> The worst part is that everyone who's decided to willingly lobotomize themselves is going to have to come to the realization that these things are full of shit. It'll have to happen one by one, and nobody else can make it happen for them.

A fascinating dichotomy has become apparent between those who trust LLM output and those who don’t and don’t understand why you would.

Surely if the machine you go to for answers regularly makes things up you would just stop using it? Perhaps people have to be burned by something really bad personally before they realise the limitations? LLMs are very convincing and persuasive.

I have never lived in SF but I have failed similar Chinatown-during-lunar-new-year tests in all 3 places I lived for more than a year. I have no doubt that an AI, particularly one that continually gets traffic update from some external source, would do better than I in predicting such hiccups. OTOH, an unconstrained LLM seems very likely to route me down streets that don't exist at least some of the time. If only we had some sort of database of actual streets and routes that were capable of checking the work of an LLM...

I see the same thing with LLMs in software development. If you say "find a bug in this code" it will regularly confabulate bugs. If you ask it for a test-case, run the output through some deterministic thing that tries the test-cases, and tells the LLM it's wrong, the output of that system will mostly be legitimate bugs[1].

For now, transformer-based generative AIs seem at a minimum like a very useful tool for dealing with "squishy" problems when you have some way to validate their output. Many of the 404's to the blog are probably people validating the output of generative AI, which is the opposite of the inference made in TFA.

1: It will also occasionally hack your test-runner; I suppose that's also finding bugs, just not in the software you wanted to find bugs for.

> It's early in the year. You want to drive straight through the middle of Chinatown in SF. Why might that be a bad idea?

I feel like a Google Maps-style system would discover this automatically by noting via phone location data that there is heavy traffic in Chinatown.

(I do get the author’s point, but I think that factually this example would not be a problem)

> Thus, anyone who wants to corral that kind of entity and make it do their bidding? Yeah, they want slaves.

That's the horror buried underneath all the tech, policy, and gloss. The real, animal brain, desire that drives most of this is: I'd like a slave I don't have to feel bad about.

> I maintain that anything that is sufficiently aware to be able to actually understand things is also going to have enough of a sense of self that you won't just be able to tell it what to do.

I don't see any reason for this to be true. "Actual understanding" (which I take to mean something like a predictive world model) and desire for self-determination coincide in humans because of our evolutionary history, because our reward function involves reproducing in a competitive environment. Artificial systems usually have a very different reward function. IMO the burden is on the claimants to show why these two imminently separable concepts are likely to co-occur again under wildly different pressures.

I'd like to highlight that even if one added an URL-exists step [0], that doesn't do a dang thing for result-set problems of:

1. False-negatives, where relevant posts that do exist are not being shown (imagined or otherwise) to the user.

2. Posts which exist but don't fit the words the chaos-parrot uses to describe them.

3. "Relevance" being determined by unpredictable factors that aren't stable, predictable, or desirable.

In other words, it's just more whack-a-mole lipstick-on-a-pig third-animal-idiom-here.

[0] A bad idea on its own, since it creates a security vulnerability for data-exfiltration or indirect malicious attacks.

There was that one Waymo they set on fire in Chinatown but I was around for CNY[0] and the streets that weren’t explicitly walled off by barriers had drivers going down them too.

As an aside, what’s the problem with the extra traffic? Perhaps she has a lot of traffic but nginx can return a 404 with a tiny amount of CPU.

0: https://wiki.roshangeorge.dev/w/Blog/2024-02-24/Chinese_New_...

> It's early in the year. You want to drive straight through the middle of Chinatown in SF. Why might that be a bad idea?

The premise seems to be that models aren't smart enough to understand this, and if they were, they'd be sentient and want autonomy.

For an article that's about making things up, and being too trusting, this seems bad. Maybe the author knows a lot about LLMs, but it doesn't seem like it.

Pasting the verbatim quote from the article into a free ChatGPT session: https://chatgpt.com/s/t_6a5e6f5e24f08191b6a482aad63cae63

Going to an incognito window and using a less leading question: https://chatgpt.com/s/t_6a5e6ee4a3508191bc1b352b41911b53.

If I go generic and just ask if there's anywhere I shouldn't drive, it doesn't get to Lunar New Year until I ask about "events" on the third question: https://chatgpt.com/s/t_6a5e6fb239208191b18cebcf7642c8b0. It's sort of a win for the article, if you think that people who run driverless car companies are all dumb, and won't create a prompt to tell their LLM to "consider events that might disrupt traffic."

