A paper I recently linked
-- "Philosophy and Cognitive Science" by Serge Sharoff
-- might be useful in discussing the issues raised concerning the Tegmark paper. Here is an extract:
"The history of artificial intelligence began with efforts to create thinking machines designed to cover the widest possible domain of human intellectual activity, and the declared objective of such program development was to exceed usual human reasoning, at least in certain fields. Programming served as a basis for these investigations. In practical terms, the relative failure of these early attempts--the discrepancy between their announced intentions and their real successes--has led to the creation of effective programs operating successfully in carefully constrained problem domains. Conceptually, the goal now of theoretical research in AI is to investigate the human mind, an end to which both psychological research and pure programming tricks have been put. But many AI problems also have a direct relationship to old philosophical problems: mental category determination, hermeneutic circle problems, the balance between empirical and
a priori knowledge, the interrelations between abstract and specific knowledge, and so on. If we look at theoretical investigations in AI, we discover that they are always based on some philosophical background; this framework helps in large part to determine the structure of AI models as they are elaborated.
Dreyfus and Dreyfus offer one attempt to trace the links between classic philosophy and cognitive science and to interpret the former from the standpoint of the latter. As they describe the history of European philosophy, they identify a sequence of AI predecessors: Plato, Galileo, Descartes, Leibniz, Kant, and Husserl. For example, they write:
Kant had a new idea as to how the mind worked. He held that all concepts were really rules. For example, the concept for dog is something like the rule: If it has four legs, barks, and wags its tail, then it's a dog.... Husserl, who can be regarded as the father of the information-processing model of the mind, [extended Kant's ideas and]
argued that concepts were hierarchies of rules, rules which contained other rules under them. For example, the rule for recognizing dogs contained a subrule for recognizing tails. Husserl also saw that such rules would have to tell us not about any particular dog, or dogs in general, but about the typical dog. All the basic ideas used by Minsky and his students of artificial intelligence were in place.2
But even though many of the basic ideas of AI may be in place in classic philosophy, AI researchers must actively develop the particular philosophical systems they use: they must clarify obscure propositions and
develop many lines of inquiry left out of the "frame" of the original philosophical systems. For example, in "On the Art of Combinations"(1666), Leibniz proposed that all reasoning can be reduced to an ordered combination of elements. If we could define such an algebra of thought, it would become possible for a machine to reason, like clockwork. Such a machine would be capable of resolving every philosophical controversy, as well as making discoveries by itself. Leibniz's thesis amounts to a theory of artificial intelligence for the seventeenth century. However, Leibniz did not have to develop many concrete questions about the correlation between his elements, about the problems of their sufficiency, or about ensuring right outcomes from right premises; that is, he never had to
debug his program. The "General Problem Solver" (GPS) developed mainly by Allen Newell and Herbert Simon is one of the earliest and most general approaches in cognitive science. The GPS-style description of reasoning (in terms of simple algebraic symbols and operations that combine these symbols into expressions) directly follows from Leibniz's thoughts and "debugs" them. As far as I know, developers of AI systems have never emphasized just how much their work relies upon and develops related philosophical theories. So, for example, the discussion about interrelations between GPS representations and Leibniz's "combinations" is rather suggestive--and unusual.
a realization of philosophy
Another example from classic philosophy can serve as a metaphor for the interpretation of AI investigations as philosophy. Drawing on the distinction between the thing-in-itself and the phenomenon it presents to us, Kant wrote in his
Critique of Pure Reason:
I cannot explore
my soul as a thing-in-itself by means of theoretical reasoning (still less by means of empirical observation); hence, I cannot explore free will as a feature of a being.... Nevertheless, I can think
about freedom, that is, the representation of it is at least without contradictions.
3
To shift this Kantian example into the domain of AI: researchers, as conscious beings, probably cannot
create artificial consciousness, but they can think about their own consciousness and express their thoughts in some language--in the language of philosophical concepts (in Kant's case), or in a programming language (in the case of AI researchers).
