Open-endedness in AI systems, cellular evolution and intellectual discussions
This work addresses the problem of limited skill diversity in AI for researchers aiming to mimic biological evolution, though it appears incremental as it builds on existing analogies without presenting new empirical results.
The paper tackles the challenge of developing AI systems capable of open-ended innovation by drawing an information-theoretic analogy between intellectual discussions and cellular molecular dynamics, proposing a framework for such systems.
One of the biggest challenges that artificial intelligence (AI) research is facing in recent times is to develop algorithms and systems that are not only good at performing a specific intelligent task but also good at learning a very diverse of skills somewhat like humans do. In other words, the goal is to be able to mimic biological evolution which has produced all the living species on this planet and which seems to have no end to its creativity. The process of intellectual discussions is also somewhat similar to biological evolution in this regard and is responsible for many of the innovative discoveries and inventions that scientists and engineers have made in the past. In this paper, we present an information theoretic analogy between the process of discussions and the molecular dynamics within a cell, showing that there is a common process of information exchange at the heart of these two seemingly different processes, which can perhaps help us in building AI systems capable of open-ended innovation. We also discuss the role of consciousness in this process and present a framework for the development of open-ended AI systems.