Quantum mechanics

Natural language

Scale (ratio)

Linguistics

Philosophy

Pure mathematics

Biology

Language identification

Field (mathematics)

Grammar

Computer science

Turing

Turing test

Language model

Paleontology

Natural language processing

Physics

Test (biology)

Mathematics

Artificial intelligence

Heuristic

Programming language

Journal of Cultural Analytics

Until recently the field of natural language generation relied upon formalized grammar systems, small-scale statistical models, and lengthy sets of heuristic rules. This older technology was fairly limited and brittle: it could remix language into word salad poems or chat with humans within narrowly defined topics. Recently, very large-scale statistical language models have dramatically advanced the field, and GPT-3 is just one example. It can internalize the rules of language without explicit programming or rules. Instead, much like a human child, GPT-3 learns language through repeated exposure, albeit on a much larger scale. Without explicit rules, it can sometimes fail at the simplest of linguistic tasks, but it can also excel at more difficult ones like imitating an author or waxing philosophical.

Can GPT-3 Pass a Writer’s Turing Test?