 Second, at a slightly higher cognitive level, there are certain kinds of “ratings games” that French supposes will be very reliable discriminators between humans and machines. For instance, the “Neologism Ratings Game”—which asks participants to rank made-up words on their appropriateness as names for given kinds of entities—and the “Category Rating Game”—which asks participants to rate things of one category as things of another category—are both, according to French, likely to prove highly reliable in discriminating between humans and machines. For, in the first case, the ratings that humans make depend upon large numbers of culturally acquired associations (which it would be well-nigh impossible to identify and describe, and hence which it would (arguably) be well-nigh impossible to program into a computer). And, in the second case, the ratings that people actually make are highly dependent upon particular social and cultural settings (and upon the particular ways in which human life is experienced). To take French’s examples, there would be widespread agreement amongst competent English speakers in the technologically developed Western world that “Flugblogs” is not an appropriate name for a breakfast cereal, while “Flugly” is an appropriate name for a child’s teddy bear. And there would also be widespread agreement amongst competent speakers of English in the developed world that pens rate higher as weapons than grand pianos rate as wheelbarrows. Again, there are questions that can be raised about French’s argument here. It is not clear to us that the data upon which the ratings games rely is as reliable as French would have us suppose. (At least one of us thinks that “Flugly” would be an entirely inappropriate name for a child’s teddy bear, a response that is due to the similarity between the made-up word “Flugly” and the word “Fugly,” that had some currency in the primarily undergraduate University college that we both attended. At least one of us also thinks that young children would very likely be delighted to eat a cereal called “Flugblogs,” and that a good answer to the question about ratings pens and grand pianos is that it all depends upon the pens and grand pianos in question. What if the grand piano has wheels? What if the opponent has a sword or a sub-machine gun? It isn’t obvious that a refusal to play this kind of ratings game would necessarily be a give-away that one is a machine.) Moreover, even if the data is reliable, it is not obvious that any but a select group of interrogators will hit upon this kind of strategy for trying to unmask the machine; nor is it obvious that it is impossibly hard to build a machine that is able to perform in the way in which typical humans do on these kinds of tests. In particular, if—as Turing assumes—it is possible to make learning machines that can be “trained up” to learn how to do various kinds of tasks, then it is quite unclear why these machines couldn’t acquire just the same kinds of “subcognitive competencies” that human children acquire when they are “trained up” in the use of language.