Most artificial intelligence researchers think unlikely the notion that a robot apocalypse or some kind of technological singularity is coming anytime soon. I’ve said as much, too. Guessing about the likelihood of distant futures is fraught with uncertainty; current trends are almost impossible to extrapolate.
But if we must, what are the best ways for guessing about the future? In the late 1950s the Delphi method was developed. Get a group of experts on a given topic and have them answer questions anonymously. Then iteratively publish back the group results and ask for feedback and revisions. Similar methods have been developed for face-to-face group decision making, like Kevin O’Connor’s approach to generating ideas in The Map of Innovation: generate ideas and give participants votes equaling a third of the number of unique ideas. Keep iterating until there is a consensus. More broadly, such methods are called “nominal group techniques.”
Most recently, the notion of prediction markets has been applied to internal and external decision making. In prediction markets, a similar voting strategy is used but based on either fake or real money, forcing participants towards a risk-averse allocation of assets.
Interestingly, we know that optimal inference based on past experience can be codified using algorithmic information theory, but the fundamental problem with any kind of probabilistic argument is that much change that we observe in society is non-linear with respect to its underlying drivers and that the signals needed are imperfect. As the mildly misanthropic Nassim Taleb pointed out in The Black Swan, the only place where prediction takes on smooth statistical regularity is in Las Vegas, which is why one shouldn’t bother to gamble. Taleb’s approach is to look instead at minimizing the impact of shocks (or hedging them in financial markets).
But maybe we can learn something from philosophical circles. For instance, Evolutionary Epistemology (EE), as formulated by Donald Campbell, Sir Karl Popper, and others, posits that central to knowledge formation is blind variation and selective retention. Combined with optimal induction, this leads to random processes being injected into any kind of predictive optimization. We do this in evolutionary algorithms like Genetic Algorithms, Evolutionary Programming, Genetic Programming, and Evolutionary Strategies, as well as in related approaches like Simulated Annealing. But EE also suggests that there are several levels of learning by variation/retention, from the phylogenetic learning of species through to the mental processes of higher organisms. We speculate and trial-and-error continuously, repeating loops of what-ifs in our minds in an effort to optimize our responses in the future. It’s confounding as hell but we do remarkable things that machines can’t yet do like folding towels or learning to bake bread.
This noosgeny-recapitulates-ontogeny-recapitulates-phylogeny (just made that up) can be exploited in a variety of ways for abductive inference about the future. We can, for instance, use evolutionary optimization with a penalty for complexity that simulates the informational trade-off of AIT-style inductive optimality. Further, the noosgeny component (by which I mean the internalized mental trial-and-error) can reduce phylogenetic waste in simulations by providing speculative modeling that retains the “parental” position on the fitness landscape before committing to a next generation of potential solutions, allowing for further probing of complex adaptive landscapes.