Which of course they are going to try to brush it all away. Better than admitting that this problem very much still exists and isn’t going away anytime soon.
The section about hallucinations is deeply relevant.
Namely, Claude sometimes provides a plausible but incorrect chain-of-thought reasoning when its “true” computational path isn’t available. The model genuinely believes it’s giving a correct reasoning chain, but the interpretability microscope reveals it is constructing symbolic arguments backward from a conclusion.
https://en.wikipedia.org/wiki/On_Bullshit
This empirically confirms the “theory of bullshit” as a category distinct from lying. It suggests that “truth” emerges secondarily to symbolic coherence and plausibility.
This means knowledge itself is fundamentally symbolic-social, not merely correspondence to external fact.
Knowledge emerges from symbolic coherence, linguistic agreement, and social plausibility rather than purely from logical coherence or factual correctness.
https://link.springer.com/article/10.1007/s10676-024-09775-5
> # ChatGPT is bullshit
> Recently, there has been considerable interest in large language models: machine learning systems which produce human-like text and dialogue. Applications of these systems have been plagued by persistent inaccuracies in their output; these are often called “AI hallucinations”. We argue that these falsehoods, and the overall activity of large language models, is better understood as bullshit in the sense explored by Frankfurt (On Bullshit, Princeton, 2005): the models are in an important way indifferent to the truth of their outputs. We distinguish two ways in which the models can be said to be bullshitters, and argue that they clearly meet at least one of these definitions. We further argue that describing AI misrepresentations as bullshit is both a more useful and more accurate way of predicting and discussing the behaviour of these systems.