A New Framework for Machine Intelligence: Concepts and Prototype
This addresses the problem of achieving general and continuous learning in AI, but it appears incremental as it builds on existing concepts without demonstrating broad breakthroughs.
The paper tackles the challenge of extending machine learning to general solutions with continuous learning by proposing a theoretical framework combining Mirror Compositional Representations and a Solution-Critic Loop, with a prototype tested on document comparison using the English Wikipedia corpus.
Machine learning (ML) and artificial intelligence (AI) have become hot topics in many information processing areas, from chatbots to scientific data analysis. At the same time, there is uncertainty about the possibility of extending predominant ML technologies to become general solutions with continuous learning capabilities. Here, a simple, yet comprehensive, theoretical framework for intelligent systems is presented. A combination of Mirror Compositional Representations (MCR) and a Solution-Critic Loop (SCL) is proposed as a generic approach for different types of problems. A prototype implementation is presented for document comparison using English Wikipedia corpus.