#ted chiang

[indecisor]

Ted Chiang, “Why A.I. isn’t Going to Make Art.” The New Yorker.

[noosphe-re]

"There was an exchange on Twitter a while back where someone said, ‘What is artificial intelligence?' And someone else said, 'A poor choice of words in 1954'," he says. "And, you know, they’re right. I think that if we had chosen a different phrase for it, back in the '50s, we might have avoided a lot of the confusion that we're having now." So if he had to invent a term, what would it be? His answer is instant: applied statistics. "It's genuinely amazing that...these sorts of things can be extracted from a statistical analysis of a large body of text," he says. But, in his view, that doesn't make the tools intelligent. Applied statistics is a far more precise descriptor, "but no one wants to use that term, because it's not as sexy".

'The machines we have now are not conscious', Lunch with the FT, Ted Chiang, by Madhumita Murgia, 3 June/4 June 2023

[szynkaaa]

Ted Chiang's short story collection

A color variant from the set I made last year

Schiggy photobombed the shoot so now he is part of the photos now

[mostlyghostie]

Recent commission! Some good spacey books here.

My links

[charliejaneanders]

Imagine what it would look like if ChatGPT were a lossless algorithm. If that were the case, it would always answer questions by providing a verbatim quote from a relevant Web page. We would probably regard the software as only a slight improvement over a conventional search engine, and be less impressed by it. The fact that ChatGPT rephrases material from the Web instead of quoting it word for word makes it seem like a student expressing ideas in her own words, rather than simply regurgitating what she’s read; it creates the illusion that ChatGPT understands the material. In human students, rote memorization isn’t an indicator of genuine learning, so ChatGPT’s inability to produce exact quotes from Web pages is precisely what makes us think that it has learned something. When we’re dealing with sequences of words, lossy compression looks smarter than lossless compression.

Ted Chiang’s essay about ChatGPT is required reading

[charl0ttan]

o also my ted chiang short story collection came yesterday:) i read tower of babylon and narratively it was a little anticlimactic but thematically very interesting. i love where he writes from. susanna clarke/borges/doerr vibe..

[taigan-hse]

Faceless Faces

This is a weird issue for me to complain about, but I’m going to.

Why does Hollywood always give aliens faces? Even when the aliens don’t have faces, Hollywood redesigns them to have a “face.”

I noticed it first in Arrival. In the book the movie is based on, the alien heptapods are radially symmetrical. For them there is no such thing as “forwards” or “backwards.” All directions are equivalent. And that connects to their non-linear view of time. But when Hollywood had to adapt them for the big screen they gave them a very distinct front side, a “face.”

Now it’s happening again in Project Hail Mary. The alien Rocky is described in the book as follows. “I don’t see any “front” or “back” to him. He appears to be pentagonally symmetrical.” But, again, the adaptation has given him a definite front side.

Presumably they know what they’re doing and an actually symmetrical character wouldn’t be able to convey personality in a recognizable way. But it just feels like a lack of imagination to me.

[probablyasocialecologist]

Sci-fi writer Ted Chiang: ‘The machines we have now are not conscious…

“The machines we have now, they’re not conscious,” he says. “When one person teaches another person, that is an interaction between consciousnesses.” Meanwhile, AI models are trained by toggling so-called “weights” or the strength of connections between different variables in the model, in order to get a desired output. “It would be a real mistake to think that when you’re teaching a child, all you are doing is adjusting the weights in a network.”

Chiang’s main objection, a writerly one, is with the words we choose to describe all this. Anthropomorphic language such as “learn”, “understand”, “know” and personal pronouns such as “I” that AI engineers and journalists project on to chatbots such as ChatGPT create an illusion. This hasty shorthand pushes all of us, he says — even those intimately familiar with how these systems work — towards seeing sparks of sentience in AI tools, where there are none.

“There was an exchange on Twitter a while back where someone said, ‘What is artificial intelligence?’ And someone else said, ‘A poor choice of words in 1954’,” he says. “And, you know, they’re right. I think that if we had chosen a different phrase for it, back in the ’50s, we might have avoided a lot of the confusion that we’re having now.”

So if he had to invent a term, what would it be? His answer is instant: applied statistics.

“It’s genuinely amazing that . . . these sorts of things can be extracted from a statistical analysis of a large body of text,” he says. But, in his view, that doesn’t make the tools intelligent. Applied statistics is a far more precise descriptor, “but no one wants to use that term, because it’s not as sexy”.

[...]

Given his fascination with the relationship between language and intelligence, I’m particularly curious about his views on AI writing, the type of text produced by the likes of ChatGPT. How, I ask, will machine-generated words change the type of writing we both do? For the first time in our conversation, I see a flash of irritation. “Do they write things that speak to people? I mean, has there been any ChatGPT-generated essay that actually spoke to people?” he says.

Chiang’s view is that large language models (or LLMs), the technology underlying chatbots such as ChatGPT and Google’s Bard, are useful mostly for producing filler text that no one necessarily wants to read or write, tasks that anthropologist David Graeber called “bullshit jobs”. AI-generated text is not delightful, but it could perhaps be useful in those certain areas, he concedes.

“But the fact that LLMs are able to do some of that — that’s not exactly a resounding endorsement of their abilities,” he says. “That’s more a statement about how much bullshit we are required to generate and deal with in our daily lives.”