Intelligence is the ability to process external or internal information with the goal of becoming more adaptive in particular contexts. Learning and creativity are key aspects of this capacity. This project is part of a new wave of interest in the relationship between evolution and learning, but it goes beyond earlier work by specifying the shared mechanisms.

We begin from the observation of deep similarities and formal relationships between the two, and examine the idea that there is one unified theory behind the two manifestations. Our project has two major streams. In “Learning in Evolution” (i.e., how learning processes can be effective in evolution), we will elaborate on this link as applied to (i) unsupervised learning in ecosystems, and (ii) reinforcement learning in embryonic development. We also aim to prove the hypothesis that (iii) the deep learning mechanism can help us re-formulate my book The Major Transitions from lower to higher evolutionary units (as passage from lower to higher level features of hierarchical representations). In “Evolution in Learning” (i.e., how evolutionary processes can be effective in learning), we test our hypothesis that Darwinian neurodynamics can provide novel understanding of (i) basic linguistic operations, and (ii) the scope of facilitated variation in generating new ideas.

This project aims to synthesize the two approaches by linking and mapping their respective logical and mathematical foundations. We will use logical–conceptual analysis and computational modeling to tackle these questions. If we are correct, scholars will have an altered view about both evolution and learning, and the field may move closer to hypothesizing one theory encompassing different subcases. We will produce up to six high-profile research papers (roughly divided into the two areas) and one overarching review.