GPT-3: Its Nature, Scope, Limits, and Consequences
Luciano Floridi, Massimo Chiriatti
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.
What this paper cites, inside the corpus
| Paper | Year | Cited |
|---|---|---|
| I.—COMPUTING MACHINERY AND INTELLIGENCE | 1950 | 9,966 |
What cites it, inside the corpus
| Paper | Year | Cited |
|---|---|---|
| A Survey on Evaluation of Large Language Models | 2024 | 2,736 |
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Topics
| Computational Physics and Python Applications | Computer Science |
| Computability, Logic, AI Algorithms | Computer Science |
| Topic Modeling | Computer Science |
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