The Register reports:

In May, Sam Altman, CEO of $80-billion-or-so OpenAI, seemed unconcerned about how much it would cost to achieve the company’s stated goal. “Whether we burn $500 million a year or $5 billion – or $50 billion a year – I don’t care,” he told students at Stanford University. “As long as we can figure out a way to pay the bills, we’re making artificial general intelligence. It’s going to be expensive.”

Statements like this have become commonplace among tech leaders who are scrambling to maximize their investments in large language models (LLMs). Microsoft has put $10 billion into OpenAI, Google and Meta have their own models, and enterprise vendors are baking LLMs into products on a large scale. However, as industry bellwether Gartner identifies GenAI as nearing the peak of the hype cycle, it’s time to examine what LLMs actually model – and what they do not.

“Large Models of What? Mistaking Engineering Achievements for Human Linguistic Agency” is a recent peer-reviewed paper that aims to take a look at how LLMs work, and examine how they compare with a scientific understanding of human language.

Amid “hyperbolic claims” that LLMs are capable of “understanding language” and are approaching artificial general intelligence (AGI), the GenAI industry – forecast to be worth $1.3 trillion over the next ten years – is often prone to misusing terms that are naturally applied to human beings, according to the paper by Abeba Birhane, an assistant professor at University College Dublin’s School of Computer Science, and Marek McGann, a lecturer in psychology at Mary Immaculate College, Limerick, Ireland. The danger is that these terms become recalibrated and the use of words like “language” and “understanding” shift towards interactions with and between machines.

“Mistaking the impressive engineering achievements of LLMs for the mastering of human language, language understanding, and linguistic acts has dire implications for various forms of social participation, human agency, justice and policies surrounding them,” argues the paper published in the peer-reviewed journal Language Sciences.

The risks are far from imagined. [Continue reading…]