It may be difficult to get used to dealing with a volatile distributed entity. Suppose your robot made some really stupid mistake. You are mad at it. The robot explains that the action was caused by a temporary condition in the experimental semantic subnetwork and suggests to present to you a hundred-terabyte volume of incremental archives, memory snapshots and audit trails from numerous servers involved in the making of the unfortunate decision, containing a partial description of the state of the relevant parts of the system at the time. If you can even find the culprit, it's non-material, distributed, and long gone.

Now, what do you kick?

The incremental growth of computer power suggests an incremental approach to developing robot intelligence, probably an accelerated parallel to the evolution of biological intelligence that's its model. Unlike other approaches, this path demands no great theories or insights (helpful though they can be): natural intelligence evolved in small steps through a chain of viable organisms, artificial intelligence can do the same. Nature performed evolutionary experiments at an approximately steady rate, even when evolved traits such as brain complexity grew exponentially. Similarly, a steady engineering effort should be able to support exponentially growing robot complexity (especially as ever more of the design search is delegated to increasingly powerful machines). The journey will be much easier the second time around: we have a guide, with directions and distances, in the history of vertebrate nervous systems.