Communications of the ACM

bringing back “wild” problems suitable for algorithmic study is not adequately awarded. Hence, the computing problems driven by scientific domains are often developed by the domain scientists, not the computer scientists. This gives rise to an obvious question: Are we sure these are the best algorithms for the tasks at hand? If our answer is yes, then we have a problem: either all domain-driven problems are trivial (very unlikely) or expert algorithms can be developed by nearly anyone without formal computer science training, and our discipline is extraneous. Clearly, there is a big opportunity for domain-driven algorithmic thinking and a need for the broader computer science community to embrace scientific problems and engineering opportunities. Should we fail to do so, we contribute to a balkanization of computing, in which computing is reimagined within each scientific community by non-experts in computing sciences.

A great example of algorithmic innovation comes from DARPA’s MAKE-IT,d a program that is reimagining the process of synthesizing chemical reaction pathways into a search algorithm. The key insight of MAKE-IT is that chemical reactions can be viewed as nodes in a directed graph: given certain precursor compounds, a reaction produces certain chemical products. New kinds of search algorithms will enable chemists to find new and better methods of producing important compounds.

˲ The human-machine symbiosis:5 Computing over the decades has been focused on understanding computing machinery, building it, controlling it and understanding the consequences. A computer, or piece of software, is principally a device that enables a person to do a job, usually an existing job, perhaps in a better manner than before. As electronic spreadsheets have largely replaced accountants’ physical ones, the human-centric task has evolved but not fundamentally changed. Computing machinery is now poised to go beyond just assisting with human problems—it enables us to fundamentally rethink them. This is seen across science and engineering domains in a particularly pointed way: progress has become intimately tied to computation and data.

What other disciplines
can we disrupt
with computational
tools that augment
our intelligence?

As this scientific revolution unfolds we are witnessing a shift from merely computational methods (for example, computer as calculator) to those in which humans and machines are partners. Over a decade ago, the Sloan Digital Sky Survey enabled astronomers to pose questions that were previously impossible to answer. Currently, DARPA is working to advance machine reading under our Big Mechanisme program with the goal of having computers that can help scientists harvest vast collections of experimental results and produce a shared human-machine understanding of certain cancer pathways. An emerging challenge is to consider how problems can be broken up and distributed across human-machine teams in order to exploit the power of silicon-based machines while optimizing the insight and creativity of the carbon-based ones. What other disciplines can we disrupt with computational tools that augment our intelligence?

The computer scientist is the toolsmith that enables domain scientists and engineers to reinvent and reimagine their disciplines as algorithmic or information-centric. However, if computing really is going to be the new framework for scientific discovery and engineering innovation, we—the computer scientists—need to support this kind of interdisciplinary study among students as well as evangelize the unconverted. Sometimes we might find those outside computing with a “not invented here” bias—we need to meet them halfway or more than halfway. Sometimes we may find disciplines are not as much converted to computing, as they are becoming assimilated under the assault of ideas from computing.

Scientific disciplines are refactoring key problems around systems for vast data and computation, and computer scientists should be working deeply with these domain scientists and engineers to bring about revolutions.

Many of the most important problems facing the U.S. and the world today represent a call to service similar to that which began during the Second World War, when many of the best and brightest minds (mostly physicists) in the country put commercial or academic interests on hold and dedicated themselves to innovations in service of national need. Students—pulled by the lure of the Apollo program or the needs of the Cold War—pursued careers of national service in science and engineering in order to solve big problems.

I would love our best and brightest computer science students to feel that pull now, and have the opportunity to be captivated by these major interdisciplinary challenges. But with the current din of opportunities, we must work harder to instill the passion for such service. Computing is no longer a new discipline and is well into adolescence—it needs to make some decisions about what it really wants to be when it grows up. Rather than just disrupting industries and creating the next great mobile app, I long for an expanded view of what constitutes legitimate computer science inquiry and for increased scientific citizenship.

References

1. Aho, A.V., Hopcroft, J.E.,and Ullman, J.D. The Design and Analysis of Computer Algorithms. Addison-Wesley, Reading, MA, 1976.

2. Brooks, F. P., Jr. The computer scientist as toolsmith II. Commun. ACM 39, 3 (Mar. 1996), 61–68.

3. Grand Challenges For Engineering, The National Academy of Engineering, 2008.

4. Vonavar, V., Hill, M., and Yelick. K. Accelerating science: A computing research agenda: A white paper prepared for the computing community consortium committee of the computing research association. Technical report, CRA/CCC, 2016.