Bruce Edmonds (Manchester), with Carlos Gershenson (Mexico)
Learning, Social Intelligence and the Turing Test - why an "out-of-the-box" Turing Machine will not pass the Turing Test

The Turing Test (TT) checks for human intelligence, rather than any putative general intelligence. It involves repeated interaction requiring learning in the form of adaption to the human conversation partner. It is a post-hoc test in contrast to the definition of a Turing Machine (TM) which is a micro-level definition. This raises the question of whether learning is just another computational process, i.e. can be implemented as a TM. Here we argue that learning or adaption is fundamentally different from computation, though it does involve processes that can be seen as computations. To illustrate this difference we compare (a) designing a TM and (b) learning a TM, defining them for the purpose of the argument. We show there is a well-defined sequence of problems that are not effectively designable but are learnable, in the form of the bounded halting problem. Some characteristics of human intelligence are reviewed including its: interactive nature, learning abilities, imitative tendencies, linguistic ability and context-dependency. A story that explains some of these is the Social Intelligence Hypothesis. If this is broadly correct this points to the necessity of a considerable period of acculturation (social learning in context) if an artificial intelligence is to pass the TT. Whilst it is always possible to 'compile' the results of learning into a TM, this would not be a designed TM and would not be able to continually adapt (pass future TTs). We conclude that since intelligence necessarily involves learning that there is not such thing as a general intelligence and that human intelligence is not (only) a computation in the sense of a TM.