Key Arguments

• Digital computers can only operate on data that are "discrete, explicit, and determinate; otherwise, it will not be the sort of information which can be given to the computer so as to be processed by rule," yet there is no reason to believe such data about the human world are actually available or even exist.
• AI researchers rely on the ontological assumption to overlook the problem that the world may not be naturally given in terms of discrete elements suitable for symbolic encoding.
• Like the epistemological assumption, this ontological assumption is taken as self‑evident due to "two thousand years of philosophical tradition reinforced by a misinterpretation of the success of the physical sciences," rather than being explicitly argued for.
• Once the assumption is made explicit, "no arguments have been brought forward in its defense," revealing that it functions as an unexamined axiom rather than a justified thesis.
• When the ontological assumption is used as the basis for a theory of practice such as AI, "the ontological assumption leads to profound conceptual difficulties," including the large data base problem and the inability to account for context‑dependence and implicit understanding.

Source Quotes

And we have seen that the biological, psychological, and epistemological assumptions which allow workers to view these difficulties as temporary are totally unjustified and may well be untenable. Now we turn to an even more fundamental difficulty facing those who hope to use digital computers to produce artificial intelligence: the data with which the computer must operate if it is to perceive, speak, and in general behave intelligently, must be discrete, explicit, and determinate; otherwise, it will not be the sort of information which can be given to the computer so as to be processed by rule. Yet there is no reason to suppose that such data about the human world are available to the computer and several reasons to suggest that no such data exist.

Now we turn to an even more fundamental difficulty facing those who hope to use digital computers to produce artificial intelligence: the data with which the computer must operate if it is to perceive, speak, and in general behave intelligently, must be discrete, explicit, and determinate; otherwise, it will not be the sort of information which can be given to the computer so as to be processed by rule. Yet there is no reason to suppose that such data about the human world are available to the computer and several reasons to suggest that no such data exist. The ontological assumption that everything essential to intelligent behavior must in principle be understandable in terms of a set of determinate independent elements allows AI researchers to overlook this prob- lem.

Yet there is no reason to suppose that such data about the human world are available to the computer and several reasons to suggest that no such data exist. The ontological assumption that everything essential to intelligent behavior must in principle be understandable in terms of a set of determinate independent elements allows AI researchers to overlook this prob- lem. We shall soon see that this assumption lies at the basis of all thinking in AI, and that it can seem so self-evident that it is never made explicit or questioned.

We shall soon see that this assumption lies at the basis of all thinking in AI, and that it can seem so self-evident that it is never made explicit or questioned. As in the case of the epistemological assumption, we shall see that this conviction concerning the indubitability of what in fact is only an hypothesis reflects two thousand years of philosophical tradition reinforced by a misinterpretation of the success of the physical sciences. Once this hypothesis is made explicit and called into question, it turns out that no arguments have been brought forward in its defense and that, when used as the basis for a theory of practice such as AI, the ontological assumption leads to profound conceptual difficulties.

As in the case of the epistemological assumption, we shall see that this conviction concerning the indubitability of what in fact is only an hypothesis reflects two thousand years of philosophical tradition reinforced by a misinterpretation of the success of the physical sciences. Once this hypothesis is made explicit and called into question, it turns out that no arguments have been brought forward in its defense and that, when used as the basis for a theory of practice such as AI, the ontological assumption leads to profound conceptual difficulties. In his introduction to Semantic Information Processing, Minsky warns against the dreadfully misleading set of concepts that people get when they are told (with the best intentions) that computers are nothing but assemblies of flip-flops; that their programs are really nothing but sequences of operations upon binary numbers, and so on.

Key Concepts

• the data with which the computer must operate if it is to perceive, speak, and in general behave intelligently, must be discrete, explicit, and determinate; otherwise, it will not be the sort of information which can be given to the computer so as to be processed by rule.
• Yet there is no reason to suppose that such data about the human world are available to the computer and several reasons to suggest that no such data exist.
• The ontological assumption that everything essential to intelligent behavior must in principle be understandable in terms of a set of determinate independent elements allows AI researchers to overlook this prob- lem.
• this conviction concerning the indubitability of what in fact is only an hypothesis reflects two thousand years of philosophical tradition reinforced by a misinterpretation of the success of the physical sciences.
• it turns out that no arguments have been brought forward in its defense and that, when used as the basis for a theory of practice such as AI, the ontological assumption leads to profound conceptual difficulties.

Context

Dreyfus opens the section 'The Ontological Assumption' by defining what he means by the ontological assumption in AI and arguing that it is both foundational for AI work and philosophically unsupported.