Review of State-of-the-Art in Deep Learning Artificial Intelligence

Abstract

The current state-of-the-art in Deep Learning (DL) based artificial intelligence (AI) is reviewed. A special emphasis is made to compare the level of a concrete AI system with human abilities to show what remains to be done to achieve human level AI. Several estimates are proposed for comparison of the current “intellectual level” of AI systems with the human level. Among them is relation of Shannon’s estimate for lower bound on human word perplexity to recent progress in natural language AI modeling. Relations between the operation of DL constructions and principles of live neural information processing are discussed. The problem of AI risks and benefits is also reviewed based on arguments from both sides.