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Model-based coding, described by John Pierce in 1961, has great potential toreduce the volume of information that needs to be transmitted in moving bigdata, without loss of information, from one place to another, or in losslesscommunications via the internet. Compared with ordinary compression methods,this potential advantage of model-based coding in the transmission of dataarises from the fact that both the transmitter ("Alice") and the receiver("Bob") are equipped with a grammar for the kind of data that is to betransmitted, which means that, to achieve lossless transmission of a body ofdata from Alice and Bob, a relatively small amount of information needs to besent. Preliminary trials indicate that, with model-based coding, the volume ofinformation to be sent from Alice to Bob to achieve lossless transmission of agiven body of data may be less than $6\%$ of the volume of information thatneeds to be sent when ordinary compression methods are used.  Until recently, it has not been feasible to convert John Pierce's vision intosomething that may be applied in practice. Now, with the development of the "SPtheory of intelligence" and its realisation in the "SP computer model", thereis clear potential to realise the three main functions that will be needed:unsupervised learning of a grammar for the kind of data that is to betransmitted using a relatively powerful computer that is independent of Aliceand Bob; the encoding by Alice of any one example of such data in terms of thegrammar; and, with the grammar, decoding of the encoding by Bob to retrieve thegiven example. It appears now to be feasible, within reasonable timescales, tobring these capabilities to a level where they may be applied to thetransmission of realistically large bodies of data.

In the transmission of information, the great potential of model-based coding with the SP theory of intelligence