Artificial Intelligence has enjoyed tremendous success over the last twenty five years. Its tools and techniques are now main stream within computer science, and at the core of so many of the systems we use every day. Search algorithms, the backbone of traditional AI, are used throughout operating systems, compilers and networks. More modern machine learning techniques are used to adapt these same systems in real-time. Satisfiability of logic formulas has become a central notion in understanding computability questions and once esoteric notions like semantic ontologies are being used to power the search engines that have become organizers of the world's knowledge, replacing libraries, encyclopedias, and automating business interfaces. And who would have guessed that AI powered robots in people's homes would now be counted in the millions. So much accomplishment to bring pride to us all.
But at the same time Artificial Intelligence has not yet succeeded in its most fundamental ambitions. Our systems are still fragile when outside their carefully circumscribed domains. The best poker playing program can't even understand the notion of a chess move, let alone the conceptual idea of animate versus inanimate. A six year old child can discuss all three domains, but may not be very good at any of them compared to our specialized systems. The challenge for AI, still, is to capture the fundamental nature of generalized perception, intelligence, and action. Worthy challenges for AI that would have tremendous practical impact, are, in my opinion: