Language Log

From Charles Belov:

YouTube music's algorithm suggested to me an album, 24 Hours in Soweto, in the amapiano genre that I love which mostly comes from the Zulu community in South Africa. I was struck by the album cover, which seems to have some random Chinese characters, some garbled. Wondering if it's AI art. Can you make any sense of it?

There's a big snowstorm expected this weekend, and so lots of Monday events are being cancelled. One of the notices that I got today had this Subject line:

Canceled January 26 colloquium hey Siri hey Alexa, what is Monday’s date?

Jesse Ventura has had a successful career as a pro wrestler, actor, and politician — all largely built on the foundations of his mastery of pro wrestling rhetoric.  And recent events have brought him back into the public eye. His Jan. 8 interview on the Minneapolis Fox News channel got 2.7 million likes and more than 47 thousand comments on TikTok, lots of play on other news-ish outlets, 295k views and more than 7400 comments on YouTube,  and 3.7 million views and more than 1400 comments on X.

See "The art of the promo" (10/31/2020) for some background on this rhetorical style, including its role in Donald Trump's career. And if you haven't listened to Ventura's interview, you should do so as background for this post.

Melissa Heikkilä, "LeCun: 'Intelligence really is about learning'", Financial Times 1/2/2026:

(The AI pioneer on stepping down from Meta, the limits of large language models — and the launch of his new start-up)

LeCun’s lightbulb moment came as a student at the École Supérieure d’Ingénieurs en Électrotechnique et Électronique in Paris in the 1980s, when he read a book about a debate on nature versus nurture between the linguist Noam Chomsky and Jean Piaget, a psychologist. Chomsky argued that humans have an inbuilt capacity for language, while Piaget said there is some structure but most of it is learnt.

“I’m not gonna make friends saying this . . . ” he tells me, “but I was reading this and I thought everything that Chomsky . . . was saying could not possibly be true, [because] we learn everything. Intelligence really is about learning.”

AI research — or neural networks, as the technology was then called, which loosely mimic how the brain functions — was practically a dead field and considered taboo by the scientific community, after early iterations of the technology failed to impress. But LeCun sought out other researchers studying neural networks and found intellectual “soulmates” in the likes of Geoffrey Hinton, then a faculty member at Carnegie Mellon.

Friday's On The Media, "Deep Fakes, Data Centers, And AI Slop — Are We Cooked?" has some linguistically-interesting discussion, especially the part about the rise of AI-generated trolling — more on that later. But this post is just a quick note on a widespread symptom of current end-to-end speech-to-text technology, where the text end of the process is letter-sequence tokens of obscure origin, yielding some peculiar spelling errors.

Yesterday I pointed out the trombonish glissando in Bobby Vinton's "Blue Velvet"; today, during my morning ablutions, on the radio I heard a jazz singer do a whole song sounding like a musical instrument.  I don't think there was any digital or electronic assistance, just his natually endowed voice.

In "Agentic culture" (8/30/2025) and "'Moloch's bargain'?" (10/12/2025) I cited some work on how interactions among algorithmic "agents" can create (socially) bad results that were not directly programmed by their inventors. I continue to be surprised at how little attention has been paid to this issue in the media, given the excitement over agentic AI. I've found a fair amount of other research with similar content, as  searches like this illustrate, which makes me wonder even more about the relative lack of uptake.

Yesterday afternoon, Andrej Karpathy posted llm-council on github:

The Vastness of Language Variation Across the Globe
Panel. AAAS 2026 Annual Meeting.  Coming in February, 2026.

Organizer:  Lenore Grenoble, University of Chicago, Chicago, IL
Co-Organizer:  Jeff Good, University at Buffalo, Buffalo, NY
Moderator:  Jeff Good, University at Buffalo, Buffalo, NY

Panelists

"Multilingual Language Ecologies and Linguistic Diversity",
Wilson de Lima Silva, Linguistics, University of Arizona, Tucson, AZ

"AI Approaches to the Study of Gesture, Prosody, and Linguistic Diversity",
 Kathryn Franich, Linguistics, Harvard University, Cambridge, MA

"Sometimes Big Questions Call for Small Data",
Gareth Roberts, Linguistics, University of Pennsylvania, Philadelphia, PA

A year and a half ago, we learned of the initial AI-assisted decipherment of a charred scroll that had been buried for two millennia under the volcanic ashes of Mt. Vesuvius (eruption 79AD) in the city of Herculaneum:  "AI (and human ingenuity) to the rescue" (2/6/24).

Since then, researchers have continued to work on the scroll until now they have identified the precise text on it:

Lost Work of Greek Philosopher Philodemus Unearthed from Herculaneum Scroll
By Tasos Kokkinidis, Greek Reporter (May 6, 2025)

In “Agentic Culture” (8/30/2025), I cited some work by economists about agentic collusion in fixing prices and dividing markets — to which I might add links here, here, and here. And in that post, I noted that the problematic effects of AI agents learning from their social interactions in other areas have been mostly ignored.

But here it comes: Batu El and James Zou, "Moloch's Bargain: Emergent Misalignment When LLMs Compete for Audiences", 10/7/2025.

An interesting recent paper (Adithya Bhaskar, Xi Ye, & Danqi Chen, “Language Models that think, chat better”, arXiv.org 09/24/2025) starts like this:

THINKING through the consequences of one’s actions—and revising them when needed—is a defining feature of human intelligence (often called “system 2 thinking”, Kahneman (2011)). It has also become a central aspiration for large language models (LLMs).¹

The footnote:

¹Language models think, therefore, language models are?

Martha Gimbel et al., "Evaluating the Impact of AI on the Labor Market: Current State of Affairs", The Budget Lab (Yale) 10/1/2025:

Overall, our metrics indicate that the broader labor market has not experienced a discernible disruption since ChatGPT’s release 33 months ago, undercutting fears that AI automation is currently eroding the demand for cognitive labor across the economy.

While this finding may contradict the most alarming headlines, it is not surprising given past precedents. Historically, widespread technological disruption in workplaces tends to occur over decades, rather than months or years. Computers didn’t become commonplace in offices until nearly a decade after their release to the public, and it took even longer for them to transform office workflows. Even if new AI technologies will go on to impact the labor market as much, or more, dramatically, it is reasonable to expect that widespread effects will take longer than 33 months to materialize.