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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.
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.
AI research is almost as purely empirical as the gradient descent loops its practitioners use to optimize their models. “Why” anything at all works is barely an afterthought.
I think the field deserves more credit than that, there are plenty of interpretability tools like
* natural language autoencoders for explanations of activations: https://transformer-circuits.pub/2026/nla/index.html (demo at https://www.neuronpedia.org/llama3.3-70b-it/nla )
* easier-to-interpret language model families like Backpack models: https://aclanthology.org/2023.acl-long.506/
* attribution graphs to trace internal reasoning steps: https://www.anthropic.com/research/open-source-circuit-traci... (demo at https://www.neuronpedia.org/gemma-2-2b/graph)
* functional analyses which have identified how LLMs do arithmetic - https://arxiv.org/html/2502.00873v1 - and how refusal happens: https://arxiv.org/abs/2406.11717
* data attribution methods linking training data to specific attention heads https://arxiv.org/abs/2601.21996
If we could give a comprehensive and global explanation of an LLM's behavior in a single paragraph, we wouldn't need the model to begin with, but that doesn't mean there's absolutely no understanding of the model internals whatsoever
We might not understand particular "emergent" capabilities, but the low level mechanism is not just understood, but a deterministic algorithm with a handful of basic componets, that are well understood themselves.
That's like saying "my d20 decided to roll a 17"
> 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".
In any other software it would be an error, regression, bug. And in a human process it would be at ~least something someone would call 'bullshit'.
We are, through this process, simulating intelligence. These models aren’t intelligent, but they can simulate it. Every simulation has a degree of fidelity, and we’re not at 100%, not even with the top models. When you think about it in those terms, I find it becomes a lot easier to keep their limitations in mind. Additionally, it becomes easier to remember that this is an algorithm that you are running, and are responsible for, not another being that you can ascribe blame to.
"literally" is a great example of this, because it can also mean "not literally, but with emphasis".
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.
Then there's old-fashioned F'ups that don't fit your political agenda and are often quite damaging and embarrassing, not to mention lethal for people who don't deserve it. e.g. The U.S. used AI tools meant for rapidly picking targets in the middle of a war to plan their initial strikes on Iran. They had time to double check everything and do their due diligence before striking, but they didn't. So, a school next to a military base was targeted and a lot of kids died. This was a genuine F'up resulting from relying on a tool meant to give rapid but merely okay target selection under time pressure when there was no time pressure. The real mistake was made by humans.
The current case of the mistaken nuclear weapon parts shipment seems like an old-fashioned F'up, updated for the times. The people who didn't simply trust the tools and actually double checked should be commended. Others in their situation wouldn't have. I fully expect AI will be scapegoated for a lot of similar F'ups in the future even though it's still the responsibility of human beings to use ethics, caution, and restraint. AI doesn't get fired. Doesn't sue. It's actually pretty awesome for taking the blame.
https://en.wikipedia.org/wiki/Stanislav_Petrov
https://en.wikipedia.org/wiki/1983_Soviet_nuclear_false_alar...
Or the War Games movie and the Norad training mistake that inspired it.
The English translation "99 Red Balloons" is considerably different as far as the details go.
This case, its bullshit machine bullshitting randomly in between specs of stolen wisdom. Nobody asked for that, nobody is in control. We all humans lose in all cases. Quite different scenarios if you asked me.
"You're absolutely right, and that's on me. That's not just a mistake — it's a failure."
Checking to see if there are better targets...
Clauding...
[0] https://en.wikipedia.org/wiki/2026_Minab_school_attack#:~:te...
A few months ago I listened to a talk a General (Admiral?) gave at CSIS where he said that the US purposefully announced their drone-hellscape plan for a Taiwanese invasion in order to force the PLA to reconsider their options/success-likelihood. I wonder if something similar could be coming of this reporting, on the face it looks like an embarrassing fumble, but it implies:
a) the US is able to, and regularly is, tracking and analyzing the manifests of ships between Iran and China.
b) the US is ready and willing to interdict and board vessels even from the PLA.
That these facts are now public might deter the Chinese leadership from attempting to share nuclear tech with Iran or other countries in the future.
The PRC has been hard against nuclear proliferation as a policy over decades, it is highly compliant with IAEA inspection norms, despite the NPT not making it mandatory to be under those inspections. This policy is not something the US has in the past or will in the future engender into it through force.
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