**9. Newer Turing tests**

In [8] the externalist foundations of a truly Total Turing test are studied. The paper begins by examining the original Turing test (TT) and Searle's antithetical Chinese Room Argument, which is intended to refute the TT. It is argued that Searle's "internalist" strategy is unable to deflect Dennett's combined robotics systems reply and the allied Total Turing Test.

In [9] an argument against the feasibility of the TT imitation game as a test for thinking or language understanding is presented. The argument is different from the five objections presented by Turing in his original paper, although it tries to maintain his original intention.

It is therefore called "the sixth argument" or "the argument from context." It is shown that—although the argument works against the original version of the imitation game—it may suggest a new version of the Turing test, still coherent with the idea of thinking and understanding as symbol manipulation.

In [10] the anti-behaviorist arguments against the validity of the Turing test as a sufficient condition for attributing intelligence based on a memorizing machine, which has recorded within it responses to every possible Turing test interaction of up to a fixed length are considered.

The possibility of memorizing machines is considered and how long a Turing test they can pass based on the age of the universe. It is concluded that the memorizing machine objection to the Turing test as a sufficient condition for attributing intelligence is invalid.

In [11] a similar issue is studied namely the claim that passing the Turing test would not be sufficient to prove that a computer program was intelligent because a trivial program could do it, namely, the "humongous table (HT) program." HT simply looks up in a table what to say next.

Three ground rules are argued for in [11] namely:


In [12] the authors raise the question of whether learning is just another computational process, that is. can be implemented as a Turing machine (TM).

They argue that learning or adaption is a process fundamentally different from simple computation. They accept, however, that learning involves processes that can be seen as computations. To illustrate this difference, they compare.

*Analysis Dialogs and Machine Consciousness DOI: http://dx.doi.org/10.5772/intechopen.112476*

(a) Designing a TM and (b) learning a TM. They show that there is a well-defined sequence of problems, which are not effectively designable but are learnable. Some characteristics of human intelligence are reviewed including interactive nature, learning abilities, imitative tendencies, linguistic ability, and context dependency. They consider the necessity of a considerable period of acculturation (social learning in context) if an artificial intelligence is to pass the Turing test. They conclude three things, namely that: a purely "designed" TM will never pass the Turing test; that there is no such thing as a general intelligence since it necessarily involves learning, and that learning, or adaption should be clearly distinguished from computation.
