What I keep coming back to is McCarthy’s refusal to offer John Grady an easy out. He doesn’t get to be right and win. He doesn’t get to reject modernity and find some pastoral sanctuary where his skills still matter. He just gets to be good at something beautiful while the world moves on without him.There’s a question buried in that: what does it mean to keep practicing a craft you love when you suspect it’s dying?

Wikipedia’s high-level guidance noting some ways to identify LLM writing

A wide ranging talk about the role of universities with regards to AI – centers are Illich’s Tools for Conviviality.

Despite all this effort, the system still failed. In collaboration with MIT Technology Review and Trouw, we set out to understand why. We obtained unprecedented access to the system, the officials who built it, and the critics who fought against it.

At Spotify they call this the “Perfect Fit Content” (PFC) program. Musicians who provide PFC tracks “must often give up control of certain royalty rights that, if a track becomes popular, could be highly lucrative.”

Spotify apparently targeted genres where they could promote passive consumption. They identified situations in which listeners use playlists for background music. That’s why I noticed the fake artists problem first in my jazz listening.

The story of Ella was an example of a chatbot working badly. It was also an example of a chatbot working wonderfully. Not once was a landlord’s silence disturbed by this woman and her problems. She was not even a person in the database, but a hysterical pronoun. And how apt, in the end, for her troubles to divert to us, a group of poets and novelists hired specifically for our feelings, who could feel for her endlessly but do nothing else, as we did not know the landlord’s name or how to reach him and lived very far away.

ALISON GOPNIK: A very common trope is to treat LLMs as if they were intelligent agents going out in the world and doing things. That’s just a category mistake. A much better way of thinking about them is as a technology that allows humans to access information from many other humans and use that information to make decisions. We have been doing this for as long as we’ve been human. Language itself you could think of as a means that allows this. So are writing and the internet. These are all ways that we get information from other people. Similarly, LLMs give us a very effective way of accessing information from other humans. Rather than go out, explore the world, and draw conclusions, as humans do, LLMs statistically summarize the information humans put onto the web.

It cannot find its own problems; it cannot feed itself. … This, however, is not a defect in a machine; we do not want it to do its own business, but ours.

In a newly translated column called “The Author of the Robots Defends Himself,” published in Lidové Noviny on June 9, 1935, Čapek expresses his frustration about how his original vision for robots was being subverted. His arguments still apply to both modern robotics and AI. In this column, he referred to himself in the third-person:

For his robots were not mechanisms. They were not made of sheet metal and cogwheels. They were not a celebration of mechanical engineering. If the author was thinking of any of the marvels of the human spirit during their creation, it was not of technology, but of science. With outright horror, he refuses any responsibility for the thought that machines could take the place of people, or that anything like life, love, or rebellion could ever awaken in their cogwheels. He would regard this somber vision as an unforgivable overvaluation of mechanics or as a severe insult to life.

I suspect that the answer lies somewhere between Rotenstreich’s authoritarian view on technology and politics—that any change in the direction of technology must be accompanied by a change in the direction of society—and an internalized belief in the dominionist mindset that underscores American culture. Effective altruism is a political gift to the wealthy, packaged absolution that gives them moral permission to extract as much as they want. It is also perilously close to the edge of the cliff of fascism.

Summary: AI is introducing the third user-interface paradigm in computing history, shifting to a new interaction mechanism where users tell the computer what they want, not how to do it — thus reversing the locus of control.

…me, salty Kuhn fan will add that “this is not a paradigm shift, it is just a new thing.”

It’s also important for journalists to ask what metric the company is using to evaluate the model—and whether that is the right one to use. A useful question to consider is whether a false positive or false negative is worse. For example, in a cancer screening tool, a false positive may result in people getting an unnecessary test, while a false negative might result in missing a tumor in its early stage when it is treatable. 

a brief academic paper that reviews the situations where automation ends up producing more work for humans, rather than less – posits that this is sort of the norm, because automation almost always adds a layer (or two) of complexity and abstraction on to a problem space. While not directly written about AI (this is from 1983) I feel it is a solid foundation from which to build discussion (in the negative) on AI in modern system.

Appen is among dozens of companies that offer data-labeling services for the AI industry. If you’ve bought groceries on Instacart or looked up an employer on Glassdoor, you’ve benefited from such labeling behind the scenes. Most profit-maximizing algorithms, which underpin e-commerce sites, voice assistants, and self-driving cars, are based on deep learning, an AI technique that relies on scores of labeled examples to expand its capabilities. 

One of the relatively little-discussed phenomena in the social critique of AI is the fact that it is not only a centralizing technology, but also one that increases the distance between people and the power of the owners of the coordinating infrastructure. Of course, there is no direct determinism of the infrastructure, but it matters in which way the playing field is tilted.

Audience: I assume you heard of chatGPT, maybe played with it a little, and was imressed by it (or tried very hard not to be). And that you also heard that it is “a large language model”. And maybe that it “solved natural language understanding”. Here is a short personal perspective of my thoughts of this (and similar) models, and where we stand with respect to language understanding.