An anarchy of methods: Current trends in how intelligence is abstracted in AI

Lehman J
Clune J
Risi S

While researchers in AI all strive to create intelligent machines, separate AI communities view intelligence in strikingly different ways. Some abstract intelligence through the lens of connectionist neural networks, while others use mathematical models of decision processes or view intelligence as symbol manipulation. Similarly, researchers focus on different processes for generating intelligence, such as learning through reinforcement, natural evolution, logical inference, and statistics. The result is a panoply of approaches and subfields.
Because of independent vocabularies, internalized assumptions, and separate meetings, AI sub-communities can become increasingly insulated from one another even as they pursue the same ultimate goal. Further deepening the separation, researchers may view other approaches only in caricature, unintentionally simplifying the motivations and research of other researchers. Such isolation can frustrate timely dissemination of useful insights, leading to wasted effort and unnecessary rediscovery.
To address such dangers, we organized an AAAI Fall Symposium called “How Should Intelligence Be Abstracted in AI Research” that gathered experts with diverse perspectives on biological and synthetic intelligence. The hope was that such a meeting might lead to a productive examination of the value and promise of different approaches, and perhaps even inspire syntheses that cross traditional boundaries. However, organizing a cross-disciplinary symposium has risks as well. Discussion could have focused narrowly on intractable disagreements, or on which singular abstraction is “the best.” An unhelpful slugfest of ideas could have emerged instead of collaborative cross-pollination, leading to a veritable AI Tower of Babel.
In the end, there were world-class keynote speakers spanning AI and biology (see Table 1), and participants were indeed collaborative. Some traveled to the United States from as far as Brazil, Australia, and Singapore; but beyond geographic diversity, there were representatives from many disciplines and approaches to AI (see Figure 1). Drawing from the symposium’s talks and events, we now summarize recent progress across AI fields, as well as the key ideas, debates, and challenges identified by the attendees. (See also the sidebar, “Straight from the Experts,” which showcases and summarizes the direct viewpoints of some of the keynote speakers.)

Pub. Info: 
IEEE Intelligent Systems. 56-62

Author = {Lehman, J. and Clune, J. and Risi, S.},
Journal = {IEEE Intelligent Systems},
Title = {An Anarchy of Methods: Current Trends in
How Intelligence Is Abstracted in AI},
Year = {2014}}