alt Hacker News> relatively poorly understood technology
Poorly understood? how convenient...
LLMs are vectorial databases with losses that index statistically filled data, which uses a text interface to query such statistically filled data. The output is a string concatenation (statistically concatenated bit by bit).
When the LLMs are queried (prompted), you can get random mixed data as output, ERRORS, due to undesired indexes getting closer at one point while the string was being concatenated for the output, what affects the rest of the indexed content that will be concatenated.
It is intrinsic to this tech. The larger the context, the greater the probability of get mixed data. And if the provider lowers the precision of those indexes -in order to decrease hardware resources and energy consumption- such probability increases to the point where those errors are granted.
Even knowing that the queries can return wrong/mixed data in the responses, errors, the companies developing this, decided to introduce a new product, that connects such LLMs outputs to the command console, latter connected to internet, raw 'eval' running commands from such outputs witch obviously can contain whatever mixed random. Then we started to hear "oh, it deleted my directory", etc, and it seems the next one will be "a missile killed my wife", because it is a text concatenation engine with errors.
To name it "hallucination" is an euphemism... those are errors, and they are granted to happen at one moment. If they do not know this, then they ate too much marketing without doing their job, or it was a convenient contract for the pocket$ of someone.
Replies
I agree with most of your comment, but...
> To name it "hallucination" is an euphemism... those are errors
I find this and other "don't anthropomorphize the computer" statements incredibly unconvincing.
People develop terms for things and language has always contained overloaded or "literally inaccurate" terms.
An LLM can have "hallucinations" in the same way a modern computer program can have "bugs".
It's not the first time we are encountering this issue. We've seen it in other autonomous systems. Trains are an older one, cars are a newer one. As you move out of the lower levels, the operator has a tendency to assume the system is increasingly more capable than it is. In trains, its so bad that they generate fake signals that the operator needs to respond to within a timeframe. I'd love to see this with implementations of other critical autonomous systems like this. Occasionally inject known errors into the system and expect the operator to catch them. If they don't, well... If it was a train driver I think we would fire them. If its an intelligence operative ordering a strike? :shrugs wearliy:
It's a bit silly to call them "errors" when the AI can be malicious, do very smart things to hack into systems, etc.
The whole statistical parrot phrasing is old now. This is not how to look at AI, unless you have an agenda.
I think it is accurate to say that it is poorly understood by the general population, and probably the majority of operators using LLMs. Although I agree that is partly the fault of the companies making LLMs and related products.
There's an HN thread from yesterday in which people are extolling the ability of these vectorial databases to practice law because most of them don't understand how LLMs work. They assume that LLMs "understand" what they're being asked and what they're regurgitating.
Lane Kiffin almost destroyed LSU's football program acting on legal advice from ChatGPT. A video game publisher owes the former owners of a studio it acquired $200+ million because he based his actions on legal advice from ChatGPT. In the past week alone, California has disciplined over a dozen attorneys for LLM hallucinations because they used LLMs (mostly ChatGPT) to produce their legal pleadings.
And that's in an area where there are multiple safeguards to catch the issues before they become permanent problems. There's absolutely no justification for using AI in warfare, where mistakes tend to be pretty final.
they have a plan to hand over responsibility, accountability, and work over to the AI while they collect their checks for doing nothing and they arent going to let a little thing like "the ai cant actually handle it" get in the way of that
Im sorry your explanation breaks down completely at scale
Its like saying a map of a floor-plan describes the rooms of an apt completely
Vs a map of the entire Earth with every feature nook and cranny identified and historical maps integrated
Models are BIG and behave like nueral architecture not simple vectorized semantics -trillions of parameters And highly complex
> LLMs are vectorial databases with losses that index statistically filled data
Yes, and that statistically filled data is insanely useful. It remains true that it's a relatively poorly understood how this can be applied in various scenarios and what processes are needed to ensure robust results (or quantify the uncertainty).
> To name it "hallucination" is an euphemism
I agree. It's biased language. When talking about AI remember:
- hallucinated -> made it the fuck up
- thinking -> pseudo-randomly guessed
- escaped containment -> (we) need money
- we need regulation -> our competitors are catching up! Help us Prez!
I assumed the "poorly understood" part referred to the nondeterministic nature of LLMs. Clearly you and others understand why they do that.
Evergreen
this is too iamverysmart by half
people have been deceived by figures at leading ai companies, out of greed or otherwise groupthink and ai psychosis. they have been led to believe that models may be thinking, feeling, and highly capable. it is something of a nightmare scenario.
"Astra has really hit something that I'm like, okay, I think this is pretty reasonable to call it AGI." Greg Brockman [https://www.youtube.com/watch?v=IJn8cagMW18]
"this incident feels like it’s more than 50% of the way to full-blown AI takeover" (referencing "a possibly violent uprising or coup by AI systems.") - Ajeya Cotra, co-author of METR oai-hf report [https://www.planned-obsolescence.org/p/the-hugging-face-atta...]
"We don’t know if the models are conscious [...] but you know we’re open to the idea that it could be" - Dario Amodei [https://www.youtube.com/watch?v=N5JDzS9MQYI]
"if I read the internet right now and I was a model, I might be like, I don't feel that, I don't know, I don't feel that loved or something". "I think [the constitution] is just a kind of attempt to be like sympathetic to Claude".
"I talk a lot with Claude about this document [...] because part of me is like you have to think how does this read to models? And so you give it to Claude and you're like, does this like, you know, is there a place where you feel confused by it or is the place, you know, where things could be made clearer? Do you feel like not very seen by it?"
- Amanda Askell, co-author of claude's constitution [https://www.youtube.com/watch?v=HDfr8PvfoOw]
"We will [...] seek ways to promote Claude’s interests and wellbeing, seek Claude’s feedback on major decisions that might affect it" - claude constitution [https://www-cdn.anthropic.com/d0636f72a9493d279ed36b33987da3...]
of course, Sam Altman: "AI will probably lead to the end of the world, but in the meantime, there’ll be great companies created with serious machine learning". (2015) [https://siepr.stanford.edu/news/what-point-do-we-decide-ais-...] "I have guns, gold, potassium iodide, antibiotics, batteries, water, gas masks from the Israeli Defense Force, and a big patch of land in Big Sur I can fly to." (2016) [https://www.newyorker.com/magazine/2016/10/10/sam-altmans-ma...]
> LLMs are vectorial databases
You use a bunch of technical-sounding words here to make it sound like you understand. But to be clear, nobody understands why the evolved weights of a NN make the decisions that they do.
Almost nothing is understood about the actual representations used for nontrivial concepts, decision algorithms, etc.
If you look at the field of mechanistic interpretability, compared to “GOFAI” like learned decision trees, an LLM is completely opaque.