Reed's Ruminations: A Blog by Dan Reed

All Grown Up Now: Service and Serial Reciprocity

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

Though the frequency and ease of electronic communication now make it less common than when families were connected by little more than the postal service, each of us has experienced the momentary sense of surprise when seeing the child of a relative who has grown and matured since the last family gathering. It's a case of cognitive dissonance, where expectations and memory collide with the reality of change. The child became an adult when we were not watching.

As a set of disciplines, computing is a bit like that too. Within the world of science, technology, engineering and mathematics (STEM), we've become an adult. As with any transition from childhood toys and recreational endeavors, adulthood brings certain family and societal responsibilities and obligations. These are the quid pro quo for daily life's rights and privileges. In many cases, though, I fear that in computing we are still overly enamored with our technological toys rather than honoring all of our responsibilities.

Others, including CACM's editor-in-chief Moshe Vardi, have written about the challenges we face in timely and appropriate review of conference and journal submissions. I also touched on this topic in another blog post and an article for CRA's Computing Research News (CRN). In turn, Jeannette Wing discussed the differential rejection rates for computing proposals at the U.S. National Science Foundation. We also know that our funding agencies struggle to recruit staff from our ranks. However, I believe these may be merely small aspects of a larger issue, the need for all of us to shoulder more responsibility in the global science and engineering community.

Pipelining and parallelism are concepts we understand well. They, like the transition from childhood to adulthood, are metaphors for the shifting nature of our communal responsibilities. As a young student, one looks to teachers, advisors and mentors for instruction and guidance. As a mid-career researcher or practitioner, one is expected to be facile with the tools and technologies of the discipline, able to draw on one's skills to advance the state of the art, cognizant of the broader context in which the technologies and products are used. As an older and wiser senior leader, one's role is to offer advice and guidance to those younger, taking a perspective broader than one's own career or even one's own discipline.

In computing, we used to rationalize our lack of presence in key debates as a consequence of the newness of the field. Today, we are "all grown up" – a set of mature, though still rapidly evolving disciplines, that touch almost all aspects of society. I believe we have a social responsibility to both the computing community and the society to act accordingly. Within the discipline, that means taking on the sometimes thankless tasks that ensure its continuing vibrancy and success – providing international, national, regional and local service on policy and strategy committees; volunteering as organizational leaders; serving as government agency staffers, program officers and leaders; and writing, speaking and testifying about critical issues (diversity and inclusion, education and curricula, research priorities, funding and resources).

Across the broader STEM community and society, computing pervades science and engineering research; it impinges on critical infrastructure function and safety, personal health and well-being; its prevalence raises new and thorny issues around personal privacy and digital security; its capabilities create new opportunities and challenges around education, digital literacy and societal inclusion; and it is the digital messenger of social, political and cultural change. Simply put, computing is central to the debate about so many aspects of our present and future that we have a duty and responsibility to be active and enthusiastic participants. That means explaining technical issues, their implications and their possibilities in terms understandable by those outside the discipline, indeed outside science and technology. It means helping frame the discussion, raising awareness of both the risks and the rewards of technological change, so that societal decisions can be made wisely and collaboratively. As I have noted elsewhere, this requires fluency in technology and policy and a willingness to look beyond one's own personal self-interests.

As a community, we are mature. Our community is filled with individuals who have both the skills and wisdom to serve our discipline, our organizations and our countries. All of us, and I include myself, need to look beyond our personal interests and give more for the benefit of others. Many of us are, but we can and should do more. Our colleagues in the physical sciences, engineering and the social, biological and biomedical sciences are active and engaged. We need to follow their example. By paying it forward (serial reciprocity), we create an environment where a new generation can thrive, just as the founders of the field did for us.

Predicting Our Technological Future

What does the future hold? Those who are fortunate or lucky enough to anticipate major social, economic or technical changes will generally be more successful than those who do not, though sometimes luck plays a role. The factors leading to change are manifold, and I am no expert in either economic theory or social dynamics. That leaves technology, and I am perhaps foolhardy enough to hazard a few guesses about what the future may hold.

Foolhardy, foolishly rash, unwisely bold, those are all operative, for the Brownian motion of component technology changes that together enable new products is complex indeed. It does not take a rocket scientist, though I was once a member of a rocket simulation team, to extrapolate the continued proliferation of mobile and embedded devices, the rapid growth of "big data," and the seemingly insatiable demand for more wired and wireless broadband capability. Beyond devices, big data and broadband, lie the challenges and opportunities posed by the Internet of Things, the dark silicon challenges beyond Dennard scaling, the rise of post-GUI natural user interfaces and a shift from explicit to implicit computing.

Dennis Gannon, Jim Larus and I speculated about all of these topics in the January 2012 issue of IEEE Computer in the cover article entitled, Imagining the Future: Thoughts on Computing. We also discussed the social and policy implications of new technologies, including the nature of privacy and security in a sensor-rich, big data world, the need for new approaches to adaptive spectrum allocation that respond more quickly to shifting demand, and social and technical expectations for new computing curricula.

All of us who are or have been researchers know that creating new technologies is exciting and fulfilling. Combining technologies to create viable products within a shifting policy landscape is a challenge of a different sort. When thinking about product and process failures, all of us would do well to recall Richard Feynman's telling denouement of human folly when serving on the review commission following the space shuttle Challenger's explosion, "For a successful technology, reality must take precedence over public relations, for nature cannot be fooled."

