Sciences of the Artificial (The Sciences of the Artificial) Paperback – 31 Oct 1996
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-- George A. Miller, "Complex Information Processing"
" People sometimes ask me what they should read to find out about artificial intelligence. Herbert Simon's book The Sciences of the Artificial is always on the list I give them. Every page issues a challenge to conventional thinking, and the layman who digests it well will certainly understand what the field of artificial intelligence hopes to accomplish. I recommend it in the same spirit that I recommend Freud to people who ask about psychoanalysis, or Piaget to those who ask about child psychology: If you want to learn about a subject, start by reading its founding fathers." -- George A. Miller, "Complex Information Processing"
& quot; People sometimes ask me what they should read to find out about artificial intelligence. Herbert Simon's book The Sciences of the Artificial is always on the list I give them. Every page issues a challenge to conventional thinking, and the layman who digests it well will certainly understand what the field of artificial intelligence hopes to accomplish. I recommend it in the same spirit that I recommend Freud to people who ask about psychoanalysis, or Piaget to those who ask about child psychology: If you want to learn about a subject, start by reading its founding fathers.& quot; -- George A. Miller, Complex Information Processing
"People sometimes ask me what they should read to find out about artificial intelligence. Herbert Simon's book The Sciences of the Artificial is always on the list I give them. Every page issues a challenge to conventional thinking, and the layman who digests it well will certainly understand what the field of artificial intelligence hopes to accomplish. I recommend it in the same spirit that I recommend Freud to people who ask about psychoanalysis, or Piaget to those who ask about child psychology: If you want to learn about a subject, start by reading its founding fathers."--George A. Miller, "Complex Information Processing"
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As the Earth has made more than 5,000 rotations since The Sciences of the Artificial was last revised, in 1981, it is time to ask what changes in our understanding of the world call for changes in the text.See all Product description
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It gave you so many provoking thoughts even before the big data era.
Ok my general impression about the book: I think historically it's a groundbreaking book; it's a book written by a visionary; it's a book that at the time must have challenged a lot of people's opinions on a lot of things; in short it's an extremely important book! Having said that, here one needs to ask the all important question, reviewing it as one is, after a gap of more than 40 years since it was first published: Overall, has the book stood the test of time?
The answer, surprisingly, is: `Yes' and `No'! Some of its insights are still very relevant, while some others are pretty outdated (which makes one wonder why Simon in later editions did not feel the need to say at least a few words about where he had gone wrong, and where he had over-simplified things to an astonishing degree).
But before talking about both the great and not so great parts, let me briefly sketch the central idea that Simon has delineated in this book, which in fact drives the entire book. Simply, it can be described as the importance of concentrating upon the interface of a system with its outer and inner environments, without having to understand in detail either the inner or outer environments. In Simon's words, "We might look toward a science of the artificial that would depend on the relative simplicity of the interface as its primary source of abstraction and generality".
Let's start with the parts that he got right. Well, first off, "Bounded rationality" of course. Simon states that the concept of bounded rationality was used by economists in some domains, even in his day (though he did coin this specific term). But he shows it quite clearly, without being antagonistic, through many examples that the concept of perfect rationality is incorrect, not only in reality (which everyone including its saner proponents accept) but also for practical purposes. Its not even good enough for practice, Simon argues persuasively. It is better to view people as bounded rational agents who adapt and satisfice rather than as perfectly rational agents who can optimize and possess an unrealistic degree of information and computational ability.
Simon also says something extremely important that people tend to often forget. He highlights the fact that the debate between markets and hierarchical organizations often misses a very important empirical fact: "Roughly eighty percent of the human economic activity in the American economy, usually regarded as almost the epitome of a "market" economy, takes place in the internal environments of business and other organizations and not in the external, between-organization environments of markets". (For other, even more "shocking" facts about `free' markets, I would refer the reader to Chomsky's writings). What Simon says about organizations, about centrally planned systems utilizing markets and vice versa, is enlightening to read and merits close attention!
Ok, now onto the bad parts. These are ironically the parts for which I know Herbert Simon best: Artificial Intelligence. And it is here that Simon gets it quite wrong; his vision quite flawed and again, I would say, it is quite strange that he didn't deem it fit to acknowledge his mistakes in later editions.
