Advances in Artificial Intelligence: Deep Intentions, Shallow Achievements

TL;DR

This paper discusses recent AI progress driven by Deep Learning, introduces a new information definition emphasizing physical and semantic aspects, and explores implications for AI design philosophy.

Contribution

It proposes a novel definition of information as a coupling of physical and semantic aspects, influencing AI development approaches.

Findings

Deep Learning has significantly advanced AI capabilities.

A new information framework links data processing and interpretation.

Implications for AI design philosophy are discussed.

Abstract

Over the past decade, AI has made a remarkable progress due to recently revived Deep Learning technology. Deep Learning enables to process large amounts of data using simplified neuron networks that simulate the way in which the brain works. At the same time, there is another point of view that posits that brain is processing information, not data. This duality hampered AI progress for years. To provide a remedy for this situation, I propose a new definition of information that considers it as a coupling between two separate entities - physical information (that implies data processing) and semantic information (that provides physical information interpretation). In such a case, intelligence arises as a result of information processing. The paper points on the consequences of this turn for the AI design philosophy.