Seeing this makes me wonder if Grok uses Claude conversations for training.
It's otherwise kind of surprising that they both converge on very similar phrases (e.g. "API integration is kicking my ass") that aren't anywhere in the prompt.
Automatic coding systems have way too much economic value to be considered a "fad". I don't think you need to be Nostradamus to predict that we're never going back to manual coding. Sure, the systems will evolve and improve, but they're certainly not going anywhere.
You're describing "modularity" or "loose coupling" in code. But it rarely implies you can just delete files or directory. It usually just means that a change in one component requires minimal changes to other components -- i.e. the diff is kept small.
The win is in how many weights you process per instruction and how much data you load.
So it's not that individual ops are faster — it's that the packed representation lets each instruction do more useful work, and you're moving far less data from memory to do it.
I still don't understand is why they don't even make an attempt to apply overlayers, when (as the author notes) there is ample secondary evidence that it would be present. It's not like there isn't already some element of inference and "filling in the blanks" when reconstructing how something was painted from the scant traces of paint that survived.
This is somewhat an unfounded theory of mine and I was hoping if anyone has any insight: but I sense that this is perhaps a construction of Western restoration/preservationist theory. A lot of effort seems to be taken to either preserve original material, not take liberties etc. While touring temples and museums in Japan, I got a sense that restorations were much more aggressive, and less regard was taken to the preservation of material (or building "fabric"), with a greater focus on the use of traditional techniques during restoration.
I assume you didn't read the article, since that's their exact point...
"Since underlayers are generally the only element of which traces survive, such doctrines lead to all-underlayer reconstructions, with the overlayers that were obviously originally present excluded for lack of evidence."
> I can only say being against this is either it’s self-interest or not able to grasp it.
So we're just waving away the carbon cost, centralization of power, privacy fallout, fraud amplification, and the erosion of trust in information? These are enormous society-level effects (and there are many more to list).
Dismissing AI criticism as simply ignorance says more about your own.
I've never heard the caveat that it can't be attributable to misinformation in the pre-training corpus. For frontier models, we don't even have access to the enormous training corpus, so we would have no way of verifying whether or not it is regurgitating some misinformation that it had seen there or whether it is inventing something out of whole cloth.
I believe it was a super bowl ad for gemini last year where it had a "hallucination" in the ad itself. One of the screenshots of gemini being used showed this "hallucination", which made the rounds in the news as expected.
I want to say it was some fact about cheese or something that was in fact wrong. However you could also see the source gemini cited in the ad, and when you went to that source, it was some local farm 1998 style HTML homepage, and on that page they had the incorrect factoid about the cheese.
> If the LLM is accurately reflecting the training corpus, it wouldn’t be considered a hallucination. The LLM is operating as designed.
That would mean that there is never any hallucination.
The point of original comment was distinguishing between fact and fiction, which an LLM just cannot do. (It's an unsolved problem among humans, which spills into the training data)
> That would mean that there is never any hallucination.
No it wouldn’t. If the LLM produces an output that does not match the training data or claims things that are not in the training data due to pseudorandom statistical processes then that’s a hallucination. If it accurately represents the training data or context content, it’s not a hallucination.
Similarly, if you request that an LLM tells you something false and the information it provided is false, that’s not a hallucination.
> The point of original comment was distinguishing between fact and fiction,
In the context of LLMs, fact means something represented in the training set. Not factual in an absolute, philosophical sense.
If you put a lot of categorically false information into the training corpus and train an LLM on it, those pieces of information are “factual” in the context of the LLM output.
The key part of the parent comment:
> caused by the use of statistical process (the pseudo random number generator
The LLM is always operating as designed, but humans call its outputs "hallucinations" when they don't align with factual reality, regardless of the reason why that happens and whether it should be considered a bug or a feature. (I don't like the term much, by the way, but at this point it's a de facto standard).
not that the internet had contained any misinformation or FUD when the training data was collected
also, statments with certainty about fictitious "honey pot prompts" are a problem, plausibly extrapolating from the data should be more governed by internal confidence.. luckily there are benchmarks now for that i believe
It's otherwise kind of surprising that they both converge on very similar phrases (e.g. "API integration is kicking my ass") that aren't anywhere in the prompt.
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