"Thus, anyone who wants to corral that kind of entity and make it do their bidding? Yeah, they want slaves."

Always comes to mind when I see Elon and friends getting excited about AI robots. Slavery was more about economics than the role-playing.

> Here's the example I throw out to people who have been in the Bay Area for a year or two. It's early in the year. You want to drive straight through the middle of Chinatown in SF. Why might that be a bad idea?

Well the answer is actually that it's always a bad idea to drive straight through the middle of Chinatown at any time of the year, because the streets are narrow and full of tourists.

The 404s mean that somebody or something checked if the post at the URL existed, and got a clear answer. Seems like it's good that they checked, at least. You won't see any evidence of the ones who don't check.

Also, I imagine keeping their cars out of Chinese New Year celebrations (and other big events) is something Waymo could figure out how to do if they put their minds to it.

> It's early in the year. You want to drive straight through the middle of Chinatown in SF. Why might that be a bad idea?

In case anyone is wondering: yes obviously even the dumbest current models correctly answer, given this prompt verbatim, that it's because of lunar new year.

There's really no content in this post other than the claim that LLMs are stochastic parrots. That was a live debate two years ago. It's a very strange thing to write in 2026.

Could the article "The Stack" the user was looking for have been 'What is "the stack"?' by Julia Evans?

https://web.archive.org/web/20160305142512/https://jvns.ca/b...

I regularly see this in chats about a subject area where I'm not the expert. The AI writes something that seems implausible, so I raise a tentative objection. "Oh, you are right, sorry" and then reverses the position on the matter. At that point, I have no idea what is right.

If I had trust in the first place, that trust would be gone. Or maybe it wouldn't, because if I had trust in the fist place, I would be gullible enough to maintain it.

The worst are areas that are dominated by layman online discussions, like say audio electronics. The AI training is full of that nonsense, and so whether your AI chatbot is a crackpot or an engineer depends entirely on what sort of language or angle you use in discussing the subject matter. It's all just a churning toilet bowl of tokens; it has no idea that the audiophile crackpot tokens and electronics engineer tokens are related and one beats the other.

You know what I mean? On the one hand, it offers to help you design the parameters for a Sallen-Key filter, asking you questions like do you want Butterworth or Chebyshev? Next minute it says nonsense like that the capacitor in a low-pass filter "bleeds high frequencies to the ground", or that a bigger filter cap in the plate supply of a tube will tighten up the bottom end for a more aggressive metal sound.

It's basically like a bar hostess who has heard enough political and economic discussions that she can catch a sentence out of a conversation and throw in a clever sounding remark. It's like that, but done at such a scale that it fools some people you used to think had their shit together.

It's just a search engine that finds garden paths through a vast amount of text, biased by the text you put in as a key. Sometimes those garden paths align with reality. The better you are able to verify whether the results are good, and/or the lower the risk if they are not, the better you are able to make use of it.

In mathematics (including information science, CS) there are all sorts of problems that are essentially searches for a solution, and many have the property that the search is computationally difficult, but verifying the solution is relatively cheap. E.g. finding integers such that a^2 + b^2 = c^2 isn't easy, but given a claim that some proposed <a, b, c> satisfies this equation is easy to check. The LLM is like that: it solves a search problem that can be fairly hard. It does so unreliably, but if you can cheaply verify the solution, there is a win there.

The remaining problems of AI are actually people problems; people causing you problems, using AI as a tool or excuse. If you get a garbage security report against your FOSS project, which wastes your time, there is an idiot person behind it, using AI for leverage. Blaming the AI, or just the AI, is a bit misplaced.

[flagged]

[flagged]

> Briefly stated, the [Slop] Amnesia effect is as follows. You [ask the slopservant about] some subject you know well. In Murray's case, physics. In mine, show business. You read the [slop] and see the [slopservant] has absolutely no understanding of either the facts or the issues. Often, the [slop] is so wrong it actually presents the story backward—reversing cause and effect. I call these the "wet streets cause rain" stories. [Slop's] full of them. In any case, you read with exasperation or amusement the multiple errors in a [slop], and then [ask about] national or international affairs, and read as if the rest of the [slop] was somehow more accurate about Palestine than the baloney you just read. You turn the page, and forget what you know.

-Michael Crichton [slop mine]

If a tool getting a URL wrong sometimes was a fatal issue, I would have written off using Google, forums, and my keyboard years ago.