#techisland

Last Thursday (Feb. 14), the nonprofit research firm OpenAI released a new language model capable of generating convincing passages of prose. So convincing, in fact, that the researchers have refrained from open-sourcing the code, in hopes of stalling its potential weaponization as a means of mass-producing fake news. While the impressive results are a remarkable leap beyond what existing language models have achieved, the technique involved isn’t exactly new. Instead, the breakthrough was driven primarily by feeding the algorithm ever more training data—a trick that has also been responsible for most of the other recent advancements in teaching AI to read and write. “It’s kind of surprising people in terms of what you can do with more data and bigger models,” says Percy Liang, a computer science professor at Stanford.  Sign up for The Algorithm — artificial intelligence, demystified The passages of text that the model produces are good enough to masquerade as something human-written. But this ability should not be confused with a genuine understanding of language—the ultimate goal of the subfield of AI known as natural-language processing (NLP). (There’s an analogue in computer vision: an algorithm can synthesize highly realistic images without any true visual comprehension.) In fact, getting machines to that level of understanding is a task that has largely eluded NLP researchers. That goal could take years, even decades, to achieve, surmises Liang, and is likely to involve techniques that don’t yet exist. Four different philosophies of language currently drive the development of NLP techniques. Let’s begin with the one used by OpenAI.

#1. Distributional semantics