Tuesday 2/4

The History of Artificial Intelligence





Our assumption this semester is that we are meat machines busily flapping our meat at one another. Understanding how meat machines might be able to reason, however, requires a brief foray into the development of contemporary science, starting with the Copernican Revolution.

That is, we began today by briefly reviewing the thread of argument I've been laying out that begins with Plato and ends with Machine Functionalism. Yet there is another thread altogether from the history of mathematics that begins with the Copernican Revolution and ends with a proposal complementary to Machine Functionalism, Computationalism, which we might also call the Fundamental Hypothesis of Traditional Artificial Intelligence:

Thought consists of the rule-governed manipulation of (strings of) symbols.

Haugeland, "Artificial Intelligence: The Very Idea", does an excellent job explaining the history of this idea, including i) Galileo's astonishing intellectual leap in recognizing that the lines, points, and planes of Euclidian Geometry can be reinterpreted to be, not about spatial relations (viz., earth measure), but about other kinds of magnitudes including velocity and acceleration, ii) Descartes' brilliant synthesis of geometry and algebra in analytic geometry to show how systems of equations and operations on them can be variously interpreted depending on the domain of inquiry, and iii) Hobbes' proclamation that "By ratiocination, I mean computation." That is,

When a man reasoneth, hee does nothing else but conceive a summe totall, from Addition of parcles; or conceive a remainder, from Subtraction of one summe from another: which (if it be done by Words) is conceiving of the consequence of the names of all the parts, to the name of the whole; or from the names of the whole and one part, to the name of the other part... Out of which we may define (that is to say determine,) what that is, which is meant by this word Reason, when we reckon it amongst the Faculties of the mind. For Reason, in this sense, is nothing but Reckoning (that is, Adding and Subtracting) of the Consequences of generall names agreed upon, for the marking and signifying of our thoughts; I say marking them, when we reckon by ourselves; and signifying, when we demonstrate, or approve our reckonings to other men.

--Thomas Hobbes, Leviathan, 1651. (Quoted in Tim Crane, "The Mechanical Mind", p. 130.)

The Fundamental Hypothesis of Traditional Artificial Intelligence, however, gives rise to what Haugeland calls "the Paradox of Mechanical Reason":

Reasoning (on the computational model) is the manipulation of meaningful symbols according to rational rules (in an integrated system). Hence there must be some sort of manipulator to carry out those manipulations. There seem to be two basic possibilities: either the manipulator pays attention to what the symbols and rules mean or it doesn't. If it does pay attention to the meanings, then it can't be entirely mechanical--because meanings (whatever exactly they are) don't exert physical forces. On the other hand, if the manipulator does not pay attention tot he meanings, then the manipulations can't be instances of reasoning--because what's reasonable or not depends crucially on what the symbols mean.

In a word, if a process or system is mechanical, it can't reason; if it reasons, it can't be mechanical.

--John Haugeland, "Artificial Intelligence: The Very Idea", p. 39.

To fully understand the Paradox of Mechanical Reason, however, we need to understand mechanism and we need to have some account of intelligence. For both these problems, we turn to none other than Alan Turing, who did more than anyone to frame the issues.

Recall Dretske's Dictum:

You don't understand it if you don't know how to build it.

If our puzzle is whether we can indeed build something that has a mind (the dream of AI!), then out of what parts shall we build it, and how will we know when we've succeeded?

This is the question Alan Turing set out solve in his justly famous essay, "Computing Machinery and Intelligence". Next time we explore Turing's solution so as to lay the groundwork for understanding Turing's remarkable contributions to computability theory.