process reminds us that everything is fraught with some risk, and that we habitually and instinctively calculate risk-reward ratios every day. It is only the extraordinary risks that we consciously contemplate, and even then only rarely.

Risk and Remembrance

The question also illuminates two other salient issues. The first is one's personal risk-reward ratio. For a very few, perhaps the most reckless and thrill seeking among us, probability zero is an acceptable answer. For others, the adventuresome and the explorers, those with the putative "right stuff," almost any carefully quantified, non-zero probability is acceptable, for it offers the hope, even if improbable, of safe return. Many opt for an answer where the prospects of success exceed those for failure, for they value their lives.

The second aspect cuts to the centrality of our humanity, our insatiable curiosity and our hope to be remembered for having done something new, for having made a difference. To set foot Mars offers a chance to answer some deeply intriguing and vexing scientific questions about planetary formation and history and about the possibility of life on Mars, past or present. To be the first offers something else, the opportunity to be remembered, at least in the brief span of recorded human history, for having done something extraordinary.

What Is Your Answer?

It is an interesting and thought provoking question. It is a presentation unto itself. What probability of successful return would you accept?

As for me, as John Glenn once said, if there were a bird on the pad and they offered me a ride, I would be on it. I would have calculated the risks with the engineers, worked with the scientists and made an informed choice. Beyond that, the science, the technology, the curiosity, the opportunity – I feel their call.