Artificial Intelligence and Intelligent Systems

The original vision of AI was to develop intelligent artifacts with the same broad range of capabilities as we observe in humans. None of the field’s early programs achieved this lofty goal, but the tone of many seminal papers makes this motivation very clear. When we entered graduate school at Carnegie Mellon University in the mid 1970s, this aim was a central tenet of many researchers in the field, and it was still important in many circles when we became professors in the mid 1980s. At that time, AI was still generally viewed as a single field with a common set of goals. By the late 1980s, that situation had started to change. Subfields like machine learning, knowledge representation, and planning began to break away from AI, establishing their own conferences, journals, and criteria for progress. One of us served as an active proponent of such developments in the area of machine learning, which launched one of the first specialized journals and which played a leading role in introducing careful experimental evaluation. To researchers who were involved in these movements, these changes seemed necessary at the time for advancing the parent field. However, the down side of this speciation was that students began to identify more with their subfield than with AI in general. They began to focus their energies on solving component problems, like supervised learning or constraint satisfaction, with little concern for how their results might be used in the context of larger AI systems. Over the past 20 years, this trend has continued unabated. Clearly, it has produced considerable technical progress within each of AI’s subfields, but it has also led to a narrowness of vision among many otherwise excellent researchers. Today, many AI practitioners consider their main affiliation to be not with AI itself but with their subfield, and their primary conference is not AAAI but one of the specialized meetings. In many cases, they lack the training to understand results in other areas or even to appreciate their goals, an effect that is exacerbated by the specialized jargons that have emerged. In fact, graduate education in AI subfields has become so specialized that the only common knowledge concerns al-