“We need to do away with the myth that computer science is about computers. Computer science is no more about computers than astronomy is about telescopes, biology is about microscopes or chemistry is about beakers and test tubes. Science is not about tools, it is about how we use them and what we find out when we do.”
— “SIGACT trying to get children excited about CS,”
by Michael R. Fellows and Ian Parberry
There have been many times where I’ve thought about writing about this over the years, but I’ve felt like I’m not sure what I’m doing yet, or what I’m learning. So I’ve held off. I feel like I’ve reached a point now where some things have become clear, and I can finally talk about them coherently.
I’d like to explain some things that I doubt most CS students are going to learn in school, but they should, drawing from my own experience.
First, some notions about science.
Science is not just a body of knowledge (though that is an important part of it). It is also venturing into the unknown, and developing the idea within yourself about what it means to really know something, and to understand that knowing something doesn’t mean you know the truth. It means you have a pretty good idea of what the reality of what you are studying is like. You are able to create a description that somehow resembles the reality of what you’re studying to such a degree that you are able to make predictions about it, and those predictions can be confirmed within some boundaries that are either known, or can be discovered, and subsequently confirmed by others without the need to revise the model. Even then, what you have is not “the truth.” What you have might be a pretty good idea for the time being. As Richard Feynman said, in science you never really know if you are right. All you can be certain of is that you are wrong. Being wrong can be very valuable, because you can learn the limits of your common sense perceptions, and knowing that, direct your efforts towards what is more accurate, what is closer to reality.
The field of computing and computer science doesn’t have this perspective. It is not science. My CS professors used to admit this openly, but my understanding is CS professors these days aren’t even aware of this distinction anymore. In other words, they don’t know what science is, but they presume that what they practice is science. CS, as it’s taught, has some value, but what I hope to introduce people to here is the notion that after they have graduated with a CS degree, they are in kindergarten, or perhaps first grade. They have learned some basics about how to read and write, but they have not learned what is really powerful about that skill. I will share some knowledge I have gleaned as someone who has ventured as a beginner in this perspective.
The first thing to understand is that an undergraduate computer science education doesn’t tend to give you any notions about what computing is. It doesn’t explore models of computing, and in many cases, it doesn’t even present much in the way of different models of programming. It presents one model of computing (the Von Neumann model), but it doesn’t venture deeply into concepts of how the model works. It provides some descriptions of entities that are supposed to make it work, but it doesn’t tie them together so that you can see the relationships; see how the thing really works.
Science is about challenging and forming models of phenomena. A more powerful notion of computing is realized by understanding that the point of computer science is not about the application of computing to problems, but about creating, criticizing, and studying the strengths and weaknesses of different models of computing systems, and that programming is a means of modeling our ideas about them. In other words, programming languages and development environments can be used to model, explore, and test, and criticize our notions about different computing system models. It behooves the computer scientist to either choose an existing computing architecture represented by an existing language, or to create a new architecture, and a new language, that is suitable to what is being attempted, so that the focus can be maintained on what is being modeled, and testing that model without a lot of distraction.
As Alan Kay has said, the “language” part of “programming language” is really a user interface. I suggest that it is a user interface to a model of computing, a computing architecture. We can call it a programming model. As such, it presents a simulated environment of what is actually being accomplished in real terms, which enables a certain way of seeing the model you are trying to construct. (As I said earlier, we can use programming (with a language) to model our notions of computing.) In other words, you can get out of dealing with the guts of a certain computing architecture, because the runtime is handling those details for you. You’re just dealing with the interface to it. In this case, you’re using, quite explicitly, the architecture embodied in the runtime, not an API, as a means to an end. The power doesn’t lie in an API. The power you’re seeking to exploit is the architecture represented by the language runtime (a computing model).
This may sound like a confusing round-about, but what I’m saying is you can use a model of computing as a means for defining a new model of computing, whatever best suits the task. I propose that, indeed, that’s what we should be doing in computer science, if we’re not working directly with hardware.
When I talk about modeling computing systems, given their complexity, I’ve come to understand that constructing them is a gradual process of building up more succinct expressions of meaning for complex processes, with a goal of maintaining flexibility, in terms of how sub-processes that are used for higher levels of meaning can be used. As a beginner, I’ve found that starting with a powerful programming language in a minimalist configuration, with just basic primitives, was very constructive for understanding this, because it forced me to venture into constructing my own means for expressing what I want to model, and to understand what is involved in doing that. I’ve been using a version of Lisp for this purpose, and it’s been a very rewarding experience, but other students may have other preferences. I don’t mean to prejudice anyone toward one programming model. I recommend finding, or creating one that fits the kind of modeling you are pursuing.
Along with this perspective, I think, there must come an understanding of what different programming languages are really doing for you, in their fundamental architecture. What are the fundamental types in the language, and for what are they best suited? What are the constructs and facilities in the language, and what do they facilitate, in real terms? Sure, you can use them in all sorts of different ways, but to what do they lend themselves most easily? What are the fundamental functions in the runtime architecture that are experienced in the use of different features in the language? And finally, how are these things useful to you in creating the model you want to attempt? Seeing programming languages in this way provides a whole new perspective on them that has you evaluating how they facilitate your modeling practice.
Application comes along eventually, but it comes late in the process. The real point is to create the supporting architecture, and any necessary operating systems first. That’s the “hypothesis” in the science you are pursuing. Applications are tests of that hypothesis, to see if what is predicted by its creators actually bears out. The point is to develop hypotheses of computing systems so that their predictive limits can be ascertained, if they are not falsified. People will ask, “Where does practical application come in?” For the computer scientist involved in this process, it doesn’t, really. Engineers can plumb the knowledge base that’s generated through this process to develop ideas for better system software, software development systems, and application environments to deploy. However, computer science should not be in service to that goal. It can merely be useful toward it, if engineers see that it is fit to do so. The point is to explore, test, discuss, criticize, and strive to know.
These are just some preliminary thoughts on computer systems modeling. In my research, I’ve gotten indications that it’s not healthy for the discipline to be insular, just looking at its own artifacts for inspiration for new ideas. It needs to look at other fields of study to introduce unorthodox models into the discussion. I haven’t gotten into that yet. I’m still trying to understand some basic ideas of a computer system, using a more mathematical approach.
Does computer science have a future?
The necessary ingredients for computer science
— Mark Miller, https://tekkie.wordpress.com