N.B. I also write for the Communications of the ACM (CACM). The following essay recently appeared on the CACMblog.
How often have you picked up a scholarly journal in a discipline far removed from your expertise, only to be stymied and mystified by the disciplinary jargon? It can be humbling and intimidating when one fails to understand the meaning of all the words in an article's title or the abstract. When coupled with the contextual knowledge often implicitly assumed by the authors, the gulf of understanding yawns wide and deep.
This epistemological and linguistic chasm separates and isolates even within the broad tent of our own discipline, which spans everything from the fundamental theory of computability to the professional practice of informatics. If you have any doubt, open the ACM Digital Library and scan a few articles in a specialty far removed from your own.
In a world where discoveries increasingly lie at the boundaries of traditional research disciplines, simplifying communication and encouraging multidisciplinary dialog and partnership have never been more necessary. In almost every case, computing is an essential element of disciplinary and multidisciplinary research. Thus, it is time for us to embrace writing as a collaborative enabler, rather than a research burden.
All too often, we academics write in a strange argot of disciplinary esoterica that can obscure the very ideas we seek to communicate. If you have ever encountered an article like the following, you know what I mean.
"Spatiotemporal domicile proximity to retroverting domestic ruminants," I. B. Smart, I. A. Postdoc & O. Authors, International Journal of Bovine Mobility, Vol. 123, No. 11, pp. 2143-2147, 2013
However linguistically facile and intellectually adept, the authors and putative ruminant experts failed to say what they really meant ("wait for the cows to come home") and why that might matter.
In a similar spirit, the late Richard Hamming once famously noted, "The purpose of computing is insight, not numbers." The academic publishing cognate is best summarized as, "The purpose of writing is communication, not obscuration." There is also an important corollary, "Write to communicate, not to impress or intimidate." Yes, subtlety and nuance are important, but they are mere handmaidens to clarity and lucidity.
Even when we avoid these linguistic traps, another, equally deadly one waits to ensnare – turgid and passive prose that invites only slumber. As anyone knows who has either served as a journal editor or reviewed a seemingly endless stack of conference paper submissions, passive, wordy and meandering prose makes identifying the key ideas and assessing their importance even more difficult.
Technical papers are not page turning spy novels, nor should they be, but they can still be interesting, clear and engaging as they convey the essential facts. As a writer, one's job is to make the reading easy; you want your papers to be read and appreciated.
The Message is the Message
It is always dangerous to write an essay about writing, lest one be lampooned for the very deficiencies one seeks to highlight. Such is life. My goal is to focus attention on an important issue.
While continuing to pursue core research in our own discipline of computing, I believe we must also communicate effectively with our peers in the arts and humanities, science and engineering, medicine and public policy. We cannot all be polymaths, but as writers, we can do more to lower the disciplinary drawbridges and invite readers into our intellectual castles.
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.
After the United States Congress Joint Select Committee on Deficit Reduction, otherwise known as the Supercommittee, failed to create an acceptable bipartisan proposal for addressing the U.S. Federal budget deficit, both parties decided to defer further discussions until after the November 2012 elections. As the January 2013 deadline for automatic, across the board cuts is now drawing ever nearer, the discussions have begun again, albeit with accusations and finger pointing on both sides of the political aisle.
Research Interests
Amidst this backdrop, all of us in the research community have been sounding the alarm regarding the consequences of the research cuts that sequestration would necessitate. There is no doubt that substantial cuts to basic research would adversely affect the long-term future of U.S. innovation and global competitiveness, upset already strained university budgets, damage current research projects in a wide range of disciplines, and disrupt the lives of thousands of faculty, post-doctoral associates and students.
That said, is important to acknowledge that we in research are a special interest group, though one whose interests are vital to the future. I realize that some would take umbrage at my description of research as a special interest group, but in the political lexicon, we are, just as are health care and environmental protection. Unless one accepts the realpolitikof budget exigencies and the conflicting goals and objectives of large, disparate multiparty negotiations, the research community will neither be effective in making the case nor realistic in managing the process and likely outcomes.
One cannot simply cry, "This is good, or this is bad," one must make cogent arguments about why certain choices yield differential benefits to the budget negotiators' positions and policies and why those choices are better than other ones. (See Being Bilingual: Speaking Technology and Policy.) Remember, there are far more good ideas than there are resources, and this is equally true in government and science.
Creating Opportunity
Despite the political polarization in Washington, I still believe a budget compromise will emerge. It will not be perfect – such is the very nature of compromise, but I suspect it will include some acceptable combination of revenue increases and budget reductions. Despite its politicization, there is still broad recognition of the importance of basic research; I believe it will fare relatively well when the Sturm und Drang are done.
However, with research proposal success rates plummeting and Hobbesian choices between research infrastructure and investigator support now necessary, we face major challenges. In the apocryphal phrasing of Ernst Rutherford, "We have no money. We must think."
