Whenever we consider technology’s replication, variation, interaction, selection, and convergence, as its own complex adaptive system, we differentiate it from the evolutionary development of social ideas and behaviors in culture (including the scientific method), the topic of our previous theme. Since modern human emergence ~100 kya, our technology has been improving exponentially, occasionally at rates that make the rest of human culture appear static in comparison. Since the 1890s, our information technology has been doubling its price-performance capabilities in computing, communication, and storage roughly every two to three years, via miniaturization, materials science, and human ingenuity (Kurzweil 1999, Koh and Magee 2006, Magee 2009, Nagy 2010).
Our technological systems can be usefully analyzed as physical and informational systems that employ both bottom-up, divergent, contingent and unpredictable evolutionary processes, and top-down, convergent, globally-optimized and predictable developmental processes. Our information technology is presently dependent on human culture for its replication and selection, but as it grows in intelligence and adaptability, we can imagine a future time in which it may become even more autonomous than living systems. Some scholars (Blackmore 2008) discuss how ‘temes’ (replicating technological algorithms, in increasingly intelligent machines), are becoming increasingly analogous to ‘memes’ (replicating ideas and behavioral algorithms, in brains), the more brain-like our machines become.
Various endings of Moore’s law, which began in 2005 with the breakdown of Dennard scaling, are for the first time allowing ‘horizontal exponentiation’ in processor connectivity, and the first economical construction of massively parallel, neural-network based, “biologically-inspired” computing machines (Floreano and Mattiusi 2008). Major advances in the neuroscience of learning and memory (Yuste 2010, Liu et al. 2012), and in computational neuroscience (O’Reilly and Munakata 2014) are teaching us how to make truly self-learning and self-programming machines, like human brains. Superhuman performance in several pattern recognition tasks has recently been achieved by self-training “deep learning” computers (Howard 2014). Such work suggests a time in which technology will be a fully autonomous complex system.
Thinking developmentally, the physics principle of least action appears fundamental to technology’s acceleration and adaptive complexification. So too do other developmental factors, including the constructal principle (Bejan and Zane 2012), various technology learning and performance curves (Dutton, 1984), and several of the causes, constraints, trends and cycles identified in technological forecasting (Martino 1992). Scholars of technological invention, diffusion and substitution (Rogers 2003), and of technology’s variation, cultural selection, and independent parallel convergence on a subset of optimal forms and functions (Basalla 1989, Kelly 2011, Johnson 2015) are making early steps toward a future discipline of “astrotechnology.” Such a discipline will eventually tell us which technologies are likely to be developmental universals in intelligent civilizations (from symbolic language, fire, stone tools, the club, and the wheel, to electricity, the digital computer and beyond), and which are likely to be evolutionary, and thus unpredictably different in form and function from one civilization to the next.
Finally, when we attempt to relate accelerating local intelligence to the idea of the universe as a complex system (evo devo cosmology), evo devo thinking can allow us to hypothesize on the role of intelligent life in a possible cosmologic replication cycle (Harrison 1995, Balazs 2002, Gardner 2003), though what that role may be is far from clear today. Nevertheless, models of technological evolution and development can make specific and falsifiable predictions about the future dynamics and role of universal intelligence and its technology. Such models include the expansion hypothesis (Kardashev 1997), the postbiological universe (Dick 2003), the biocosm (Gardner 2003), the transcension hypothesis (Smart 2012) and stellivores (Vidal 2014), and may help us resolve fascinating questions in astrosociology, including the Fermi Paradox (Webb 2015).
We are still early in applying evolutionary and developmental models to technology today, but the scientific, technical, and policy potentials for scholarship and collaboration in this emerging area have never been greater. We hope you’ll join us in discussing them, and our other three research themes, at this blog.
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