A system of different layers of abstraction for artificial intelligence

TL;DR

This paper proposes a five-layer abstraction system for AI, providing a conceptual map to understand, innovate, and optimize AI systems across different complexity and performance trade-offs.

Contribution

It introduces a multi-layered abstraction framework for AI, clarifying how various subfields interconnect and guiding innovation and safety considerations.

Findings

Identifies five key layers of AI abstraction.

Highlights complexity-performance trade-offs at each layer.

Provides a conceptual map for AI development and safety.

Abstract

The field of artificial intelligence (AI) represents an enormous endeavour of humankind that is currently transforming our societies down to their very foundations. Its task, building truly intelligent systems, is underpinned by a vast array of subfields ranging from the development of new electronic components to mathematical formulations of highly abstract and complex reasoning. This breadth of subfields renders it often difficult to understand how they all fit together into a bigger picture and hides the multi-faceted, multi-layered conceptual structure that in a sense can be said to be what AI truly is. In this perspective we propose a system of five levels/layers of abstraction that underpin many AI implementations. We further posit that each layer is subject to a complexity-performance trade-off whilst different layers are interlocked with one another in a control-complexity trade-off. This overview provides a conceptual map that can help to identify how and where innovation should be targeted in order to achieve different levels of functionality, assure them for safety, optimise performance under various operating constraints and map the opportunity space for social and economic exploitation.