I've been using "off-by-one" errors to describe one of my biggest concerns with LLMs replacing search, or acting as research agents, or functionally being expected to be reliable narrators in general. If you ask ChatGPT when George Washington was born, and it comes back with March 4th, 2017, you'll reject that outright and recognize it's hallucinated a garbage response, presuming you have enough context to have understood who George Washington was in the first place and that your brain hasn't completely succumbed to rot yet.
But if it returns February 20th, 1731... that... man, that sounds close? Is that right? It sounds like it _could_ be right... Isn't Presidents' Day essentially based on Washington's birthday? And _that's_ in February, right? So, yeah, February 20th, 1731. That's probably Washington's birthday.
And so the LLM becomes an arbiter of capital-T Truth and we lose our shared understanding of actual, factual data, and actual, factual history. It'll take less than a generation for the slop factories to poison the well, and while the idea is obviously that you train your models on "known good", pre-slop content, and that you weight those "facts" more heavily, a concerted effort to degrade the Truthfulness of various facts could likely be more successful than we anticipate, and more importantly: dramatically more successful than any layperson can easily understand.
We already saw that with the early Bard Google AI proto-Gemini results, where it was recommending glue as a pizza topping, _with authority_. We've been training ourselves to treat responses from computers (and specifically Google) as if they have authority, we've been eroding our own understanding and capabilities around media literacy, journalism, fact-checking, and what constitutes an actual "fact", and we've had a shared understanding that computers can _calculate_ things with accuracy and fidelity and consistency. All of that becomes confounded with an LLM that could reasonably get to a place where it reports that 2+2=5.
The worst part about the nature of this particular pathway to ruin is that the off-by-one nature of these errors are how they'll infiltrate and bury themselves into some system, insidiously, and below the surface, until days or months or years later when the error results in, I don't know, mega-doses of radiation because of a mis-coded rounding error that some agentic AI got wrong when doing a unit conversion and failed to catch it. We were already making those errors as humans, but as our dependence and faith on LLMs to be "mostly right" increases, and our willingness and motivation to check it for errors dwindles, especially when results "look" right, this will go from being a hypothetical issue to being a practical one extremely quickly and painfully, and probably faster than we can possibly defend against it.
Interesting times ahead, I suppose, in the Chinese-curse sense of the word.
At every point, during a knowledge/data search for reaching a particular goal, the onus is _always_ on the person searching to do their best to ensure that the sources they use are accurate, and they do the effort required to ensure that they translate that properly to fit that goal.
The education system I grew up in was not perfect. Teachers were not experts in their field, but would state factual inaccuracies - as you say LLMs do - with authority. Libraries didn't have good books; the ones they had were too old, or too propaganda-driven, or too basic. The students were not too interested in learning, so they rote-learned, copied answers off each other and focussed on results than the learning process. If I had today's LLMs then, I'd have been a lot better off, and would've been able to learn a lot more (assuming that I went through the effort to go through all the sources the LLM cited).
The older you grow, you know that there is no arbiter of T-Truth; you can make someone/something that for yourself, but times change, "actual, factual history" could get proven incorrect, and you will need to update your knowledge stores and beliefs along with it, all the while being ready to be proved incorrect again. This has always been the case, and will continue to be, even with LLMs.
But if it returns February 20th, 1731... that... man, that sounds close? Is that right? It sounds like it _could_ be right... Isn't Presidents' Day essentially based on Washington's birthday? And _that's_ in February, right? So, yeah, February 20th, 1731. That's probably Washington's birthday.
And so the LLM becomes an arbiter of capital-T Truth and we lose our shared understanding of actual, factual data, and actual, factual history. It'll take less than a generation for the slop factories to poison the well, and while the idea is obviously that you train your models on "known good", pre-slop content, and that you weight those "facts" more heavily, a concerted effort to degrade the Truthfulness of various facts could likely be more successful than we anticipate, and more importantly: dramatically more successful than any layperson can easily understand.
We already saw that with the early Bard Google AI proto-Gemini results, where it was recommending glue as a pizza topping, _with authority_. We've been training ourselves to treat responses from computers (and specifically Google) as if they have authority, we've been eroding our own understanding and capabilities around media literacy, journalism, fact-checking, and what constitutes an actual "fact", and we've had a shared understanding that computers can _calculate_ things with accuracy and fidelity and consistency. All of that becomes confounded with an LLM that could reasonably get to a place where it reports that 2+2=5.
The worst part about the nature of this particular pathway to ruin is that the off-by-one nature of these errors are how they'll infiltrate and bury themselves into some system, insidiously, and below the surface, until days or months or years later when the error results in, I don't know, mega-doses of radiation because of a mis-coded rounding error that some agentic AI got wrong when doing a unit conversion and failed to catch it. We were already making those errors as humans, but as our dependence and faith on LLMs to be "mostly right" increases, and our willingness and motivation to check it for errors dwindles, especially when results "look" right, this will go from being a hypothetical issue to being a practical one extremely quickly and painfully, and probably faster than we can possibly defend against it.
Interesting times ahead, I suppose, in the Chinese-curse sense of the word.