It's #Pride month, and #PatRobertson kicked it off with a bang by...kicking off. What better way to start Pride than with a piece of fanfic by #WilWheaton about Robertson's arrival in Hell?

https://wilwheaton.net/2023/06/the-wait/

Once you've had your fill of #Schadenfreude, get your Pride/#Trekkie (or, if you prefer, #Trekker) gear on with Wil's Acting Ensign Pride collection:

https://shopstands.com/collections/wil-wheaton

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Seguing smoothly into science fiction by way of Trek, let's turn to #TedChiang, a titan of the field, who is also a wicked-sharp critic of #AIHype. Ted's been at this since at least 2017, when he identified tech CEOs' fears of #AI as a form of transference of their fear of corporations, which are, after all, autonomous artificial life-forms that are rapidly devouring the human race:

https://www.buzzfeednews.com/article/tedchiang/the-real-danger-to-civilization-isnt-ai-its-runaway

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When it comes to inventing devastating metaphors for AI, this is Ted's busy season. "AI is a blurry JPEG of the web":

https://www.newyorker.com/tech/annals-of-technology/chatgpt-is-a-blurry-jpeg-of-the-web

It's also "the new McKinsey":

https://www.newyorker.com/science/annals-of-artificial-intelligence/will-ai-become-the-new-mckinsey

And now, in the *#FinancialTimes*, Ted sits down for lunch with Madhumita Murgia to talk about the linguistic game we play when we describe a statistical inference tool as "artificial intelligence," given that it is neither "artificial," nor "intelligent":

https://www.ft.com/content/c1f6d948-3dde-405f-924c-09cc0dcf8c84

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Start with whether machines "learn": "#MachineLearning" is just adjusting weights in a statistical model. When you teach a child something, you're not adjusting weights! "Machine learning" is a useful metaphor for thinking about a subset of applied stats, but it's also a trap, tricking us into unconsciously anthropomorphizing an intelligence behind plausible sentence generators. We talk about AI models "hallucinating" - another linguistic trap.

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Applied statistics can automate a lot of work away, but what it's best at is automating the #BullshitJobs that #DavidGraeber (#RestInPower) described in his brilliant 2018 book:

https://memex.craphound.com/2018/06/20/david-graebers-bullshit-jobs-why-does-the-economy-sustain-jobs-that-no-one-values/

An academic friend tells me that they use #LLMs to write recommendation letters for grad students, and that grad students use LLMs to take minutes on department meetings.

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Presumably, someone else is using LLMs to ingest and summarize these recommendation letters and departmental meetings. LLMs can inflate a few bullet points into several florid paragraphs, and deflate them back into bullet points, with significant semantic losses on the way.

Is there a non-bullshit use for these? Maybe. I have spent a lot of my activist career as an anti-bullshit actor.

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For example, I was among the first public interest delegates to #WIPO, the most industry captured UN specialized agency, which has the same relationship to terrible copyright proposals that Mordor has to evil - a kind of infinite wellspring of bad ideas.

These ideas emerge out of an extremely bullshit process.

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The Standing Committee on Copyright and Related Rights meets periodically in Geneva for a highly stylized, multi-day session in which national delegates rise and deliver cryptic, stilted remarks about various proposed clauses to awful treaties like the #BroadcastTreaty.

These remarks are recorded by the Secretariat, who then offers each delegation the opportunity to redact or alter the official record of their remarks.

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Then, six months later, the Secretariat publishes this revisionist version of the session, which would be impossibly dull and cryptic even without all the revisions. This is the Shield of Boringness (h/t @danacorn) in action.

One day at one of these meetings, @wseltzer had a brilliant idea: let's make our *own* transcript.

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