As a technologist who heads a company developing state-of-the-art software and processes, I’m always interested in the latest developments in the field of computing. For years, computer “science” received a wary glance from “real” scientists, who viewed the emerging field with the same disdain they offered the social “sciences.” That has all changed according to a New York Times article by Steve Lohr [“Computing, 2016: What Won’t be Possible?” 31 October 2006].
The more generous perspective today is that decades of stunningly rapid advances in processing speed, storage and networking, along with the development of increasingly clever software, have brought computing into science, business and culture in ways that were barely imagined years ago. The quantitative changes delivered through smart engineering opened the door to qualitative changes. Computing changes what can be seen, simulated and done. So in science, computing makes it possible to simulate climate change and unravel the human genome.
As Lohr notes, computers have had a dramatic impact in almost all aspects of human endeavor including culture and business.
In business, low-cost computing, the Internet and digital communications are transforming the global economy. In culture, the artifacts of computing include the iPod, YouTube and computer-animated movies.
Lohr then rhetorically asks, “What’s next?” Some of the answers to that question, he reports, were discussed last month during a Washington, DC, symposium held by the Computer Science and Telecommunications Board. At the “2016” symposium, Lohr reports:
Computer scientists from academia and companies like I.B.M. and Google discussed topics including social networks, digital imaging, online media and the impact on work and employment. But most talks touched on two broad themes: the impact of computing will go deeper into the sciences and spread more into the social sciences, and policy issues will loom large, as the technology becomes more powerful and more pervasive.
Policy is always racing to keep up with technology, but Lohr implies that policymakers are going to have to move even faster. We’re all aware of privacy issues, but ethical issues are also likely to arise about how data is collected and used. For someone whose company markets software that helps corporations keep up with policy changes, I read such statements with great interest. Lohr focuses on one particular talk:
Richard M. Karp, a professor at the University of California, Berkeley, gave a talk whose title seemed esoteric: “The Algorithmic Nature of Scientific Theories.” Yet he presented a fundamental explanation for why computing has had such a major impact on other sciences, and Dr. Karp himself personifies the trend. His research has moved beyond computer science to microbiology in recent years. An algorithm, put simply, is a step-by-step recipe for calculation, and it is a central concept in both mathematics and computer science. “Algorithms are small but beautiful,” Dr. Karp observed. And algorithms are good at describing dynamic processes, while scientific formulas or equations are more suited to static phenomena. Increasingly, scientific research seeks to understand dynamic processes, and computer science, he said, is the systematic study of algorithms. Biology, Dr. Karp said, is now understood as an information science. And scientists seek to describe biological processes, like protein production, as algorithms. “In other words, nature is computing,” he said.
More and more people are starting to abandon what Robert Frenay refers to as the machine model of the world and adopting a natural model. One thing that all successful natural systems possess is feedback loops, which means information sharing plays a major role in any successful system. Resilient enterprises understand this. Technology has made information sharing easier (so easy, in fact, that many decision makers lament information overload). Natural systems understand what information is critical and they fine tune receptors accordingly. Resilient enterprises do the same thing. Enterra Solutions helps them do this through what we call Transparent Intelligent Interfaces, rich Internet applications that deliver the desired information in the desired format.
Lohr also writes about a talk by Jon Kleinberg, a professor at Cornell, that examined social networks, which Kleinberg notes are “pre-technological creations that sociologists have been analyzing for decades.” Lohr discusses how Karp’s and Kleinberg’s talks intersect:
With the rise of the Internet, social networks and technology networks are becoming inextricably linked, so that behavior in social networks can be tracked on a scale never before possible. … The new social-and-technology networks that can be studied include e-mail patterns, buying recommendations on commercial Web sites like Amazon, messages and postings on community sites like MySpace and Facebook, and the diffusion of news, opinions, fads, urban myths, products and services over the Internet. Why do some online communities thrive, while others decline and perish? What forces or characteristics determine success? Can they be captured in a computing algorithm? Social networking research promises a rich trove for marketers and politicians, as well as sociologists, economists, anthropologists, psychologists and educators. … Future trends in computer imaging and storage will make it possible for a person, wearing a tiny digital device with a microphone and camera, to essentially record his or her life. The potential for communication, media and personal enrichment is striking.
Lohr ends his piece with a note that privacy as we know it is likely a thing of the past. We are quickly becoming a surveillance society and will have to wrestle with the privacy and ethical issues that come with it. While Lohr’s article was principally about the science behind computing, he quotes a computer scientist who notes that society, not scientists, will determine how technology is used.