On my current NLP project I’ve taken to calling a certain class of data “the text”. This refers to literal text—the natural language I’m being paid to analyze—but also machine learning models, embedding vectors…any string of bits that is useful … Continue reading
A big thing in machine learning-driven artificial intelligence for language processing right now is word embedding vectors. The word cat is represented to the computer not as a string of letters c-a-t, nor as an item in a vocabulary (which … Continue reading
Part of speech. Part of life. Continue reading
My career as an artificial intelligence engineer began in a master’s program in linguistics. There I memorized the International Phonetic Alphabet, played hunt-the-allophone, read the literature on control verbs and code switching, and generally made a good faith effort to … Continue reading
Ferdinand de Saussure: Meaning is difference. Claude Shannon: Difference can be quantified. Alan Turing: Quantification can be automated. Go!
“NEW SIGN. WE MAKE. YOU SEE.” At first I thought I might have been misinterpreting BoBo, but he kept repeating the signs until it was clear what he meant. “YES YOU SEE” Dian added. “WE MAKE SIGN. YOU HAPPY.” Noam … Continue reading
The king of France is bald. What does this mean? Is it this? ∃x [KingOfFrance(x) ∧ ∀y [KingOfFrance(y) → x=y] ∧ Bald(x) Or maybe this? How about this? All of these depictions are useful. The translation of natural language into predicate calculus—pioneered … Continue reading