In order to develop cognitive science as rigorous philosophy, it is necessary to adopt the premise that a description of states of consciousness as representational states can be consistent.
4 States of consciousness themselves, along with skills, emotions, and so forth, are not representations in themselves and do not belong to the realm of language; however, the fact that these states may find expression in verbal forms demonstrates that some kind of symbolic representation is possible. Moreover, states of consciousness have an inherent need for some kind of expression in order to be grasped, and language is
the medium for symbolizing internal states. Schütz refers to this process as explication.
5 Admittedly, explication is possible only for some part of consciousness, and it cannot be done to "absolute zero," to the
nth degree. But interpreting situations is one of the main activities of consciousness, and explaining them through language is a necessary way of socializing and expanding the conscious "stock of knowledge." Schütz uses the phrase "taken for granted" to describe the seemingly natural attitude one adopts in everyday life towards phenomena such as the characteristics of the world and of other conscious beings. In fact, what this "natural" attitude takes for granted is precisely the possibility of describing consciousness. We may recall a quotation from Pascal that Dreyfus and Dreyfus use as the title for their book's prologue: "The heart has its reasons that reason does not know." Undoubtedly, there is a reason why the European philosophical tradition has for so long attempted to explicate the processes of consciousness. There is no reason to declare this attempt no longer valid.
basic concepts for computer phenomenology
Many of the primary phenomenological ideas of Husserl and the early Heidegger lend themselves to interpretation from the viewpoint of cognitive science: notions of the phenomenon, the constitution of meaning, readiness-to-hand (
Zuhanden), intentionality, horizon, and internal time-consciousness.
For the purposes of this article, phenomenology may be described as the philosophy of dynamic representations. In
Truth and Method, Hans-Georg Gadamer cites Schleiemacher's words as a slogan for this philosophy: "Blooming is the real maturity. A ripe fruit is only a chaotic surface that does not belong to the organic plant." The purpose of phenomenological description is to probe the thinking life hidden within us:
In contrast to an analytic philosophy that substitutes simplified constructions for the immediately given in all of its complexity and applies 'Ockham's razor,' phenomenology resists all transforming reinterpretations of the given, analyzing it for what it is in itself and on its own terms.
6
Phenomenology's key concept is the notion of constitution, a description of the creative dynamics of the phenomena of consciousness. As Husserl wrote, "it is necessary to show in each concrete constituting act how the sense of the phenomenon is being created."
7 Phenomenology uses a complex description of the phenomenon as "that which shows its selfness through itself." For our purpose--that of describing a computer phenomenology--it is sufficient to consider a phenomenon as a mental construct that is placed in consciousness, complies with other phenomena, and has the ability to reveal itself.
Husserl's methodological solipsism corresponds closely to the nature of computer representations. His descriptions deal exclusively with subjective phenomena. The external world is taken out of brackets; as Husserl says,
epoché is committed. A mental act, as phenomenology describes it, is concerned not with material things but with itself. Husserl uses the notion of intentionality, the direction of consciousness toward a perceived object, to describe the interaction between consciousness and objects in the external world. Through intentionality, consciousness comes to represent the object as a phenomenon.
Intentionality expresses the fundamental feature of consciousness: it is always
consciousness about something. Consciousness is not an abstract mechanism that processes raw data; its
core structure correlates with and, therefore, depends on grasped phenomena. This ensures the impossibility of a description of consciousness which is separate from perceived objects.
Husserl wrote:
In all pure psychic experiences (in perceiving something, judging about something, willing something, enjoying something, hoping for something, etc.) there is found inherently a being-directed-toward.... Experiences are intentional. This being-directed-toward is not just joined to the experience by way of a mere addition, and occasionally as an accidental reaction, as if experiences could be what they are without the intentional relation. With the intentionality of the experiences there announces itself, rather, the essential structure of [the] purely psychical.
8 . . . . .
Phenomenology and Cognitive Science