The Turing test was originally formulated as an operational answer to the question, “Can a machine think?”. Turing provided this operational test because there is no precise, scientific definition of what it means to think. But, as can be seen from the large volumes of writing on the subject since his initial essay, Turing’s operational test never fully satisfied all scientists and philosophers. Moreover, in the age of large language models, there is even disagreement as to whether current models can pass Turing’s test, and, if we claim that they cannot, then many humans cannot pass the test either. I will, therefore, argue that it is time to abandon the Turing test and embrace ambiguity around the question of whether AI “thinks”. Much as biologists stopped worrying about the definition of “life” and simply engaged in scientific exploration, so too can neuroscientists, cognitive scientists, and AI researchers stop worrying about the definition of “thought” and simply move on with our explorations of the human brain and the machines we design to mimic its functions.

 

References

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Golan, T., Taylor, J., Schütt, H., Peters, B., Sommers, R. P., Seeliger, K., … & Kriegeskorte, N. (2023). Deep neural networks are not a single hypothesis but a language for expressing computational hypotheses. Behavioral and Brain Sciences, 46.

Zador, A., Escola, S., Richards, B., Ölveczky, B., Bengio, Y., Boahen, K., … & Tsao, D. (2023). Catalyzing next-generation artificial intelligence through neuroai. Nature Communications, 14(1), 1597.

Sejnowski, T. J. (2023). Large language models and the reverse turing test. Neural Computation, 35(3), 309-342.