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.