Marketing does not make successful products, at least not for long. Instead, they must have real and intrinsic value to the users, and they must operate reliably as advertised. Predicting user value is, of course, far more difficult than it superficially seems, else venture capitalists would have higher success rates, and major corporations would not be foundering on the rocks of technological change.

Nor is past or present success a guarantee of future profitability. Shot any Polaroids lately? Written any VAX assembly code this month for anything other than legacy systems? Still driving your Rambler to work? All three products were icons of the 1960s and 1970s, yet their parent companies are no more.

There are many reasons why initial success becomes the father of later failure. Sometimes designers cling too long to old technology, sometimes companies make ill-advised leaps to new and untried technology, and sometimes leaders cling to old business models when change is essential to survival.

As William Gibson famously noted, "The future is already here. It's just not very evenly distributed." Though both pithy and true, it is hardly a guide to navigating the implied temporal mixing of present and future. Instead, I take more comfort from Søren Kierkegaard, "Life must be lived forward, but can only be understood backwards."

Research Data Sustainability and Access

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

How recently have you mounted a 9-track open reel tape, hoping to access the irreplaceable data that was the foundation of your first research paper? At this point, you may not even remember if it was 800, 1600 or 6250 bits per inch (bpi), EBCDIC or ASCII, blocked or unblocked. You are not that old, you say? What about your 5.25" or 3.5" floppy disks or DAT archive? Odds are you haven't accessed the data because you can't without seeking the services of a conversion company that specializes in data retrieval from obsolete media.

Have you ever been involved in a research project, either individually or as part of a multi-institutional team, that produced data intended for broader community use? If so, then you probably placed it on the project web site, perhaps with the research software needed to decode and process the data. A decade later, is the data still accessible and does the software even compile or execute on current systems?

Personally, I still have some 9-track computer tapes, a punched card deck and a paper tape in an office desk drawer, saved for both reasons of pedagogy and nostalgia. I also have some data analytics tools originally designed for workstations now found in the Computer History Museum.

These examples may seem quaint, and perhaps they are, but each of us has some variant of this data obsolescence and inaccessibility experience. They are the analog (pun intended) of our previous consumer media experiences. After all, have you played any of your 45s, 8-track tapes, or cassettes lately?

These are the symptoms of three bigger issues: the rapid obsolesce of specific storage technologies, the explosive growth of research data across all scientific and engineering disciplines, and the even more difficult task of sustaining data access past the end of research projects.

The first is a natural consequence of technological change, one that we have collectively managed across the history of modern digital computing. In turn, "big data" is a hot topic of research and business innovation, with new tools and techniques appearing to extract insights from large volumes of unstructured or ill-structured data. However, the social and economic challenges around research data preservation are profound and not yet resolved.

In the U.S., the Office of Science and Technology Policy (OSTP) on behalf of the National Science and Technology Council (NSTC) recently issued a request for information (RFI) on Public Access to Digital Data Resulting from Federally Funded Scientific Research. In addition, the National Science Foundation has instituted a requirement that research proposals include a data management plan, which notes that "Investigators are expected to share with other researchers, at no more than incremental cost and within a reasonable time, the primary data, samples, physical collections and other supporting materials created or gathered in the course of work under NSF grants."

Several issues are convolved in our desire and expectations for data sharing. One is advancing discovery and innovation via the free flow of information, replicating and expanding experiments and sharing data from increasingly expensive national and international scientific instrumentation. This is central to the scientific process, though it brings the cultural disparity that exists across disciplines to the fore – astronomy, biology and computing are culturally quite different.

The second is the need for multidisciplinary sharing and cross-domain fertilization. Increasingly, new insights emerge from fusing and analyzing data drawn from diverse sources. Such integration places a premium on metadata schemas, well documented data formats and service access protocols. In turn, these require standards and coordination, both within and across disciplines.

The third is the distinction between research, which produces data, and data preservation, documentation and dissemination. In my experience, the skills and expertise, as well as reward metrics, are distinctly different for the two activities. This is an oblique way of saying that researchers will generally optimize for research advancement over data preservation when forced to choose between the two. This is only natural, given our current reward structure.

Research and data preservation also differ markedly in their timescales, for data preservation and dissemination services often require decadal planning, with associated infrastructure and professional staffing, rather than the 3-5 year funding for principal investigators, graduate students and post-doctoral research associates that is typical of research grants and contracts.

Finally, data preservation and dissemination can be expensive, rivaling or exceeding that of the initial research investment. Quite clearly, not everything can and should be preserved in perpetuity, but predicting the future value of data is both difficult and perilous.

This suggests that we need economic and social processes that more rigorously access the present and future value of data. They might include combinations of commercial, fee-based models, where researchers and organizations vote with their funds for access to and retention of certain data (i.e., a cloud-based research data marketplace), government funded and managed repositories where key data is retained (e.g., NIH's GenBank), or distributed but interconnected archives funded by multiple agencies and governments (e.g., the Worldwide LHC Computing Grid).

What is clear is that the dramatic growth of research data, the collaborative and competitive nature of international science and engineering research, expectations for economic returns from research investments and disciplinary differences all make this a pressing and difficult problem. Our current, ad hoc approaches are inadequate and not sustainable.