For years we humans have worried about plagues, asteroids, earthquakes, eruptions, fires, floods, famines, wars, genocides, and other uncontrollable events that could wipe away our civilization. In the modern age, with so much depending on computing and communications, we have added computers to our list of potential threats. Could we perish from the increasing intelligence of computers? Denning thinks that is less of a threat than the apparently mundane march of automated bureaucracies. He also asserts that none of the possible negative outcomes is a forgone conclusion because humans teaming with machines are far more intelligent than either one alone.

Liah Greenfeld and Mark Simes have long worked together, integrating the perspectives of two very different disciplinary traditions: cultural history/historical sociology and human neuroscience. The combination of their areas of expertise in the empirical investigation of mental disorders, which severely affect intelligence---among other things---has led them to certain conclusions that may throw a special light on the question of this symposium: Will computers outcompete us all?

The Priesthood of the Singularity posits a fast approaching prospect of machines overtaking human abilities (Ray Kurzweil's The Singularity is Near, Viking Press, 2006) on the basis of the exponential rate of electronic integration---memory and processing power. In fact, they directly correlate the growth of computing technology with that of machine intelligence as if the two were connected in some simple-to-understand and predictable way. Here we present a different view based upon the fundamentals of intelligence and a more likely relationship. We conclude that machine intelligence is growing in a logarithmic (or at best linear fashion) rather than the assumed exponential rate.

In this paper a look is taken at artificial intelligence and the ways it can be brought about, either by means of a computer or through biological growth. Ways of linking the two methods are also discussed, particularly the possibilities of linking human and artificial brains together. In this regard practical experiments are referred to in which human enhancement can be achieved though linking with artificial intelligence.

The state of the art in automating basic cognitive tasks, including vision and natural language understanding, is far below human abilities. Real-world reasoning, which is an unavoidable part of many advanced forms of computer vision and natural language understanding, is particularly difficult---suggesting the advent of computers with superhuman general intelligence is not imminent. The possibility of attaining a singularity by computers that lack these abilities is discussed briefly.

To jumpstart this symposium, Espen Andersen describes the debate surrounding "technological singularity" and questions whether this is something that will happen---and if so, what the consequences might be.

While evolution has inspired algorithmic methods of heuristic optimization, little has been done in the way of using concepts of computation to advance our understanding of salient aspects of biological phenomena. The authors argue under reasonable assumptions, interesting conclusions can be drawn that are of relevance to behavioral evolution. The authors will focus on two important features of life---robustness and fitness---which, they will argue, are related to algorithmic probability and to the thermodynamics of computation, disciplines that may be capable of modeling key features of living organisms, and which can be used in formulating new algorithms of evolutionary computation.

The goal of the LabRats Science Education Program is to inspire secondary school-age students from all backgrounds to love learning about science and technology. Shawn Carlson, the Executive Director of LabRats, presents five key insights that can be integrated into any science and technology program. The purpose of which is to overhaul students' attitudes and motivation to learn. Carlson also offers detailed suggestions on how educators can use these insights to inspire their students to become lifelong learners of science and technology.

Phil Yaffe has provided numerous commentaries on various aspects of professional communication, which have helped readers more effectively articulate their own ideas about the future of computing. Here he tells us about how scientists see the world---the "scientific approach," he calls it---because he thinks many non-scientists see the world in a similar way. This realization can lower barriers of communication with scientists.

In this ninth symposium article, Lukáš Sekanina addresses evolutionary and evolvable hardware; answering the questions what it means for a physical system to be designed evolutionarily and on what kinds of computations such physical systems perform will be answered.

Until 2000, computer science enrollments were steadily increasing. Then suddenly students started turning to other fields; by 2008, enrollments had dropped by 50 percent. To that end, Jan Cuny has been leading a program at the National Science Foundation to increase both the number and diversity of students in computing. In this interview with Ubiquity, she discusses the magnitude of the problem and the initiatives underway to turn it around.

In the third installment of this symposium, which originally appeared in the Communication the ACM, we go back to 1981. More than 30 years ago, I called on ACM members to employ more experimental methods and avoid confusing hacking (tinkering) with true science. Joining a long tradition of ACM Presidents speaking up about computing as science.

In this article, David Fogel discusses the relationship between evolutionary computation and evolutionary game theory. The mathematics of evolutionary game theory relies on assumptions that often fail to describe the real-world conditions that the theory is intended to model. This article highlights those assumptions and suggests evolutionary computation may ultimately serve as a more useful approach to understanding complex adaptive systems in nature.

In this article, Moshe Sipper discusses a foreseeable future in which an entirely new paradigm of producing software will emerge. Sipper calls this software engineering revolution, "Darwinian Software Engineering"---a time when it will be possible to program computers by means of evolution.

The first article in this symposium, which originally appeared in the Communication the ACM, is courtesy of ACM President Vinton Cerf. Earlier this year, he called on all ACM members to commit to building a stronger science base for computer science. Cerf cites numerous open questions, mostly in software development, that cry out for experimental studies.

The recent interest in encouraging more middle and high school students to prepare for careers in science, technology, engineering, or mathematics (STEM) has rekindled the old debate about whether computer science is really science. It matters today because computing is such a central field, impacting so many other fields, and yet it is often excluded from high school curricula because it is not seen as a science. In this symposium, fifteen authors examine different aspects from what is science, to natural information processes, to new science-enabled approaches in STEM education.

In this article evolutionary computation (EC) is considered as a kind of nature-inspired computing (NIC) paradigm. EC not only has great effect on the development of computing methods from structure to process, but also has great effect on many aspects of our society as a ubiquitous or general computational thinking. EC is still one of the best choices for problem solving among all methods when people face more and more complex problems.

