The virtues of generalityFewer preconceptionsMost machine intelligence systems today are expert systems - they operate in a narrow domain.If you look at the world of compressors, there are lots of specific-purpose compressors - compressing images, videos and text mostly. These do most of the world's heavy lifting in the area. However, there are also a range of general-purpose compressors. These are not, typically, a collection of specific-purpose compressors clumped together, with adaptive selection of the algorithm depending on the data type. Rather they just have fewer preconceptions about the form of the data.
A second path to machine intelligenceI think this illustrates a "second path" to machine intelligence - besides the obvious one of making lots of expert systems in different fields and allowing their scope to broaden.General-purpose compressors are interesting - in part - because if you have a good one, you can use it to build specific-purpose compressors of any kind you like. General-purpose compressors may not be very competitive on particular tasks - when compared with specific-purpose compressors today - but their generality means that they can still find a niche in the marketplace. The significance of general purpose stream compression for machine intelligence suggests that such systems may become more important in the future.
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