artificial intelligence is to find algorithms which have the greatest efficiency of intelligence, that is, which achieve the most intelligence per unit of computational resources consumed.

It should also be pointed out that although universal intelligence does not test the efficiency of an agent in terms of the computational resources that it uses, it does however test how quickly the agent learns from past data. In a sense, an agent which learns very quickly could be thought of as being very "data efficient".

## 4.2 Formal Definitions And Tests Of Machine Intelligence

Turing test and derivatives. The classic approach to determining whether a machine is intelligent is the so called Turing test [Tur50] which has been extensively debated over the last 50 years [SCA00]. Turing realised how difficult it would be to directly definite intelligence and thus attempted to side step the issue by setting up his now famous imitation game: If human judges can not effectively discriminate between a computer and a human through teletyped conversation then we must conclude that the computer is intelligent.

Though simple and clever, the test has attracted much criticism. Block and Searle argue that passing the test is not sufficient to establish intelligence [Blo81, Sea80, Eis91]. Essentially they both argue that a machine could appear to be intelligent without having any "real intelligence", perhaps by using a very large table of answers to questions. While such a machine might be impossible in practice due to the vast size of the table required, it is not logically impossible. In which case an unintelligent machine could, at least in theory, consistently pass the Turing test. Some consider this to bring the validity of the test into question. In response to these challenges, even more demanding versions of the Turing test have been proposed such as the Total Turing test [Har89], the Truly Total Turing test [Sch98] and the inverted Turing test [Wat96]. Dowe argues that the Turing test should be extended by ensuring that the agent has a compressed representation of the domain area, thus ruling out look-up table counter arguments [DH98]. Of course these attacks on the Turing test can be applied to any test of intelligence that considers only a system's external behaviour, that is, most intelligence tests.

A more common criticism is that passing the Turing test is not necessary to establish intelligence. Usually this argument is based on the fact that the test requires the machine to have a highly detailed model of human knowledge and patterns of thought, making it a test of humanness rather than intelligence [Fre90, FH98]. Indeed, even small things like pretending to be unable to perform complex arithmetic quickly and faking human typing errors become important, something which clearly goes against the purpose of the test.

The Turing test has other problems as well. Current AI systems are a long way from being able to pass an unrestricted Turing test. From a practical point of view this means that the full Turing test is unable to offer much guidance to our work. Indeed, even though the Turing test is the most famous test of machine intelligence, almost no current research in artificial intelligence is specifically directed toward being able to pass it. Unfortunately, simply restricting the domain of conversation in the Turing test to make the test easier, as is done in the Loebner competition [Loe90], is not sufficient. With restricted conversation possibilities the most successful Loebner entrants are even more focused on faking human fallibility, rather than anything resembling intelligence [Hut96]. Finally, the Turing test returns different results depending on who the human judges are. Its unreliability has in some cases lead to clearly unintelligent machines being classified as human, and at least