The Turing Test (TT) is best regarded as a model to test for intelligence, where an entity’s intelligence is inferred from its ability to be attributed with ‘human-likeness’ during a text-based conversation. The problem with this model, however, is that it does not care if or how well an entity produces a meaningful conversation, as long as its interactions are humanlike enough. As a consequence, the TT attracts projects that concentrate on how best to fool the judges. In light of this, I propose a new version of the TT: the Questioning Turing Test (QTT). Here, the entity has to produce an enquiry rather than a conversation; and it is parametrised along two further dimensions in addition to ‘human-likeness’: ‘correctness’, evaluating if the entity accomplishes the enquiry; and ‘strategicness’, evaluating how well the entity accomplishes the enquiry, in terms of the number of questions asked (the fewer, the better).

中文翻译：

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质疑图灵测试

图灵测试 (TT) 最好被视为测试智能的模型，其中实体的智能是从其在基于文本的对话中被归因于“人类相似性”的能力来推断的。然而，这个模型的问题在于，它并不关心一个实体是否或如何产生有意义的对话，只要它的交互足够人性化。因此，TT 吸引了专注于如何最好地愚弄评委的项目。有鉴于此，我提出了一个新版本的 TT：质疑图灵测试（QTT）。在这里，实体必须进行询问而不是对话；除了“人类相似性”之外，它还沿着两个进一步的维度进行参数化：“正确性”，评估实体是否完成了查询；和“战略”，