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Cake day: March 22nd, 2024

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  • I mean I think the whole AI consciousness emerged from science fiction writers who wanted to interrogate the economic and social consequences of totally dehumanizing labor, similar to R.U.R. and Metropolis. The concept had sufficient legs that it got used to explore things like “what does it mean to be human?” in a whole bunch of stories. Some were pretty good (Bicentennial Man, Aasimov 1976) and others much less so (Bicentennial Man, Columbus 1999). I think the TESCREAL crowd had a lot of overlap with the kind of people who created, expanded, and utilized the narrative device and experimented with related technologies in computer science and robotics, but saying they originated it gives them far too much credit.




  • I recommend it because we know some of these LLM-based services still rely on the efforts of A Guy Instead to make up for the nonexistence and incoherence of AGI. If you’re an asshole to the frontend there’s a nonzero chance that a human person is still going to have to deal with it.

    Also I have learned an appropriate level of respect and fear for the part of my brain that, half-asleep, answers the phone with “hello this is YourNet with $CompanyName Support.” I’m not taking chances around unthinkingly answering an email with “alright you shitty robot. Don’t lie to me or I’ll barbecue this old commodore 64 that was probably your great uncle or whatever”









  • This is a good example of something that I feel like I need to drill at a bit more. I’m pretty sure that this isn’t an unexpected behavior or an overfitting of the training data. Rather, given the niche question of “what time zone does this tiny community use?” one relatively successful article in a satirical paper should have an outsized impact on the statistical patterns surrounding those words, and since as far as the model is concerned there is no referent to check against this kind of thing should be expected to keep coming up when specific topics or phrases come up near each other in relatively novel ways. The smaller number of examples gives each one a larger impact on the overall pattern, so it should be entirely unsurprising that one satirical example “poisons” the output this cleanly.

    Assuming this is the case, I wonder if it’s possible to weaponize it by identifying tokens with low overall reference counts that could be expanded with minimal investment of time. Sort of like Google bombing.



  • Can confirm that about Zitron’s writing. He even leaves you with a sense of righteous fury instead of smug self-satisfaction.

    And I think that the whole bullshit “foom” argument is part of the problem. For the most prominent “thinkers” in related or overlapping spaces with where these LLM products are coming from the narrative was never about whether or not these models were actually capable of what they were being advertised for. Even the stochastic parrot arguments, arguably the strongest and most well-formulated anti-AI argument when the actual data was arguably still coming in, was dismissed basically out of hand. “Something something emergent something.” Meanwhile they just keep throwing more money and energy into this goddamn pit and the real material harms keep stacking up.




  • That’s probably true, but it also speaks to Ed Zitron’s latest piece about the rise of the Business Idiot. You can explain why Wikipedia disrupted previous encyclopedia providers in very specific terms: crowdsourced production to volunteer editors cuts costs massively and allows the product to be delivered free (which also increases the pool of possible editors and improves quality), and the strict* adherence to community standards and sourcing guidelines prevents the worse loss of truth and credibility that you may expect.

    But there is no such story that I can find for how Wikipedia gets disrupted by Gen AI. At worst it becomes a tool in the editor’s belt, but the fundamental economics and structure just aren’t impacted. But if you’re a business idiot then you can’t actually explain it either way and so of course it seems plausible



  • As the bioware nerd I am it makes my heart glad to see the Towers of Hanoi doing their part in this fight. And it seems like the published paper undersells how significant this problem is for the promptfondlers’ preferred narratives. Given how simple it is to scale the problem complexity for these scenarios, it seems likely that there isn’t a viable scaling-based solution here. No matter how big you make the context windows and how many steps the system is able to process it’s going to get out scaled by simply increasing some Ns in the puzzle itself.

    Diz and others with a better understanding of what’s actually under the hood have frequently referenced how bad Transformer models are at recursion and this seems like a pretty straightforward way to demonstrate that and one that I would expect to be pretty consistent.