PEDAGÓGICAS 1 Peso Corporal:

A few years after the first edition of his book, Simon, along with Newell, gave yet an- other characterization. In a classic paper from 1976, Newell and Simon updated their earlier characterization. Instead of saying that CS is thescienceof computers and al- gorithms, they now said that it is the “empirical” “studyof the phenomena surrounding computers”, “not just the hardware, butthe programmed, living machine” (Newell and Simon, 1976, pp. 113, 114; my italics).

The reason that they say that CS is not an “experimental”scienceis that it doesn’t always strictly follow the scientific (or “experimental”) method. (In§4.8, we’ll talk more about what that method is. For an opposing view that CS isan experimental science, see Plaice 1995.) CS is, like experimental sciences,empirical—because pro- grams running on computers areexperiments, though not necessarily like experiments in other experimental sciences. For example, often justoneexperiment will suffice to answer a question in CS, whereas in other sciences,numerousexperiments have to be run. Another difference between computer “science” and other experimental sci- ences is that, in CS, the chief objects of study (the computers and the progams) are not “black boxes” (Newell and Simon, 1976, p. 114); that is, most natural phenomena are things whose internal workings we cannot see directly but must infer from experiments we perform on them. But we know exactly how and why computer programs behave as they do (they are “glass boxes”, so to speak), becausewe(not nature) designed and built the computers and the programs. We can understand them in a way that we cannot understand more “natural” things.

However, although this is the case for “classical” computer programs, it is not the case for artificial-neural-network programs: “A neural network, however, was a black box” (Lewis-Kraus, 2016,§4). (We’ll return to this in§§3.12 and 18.8.2.)

Further Reading:

Rosenblueth and Wiener 1945, pp. 318–319, talk about “closed-box” and “open-box” problems, surely an early version of the notion of “black” and “glass” “boxes”. For more on the history of these terms, see https://en.wikipedia.org/wiki/Black box.

On black boxes, programs as experiments, and their relationship to knowing-how and knowing- that in the context of neural-network algorithms, see Knight 2017; Metz 2017; Mukherjee 2017, 2018:

Here is the strange rub of such a deep learning system: It learns, but it cannot tell us why it has learned; it assigns probabilities, but it cannot easily express the reasoning behind the assignment. Like a child who learns to ride a bicycle by trial and error and, asked to articulate the rules that enable bicycle riding, simply shrugs her shoulders and sails away, the algorithm looks vacantly at us when we ask, “Why?” It is, like death, another black box. (Mukherjee, 2018)

The computer scientist Joseph Weizenbaum (1976, pp. 40–41) considered this to be a fatal flaw: Indeed, we are often quite distressed when a repairman returns a machine to us with the words, “I don’t know what was wrong with it. I just jiggled it, and now it’s working fine.” He [sic] has confessed that he failed to come to understand the law of the broken machine and we infer that he cannot now know, and neither can we or anyone, the law of the “repaired” machine. If we depend on that machine, we have become servants of a law we cannot know, hence of a capricious law. And that is the source of our distress.

Recent work in cognitive neuroscience suggests that “recording from neurons at the highest stage of the visual system . . . [shows] that there’s no black box”, and that this might apply to compu- tational neural networks (Wade, 2017).

Neural-network CS has been likened to something other than “real” science, namely,alchemy! See a debate on this at https://www.reddit.com/r/MachineLearning/comments/7i1uer/n yann lecun response to ali rahimis nips lecture/. For discussion of this, see Fortnow 2018a, which includes the following joke:

Q: Why did the neural net cross the road? A: Who cares as long as it got to the other side.

For discussions of attempts to get such systems to be able to “account” for themselves, see Lipton 2016; Kuang 2017; Metz 2018.

Sometimes, a distinction is made between aprogram and aprocess: A program

might be a static piece of text or the static way that a computer is hardwired—a tex- tual or physical implementation of an algorithm. Aprocessis a dynamic entity—the program in the “process” of actually being executed by the computer.

Further Reading:

We’ll look at some of these distinctions in more detail in Chapter 12. On the program-process distinction, see Eden and Turner 2007b, §2.2; Denning 2010, p. 4; and Frailey 2010, p. 2. Manovich 2013, p. B11, uses the term ‘performance’ instead of ‘process’, “because what we are experiencing is constructed by software in real time. . . . we are engaging with the dynamic outputs of computation.”

