Learning and Knowledge: Theoretical Issues and Biological Applications

Dear Colleagues,

This Special Issue aims to encourage speculation and application on general topics at the frontier of many disciplines and focuses on the new perspectives of machine learning. In the broadest sense, learning is the transfer of knowledge between agents that are capable of realizing complex behaviors and processing information at many levels of meaning. Unfortunately, all of the terms in this characterization are difficult to define in precise and unambiguous ways. In other words: Knowledge emerges and is passed on, but many crucial aspects of this dynamic are unclear. Biology is essentially based on emergent passages, and although evolution is certainly driven by chance, hidden mechanisms lead to selective pathways that express fundamental forms of life intelligence. In this sense, the theme proposes to combine learning and biological emergence to gain new interpretative keys for both phenomena. The language exploded in the LLM models as the core of learning processes has a biological origin that can be fully recognized in the combinatorial power of biopolymers. The principles of general syntax, combined with mathematical properties of computations and the distributed forms of memory, could provide a new understanding of the missing points in our scientific reconstructions.

Prof. Dr. Vincenzo Manca
Guest Editor

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• machine learning
• large language models
• biological knowledge representation
• evolotionary systems