Excerpt

How  do  systems  like  the  brain,  the  immune  system,  ecosystems,  or financial markets learn and adapt over time? From the early days of computing, scientists and engineers alike have sought to abstract the key mechanisms of adaptation, learning, and intelligence and encode them in a computer. Norbert Weiner, an early cyberneticist, hypothesized that  feedback  mechanisms  were  the  key  ingredient  of  intelligence (Wiener  1948),  Ross  Ashby’s  general  theory  of  adaptive  systems emphasized  system  states  and  the  role  of  modeling  in  intelligence (Ashby 1956), and Donald O. Hebb’s influential book Organization of Behavior emphasized synaptic learning and cell assemblies (Hebb 1949). Meanwhile, engineers developed computational realizations of different aspects  of  adaptive  systems:  neural  networks  (McCulloch  and  Pitts 1943), genetic algorithms and classifier systems (Holland 1962, 1975; Holland et al. 1989), reinforcement learning and minimax algorithms (Samuel 1959; Kaelbling, Littman, and Moore 1996; Russell and Norvig 2003), and symbolic artificial intelligence models (Newell and Simon 1956). For understandable reasons, these efforts focused primarily on intelligence in the brain and on human thought processes, and over time  each  developed  its  own  formalisms,  terminology,  and  research communities specific to the type of system being studied. As computing matured, other researchers developed computational models in fields far afield from the brain, including chemical reaction networks and immunology.

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