I would feel more comfortable with such predictions if research were clearly walking the wooded path to AI. Moore's law works because<p><pre><code> 1. We already have processors
2. We have a metric to measure processor speed
</code></pre>
Then the path we're hiking is one <i>mostly</i> of incremental improvments, with occasional boulder of innovation.<p>Whereas the 'intelligent' agents we currently have do not seem particularly similar in <i>quality</i> to intelligence we already know; the path from contemporary AI to strong AI is hardly a trail at all --- it's all boulders; innovation all the way up.<p>[The claim I make about 'quality' is vague; in part necessarily so. If I could pinpoint my discomfort with current methods, I could propose a new course of action based on a new metric. Nevertheless I feel that the high-mathematical bent of current machine learning techniques (proto-value RL, statistical relational methods, etc) will lead to excellent answers, but does not point towards the flexibility of general intelligence. Yrom the other camp, low-level connectionist methods have not to my mind offered significant results in problem solving.]