prehension and knowledge in a domain is estimated by having them guess missing words from a passage of text.

While simple text compression can be performed with symbol frequencies, the resulting compression is relatively poor. By using more complex models **that capture** higher level features such as aspects of grammar, the best compressors are able to compress text to about 1.5 bits per character for English. However humans, which can also make use of general world knowledge, the logical structure of the argument etc., are able to reduce this down to about 1 bit per character. Thus the compression statistic provides an easily computed measure of how complete a machine's model of language, reasoning and domain knowledge are, relative to a human.

To see the connection to the Turing test, consider a compression test based on a very large corpus of dialogue. If a compressor could perform extremely well on such a test, this is mathematically equivalent to being able to determine which sentences are probable at a given point in a dialogue, and which are not (for the equivalence of compression and prediction see [2]). Thus, as failing a Turing test occurs when a machine (or person!) generates a sentence which would be improbable for a human, extremely good performance on dialogue compression implies the ability to pass a Turing test.

A recent development in this area is the Hutter Prize [17]. In this test the corpus is a 100 MB extract from Wikipedia. The idea is that this should represent a reasonable sample of world knowledge and thus any compressor that can perform very well on this test must have a good model of not just English, but also world knowledge in general.

One criticism of compression tests is that it is not clear whether a powerful compressor would easily translate into a general purpose artificial **intelligence.**

## 4 Linguistic Complexity

A more linguistic approach is taken by the HAL project at the company Artificial Intelligence NV [32]. They propose to measure a system's level of conversational ability by using techniques developed to measure the linguistic ability of **children.**
These methods examine things such as vocabulary size, length of utterances, response types, syntactic complexity and so on. This would allow systems to be
". . . assigned an age or a maturity level beside their binary Turing test assessment of 'intelligent' or 'not intelligent' "[31]. As they consider communication **to be the** basis of intelligence, and the Turing test to be a valid test of machine intelligence, in their view the best way to develop intelligence is to retrace the way in **which**
human linguistic development occurs. Although they do not explicitly refer to their linguistic measure as a test of intelligence, because it measures progress towards what they consider to be a valid intelligence test, it acts as one.