By “programmed, living machines”, Newell & Simon meant computers that are actually running programs—not justthe static machines sitting there waiting for some- one to use them,northe static programs just sitting there on a piece of paper waiting for someone to load them into the computer, northe algorithms just sitting there in someone’s mind waiting for someone to express them in a programming language— but“processes” that are actually running on a computer.

To study “programmed living machines”, we certainly do need to study the algo- rithms that they are executing. After all, we need to know what they are doing; that is, it seems to be necessary to know what algorithm a computer is executing. On the other hand, in order to study an algorithm, it doesnotseem to be necessaryto have a computer around that can execute it or to study the computer that is running it. It can be helpful and valuable to study the computer and to study the algorithm actually being run on the computer, but the mathematical study of algorithms and their compu- tational complexity doesn’tneed the computer. That is, the algorithm can be studied as a mathematical object, using only mathematical techniques, without necessarily ex- ecuting it. It may be very much more convenient, and even useful, to have a computer handy, as Knuth notes, but it does not seem to be necessary. If that’s so, then it would seem thatalgorithmsare really the essential object of study of CS: Both views require algorithms, but only one requires computers.

But is it really the case that you cannot study computers without studying algo- rithms? Compare the study of computers with neuroscience: the study of brains and the nervous system. Although neuroscience studies both the anatomy of the brain (its static, physical structure) and its physiology (its dynamic activity), it generally treats the brain as a “black box”: Its parts are typically named or described, not in terms of what theydo(theirfunction), but in terms ofwhere they are located(theirstructure).

Further Reading:On the function-structure distinction, see Bechtel and Abrahamsen 2005,§3.

For example, the “frontal lobe” is so-called because it is in thefrontof the brain; its

functionsinclude memory, planning, and motivation. The “temporal lobe” is so-called because it is near thetempleson your head; itsfunctionsinclude processing sensory input. And the “occipital lobe” is so-called because it is near the occipital bone (itself so-called because it is “against” (ob-) the head (caput)); its functionsinclude visual processing.

It is as if a person from the 19th century found what we know to be a laptop com- puter lying in the desert and tried to figure out what it was, how it worked, and what it did, with no documentation.

Further Reading:

See Weizenbaum 1976, Ch. 5, for the source of this kind of thought experiment. “. . . Stone- henge, the world’s largest undocumented computer” (Brooks, 1975, p. 163) and the Antikythera Mechanism (§6.5.1) are real-life examples.

They might identify certain physical features: a keyboard, a screen, internal wiring (and, if they were from the 19th century, they might describe these as buttons, glass,

and strings), and so on. More likely, they would describe the device as we do the brain, in terms of thelocationsof the parts: an array of button-like objects on the lower half, a glass rectangle on the upper half, and so on.

But without knowledge of what the entire system and each of its parts was sup- posed todo—what theirfunctionswere—they would be stymied. Yet this seems to be what neuroscientists study. Of course, modern neuroscience, especially moderncog- nitiveneuroscience, well understands that it cannot fully understand the brain without understanding its processing (its algorithms, if indeed it executes algorithms) (Dennett, 2017, p. 341). Only recently have new maps of the brain begun to identify its regions

functionally, that is, in terms of what the regions do, rather than where they are located (Zimmer, 2016). But this is a topic for another branch of philosophy: the philosophy of cognitive science.

Further Reading:

On the philosophy ofcognitivescience, relevant readings include Fodor 1968; Gazzaniga 2010; Piccinini 2010a; Rapaport 2012b.

So it seems to be necessary to study algorithms in order to fully understand computers.

Further Reading on Whether CS Is a Science or Not:

Kukla 1989 argues that at least one branch of CS—Artificial Intelligence—is not an empirical science, but ana prioriscience or discipline like mathematics. For the opposite point of view, see Burkholder 1999. Cerf 2012b argues that, even if CS might once have focused on computing machines, it should now be more focused on “predict[ing] likely outcomes based on models[, which] is fundamental to the most central notions of the scientific method”. Hsu 2013 argues that “there are no clear boundaries” between branches of knowledge. Tedre and Moisseinen 2014 is a survey of the nature of experiments in science, and whether CS is experimental in nature. Tedre 2015 is an investigation of the philosophical issues around the nature and history of computer science, examining whether it is a science, and, if so, what kind of science it might be. See also Denning 1980; Naur 1995; Feitelson 2007; Abrahams and Lee 2013; Ensmenger 2011b