Take Wing
It is a capital mistake to theorize before one has data—Sherlock Holmes
Jeannette Wing is a Professor of Computer Science at Columbia University. She was recently appointed Executive VP for Research across all of Columbia’s campuses. She is also an adjunct Professor at Carnegie Mellon University. She stays busy. See this for her CV.
Sixteen years ago she wrote a short position paper for Communications of the ACM titled “Computational Thinking.” It begins:
Computational thinking builds on the power and limits of computing processes, whether they are executed by a human or by a machine.
This is the first of sixteen sentences of the form “Computational thinking [verb]…,” plus one that follows a semicolon. The thirteenth prefaces six bulleted paragraphs giving characteristics of computational thinking. They fill page 3 of 3—the paper is shorter to read than this post.
Her 2007 talk on “Computational Thinking” has oodles of things that aren’t in the paper: pictures. It also concludes by posing five questions that she put into another famous CACM paper.
Five Questions
This second paper appeared in 2008 with the title “Five Deep Questions in Computing.” They are:
- P = NP?
- What is computable?
- What is intelligence?
- What is information?
- (How) can we build complex systems simply?
The first is one of the basic questions of theory, of course.
The next is about what are computations? This question complicates when one includes: DNA or quantum computers? Also what about man-machine joint computations? Given that humans and machines have different computing capability, now ask: What is computable? Pretty complex.
Let’s skip what is intelligence for now.
What is information? This is a perfect question: Information is not just 
Finally we will also skip the last question.
She ends with:
I pose these questions to stimulate deep thinking and further discussion. What deep questions about computing would you want answered? In 50 years, how different will they and their answers be from what we ask and are able to answer today?
Data Science
Wing is a leading expert of data science. This field is traceable back to 1962 when John Tukey described an area he called “data analysis”. That is Tukey of Fast Fourier transform fame, who I knew when I was at Princeton.
Data science combines the scientific method, math and statistics, specialized programming, advanced analytics, AI, and even storytelling to uncover and explain the business insights buried in data.
Her current research interests are in trustworthy AI. Her areas of research expertise include security and privacy, formal methods, programming languages, and distributed and concurrent systems. She is widely recognized for her intellectual leadership in computer science, and more recently in data science. Wing’s seminal essay, titled “Computational Thinking,” was published more than a fifteen years ago and is credited with helping to establish the centrality of computer science to problem-solving in all other disciplines.
Data can be precious for one of three reasons: the data set is expensive to collect; the data set contains a rare event (low signal-to-noise ratio); or the data set is artisanal—small, task-specific, and/or targets a limited audience.
- A good example of expensive data comes from large, one-off, expensive scientific instruments, for example, the Large Synoptic Survey Telescope, the Large Hadron Collider, and the IceCube Neutrino Detector at the South Pole.
- A good example of rare event data is data from sensors on physical infrastructure, such as bridges and tunnels; sensors produce a lot of raw data, but the disastrous event they are used to predict is (thankfully) rare. Rare data can also be expensive to collect.
- A good example of artisanal data is the tens of millions of court judgments that China has released online to the public since 2014.
Open Problems
Let’s take a cue from Wing: What other major aspects and objectives of computer science shall we put into crisp bullet points?
Information • The Early Years
Information = Comprehension × Extension
“Is logic a real thing?”
Everything on the math side of the world – also the computer science island – is based on logic … but has it a real counterpart?
In my 2017 survey of the nature of computer science, I argued that CS is best understood as investigating five central questions:
I compared these to Wing’s five questions, concluding that her five really boil down to two questions:
A. What is computation such that only some things can be computed? (And what can be computed efficiently, and how?)
B. (How) can we build physical devices to perform these computations?
Her (A) is equivalent to my 1–3, and her (B) is equivalent to my 4.
I concluded that “it is interesting and important to note that none of Wing’s questions correspond to [my] ethical question 5”, which has been the focus, among other things, of questions of bias in deep learning systems.
For details, see Rapaport, William J. (2017), “What Is Computer Science?”, American Philosophical Association Newsletter on Philosophy and Computers 16(2) (Spring): 2–22
http://www.cse.buffalo.edu/~rapaport/Papers/ComputersV16n2.pdf,
as well as Chapter 20 of my forthcoming book, Philosophy of Computer Science (Wiley).
(Apologies if this comment appears more than once 🙂
Yes, logic is a real thing. Mammalian neurons can recognize specific patterns of their inputs, which means they’re doing logic, and thus logic is a real thing.