Today, I hope to make clear that fashioning new priorities for science and technology will require more than just clever re-packaging. Let's start by looking at how we traditionally package the term, "priorities," in the context of science and technology. Discussions on setting priorities generally focus on a specific set of typical but increasingly less meaningful tradeoffs:
All of us have probably at one time or another been involved in priority setting discussions that focused on these and other dimensions. Trying to strike the so-called "best" balance among these tradeoffs can be an exercise in frustration: it alienates the best of us; it consumes inordinate amounts of time; and it effectively transforms integrative decision-making into an exercise in reductionism. Put another way, priorities frequently emerge as unexceptional, incremental changes and perturbations within the confines of an established system.
Today, we need to ask ourselves if these incremental and reductionist approaches are the right approaches for the times in which we live. I would say they are not. These are times of extraordinary change, and we are only beginning to grasp the full extent of the changes at hand.
New priorities for science and technology should reflect the richness of our varied disciplines and the integrative nature of the changes taking place. The rapid-fire commentators in the medial usually describe our situation in terms of one or two-word "sound bites." You've heard them many times: economic competitiveness; information explosion; virtual organization; corporate restructuring; environmental imperatives; infrastructure renewal;shared wealth; and so on.
These sound bites, however, can drown out a crucial fact. A good deal of the change has been propelled socially by growing populations with heightened human aspirations and technologically by the advent of high-speed digital computing. Computing has not necessarily been the most important driver of these changes, but it has been central to them.
These technologies have enabled new telecommunications and information technologies,making possible the sharing of information -- voice, video, and otherwise-- around and across the world. That is what's facing us. What we do with it is the question.
Today, however, even the most scientifically and technologically literate among us have difficulty grasping the full potential of the advances at our fingertips. The computer and telecommunications explosion is already prompting a profound redefinition of such concepts as "community," library," "corporation," "government,""university," "technology transfer.
We know that scientists and engineers were central to enabling the industrial revolution and the period of progress in the post-war era. Many studies indicate that during the past half century, technological innovation has been responsible for roughly 40 percent of the productivity gain her in the U.S. Our community is now being called upon to provide even greater leadership in the emerging information age. We should now look forward to enabling and shaping what is yet to come -- even though we don't quite know what it is.In a trilogy of speeches delivered in February of this year, Vice President Gore suggested the metaphor, "distributed intelligence," to describe a new age of intelligent systems. It is a complicated metaphor, based on applying the principle of parallel processing to social challenges and economic progress. It's best to think of all these terms not as labels, but as monikers at the moment.
It rests upon the notion of giving people the ability to communicate virtually instantaneously with each other via different media, as well as giving them access to the information they need and tot he tools they need to transform that information they need and to the tools they need to transform that information into useful, productive knowledge. One could say that this involves all of society getting wired, except that it won't always involve wires. This may yield an age in which the sharing of information is instantaneous and ubiquitous.
To pursue these kinds of emerging opportunities, NSF is exploring frameworks for the development and deployment of new ideas and technologies for research, education and for society as a whole, using academic science and engineering as a test bed.
Let me give you a sense of what we are considering. Again, these are monikers for describing the cutting edge and helping us think about what to do.
Let's focus on the third area, Learning and Intelligent Systems. This spans topics as diverse as cognitive and computing and algorithms and linguistics. It involves three clearly distinct sets of challenges. The first is perhaps the most immediately attainable. It is to improve our system of learning via advances in hardware and software. Schools, corporations, and other shave already begun addressing this challenge, and there are encouraging signs of progress.
A second challenge is directly related to the first, but more fundamental. It begins with questions like: Can we create an entirely new system of learning? Can we change the way we approach education and training at all levels, develop new tools and techniques that actually augment both our and our machine's capacity to learn and create? That's a key statement. Some of my colleagues argue we should just say our ability as humans to learn and create. It's more than just that. Machines can be creative, and they can help us be creative.
The third relates to better enabling the creative capabilities of all citizens through a more facile, symbiotic relationship with the computer and communications systems rapidly enveloping all of us. For scientists and engineers, the result may be a whole way of pursuing research and effecting discovery.
