Dear Commons Community,
In an article in today’s New York Times, Steven Johnson describes advances in artificial intelligence and specifically OpenAI’s GPT-3 that writes original prose with “mind-boggling fluency.” Here is an excerpt.
“A supercomputer complex in Iowa is running a program created by OpenAI, an organization established in late 2015 by a handful of Silicon Valley luminaries…. In its first few years, as it built up its programming brain trust, OpenAI’s technical achievements were mostly overshadowed by the star power of its founders. But that changed in summer 2020, when OpenAI began offering limited access to a new program called Generative Pre-Trained Transformer 3, colloquially referred to as GPT-3. Though the platform was initially available to only a small handful of developers, examples of GPT-3’s uncanny prowess with language — and at least the illusion of cognition — began to circulate across the web and through social media. Siri and Alexa had popularized the experience of conversing with machines, but this was on the next level, approaching a fluency that resembled creations from science fiction like HAL 9000 from “2001”: a computer program that can answer open-ended complex questions in perfectly composed sentences…
…GPT-3 belongs to a category of deep learning known as a large language model, a complex neural net that has been trained on a titanic data set of text: in GPT-3’s case, roughly 700 gigabytes of data drawn from across the web, including Wikipedia, supplemented with a large collection of text from digitized books. GPT-3 is the most celebrated of the large language models, and the most publicly available, but Google, Meta (formerly known as Facebook) and DeepMind have all developed their own L.L.M.s in recent years. Advances in computational power — and new mathematical techniques — have enabled L.L.M.s of GPT-3’s vintage to ingest far larger data sets than their predecessors, and employ much deeper layers of artificial neurons for their training.
Chances are you have already interacted with a large language model if you’ve ever used an application — like Gmail — that includes an autocomplete feature, gently prompting you with the word ‘‘attend’’ after you type the sentence ‘‘Sadly I won’t be able to….’’ But autocomplete is only the most rudimentary expression of what software like GPT-3 is capable of. It turns out that with enough training data and sufficiently deep neural nets, large language models can display remarkable skill if you ask them not just to fill in the missing word, but also to continue on writing whole paragraphs in the style of the initial prompt. For example, when I gave GPT-3 the prompt:
In 1958, Frank Rosenblatt introduced the Perceptron, an early version of a neural net that could only detect the location of a square on a printed card. Fifty years later, neural nets are capable of . . .
The software dutifully completed the thought, and then continued on, picking up on the historical framing of the initial text:
. . . detecting and classifying faces, finding patterns in data, and emulating the human brain. While the neural net matured in academia, it also found its way into the tech industry. In the late 1980s and early 90s, neural nets were used in speech recognition and character recognition applications. The growth in computing power and the ability to collect larger data sets allowed other neural net applications to emerge.
…So far, the experiments with large language models have been mostly that: experiments probing the model for signs of true intelligence, exploring its creative uses, exposing its biases. But the ultimate commercial potential is enormous. If the existing trajectory continues, software like GPT-3 could revolutionize how we search for information in the next few years. Today, if you have a complicated question about something — how to set up your home theater system, say, or what the options are for creating a 529 education fund for your children — you most likely type a few keywords into Google and then scan through a list of links or suggested videos on YouTube, skimming through everything to get to the exact information you seek. (Needless to say, you wouldn’t even think of asking Siri or Alexa to walk you through something this complex.) But if the GPT-3 true believers are correct, in the near future you’ll just ask an L.L.M. the question and get the answer fed back to you, cogently and accurately. Customer service could be utterly transformed: Any company with a product that currently requires a human tech-support team might be able to train an L.L.M. to replace them.”
I found the article fascinating and worth a read!