Machine lifelong learning: Challenges and benefits for artificial general intelligence

Abstract

We propose that it is appropriate to more seriously consider the nature of systems that are capable of learning over a lifetime. There are three reasons for taking this position. First, there exists a body of related work for this research under names such as constructive induction, continual learning, sequential task learning and most recently learning with deep architectures. Second, the computational and data storage power of modern computers are capable of implementing and testing machine lifelong learning systems. Third, there are significant challenges and benefits to pursuing programs of research in the area to AGI and brain sciences. This paper discusses each of the above in the context of a general framework for machine lifelong learning. © 2011 Springer-Verlag Berlin Heidelberg.