Evolution is a special kind of (machine) learning

Theoretical computer science has a long history of peering through the algorithmic lens at the brain, mind, and learning. In fact, I would argue that the field was born from the epistemological questions of what can our minds learn of mathematical truth through formal proofs. The perspective became more scientific with McCullock & Pitts’ (1943) introduction of finite state machines as models of neural networks and Turing’s B-type neural networks paving the way for our modern treatment of artificial intelligence and machine learning. The connections to biology, unfortunately, are less pronounced. Turing ventured into the field with his important work on morphogenesis, and I believe that he could have contributed to the study of evolution but did not get the chance. This work was followed up with the use of computers in biology, and with heuristic ideas from evolution entering computer science in the form of genetic algorithms. However, these areas remained non-mathematical, with very few provable statements or non-heuristic reasoning. The task of making strong connections between theoretical computer science and evolutionary biology has been left to our generation.

ValiantAlthough the militia of cstheorists reflecting on biology is small, Leslie Valiant is their standard-bearer for the steady march of theoretical computer science into both learning and evolution. Due in part to his efforts, artificial intelligence and machine learning are such well developed fields that their theory branch has its own name and conferences: computational learning theory (CoLT). Much of CoLT rests on Valiant’s (1984) introduction of probably-approximately correct (PAC) learning which — in spite of its name — is one of the most formal and careful ways to understand learnability. The importance of this model cannot be understated, and resulted in Valiant receiving (among many other distinctions) the 2010 Turing award (i.e. the Nobel prize of computer science). Most importantly, his attention was not confined only to pure cstheory, he took his algorithmic insights into biology, specifically computational neuroscience (see Valiant (1994; 2006) for examples), to understand human thought and learning.

Like any good thinker reflecting on biology, Valiant understands the importance of Dobzhansky’s observation that “nothing in biology makes sense except in the light of evolution”. Even for the algorithmic lens it helps to have this illumination. Any understanding of learning mechanisms like the brain is incomplete without an examination of the evolutionary dynamics that shaped these organs. In the mid-2000s, Valiant embarked on the quest of formalizing some of the insights cstheory can offer evolution, culminating in his PAC-based model of evolvability (Valiant, 2009). Although this paper is one of the most frequently cited on TheEGG, I’ve waited until today to give it a dedicated post.
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