Simon gets the abstraction terribly wrong in his ideas about the human mind. The same idea of artificial systems having simple interfaces, that works (or can work) in the sphere of human economic activity, at that particular level of abstraction, simply cannot and doesn't work when applied to the creative use of the human mind. As Simon says in this book, (and as others from the group of `cognitive revolutionaries' Chomsky, Miller et al would also say), the mind can be represented as an information processing system. But in line with the theme of the book, he feels that the mechanisms inside this information processing system are simple adaptive rules. His example of an ant finding its way back home is indicative of how extremely wrong he went with this kind of thinking. I quote:
"In the case of the ant (and for that matter the others) we know the answer. He has a general sense of where home lies, but he cannot foresee all the obstacles between. He must adapt his course repeatedly to the difficulties he encounters and often detour uncrossable barriers. His horizons are very close, so that he deals with each obstacle as he comes to it; he probes for ways around or over it, without much thought for future obstacles. It is easy to trap him into deep detours. Viewed as a geometric figure, the ant's path is irregular, complex, hard to describe. But its complexity is really a complexity in the surface of the beach, not a complexity in the ant" Say what!?
Let me just state, as a matter of FACT, how ants (or other insects) do path integration is still not clear, to this very day. To make a very long point short, so that its clear to the reader where Simon gets it wrong: Even if there was no beach, even if the path to home ended up tracing a straight line, even then there would be much complexity inside the ant. The complexity of the beach pales in comparison to the complexity of what is going on inside the ant. How is it that it integrates such different cues as sun position, leg movement etc, etc, is still unknown. This is where, I feel, as far as AI is concerned, Simon's whole abstraction, his central idea, his edifice (of artificial systems) falls apart.
How could a visionary like Simon go so awfully wrong? I think the answer is pretty simple. I believe it stems from the naïve hopes that he (and others) had for AI, namely, simple adaptive mechanisms could give rise to "intelligence". I feel that what he said on this subject stemmed from his exuberance for AI, which can be described thus: "If the mind is an artificial system with simple adaptive rules, then we shall soon invent "intelligent" machines as well. It's only a matter of time people!"
So in short, I think it was his excitement about the birth of AI, which led him to his mistakes. Like many early ambitious AI theorists, he probably must have felt that "intelligent" machines were just around the corner.
Having said that, is Simon to be blamed alone? Here's what one of my favorite scientists, the granddaddy of computing, Alan Turing says in his famous paper: "Instead of trying to produce a programme to simulate the adult mind, why not rather try to produce one which simulates the child's? If this were then subjected to an appropriate course of education one would obtain the adult brain. Presumably the child brain is something like a notebook as one buys it from the stationer's. Rather little mechanism, and lots of blank sheets. (Mechanism and writing are from our point of view almost synonymous.) Our hope is that there is so little mechanism in the child brain that something like it can be easily programmed"
So we see that it is Turing who should be "blamed" for the early naive exuberance for AI. He hopes that a child's mind would be more or less a blank slate with some basic, simple mechanism. (Remember, it has to be simple so that we can program it quickly, and get our "intelligent" machine ready before Christmas ). Of course now we know better. By now it's understood that even for very simple biological traits it's "fiendishly difficult," to quote a recent advanced text, to discover the genetic basis.
Coming back to the book, all in all, it is a book that I would recommend highly, primarily for its historical importance, but also for the many insights that are still relevant today! In the end I would use Simon's words (and overall sentiments) that he used to describe rational action economists, to describe his own book: Heroic but flawed!
Questions for Inquiring Minds: forty-five years after its initial publication, how many books can be found on Amazon that address “design science” or the like, especially in the sense that Simon laboriously enunciated? Ok then, what actual impact has Simon’s version of design science in itself ever had on engineering or design practice? Did actual engineering practitioners or experienced designers in general ever regard this book as consequential or relevant?
Fundamentally, Simon construes design as amenable to casting as a science per se, rather than as an endeavor wherein many of the more challenging aspects are typically dealt with largely as an art. That design is informed or facilitated by science is vacuously true, not to mention irrelevant. Moreover, engineers/designers have developed much of that sort of science, because they are resourceful in finding better ways to fashion improved products. Disconcertingly, Simon’s thesis begins with the premise of design as problem solving, rather than one mainly of resolving problem situations by first systematically formulating problem statements. Furthermore, design problem discovery proceeds well beyond design per se – at least into development testing. The lesson: finalized well-formed problem statements exist only in textbooks or in classrooms.
