In Defense of Strong AI

Semantics as Second-Order Rules

Abstract

This paper argues against John Searle in defense of the potential for computers to understand language (“Strong AI”) by showing that semantic meaning is itself a second-order system of rules that connects symbols and syntax with extralinguistic facts. Searle’s Chinese Room Argument is contested on theoretical and practical grounds by identifying two problems in the thought experiment, and evidence about “machine learning” is used to demonstrate that computers are already capable of learning to form true observation sentences in the same way humans do. Finally, sarcasm is used as an example to extend the argument to more complex uses of language