1. Large language models (LLMs), Artificial Intelligence (AI) and Natural language processing (NLP), history of NLP
Explanations, formulas, visualisations:
- Britannica entry for artificial intelligence
- Eisenstein 1.1
- Jurafsky-Martin 2
- Benoît Sagot, Apprendre les langues aux machines, leçon inaugurale, Collège de France
- Melanie Mitchell, The Turing Test and our shifting conceptions of intelligence, Science
- NLP is an important part of artificial intelligence because language is the most prominent capacity of human intelligence.
- The Turing test: another reason why language is central to AI
- Not a test in fact, but a thought experiment
- ELIZA: a case that shows how easily humans are tricked into ascribing human traits to machines
- Linguistics was central to language processing in the past, before the introduction of machine learning. Currently we see a big gap between linguistics and NLP, but the potential for bridging this gap is still there.
- Language processing was one of the first applications of machine learning and it remains a prominent challenge.
- Humans use language to name (maybe even to form) concepts by abstracting from real objects and events
- Regular expressions describe explicitly the structure of text (as a sequence of symbols), natural languages is similar but more complex
- Machine learning results in implicit knowledge, so that machines can imitate how humans use language without any access to concepts and reality
- Modern machines do develop some concepts, but the relationship with the reality is still missing
