This is a personal blog updated regularly by Professor Daniel Reed at the University of Utah.
These musing on the current and future state of technology, scientific research, innovation, and personal history, are my own and don’t necessarily represent the University of Utah's positions, strategies, or opinions.
Does it ever seem that we are imagination challenged, bound to imitate or follow a popular idea or approach, whether successful or not? (At this point, I could insert a quasi-obligatory reference to lemmings plunging into the sea. For the record, lemmings do not commit mass suicide; that is a persistent urban myth.)
Once an idea gains traction, it often seems everyone adopts a "me too" approach. There is, of course, intellectual safety in following a crowd. After all, if "everyone" is doing it, then ipso facto it must be a good idea. Alas, not necessarily. In Gartnerhype cycle parlance, this mimicry can happen early, during the "peak of inflated expectations," or later, riding the "slope of enlightenment," if the idea survives the dreaded "trough of disillusionment."
My AI friends still talk ruefully about the long and difficult AI winter that followed early, unrealistic expectations (e.g., human language translation in the 1960s). Funding became scarce and the community shrank. Today, of course, we are basking in the AI spring, when many early hopes and predictions are being realized in dramatic fashion, powered by new machine learning ideas, large volumes of data, and powerful computing systems. Despite AI's enormous success, we have not yet realized the dream of strong AI, though I retain fond hopes. And yes, I am an optimist; I do not share the fears expressed by some about strong AI. However, I digress – back to viral memes.
Uber, the ride sharing service, has spawned seemingly endless variants, all described in pitch decks as Uber for X – a new instance of the sharing economy. As Wired noted last year, Uber for X has been the headline of more than 400 news articles. If you are thinking that the Uber for homework opportunity (there are several startups) might not be the next, well Uber, you are almost certainly right. There is, as former Federal Reserve chair, Alan Greenspan, called it, some irrational exuberance.
Following the success of AirBnB, a new category of startups, somewhat whimsically called MaaS (Mom as a Service), has exploded. (Let me just state right now, I apologize to moms everywhere. You were right; we should have cleaned up our rooms.) These MaaS companies provide services such as cooking, cleaning, laundry, and food; among the best-known food examples are Grubhub (prepared food delivery) and Blue Apron (cooking ingredients delivery). As one who is culinary-challenged, I hope this segment fares well.
Because humans are involved, this faddish behavior is largely domain invariant. As such, it extends to research and scholarship as well. In the best cases, a triggering discovery stimulates the cognizant research community to shift directions and build a rich and robust intellectual infrastructure around a novel idea. The discovery of the CRISPR/Cas9 gene editing technique is a wonderful example; research groups worldwide quickly pivoted to investigate the technique and its applications. In other cases, a technology crosses a threshold (e.g., price or performance) and that quantitative change enables qualitatively new approaches to old problems. Field programmable gate arrays (FPGAs), long a niche technology, are now the subject of intense research as AI accelerators.
Whether in startups or research, advantage often accrues to first movers, those who pioneer a new idea and explore its applications. Doing so requires imagination – the ability to step out of the incremental, "me to" mindset and take transformative intellectual (and often financial) risks. That is not easy, both because creative thinking is challenging and because failure is often punished. Now there's an opportunity – IaaS (Imagination as a Service). Let me get to work on that research proposal and pitch deck.
Let me see if I can grab your attention with the lede, and not bury it with a meandering introduction that has all the directness of a random walk. Ready? Let's do this thing.
All children are born scientists, innately curious about the world. We then desiccate their enthusiasm with an arid and false notion of science as a passionless activity, one stereotypically pursued by social misfits. We then wonder why Science, Technology, Engineering and Math (STEM) careers do not excite more students.
Far too much of what we teach primary and secondary school students about the motivations, methods, and practice of science is little more than a well-meaning falsehood that does as much harm as good. Why is that, you say? Isn't the rote quartet of observation, hypothesis, experiment, and theory the scientific method's analog of the journalist's five Ws: Who, what, where, when and why?
Yes, they are, but they do not tell the entire story. The scientific quartet focus on the deductive (drawing necessary conclusions) and the inductive (inferring generalizations from samples or examples), but neglect the creative and exciting abductive (making guesses) elements. Abduction is about discovering (guessing) the simplest and most likely explanation.
Now I'm going to discuss how we would look for a new law. In general, we look for a new law by the following process. First, we guess it (audience laughter). No, don't laugh, that's the truth. Then we compute the consequences of the guess, to see what, if this is right, if this law we guess is right, to see what it would imply and then we compare the computation results to nature or we say compare to experiment or experience, compare it directly with observations to see if it works.
The very fact that the audience laughed when Feynman said, "we guess it," speaks volumes about how we teach the scientific method. It ignores the inspirational, creative aspect of science and reinforces the stereotype of a scientist as an emotionless automaton. This early cultural imprinting does a deep disservice to science and discourages many children from expressing their creativity via science, to our societal detriment.
Nor is this stereotype restricted to non-academics. To my open-mouthed astonishment, I have personally witnessed professors debating whether science is beautiful. Let me state categorically that science and mathematics are beautiful. Symmetry, simplicity, elegance, universality, and yes, beauty are all words scientists use regularly and without hesitation.
Because science is a human endeavor, the creative passions inspiring scientists to "guess" are no different from those impelling artists to paint, musicians to compose, or authors to write. It is about creativity and creative expression. Like all scientists, I have looked at a scientific theory with the same reverence and awe that a great work of art inspires in me. It touches something deep and fundamental in the human psyche.
Testing the Prediction
In the same 1964 public lecture, Feynman also succinctly summarizes the leitmotif of science:
If it [the guess] disagrees with experiment, it's WRONG. In that simple statement is the key to science. It doesn't make any difference how beautiful your guess is, it doesn't matter how smart you are, who made the guess, or what his name is… If it disagrees with experiment, it's wrong. That's all there is to it.
Equally importantly, the experiment can be repeated by others – friends, enemies, competitors, doubters – to independently verify or disprove the claim. In this brief statement, Feynman captures the essence of what may be one of humanity's greatest conceptions – the systematic, repeatable testing and public validation of ideas. It gives no pride of place to the rich, powerful, dogmatic, or ideological.
It also explains why science calls well-tested ideas "theories" rather than facts or truths. A single negative experiment (i.e., one where the experiment fails to yield the predicted result) is sufficient to disprove the theory, but no number of experiments can prove with absolute certainty that the theory always holds. However, that does not mean repeated experiments do not establish high confidence. When a scientist calls something an established theory, he or she means the "guess" has withstood repeated observation and testing by an international community.
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 21st century knowledge economy. And that is really important right now.
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.
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.
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.
N.B. I also write for the Communications of the ACM (CACM). The following essay recently appeared on the CACMblog. 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.
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 21st 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.
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'sPrincipia 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.
N.B. I also write for the Communications of the ACM (CACM). The following essay recently appeared on the CACMblog.
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.
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'sWorldCanvass 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 Trektricorder 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.
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