Thinking will undoubtedly mean questioning some perceived verities and deeply held beliefs. NIH R01 awards will no longer be de facto expectations for promotion and tenure. Research infrastructure sharing across institutions may well become the norm, and not just for large-scale instrumentation. Cross-disciplinary tradeoffs about relative investment will become even more pressing. Industry-academic partnerships will rise in importance, as we develop more effective and mutually beneficial industrial collaboration frameworks. However, these industry partnerships will not a surrogate for federal funding of basic research.
Whatever the outcomes, by revisiting some of our assumptions, we can create more free energy in the research system and dedicate precious resources to new and emerging opportunities. I am confident that many of these new opportunities lie at our disciplinary interstices, and hybridization and cross-fertilization can yield new insights and outcomes. More broadly, the coupling of the arts and humanities with public policy, science and engineering, and biomedicine can be transformative. This is consilience in its highest form. However, we must think.
Take heart and keep the faith. The future can be and will be bright – if we make it so.
N.B. I also write for the Communications of the ACM (CACM). The following essay recently appeared on the CACMblog.
To appropriate a line from The Music Man, there is trouble here in River City, and anyone who cares about the future of scientific discovery and innovation should be worried. What is that trouble you say? It is the divide separating the land of high-performance computing (HPC) and big data, and the political and funding implications created by this divide.
Whether in the U.S., Europe or Japan, the competition for research and infrastructure funding is intense. In the midst of our lingering economic malaise, budgets are being stretched to the breaking point. Should we invest in a new telescope or a new accelerator, a new polar station or a new biology initiative? These are legitimate, though painful questions, borne of budget exigencies and fiscal realities.
Many of us are concerned that in this time of limited resources, we could face a similar funding competition that pits HPC, particularly exascale plans, against big data. This competition would be disastrous for science, for computing and computational science research, for infrastructure deployment and for global innovation and economic growth. Both HPC and big data are essential elements of the research portfolio. We must make sure this rumble in the policy halls does not take place.
Understanding Cultures, Preventing Conflict
Even those who have never heard the name of the philosopher George Santayana can parrot his famous dictum, "Those who cannot remember the past are condemned to repeat it." Thus, it is worth drawing a few lessons from the Peloponnesian war, which pitted two great Greek city-states, Athens and Sparta, against one another. Although Sparta was ultimately the military victor, the adverse social, economic and political effects devastated all of Greece, and the wars marked the end of the Greek golden age. We cannot afford to reprise the Peloponnesian war in the guise of big data versus HPC.
The root of this potential conflict is the differing norms of computer science and computational science. Big data and machine learning are the creations of the academic computing and business cultures, while computational science is more the offspring of science and engineering. Both share historical roots, and both are cross-fertilized by the other. They need not and should not be inimical, especially since many of the technical challenges are common to both.
Some see big data as an egalitarian opportunity, one that could readily benefit both big and small science and yield scientific and economic benefits very quickly. Let me be clear, this statement is unquestionably true. The unprecedented richness of scientific and engineering data being produced by large-scale instruments and ubiquitous sensors is ripe for harvest and correlation via advanced data mining. Implicit this is the need for investment in both new data analysis tools and techniques and in large-scale data repositories. (See My Scientific Big Data Are Lonely.)
Others see exascale computing system proposals as a quixotic quest for national bragging rights that will benefit only a few. Let me be equally clear, this statement is unquestionably false. Yes, there are elements of national and regional competition in the Top500 rankings. However, the underlying technology challenges and scientific opportunities are profound. Low power memory and processor designs, post-Dennard device scaling and the software and reliability challenges of large, complex systems all are at the forefront of 21st century computing system design, which deep implications for the future of the information economy. Similarly, some of our most pressing scientific and social problems in climate change, energy and biomedicine are dependent on powerful, advanced computing capabilities. Implicit in this is the need for continued, balanced investment in technology research and high-end system deployment for HPC.
A Grand Concord
We need a concord and strategic research investment plan that recognizes the shared importance of HPC and big data. Both warrant investments in basic research, and both need investments in large-scale infrastructure deployments. (Make no mistake, though, research and infrastructure are different, as I noted in Research {preposition} Infrastructure.) In a time of straitened budgets, this will not be easy, and it will undoubtedly require political compromise.
Neither the proponents of big data and nor those of HPC may get all they want on the time scale they desire, but neither can be sacrificed for the other. If the proponents on each side adopt strategies that treat the other community as the enemy, the relevant lesson of the Peloponnesian wars is unmistakable -- in such a battle, there are only losers. That would be disastrous for us all, particularly when there is a win-win so tantalizingly close.
I am a member of the U.S. National AcademiesBoard on Global Science and Technology (BGST). As the name suggests, the role of BGST is to examine the shifting nature of the global science and technology enterprise and its implications. These include the global flow of intellectual talent and capital, the interplay of government policies, research and development priorities, innovation and technology transfer, and global competitiveness and security. This is a wide-ranging mandate, which is both exciting and challenging.
Lest this seem mere academic punditry, remember that 30-40 percent of U.S. net economic growth in recent decades has been due to advances in information technology. This is not just a tale of Silicon Valley, but also one for the entire country's economy.
