![]() ![]() These two phenomena related to the Turing test are sufficiently distinctive, important and general for a detailed analysis. The particular AI community rushes to replace the old benchmark by a more challenging benchmark, one for which human performance would still be beyond AI. On the other hand, AI benchmarks are suffering an adversarial situation too, with a ‘challenge-solve-and-replace’ evaluation dynamics whenever human performance is ‘imitated’. The term “Turing learning” has been used for this kind of setting. On the one hand, many generative models, such as generative adversarial networks (GAN), build imitators under an adversarial setting that strongly resembles the Turing test (with the judge being a learnt discriminative model). At the same time, however, these developments have revived some key elements of the Turing test: imitation and adversarialness.
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