Managing Director
CurrentPrecise Intelligence Ltd researches the problem of Natural Language Understanding (NLU). I learned during my research into speech recognition that it can be easier to solve a hard problem than a simple one. The beauty of mathematics is to find simple solutions to seemingly complex problems. Many researchers have tried to simplify the problem of NLU by ignoring anaphors, common sense, context, explainability and even statements, which are critical to human-quality conversations. I have had lessons in French, German, Spanish and the basics of Arabic. I have also looked at Chinese and am now learning Korean. Such diverse languages have complex peculiarities. By disregarding previous work and applying my own experience, I was able to identify the invariant features of language so that I could classify sentences. One of the issues with traditional linguistics has been an unhealthy focus on single standalone sentences. To ensure I work with human-quality language, I use short conversational tests as these provide a focus on context. There were two key issues with conventional AI research. Firstly, confusion between form and meaning, and secondly, the seeming invisibility of statements. Statements are a key component of human conversations and hence understanding statements, which require the creation of unstructured cognitive memories, is critical to Natural Language Understanding. In a conversation between two people the meaning of a sentence will be interpreted differently by each of the individuals depending on the preceding information and each person's cognitive memories. Following on from the first generation of TFTOC code in HPD I have continued to work on two key research areas:• Representation – given the novelty of Abstracts and TFTOC, it is important to have terse and simple representations – so this is an ongoing activity.• Language theory – given the novelty of the new mathematics it remains important to continue to simplify it.