Artificial Emotions for Rapid Online Explorative Learning
Proceedings of the Second International Workshop on Self-Supervised Learning, PMLR 159:63-83, 2022.
For decades, A.I. has been able to produce impressive results on hard problems, such as games playing in synthetic environments, but have had difficulty in interfacing with the natural world. Recently machine learning has enabled A.I. to interface more robustly with the real world. Statistical methods for speech understanding opened the door to voice-based systems and more recently deep-learning has revolutionized computer vision to the extent that wild speculation now predicts artificial superintelligence surpassing human intelligence, but we are a few major breakthroughs short of that being achieved. We know what some of these breakthroughs need to be. We need to replace supervised learning with unsupervised learning and we need to take on topics like motivation, attention, and emotions. In this article, we describe an architecture that touches on some of these issues drawing inspiration from neuroscience. We describe three aspects of the architecture in this article that address learning through fear and reward and address the focus of attention. These three systems are intimately linked in mammalian brains. We believe that this work represents an attempt to bridge the gap between high order reasoning and base-level support for motivation and learning in robots.