> And there is nothing in the physical word that we have understood so far that cannot be ultimatly expressed computationally
Except for those physical phenomena that can only be modelled with a recursive form that doesn't start with coefficients defined with infinite precision - which unfortunately includes a lot of useful things like weather prediction, and even the classical mechanics of n-body systems.
How about telling me what tomorrow's Dow close is going to be? Or the oil price four years from now? Or which parts of the universe that electron over there passed through on its way to your monitor?
I think a lot of the responses here just make the point for Lanier:
"There's a whole other problem area that has to do with neuroscience, where if we pretend we understand things before we do, we do damage to science, not just because we raise expectations and then fail to meet them repeatedly, but because we confuse generations of young scientists."
Anyone who believes that consciousness is computable by definition, because it just has to be, has a dilettante-level view of the problem. Philosophers have been arguing about this for centuries, and on the whole they're not so certain about it.
So I'll repeat - we don't have adequate models for human or even animal personality, or for emotional responsiveness, or even for the self-aware perception of qualia, which is apparently one of the key things that defines consciousness.
Big data isn't a solution to this, any more than throwing the works of Shakespeare into a database and looking for clustering statistics about word proximity and sentence structure will get you a new Julius Caesar.
The reality is there are levels of intelligent behaviour - especially self-aware creative behaviour - that are completely opaque to any modelling technique available in current CS. I believe that anyone who thinks the problem is simple and just needs moar code and a faster processor thrown at it is expressing a faith-based position of hopeful belief, not a reality-based fact.
Shakespeare is Shakespeare because the writing isn't word salad. Shakespeare is interesting because of the unusual the density and richness of the experiences that are referred to - not just in the plot, but in the details of the metaphors in each sentence.
You need to model experience before you can recreate that, and you can't do that unless you have a deep understanding of what experience is. Big data gives you slightly more focussed monkeys and more typewriters, but it's in no way a solution to the basic modelling problem.