Beyond Dennard Scaling
Moore's Law, the notion that the number of transistors in a given silicon area doubles roughly every two years, is not a law or even a theorem. Rather, it was an empirical observation, originally made by Gordon Moore in 1965. For over forty years, it has continued to hold true by virtue of enormous intellectual effort, ongoing architectural and software innovation, and billions of dollars of investments in process technology and silicon foundries. In turn, consumers, businesses and governments have been the happy beneficiaries of faster microprocessors, more powerful, inexpensive and ubiquitous computing devices and a rich and varied suite of software applications.
However, there is bad news. The continued and seemingly miraculous improvement in general processor performance is now over, a consequence of physical limits on transistor shrinkage that was itself a happy consequent of Dennard scaling. To be clear, Moore's law continues, with the number of transistors on a chip continuing to double, but the transistors no longer become more energy efficient as they shrink. The result has been bounds on microprocessor clock rates due to energy dissipation constraints and the concomitant rise of multicore chips and function-specific accelerators such as GPUs. (See Battling Evil: Dark Silicon and Nothing Is Forever for a few reflections and details.)
This radical shift breaks a virtuous cycle of mutually reinforcing benefits, one where software developers created feature-filled applications and stimulated demand for faster general-purpose processors, which then drove the creation of even more advanced applications. Simply put, we are the reluctant, wide-eyed denizens of a brave new world, one where the cherished and popular expectation that applications would run faster without change each time a new backward-compatible processor appeared.
There is a technical way forward, but it means embracing application parallelism and retargeting software to each new generation of non-compatible, heterogeneous multicore processors. As over fifty years of research in parallel computing has shown, this is a path fraught with pain and difficulty. In turn, this has profound implications for the future of the silicon ecosystem and the nature and locus of continued innovation. It is the trillion-dollar inflection point, pivoting on chip performance, business models and global markets.
Ecosystem Implications
The end of Dennard scaling and the emergence of heterogeneous multicore processors has coincided with another shift, the transition from an era dominated by PCs to one defined by smartphones and tablets. For much of the world, the smartphone is now the primary computing system, and in developing economies, the aspirational feature phone is the only computing device. More to the point, the majority of PC and smartphone users are not in the U.S., nor will they ever be again. Not surprisingly, these two phenomena, the end of Dennard scaling and the rise of smartphones and tablets are deeply interrelated.
The PC ecosystem has long been driven by the phenomenal success of the x86 microprocessor family and successive generations of processors from Intel and AMD, both U.S. companies. Conversely, the smartphone and tablet ecosystem is largely based on the ARM microprocessor family and low power systems-on-a-chip (SoCs) developed by ARM licensees around the world. Beyond the ongoing competition between PC and smartphone vendors, this is a battle of business models, pitting a closed x86 ecosystem with captive silicon foundries against fabless semiconductor design firms that mix and match function-specific accelerators with ARM cores and use Taiwan Semiconductor Manufacturing Company (TSMC) as a foundry.
Enormous resources are being invested in both silicon ecosystems, with x86 designers seeking to "grow down" by reducing power and integrating functionality on chip to compete in the smartphone and tablet market. Conversely, ARM designers are seeking to "grow up" by increasing performance and adding features to compete with x86 designs in the server market. This battle royal is not winner take all. Rather, it is a competition to define the global nexus of innovation, with profound implications for global IT dominance in the next decade.
Global Competitiveness and BGST
It is with this backdrop that the BGST committee examined the technical consequences from the end of Dennard scaling, the cultural and economic challenges of parallelism, the possible shifts of capital and talent, and national and regional investments in IT research. The report's broad conclusions include the following: (a) the U.S. still leads in basic IT research, but the global gap continues to shrink, (b) IT investment strategies and challenges differ markedly across countries and regions, (c) single chip performance is unlikely to continue as the predominate focus of innovation, (d) there are serious risks that growing international markets will diminish U.S. influence and (e) U.S. national security and defense readiness depends on continued rapid uptake and deployment of advanced IT.
I encourage you to download and read the complete BGST report for additional details and insights.
In IT and innovation circles, Gordon Moore is also famous for another dictum, "only the paranoid survive." What he really said is more nuanced and relevant to the global innovation competition, "Success breeds complacency. Complacency breeds failure. Only the paranoid survive." It is worth pondering as one considers the interplay of science and technology, economics, government policy, and business models.
Acknowledgments
I would be remiss if I did not express my sincere thanks to the members of the BGST report committee: Cong Gao (Nottingham), Tai Ming Cheung (UCSD), John Crawford (Intel), Dieter Ernst (East-West Center), Mark Hill (Wisconsin), Steve Keckler (NVidia), David Liddle (U.S. Venture Partners), and Kathryn McKinley (Microsoft). There were thoughtful, dedicated and indefatigable. Finally, all of the committee members are deeply indebted to Bill Berry, Ethan Chiang and Lynette Millett from the National Academies.
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