GPT-3: Its Nature, Scope, Limits, and Consequences

TLDR

The commentary examines reversible versus irreversible questions to explore how they reveal the source of answers. The authors analyze GPT‑3, a third‑generation autoregressive deep‑learning language model, through the lens of reversible and irreversible questions. The analysis demonstrates that GPT‑3 fails mathematical, semantic, and ethical tests, confirming it is not a general AI and highlighting the risks of mass production of cheap semantic artifacts.

Abstract

Abstract In this commentary, we discuss the nature of reversible and irreversible questions, that is, questions that may enable one to identify the nature of the source of their answers. We then introduce GPT-3, a third-generation, autoregressive language model that uses deep learning to produce human-like texts, and use the previous distinction to analyse it. We expand the analysis to present three tests based on mathematical, semantic (that is, the Turing Test), and ethical questions and show that GPT-3 is not designed to pass any of them. This is a reminder that GPT-3 does not do what it is not supposed to do, and that any interpretation of GPT-3 as the beginning of the emergence of a general form of artificial intelligence is merely uninformed science fiction. We conclude by outlining some of the significant consequences of the industrialisation of automatic and cheap production of good, semantic artefacts.