Unsupervised Learning in Complex Systems
Hugo Cisneros
In this thesis, we explore the use of complex systems to study learning and
adaptation in natural and artificial systems. The goal is to develop autonomous
systems that can learn without supervision, develop on their own, and become
increasingly complex over time. Complex systems are identified as a suitable
framework for understanding these phenomena due to their ability to exhibit
growth of complexity. Being able to build learning algorithms that require
limited to no supervision would enable greater flexibility and adaptability in
various applications. By understanding the fundamental principles of learning
in complex systems, we hope to advance our ability to design and implement
practical learning algorithms in the future. This thesis makes the following
key contributions: the development of a general complexity metric that we apply
to search for complex systems that exhibit growth of complexity, the
introduction of a coarse-graining method to study computations in large-scale
complex systems, and the development of a metric for learning efficiency as
well as a benchmark dataset for evaluating the speed of learning algorithms.
Our findings add substantially to our understanding of learning and adaptation
in natural and artificial systems. Moreover, our approach contributes to a
promising new direction for research in this area. We hope these findings will
inspire the development of more effective and efficient learning algorithms in
the future.