 Some defenders of AI are also concerned with how our understanding of understanding bears on the Chinese Room argument. In their paper “A Chinese Room that Understands” AI researchers Simon and Eisenstadt (2002) argue that whereas Searle refutes “logical strong AI”, the thesis that a program that passes the Turing Test will necessarily understand, Searle’s argument does not impugn “Empirical Strong AI” – the thesis that it is possible to program a computer that convincingly satisfies ordinary criteria of understanding. They hold however that it is impossible to settle these questions “without employing a definition of the term ‘understand’ that can provide a test for judging whether the hypothesis is true or false”. They cite W.V.O. Quine’s Word and Object as showing that there is always empirical uncertainty in attributing understanding to humans. The Chinese Room is a Clever Hans trick (Clever Hans was a horse who appeared to clomp out the answers to simple arithmetic questions, but it was discovered that Hans could detect unconscious cues from his trainer). Similarly, the man in the room doesn’t understand Chinese, and could be exposed by watching him closely. (Simon and Eisenstadt do not explain just how this would be done, or how it would affect the argument.) Citing the work of Rudolf Carnap, Simon and Eisenstadt argue that to understand is not just to exhibit certain behavior, but to use “intensions” that determine extensions, and that one can see in actual programs that they do use appropriate intensions. They discuss three actual AI programs, and defend various attributions of mentality to them, including understanding, and conclude that computers understand; they learn “intensions by associating words and other linguistic structure with their denotations, as detected through sensory stimuli”. And since we can see exactly how the machines work, “it is, in fact, easier to establish that a machine exhibits understanding that to establish that a human exhibits understanding….” Thus, they conclude, the evidence for empirical strong AI is overwhelming.