What this means is that even though with respect to the uniform probability on the phase space the target has exceedingly small probability, the probability for the evolutionary algorithm E to get into the target in m steps is no longer small. And since complexity and improbability are for the purposes of specified complexity parallel notions, this means that even though the target is complex and specified with respect to the uniform probability on the phase space, it remains specified but is no longer complex with respect to the probability induced by evolutionary algorithm E.
[End Quote - WA Dembski, "Specified Complexity", MetaViews 152]
The above shows the kind of bait-and-switch tactic necessary to maintain the illusion that the products of algorithms or natural processes can in principle be distinguished from the products of intelligent agency. When one examines Dembski's technical discussion of "specification", one finds that the complexity is determined from the likelihood of a solution occurring due to the *chance* hypothesis. Here, Dembski swaps that out for the likelihood that the non-chance hypothesis finds the solution. Were this a pinball game, the machine would lock up and flash "TILT!".
The relative probability for assessing the complexity of some solution is given by Dembski on page 145 of TDI as P(E|H), where H is a *chance* hypothesis.
Essentially, what Dembski proves with his analysis of evolutionary computation is not that it cannot produce actual specified complexity, but rather that the bounded complexity measure discussed on page 144 of TDI will show that a problem is solvable by evolutionary computation given a certain limited m steps.
But the problem is even worse. It follows by a combinatorial argument that for any partition of the phase space into pieces none of which has probability more than the probability of the target (which by assumption is less than 1 in 10^150), for the vast majority of these partition elements the probability of the evolutionary algorithm E entering them is going to be no better than pure random sampling. It follows that the vast majority of fitness functions on the phase space that coincide with our original fitness function on the target but reshuffle the function on the partition elements outside the target will not land the evolutionary algorithm in the target (this result is essentially a corollary of the No Free Lunch theorems by Wolpert and Macready). Simply put, the vast majority of fitness functions will not guide E into the target even if they coincide with our original fitness function on the target (see Appendix 8).
[End Quote - WA Dembski, "Specified Complexity", MetaViews 152]
A dose of reality. I'm sure you will not watch these videos but many will that want to see the real arguments.
[ame=http://www.youtube.com/watch?v=S55Vn4rVPfY]1 of 10 - Intelligent Design of the Universe - Billy Crone - YouTube[/ame]
[ame=http://www.youtube.com/watch?v=YinrToIKJtg]Mathematics Disprove Evolution? The probability of spontaneous generation (creation vs. evolution) - YouTube[/ame]
[ame=http://www.youtube.com/watch?v=qEnJYdakou4]Atheists Humbled by Order in Universe - YouTube[/ame]
[ame=http://www.youtube.com/watch?v=KuB7KMfVems]Hitchens vs Craig The existence of God part 1 - YouTube[/ame]
[ame=http://www.youtube.com/watch?v=4lIEhLwONgw]Putting The Fear of God Into Atheists - YouTube[/ame]
[ame=http://www.youtube.com/watch?v=wAXKttcbhM4]Hitchens vs Craig The existence of God part 2 - YouTube[/ame]
[ame=http://www.youtube.com/watch?v=cM9-F57i6aQ]Hitchens vs Craig The existence of God part 3 - YouTube[/ame]
[ame=http://www.youtube.com/watch?v=OTzOJNHGqNg]Hitchens vs Craig The existence of God part 4 - YouTube[/ame]
[ame=http://www.youtube.com/watch?v=81SRgFvCi7M]Hitchens vs Craig The existence of God part 5 - YouTube[/ame]