Finally, we discuss how behavioural similarity metrics could be used to create taxonomies for artificial and natural intelligence. We revisit some of the key questions surrounding the Turing test, such as ‘understanding’, commonsense reasoning and extracting meaning from the world, and explore how the new testing paradigms should work to unmask the limitations of current and future AI. After recognising the abyss that appears beyond superhuman performance, we build on Turing learning to identify two different evaluation schemas: Turing testing and adversarial testing. 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. In the last 20 years the Turing test has been left further behind by new developments in artificial intelligence.
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