  Yet there is something in the idea of ML-randomness that we might apply profitably to the case of finite sequences. Since being generated by an effective procedure is a measure zero property of infinite sequences, given that there are only countably many effective procedures, it follows immediately that no ML-random sequence can be effectively produced. This fits well with the intuitive idea that random sequences don’t have the kind of regular patterns that any finite algorithm, no matter how complex, must exploit in order to produce an infinite sequence. This contrast between random sequences which lack patterns that enable them to be algorithmic generated, and non-random sequences which do exhibit such patterns, does not apply straightforwardly to the finite case, because clearly there is an effective procedure which enables us to produce any particular finite sequence of outcomes—simply to list those outcomes in the specification of the algorithm. But a related contrast does exist—between those algorithms which are simply crude lists of outcomes, and those which produce outcomes which involve patterns and regularities in the outcome sequence. This leads us to the idea that finite random sequences, like their infinite cousins, are not able to be generated by an algorithm which exploits patterns in the outcome sequence. The outcomes in random sequences are thus patternless, or disorderly, in a way that is intuitively characteristic of random sequences.