THE idea of machines that think and act as intelligently as humans can generate strong emotions. This may explain why one of the most important accomplishments in the field of artificial intelligence has gone largely unnoticed: that some of the advances in AI can be used by ordinary people to improve their own natural intelligence and communication skills.
Chief among these advances is a form of logic called computational logic. This builds and improves on traditional logic, and can be used both for the original purpose of logic - to improve the way we think - and, crucially, to improve the way we communicate in natural languages, such as English. Arguably, it is the missing link that connects language and thought.
According to one school of philosophy, our thoughts have a language-like structure that is independent of natural language: this is what students of language call the language of thought (LOT) hypothesis. According to the LOT hypothesis, it is because human thoughts already have a linguistic structure that the emergence of common, natural languages was possible in the first place.
The LOT hypothesis contrasts with the mildly contrary view that human thinking is actually conducted in natural language, and thus we could not think intelligently without it. It also contradicts the ultra-contrary view that human thinking does not have a language-like structure at all, implying that our ability to communicate in natural language is nothing short of a miracle.
Research in AI lends little support to the first view, and some support to the second. But if we want to improve how we communicate in natural language, the AI version of the LOT hypothesis comes into its own, offering us a detailed analysis we can use as a guide.
Using this guide we can then try to express ourselves in a form of natural language that is closer to the LOT. This will make it easier for others to understand our communications because they will require less effort to translate them into thoughts of their own. But to fully exploit the guide, we need to understand the nature of the LOT and the relationship between it and natural language.