Can machines think efficiently?

TL;DR

This paper proposes an energy-efficient extension to the Turing Test, emphasizing the importance of resource consumption in evaluating machine intelligence, thus aligning the assessment with practical considerations of finite resources.

Contribution

It introduces an energy-aware version of the Turing Test, integrating resource efficiency into the evaluation of machine intelligence.

Findings

Energy constraint enhances the evaluation of AI efficiency.

The new test provides a measurable endpoint for intelligence assessment.

Encourages society to balance AI benefits against resource costs.

Abstract

The Turing Test is no longer adequate for distinguishing human and machine intelligence. With advanced artificial intelligence systems already passing the original Turing Test and contributing to serious ethical and environmental concerns, we urgently need to update the test. This work expands upon the original imitation game by accounting for an additional factor: the energy spent answering the questions. By adding the constraint of energy, the new test forces us to evaluate intelligence through the lens of efficiency, connecting the abstract problem of thinking to the concrete reality of finite resources. Further, this proposed new test ensures the evaluation of intelligence has a measurable, practical finish line that the original test lacks. This additional constraint compels society to weigh the time savings of using artificial intelligence against its total resource cost.