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A stochastic computer, designed to help an autonomous vehicle navigate, outperforms a conventional computer by three orders of magnitude, say computer scientists

These guys have applied stochastic computing to the process of pattern recognition. The problem here is to compare an input signal with a reference signal to determine whether they match.   In the real world, of course, input signals are always noisy so a system that can cope with noise has an obvious advantage.  Canals and co use their technique to help an autonomous vehicle navigate its way through a simple environment for which it has an internal map. For this task, it has to measure the distance to the walls around it and work out where it is on the map. It then computes a trajectory taking it to its destination.

Although the idea of stochastic computing has been around for half a century, attempts to exploit have only just begun. Clearly there's much work to be done. And since one line of thought is that the brain might be a stochastic computer, at least in part, there could be exciting times ahead.

several papers recently proposed to explicitly encourage the diversity of the robot behaviors, rather than the diversity of the genotypes as in classic evolutionary optimization. Such an approach avoids the need to compute distances between structures and the pitfalls of the noninjectivity of the phenotype/behavior relation; however, it also introduces new questions: how to compare behavior?

In this paper, we review the main published approaches to behavioral diversity and benchmark them in a common framework.

The results show that fostering behavioral diversity substantially improves the evolutionary process in the investigated experiments, regardless of genotype or task.