During the past 35 years the evolutionary computation research community has been studying properties of evolutionary algorithms. Many claims have been made---these varied from a promise of developing an automatic programming methodology to solving virtually any optimization problem (as some evolutionary algorithms are problem independent). However, the most important claim was related to applicability of evolutionary algorithms to solving very complex business problems, i.e. problems, where other techniques failed. So it might be worthwhile to revisit this claim and to search for evolutionary algorithm-based software applications, which were accepted by businesses and industries. In this article Zbigniew Michalewicz attempts to identify reasons for the mismatch between the efforts of hundreds of researchers who make substantial contribution to the field of evolutionary computation and the number of real-world applications, which are based on concepts of evolutionary algorithms.

In this article in the ACM Ubiquity symposium on evolutionary computation Max H. Garzon presents reflections on the connections between evolutionary computation, natural computation, and current definitions of computer science. The primary aim and result is a more coherent, comprehensive and modern definition of computer science.

In this third article in the ACM Ubiquity symposium on evolutionary computation Xin Yao provides a deeper understanding of evolutionary algorithms in the context of classical computational paradigms. This article discusses some of the most important issues in evolutionary computation. Three major areas are identified. The first is the theoretical foundation of evolutionary computation, especially the computational time complexity analysis. The second is on algorithm design, especially on hybridization, memetic algorithms, algorithm portfolios and ensembles of algorithms. The third is co-evolution, which seems to be under studied in both theory and practice. The primary aim of this article is to stimulate further discussions, rather than to offer any solutions.

In this second article in the ACM Ubiquity symposium on evolutionary computation Hans-Paul Schwefel explores the effects of simulating evolutionary mechanisms.

Evolution is one of the indispensable processes of life. After biologists found basic laws of evolution, computer scientists began simulating evolutionary processes and using operations discovered in nature for solving problems with computers. As a result, they brought forth evolutionary computation, inventing different kinds operations and procedures, such as genetic algorithms or genetic programming, which imitated natural biological processes. Thus, the main goal of our Symposium is exploration of the essence and characteristic properties of evolutionary computation in the context of life and computation.

The "computation" symposium presents the reflections of thinkers from many sectors of computing on the fundamental question in the background of everything we do as computing professionals. While many of us have too many immediate tasks to allow us time for our own deep reflection, we do appreciate when others have done this for us. Peter Freeman points out, by analogy, that as citizens of democracies we do not spend a lot of time reflecting on the question, "What is a democracy," but from time to time we find it helpful to see what philosophers and political scientists are saying about the context in which we act as citizens.

Editor's Introduction This fourteenth piece is inspired by a question left over from the Ubiquity Symposium entitled What is Computation? Peter J. Denning Editor

Computing saw the light as a branch of mathematics in the '40s, and progressively revealed ever new aspects [gol97]. Nowadays even laymen have become aware of the broad assortment of functions achieved by systems, and the prismatic nature of computing challenges thinkers who explore the various topics that substantiate computer science [mul98].

In this thirteenth piece to the Ubiquity symposium discussing What is computation? Melanie Mitchell discusses the idea that biological computation is a process that occurs in nature, not merely in computer simulations of nature.
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In this twelfth piece to the Ubiquity symposium discussing What is computation? Erol Gelenbe reviews computation in natural systems, focusing mainly on biology and citing examples of the computation that is inherent in chemistry, natural selection, gene regulatory networks, and neuronal systems.
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In this eleventh piece to the Ubiquity symposium discussing What is computation? Jeffrey P. Buzen develops a new computational model for representing computations that arise when deterministic algorithms process workloads whose detailed structure is uncertain.
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In this tenth piece to the Ubiquity symposium discussing What is computation? Joseph Traub shares his views about using the Turing Machine model and the real number model for solving continuous scientific problems.
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In this ninth piece to the Ubiquity symposium discussing What is computation? Alfred V. Aho shares his views about the importance of computational thinking in answering the question.
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In this sixth article in the ACM Ubiquity symposium,What is Computation? Ruzena Bajcsy of the University of California-Berkeley explains that computation can be seen as a transformation or function of information.
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In this fifth article in the ACM Ubiquity symposium on What is computation? Paul S. Rosenbloom explains why he believes computing is the fourth great scientific domain, on par with the physical, life, and social sciences.
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In this seventh article in the ACM Ubiquity symposium, What is Computation?, Dave Bacon of University of Washington explains why he thinks discussing the question is as important as thinking about what it means to be self-aware. —Editor

Various authors define forms of computation as specialized types of processes. As the scope of computation widens, the range of such specialties increases. Dennis J. Frailey posits that the essence of computation can be found in any form of process, hence the title and the thesis of this paper in the Ubiquity symposium discussion what is computation. --Editor

Most people understand a computation as a process evoked when a computational agent acts on its inputs under the control of an algorithm. The classical Turing machine model has long served as the fundamental reference model because an appropriate Turing machine can simulate every other computational model known. The Turing model is a good abstraction for most digital computers because the number of steps to execute a Turing machine algorithm is predictive of the running time of the computation on a digital computer. However, the Turing model is not as well matched for the natural, interactive, and continuous information processes frequently encountered today. Other models whose structures more closely match the information processes involved give better predictions of running time and space. Models based on transforming representations may be useful.

In this second article in the ACM Ubiquity symposium on 'What is computation?' Peter Wegner provides a history of the evolution of comptuation. --Editor

In the second in the series of articles in the Ubiquity Symposium What is Computation?, Prof. John S. Conery of the University of Oregon explains why he believes computation can be seen as symbol manipulation. For more articles in this series, see table of contents in the http://ubiquity.acm.org/article.cfm?id=1870596 Editors Introduction to the symposium. --Editor