Review of the state of the art in autonomous artificial intelligence

TL;DR

This paper reviews current advancements in autonomous AI, proposing a new AutoAI system that self-optimizes and self-adapts using emerging data sources and automated training tools, pushing beyond existing capabilities.

Contribution

It introduces a novel AutoAI design that incorporates self-improvement, self-optimization, and self-procreation, based on integrating recent algorithms and data sources.

Findings

AutoAI enables autonomous self-optimization.

Utilizes new sources of data for training.

Advances beyond current state-of-the-art algorithms.

Abstract

This article presents a new design for autonomous artificial intelligence (AI), based on the state-of-the-art algorithms, and describes a new autonomous AI system called AutoAI. The methodology is used to assemble the design founded on self-improved algorithms that use new and emerging sources of data (NEFD). The objective of the article is to conceptualise the design of a novel AutoAI algorithm. The conceptual approach is used to advance into building new and improved algorithms. The article integrates and consolidates the findings from existing literature and advances the AutoAI design into (1) using new and emerging sources of data for teaching and training AI algorithms and (2) enabling AI algorithms to use automated tools for training new and improved algorithms. This approach is going beyond the state-of-the-art in AI algorithms and suggests a design that enables autonomous algorithms to self-optimise and self-adapt, and on a higher level, be capable to self-procreate.