Throughout much of the twentieth century, brainwaves were believed to be su-perfluous, like the sound an engine makes without contributing to the operation of the engine. Now we must consider that these waves may be a type of emergent program for organizing the actions of neurons. In thorough reviews of the lit-erature, Kelso et al. (1991), Uhlhaas et al. (2009) and De Assis (2015) report that many neuroscientists understand the mechanisms underlying working memory and attention in terms of emergent brain waves that synchronize distant neurons, creating virtual neuronal assemblies (De Assis, 2015; Postle, 2006). It appears that waves may provide “the ‘contexts’ for the ‘content’ carried by networks of princi-pal cells” and “the precise temporal structure necessary for ensembles of neurons to perform specific functions, including sensory binding and memory formation”
(Buzsáki & Chrobak, 1995). In addition, emergent wave patterns may also define what data gets attention, that is, consciousness (see Thompson & Varela, 2001), which, in turn, affects further sensory processing.
This signal propagation theory of learning, using self-organizing signs (not codes), may help explain how people are able to form and use fluid adaptable cat-egories and deal with complex changing environments. Local fluctuations allow stochastic resonance (as with the Ls and ╚s), the similarity and proximity of possi-ble states, which in turn allows sameness to spread, instant organization. Natural selection cannot “see” to select these local interactions (it does not need to since these interactions just flow spontaneously to the lowest energy state). What can be selected for fitness are the effects of the global patterns that emerge from the local interactions (Cf. Rocha, 1998).2
No Artificial Neural Networks or Deep Learning networks are designed to im-itate the fluid interplay between self-organization and natural selection. AI de-signers are more committed to strictly selectionist, aka connectionist, approaches.
Although learning can be accomplished this way, it produces automatons, as does standardized curriculums and relentless testing, reward and punishment.
Even with the latest celebrated update (Levis-Kraus, 2016), Google Translate is still bad with puns, jokes and poetry. Psychologists Jung-Beeman et al. (2004) suggest that insight—understanding literary themes and metaphors and getting jokes—requires synchronizing distant brain areas instantly via gamma waves.
To design computers that can get allusive language, that understand people, one might need a more fluid medium for traveling waves to emerge. Atomic switch networks as per Stieg et al. (2014) seem promising; they have been used to create emergent patterns that imitate simple natural systems. Experimental chemical 2 Likewise, contrary to the selfish gene hypothesis, natural selection cannot “see” the genes per se
only their products.
reaction-diffusion computers have been around for more than a decade (Ad-amatsky et al., 2005), but although they create emergent patterns, they do away with more permanent connections. Our brains seem to use both.
Maybe we will eventually use reaction-diffusion to create more humanoid AI, but we already have eight billion human computers coupled together on the Inter-net, like so many neurons ready to organize. The potential for spectacular evolu-tion of knowledge is at our finger tips, if only we were in control of AI algorithms rather than controlled by them. With more information about the nature of AI compared to BI, we could make better choices with regard to how little or much we are willing to let AI think for us.
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