the first 30 years, AI pursued a dream of intelligent machines. When they were unable to even get close to realizing the dream, they gave up rule-based AI systems and turned instead to machine learning focused on automating simple mental tasks rather than general intelligence. They were able to build amazing automations based on neural networks without trying to imitate human brain processes. Today’s AI has become so successful with neural network models that do far better than humans at some mental tasks that we are now facing social disruptions about joblessness caused by AI-driven automation.

 

Conclusion

We welcome the enthusiasm for computer science and its ways of thinking. As professionals, we need to be careful that in our enthusiasm we do not entertain and propagate misconceptions about our field. Let us not let others oversell our field. Let us foster expectations we can fulfill.

 

References

1. Change the Equation. The hidden half. Blog post. (Dec.

7, 2015); http://changetheequation.org/blog/hidden-half

2. Computer Science Curricula 2013; https://www.acm.

org/education/CS2013-final-report.pdf

3. Denning, P. and Lewis, T.G. Exponential laws of computing growth. Commun. ACM 60, 1 (Jan. 2017).

4. Denning, P. and C. Martell. Great Principles of Computing. MI T Press, 2015.

5. Denning, P. J. et al. Computing as a discipline.

Commun. ACM 32, 1 (Jan. 1989), 9–23.

6. Guzdial, M. Learner-Centered Design of Computing Education: Research on Computing for Everyone.

Morgan-Claypool, 2015.

7. Tedre, M. Science of Computing: Shaping a Discipline.

CRC Press, Taylor & Francis, 2014.

8. Tedre, M. and Denning, P. J. The long quest for computational thinking. In Proceedings of the 16th Koli Calling Conference on Computing Education Research

(Koli, Finland, Nov. 24–27, 2016), 120–129.

9. Wing, J. Computational thinking. Commun. ACM 49, 3

(Mar. 2006), 33–35.

 

Peter J. Denning ( [e-mail]) is Distinguished Professor of Computer Science and Director of the Cebrowski Institute for information innovation at the Naval Postgraduate School in Monterey, CA, is Editor of ACM Ubiquity, and is a past president of ACM. The author’s views expressed here are not necessarily those of his employer or the U.S. federal government.

Matti Tedre ( [e-mail]) is Associate Professor of Computer and Systems Sciences at Stockholm University, Sweden, adjunct professor at University of Eastern Finland, and the author of Science of Computing: Shaping a Discipline (CRC Press, Taylor & Francis, 2014).

Pat Yongpradit ( [e-mail]) is the Chief Academic Officer for Code.org and served as staff lead on the development of the K– 12 Computer Science Framework.

A former high school computer science teacher, Pat has been featured in the book, American Teacher: Heroes in the Classroom, has been recognized as a Microsoft Worldwide Innovative Educator, and is certified in biology, physics, math, health, and technology education.

Copyright held by authors.

ing that CT is a knowledge set that
drives the programming skill. A stu-
dent who scores well on tests to explain
and illustrate abstraction and decom-
position can still be an incompetent
or insensitive algorithm designer. The
only way to learn the skill is to practice
for many hours until you master it. The
newest CSTA guidelines move to coun-
teract this upside-down story, empha-
sizing exhibition of programming skill
in contests and projects.d
Because computation has invaded
so many fields, and because people who
do computational design in those fields
have made many new discoveries, some
have hypothesized that CT is the most
fundamental kind of thinking, trump-
ing all the others such as systems think-
ing, design thinking, logical thinking,
scientific thinking, etc. This is compu-
tational chauvinism. There is no basis
to claim that CT is more fundamental
than other kinds of thinking.

When we engage in everyday step-by-step procedures we are thinking computationally. Everyday step-by-step procedures use the term “step” loosely to refer to an isolated action of a person. That meaning of step is quite different from a machine instruction; thus most “human executable recipes” cannot be implemented by a machine. This misconception actually leads people to misunderstand algorithms and therefore overestimate what a machine can do.

Step-by-step procedures in life, such as recipes, do not satisfy the definition of algorithm because not all their steps are machine executable. Just because humans can simulate some computational steps does not change the requirement for a machine to do the steps. This misconception undermines the definition of algorithm and teaches people the wrong things about computing.

Computational thinking improves
problem-solving skills in other fields.
This old claim is called the “transfer
hypothesis.” It assumes that a thinking
skill automatically transfers into other
domains simply by being present in
the brain. It would revolutionize educa-
tion if true. Education researchers have
studied automatic transfer of CT for
three decades and have never been able
d One of the K– 12 curriculum recommendations
actually cites making a peanut butter and jelly
sandwich as an example of an algorithm.
to substantiate it. 6 There is evidence on
the other side—slavish faith in a single
way of thinking can make you into a
worse problem solver than if you are
open to multiple ways of thinking.

Another form of transfer—designed transfer—holds more promise. Teachers in a non-CS field, such as biology, can bring computational thinking into their field by showing how programming is useful and relevant in that field. In other words, studying computer science alone will not make you a better biologist. You need to learn biology to accomplish that.

CS is basically science and math. The engineering needed to produce the technology is all based on the science and math. History tells us otherwise. Electrical engineers designed and built the first electronic computers without knowing any computer science—CS did not exist at the time. Their goal was to harness the movement of electrons in circuits to perform logical operations and numerical calculations. Programs controlled the circuits by opening and closing gates. Later scientists and mathematicians brought rigorous formal and experimental methods to computing. To find out what works and what does not, engineers tinker and scientists test hypotheses. In much of computing the engineering has preceded the science. However, both engineers and scientists contribute to a vibrant computing profession: they need each other.

Old CS is obsolete. The important developments in CS such as AI and big data analysis are all recent. Computing technology is unique among technologies in that it sustains exponential growth (Moore’s Law) at the levels of individual chips, systems, and economies. 3 Thus it can seem that computer technology continually fosters upheavals in society, economies, and politics—and it obsoletes itself every decade or so. Many of the familiar principles of CS were identified in the 1950s and 1960s and continue to be relevant today. The early CS shaped the world we find ourselves in today. Our history shows us what worked and what does not. The resurrection of the current belief that CS=programming illustrates how those who forget history can repeat it.

Artificial intelligence is an old subfield of CS, started in the early 1950s. For