To expand on the book’s critique in the context of the exigencies of the real world of design,
1. the only design-oriented engineering author cited by Simon is Clive Dym (p. 128)
- Dym otherwise states “as grounds for serious study, the art of engineering has lain fallow...To recognize that there is an art to engineering design does not preclude design from being worthy of serious scientific study.” (p.185 of “Engineering Design – A Synthesis of Views”)
2. the conceivability of a science of design (chapters 5 & 6 ) is dubious given the vital role of the practitioner art component typical of customary practice
- Turing Award recipient Frederick Brooks has written “I believe a ‘science of design’ to be impossible” (p. xii of “The Design of Design – Essays from a Computer Scientist”)
3. that technical rationality inherent in the science of design can serve as a practical basis for design methodology (chapter 5 ) is widely discounted
- Donald Schon counsels “Let us search, instead, for an epistemology of practice which some practitioners do bring to (design challenges)” (p. 49 of “The Reflective Practitioner- How Professionals Think in Action”)
4. that design problems are givens readily available as design requirements (e.g., p. 115) for immediate search for design alternatives (p. 121) from which to select, is wholly unrealistic
- Donald Schon points out that “with this emphasis on problem solving, we ignore problem setting...In real world practice, problems do not present themselves...as givens” (p.40)
5. even worse, Simon’s advocacy of a science-based methodology (Chapter 5 ) is questionable, especially as reliant upon an encompassing automated search/optimization process
- Christopher Alexander states “the search for the image or criterion for success is going on at the same time as the search for a solution” (p. 197 of “Notes on the Synthesis of Form”)
- Frederick Brooks observes that “as one ponders the tradeoffs, there comes a new understanding of the whole design problem as an...interplay of factors (that yields) ...a change in the weightings of the desiderata” (p. 26)
6. the value of formal logic for development (p. 115 ) is neither uncommon nor a panacea, but its use may be misleading
- Christopher Alexander notes that “however rational we should like to be...Logical methods, at best, rearrange the way in which our personal bias is to be introduced” (p.194)
7. as a response to Item 1 above, Ozgur Eris’ “Effective Inquiry for Innovative Engineering Design” is a thoughtful, systematic, and admirable exploration of design practice
- Eris’ thesis claims “the uniqueness of design thinking by identifying a specific class of questions that are characteristic of design situations.” (p. 1)
To elaborate on the fourth bullet above, in engineering development endeavors, there are typically three partly trial-and-error steps leading up to the codification of design requirements, or problem definition per se:
1, problem situation – exploration, bounding & characterization of the problem in context
2. problem setting – determination of the programmatic goals, strategies, resources, etc., for project definition/go-ahead
3. problem framing – delineation of the essential technical issues and implications to be addressed, along with reservations and success criteria
4. problem specification – particular requirements that the design effort is committed to satisfy and verify.
Upon the completion of Step 4, one then has a design problem statement in hand, albeit one subject to refinement as design proceeds. The good news is that much of the more problematic work has been accomplished at this point. Moreover, if a (hypothetical) design problem is reduced to algorithmic resolution, then there exists a relatively tractable design task to deal with, provided the algorithms’ (largely subjective) parameters remain fixed. After all, optimization algorithms per se are rather straightforward; it’s their subjective application that is highly problematic.
Although Simon had presented the notion previously, his characterization of bounded rationality here is both cogent and useful. Somewhat surprisingly though, he then attempts to overcome this phenomenon by articulating his automated design alternative via a generation-selection-optimization strategy. This technical rationality synthesis stands in complete disregard of the essential nature, context, and conduct of design. Simon’s design strategy is accordingly at best an idealization; but in my estimation, not at all a helpful or viable one. Nonetheless, bounded rationality is an important concept, and its clear explication and fruitful development may be found in Gerd Gigerenzer’s “Bounded Rationality”. In acknowledging the realities of decision making as vital to matters besides design, Gigerenzer develops the companion notion of “quasi-rationality”. As the term suggests, it obtains from an interplay of analysis and intuition, as characteristic of many human cognitive tasks. Quasi-rationality, moreover, is the basis on which designers naturally operate.
In sum, Simon’s design science and technical rationality are idealized notions resiliently contrary to successful design practice. Moreover, practical design automation itself has been introduced from at least the 1960s and applied ever more expansively. This has been done largely on the initiatives of engineering practitioners themselves. In contrast, Simon’s skewed and inordinate vision of design automation simply fails to apprehend the multifaceted nature of design and the flexible performance demanded of designers. Furthermore, Simon’s crucial expectations for operations research and artificial intelligence technologies have since this book was first published been quite notably compromised in terms of delivered results. In all then, what in tenuous principle might be done per this book’s vision, through expansive automation under ideal conditions, is unworthy of serious consideration, and especially so now in hindsight.
Even if Simon’s vision were realized, it would merely shift much of the presently perceived complexity would be shifted elsewhere in the development process, and the overall process implementation would be rendered even more complex and probably less responsive. (See Nicholas Rescher’s commentary on the inevitability of complexity escalation via the introduction of technology that appears in “Complexity: A Philosophical Overview.”) Arguably, Simon’s proposal for design science automation will likely remain unworthy of consideration, if only because of the staggering complexity concentration, flexibility/fragility issues, and development cost/time entailed in its workable implementation for real-world deployment.
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