These are difficult challenges and questions -- controversial in fact. That's ideal in my mind,because we can learn from each other's arguments and from other different approaches and perspectives we bring to the discussion. We have to have a way to argue vigorously and forcefully.
I like to tell people that one of NSF's jobs is to promote intellectual eclecticism. The reductionist approach, if uniquely prescribed as the system of acceptable academic progress,can yield homogenization of a sort -- and mute the fruits of eclecticism. You may have heard Peter Medawar's famous quote, "the human mind treats a new idea the way the body treats a strange protein; it rejects it." The same thinking applies to other subjects as well.
This past summer, the governors of 18 western states led by Colorado's Roy Romer announced plans to establish what they call the Virtual University. This news inspired our own Washington post to publish an editorial under the heading, "A No-Campus Campus." The Post is skeptical about Virtual U's prospects. It wrote: "The virtues of the Virtual University may yet trump the familiar virtues of other [universities], but it will take more than plugging in a modem." Regardless of whether these emergent virtual universities meet with success or failure, they mark a dramatic step in the evolution of scholarship in our society.
Through the middle ages, learning and scholarship were confined to monks working in cells. Then, around the 12th century, the first universities appeared. This gave us the professors/students/classroom model of learning. It has survived remarkably intact until our own era. Now, learning no longer requires that we gather on a campus or in school, just as visiting the Louvre no longer requires flying to Paris.
This concept also applies to challenges that are much closer to home. For example, I know over the past year, many have worked closely with NSF's Division of Science Resources Studies at a series of workshops on data needs in science and engineering. The feedback on these workshops has been consistently positive, and we know they have been immensely valuable to NSF. We have all benefited from each other's capabilities and expertise. Touse the Vice President's metaphor, they've enabled us to pool our distributed intelligence.
Now it becomes logical to examine the next steps for this process. Can we collect this great wealth of data sources and surveys, make them more compatible, and like them and calibrate them with national databases? The National Science Board's Task Force on Graduate Education asked such a question about data on graduate education, and we can all envision the potential benefits to policy makers of a more comprehensive data source.
There was a time, not very many years ago, when this kind of rhetoric would have been labeled wildly impractical. Different data formats, incompatible systems, and general bureaucratic barriers would have ended this kind of effort before it began.
Today, while we sill need to work on lowering bureaucratic barriers, the technological hurdles are fast disappearing. Web-based technologies make them virtually transparent. They allow us to envision intra-networks, policy tools, and collaborations that were previously unrealistic or even unimaginable.
When I was an entry level engineer at RCA, a big part of my value to the company was my mastery of the slide rule and my dexterity with french curves for developing drawings and diagrams. Those aren't core personal or professional competencies in any company today,and my expertise with them won't get me a job anywhere, anytime, anyplace, at a decent salary.
When we developed new product ideas back then, we always had to wait -- sometimes for several months -- for models to be built. My mind would often move on to other ideas while we waited for the models. Compare that with being able to sit down with a 3-D CAD program to develop virtual models in the span of a few hours. That represents a quantum leap in learning power and creative potential, and we can make it widely accessible now. This is the stuff of rewarded careers in today's societal marketplace.
New priorities for science and technology should be viewed in this context. The reductionist approach we've relied upon so intently for several generations may no longer suffice. While remaining of great intellectual value, it, at a minimum, may require thoughtful reconsideration, as we learn more about the opportunities and the responsibilities facing scientists and engineers in the information age.
We cannot see very far into the future--especially if we are on the edge. It is indeed unknown to us, yet we suspect it is likely to be different from the present. With the advent of high-speed tools for learning, creativity, and innovation, change becomes increasingly more rapid, drawing the world's people closer in globally-based markets, and creating almost continual shifts in the way we interact with each other.
To prosper in this eclectic milieu, we must become increasingly astute about making connections, working together, and integrating across science and engineering for the common good. The priorities we set for science and technology should reflect this spirit of holism and integration, as should our views of the roles and careers of professionals in science and technology.
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