Passed the Turing Test: Living in Turing Futures

TL;DR

This paper discusses the rise of generative AI models that can pass the Turing test, exploring their societal implications and the contrast with Turing's original vision of machine learning inspired by human development.

Contribution

It introduces the concept of 'child machines' inspired by Turing's vision, highlighting their potential societal impact and the differences from current AI models.

Findings

Generative AI can convincingly imitate human conversation.

Current AI models differ from Turing's vision of learning like human children.

Implications of AI passing the Turing test for society and ethics.

Abstract

The world has seen the emergence of machines based on pretrained models, transformers, also known as generative artificial intelligences for their ability to produce various types of content, including text, images, audio, and synthetic data. Without resorting to preprogramming or special tricks, their intelligence grows as they learn from experience, and to ordinary people, they can appear human-like in conversation. This means that they can pass the Turing test, and that we are now living in one of many possible Turing futures where machines can pass for what they are not. However, the learning machines that Turing imagined would pass his imitation tests were machines inspired by the natural development of the low-energy human cortex. They would be raised like human children and naturally learn the ability to deceive an observer. These ``child machines,'' Turing hoped, would be powerful enough to have an impact on society and nature.