Artificial intelligence (AI) emerged from the 1956 Dartmouth Conference. Twenty-one years later, my colleagues and I started daily operational use of what we think became the first application of AI to be used in practice: the PUFF pulmonary function system. We later described the design and initial performance of that system (Aikins et al., 1983; Snow et al., 1998). Today, easily recognizable descendants of that first “expert system” run on commercial products found in medical offices around the world (http://www.medgraphics.com/datasheet_pconsult.html), as do many other AI applications. My research now focuses on integrated concurrent engineering (ICE), a computer and AI-enabled multiparticipant engineering design method that is extremely rapid and effective (Garcia et al., 2004). This brief note compares the early PUFF, the current ICE work, and the modern AI view of neurobiological systems. This comparison shows the dramatic and surprising changes in AI methods in the past few decades and suggests research opportunities for the future. The comparison identifies the continuing crucial role of symbolic representation and reasoning and the dramatic generalization of the context in which those classical AI methods work. It suggests surprising parallels between animal neuroprocesses and the multihuman and multicomputer agent collaborative ICE environment. Finally, it identifies some of the findings and lessons of the intervening years, fundamentally the move to model-based multidiscipline, multimethod, multiagent systems in which AI methods are tightly integrated with theoretically founded engineering models and analytical methods implemented as multiagent human and computer systems that include databases, numeric algorithms, graphics, human–computer interaction, and networking.