This is a simple introduction to the philosopher John Searle's main argument against artificial intelligence (AI). This means that it doesn't come down either for or against that argument. The main body of the Searle's argument is how he distinguishes syntax from syntax. Thus the well-known Chinese Room scenario is simply Searle's means of expressing what he sees as the vital distinction to be made between syntax and semantics when it comes to debates about computers and AI generally. One way in which John Searle puts his case is by reference to reference. That position is summed up simply when Searle (in his'Minds, Brains, and Programs' of 1980) writes: "Whereas the English subsystem knows that'hamburgers' refers to hamburgers, the Chinese subsystem knows only that'squiggle squiggle' is followed by'squoggle squoggle'." So whereas what Searle calls the "English subsystem" involves a complex reference-relation which involved entities in the world, mental states, knowledge of meanings, intentionality, consciousness, memory and other such things; the Chinese subsystem is only following rules.

In this paper, we target at the problem of sketch recognition. We systematically study how to incorporate users' correction and editing into isolated and full sketch recognition. This is a natural and necessary interaction in real systems such as Visio where very similar shapes exist. First, a novel algorithm is proposed to mine the prior shape knowledge for three editing modes. Second, to differentiate visually similar shapes, a novel symbol recognition algorithm is introduced by leveraging the learnt shape knowledge. Then, a novel editing detection algorithm is proposed to facilitate symbol recognition. Furthermore, both of the symbol recognizer and the editing detector are systematically incorporated into the full sketch recognition. Finally, based on the proposed algorithms, a real-time sketch recognition system is built to recognize hand-drawn flowcharts and diagrams with flexible interactions. Extensive experiments show the effectiveness of the proposed algorithms.