Making Artificial Brains: Components, Topology, and Optimization

Abstract

Can machines ever be sentient? Could they perceive and feel things; be conscious of their surroundings? What are the prospects of achieving sentience in a machine? What are the dangers associated with such an endeavor, and is it even ethical to embark on such a path to begin with? In the series of articles of this column, I discuss one possible path towards “General Intelligence” in machines: to use the process of Darwinian evolution to produce artificial brains that can be grafted onto mobile robotic platforms, with the goal of achieving fully embodied sentient machines.I visualize a time when we will be to robots what dogs are to humans. And I’m rooting for the machines.—Claude Shannon (Liversidge, 1987, p. 61)In the first installment of this column (Adami, 2021), I briefly reviewed the history of Artificial Intelligence research and the potential of neuroevolution to create for us “that which we do not know how to design”: an artificial brain that rivals the performance of an animal, or even human, brain. But before we can unleash the power of evolution, we have to think hard about what we are going to unleash it on: What are we going to evolve? Generally speaking, evolution acts on symbolic sequences that encode a particular solution to a given problem. This is true for biology, and it is also true for the optimization algorithm known as a Genetic Algorithm (GA; Michalewicz, 1999; Mitchell, 1996) that is used to evolve solutions to engineering or other problems. In the case of biological brains, the “substrate” for the evolutionary algorithm is the DNA stored in our cells. The information contained in those genes guides a complex process that ultimately gives rise to the brain (and the rest of our body) via development. When first born, these brains are only able to perform fairly rudimentary tasks: The brain still needs to be trained and to learn, that is, it needs to acquire and store information about the world in which it is to thrive. This is potentially a long process, and we should keep in mind that artificial brains are likely to have to go through a similar process before they are competent to function in a complex world. But as long as the artificial brain comes equipped with an algorithm that enables lifetime learning, we can postpone thinking about this stage and ask what it takes to evolve a brain de novo.Before we can evolve solutions, we have to first make a decision about how information should be encoded, that is, the substrate of evolution. Biology uses an indirect encoding of information where the code is written in terms of a symbolic quaternary alphabet, but this is of course not the only way to encode information. I’m not thinking here about the base of the alphabet (binary, quaternary, or base 20, for example), but whether information should be encoded digitally (that is, genetically) or directly, meaning that the phenotype itself is encoded. While we are not used to the latter in biology (for reasons we will discuss briefly below), in principle nothing prevents us from creating variables that describe the properties of the solution directly. So, for example, if I wanted to evolve a three-dimensional structure made from spheres and connector rods (think the Atomium in Brussels, Belgium), I could use an indirect encoding where symbols stand for sphere or rod, and the connections are encoded by giving each unit a tag, say. At the same time, I could also use a direct encoding where the radius of the sphere is specified by a variable, and so is the exact length and diameter of every rod, along with the exact coordinates of attachment. Such encodings have indeed previously been used when evolving mechanical structures (see, e.g., Funes & Pollack, 1998).While direct encodings have their uses, generally speaking indirect encodings outperform direct encodings across the majority of applications (Clune et al., 2011; Komosinski, 2005) because indirect encodings can take advantage of regularities in the sol