Stochastic Parrots Day Tickets, Fri, Mar 17, 2023 at 8:00 AM | Eventbrite

This free event is running online from 3pm to 7pm UK time this Friday. The line-up features Emily Bender, Safiya Noble, Timnit Gebru and more.

Since the publication of On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?🦜 two years ago, many of the harms the paper has warned about and more, have unfortunately occurred. From exploited workers filtering hateful content, to an engineer claiming that chatbots are sentient, the harms are only accelerating.

Join the co-authors of the paper and various guests to reflect on what has happened in the last two years, what the large language model landscape currently look like, and where we are headed vs where we should be headed.

Stochastic Parrots Day Tickets, Fri, Mar 17, 2023 at 8:00 AM | Eventbrite

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Report: Thinking about using AI? - Green Web Foundation

A solid detailed in-depth report.

The sheer amount of resources needed to support the current and forecast demand from AI is colossal and unprecedented.

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First Impressions of the Pixel 9 Pro | Whatever

At this point, it really does seem like “AI” is “bullshit you don’t need or is done better in other ways, but we’ve just spent literally billions on this so we really need you to use it, even though it’s nowhere as good as what we were already doing,” and everything else is just unsexy functionality that makes what you do marginally easier or better. I’m sorry we live in a world where enshittification is being marketed as The Hot And Sexy Thing, but just because we’re in that world, doesn’t mean you have to accept it.

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Why “AI” projects fail

“AI” is heralded (by those who claim it to replace workers as well as those that argue for it as a mere tool) as a thing to drop into your workflows to create whatever gains promised. It’s magic in the literal sense. You learn a few spells/prompts and your problems go poof. But that was already bullshit when we talked about introducing other digital tools into our workflows.

And we’ve been doing this for decades now, with every new technology we spend a lot of money to get a lot of bloody noses for way too little outcome. Because we keep not looking at actual, real problems in front of us – that the people affected by them probably can tell you at least a significant part of the solution to. No we want a magic tool to make the problem disappear. Which is a significantly different thing than solving it.

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Why A.I. Isn’t Going to Make Art | The New Yorker

Using ChatGPT to complete assignments is like bringing a forklift into the weight room; you will never improve your cognitive fitness that way.

Another great piece by Ted Chiang!

The companies promoting generative-A.I. programs claim that they will unleash creativity. In essence, they are saying that art can be all inspiration and no perspiration—but these things cannot be easily separated. I’m not saying that art has to involve tedium. What I’m saying is that art requires making choices at every scale; the countless small-scale choices made during implementation are just as important to the final product as the few large-scale choices made during the conception.

This bit reminded me of Simon’s rule:

Let me offer another generalization: any writing that deserves your attention as a reader is the result of effort expended by the person who wrote it. Effort during the writing process doesn’t guarantee the end product is worth reading, but worthwhile work cannot be made without it. The type of attention you pay when reading a personal e-mail is different from the type you pay when reading a business report, but in both cases it is only warranted when the writer put some thought into it.

Simon also makes an appearance here:

The programmer Simon Willison has described the training for large language models as “money laundering for copyrighted data,” which I find a useful way to think about the appeal of generative-A.I. programs: they let you engage in something like plagiarism, but there’s no guilt associated with it because it’s not clear even to you that you’re copying.

I could quote the whole thing, but I’ll stop with this one:

The task that generative A.I. has been most successful at is lowering our expectations, both of the things we read and of ourselves when we write anything for others to read. It is a fundamentally dehumanizing technology because it treats us as less than what we are: creators and apprehenders of meaning. It reduces the amount of intention in the world.

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s19e01: Do Reply; Use plain language, and tell the truth

Very good writing advice from Dan:

Use plain language. Tell the truth.

Related:

The reason why LLM text for me is bad is that it’s insipid, which is not a plain language word to use, but the secret is to use words like that tactically and sparingly to great effect.

They don’t write plainly because most of the text they’ve been trained on isn’t plain and clear. I’d argue that most of the text that’s ever existed isn’t plain and clear anyway.

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