New #blog post: MDN’s AI Help and lucid lies.

This article on AI focused on the inherent untrustworthiness of LLMs, and attempts to break down where LLM untrustworthiness comes from. Stay tuned for a follow-up article about AI that focuses on data-scraping and the theory of labor. It’ll examine what makes many forms of generative AI ethically problematic, and the constraints employed by more ethical forms.

Excerpt:

I don’t find the mere existence of LLM dishonesty to be worth blogging about; it’s already well-established. Let’s instead explore one of the inescapable roots of this dishonesty: LLMs exacerbate biases already present in their training data and fail to distinguish between unrelated concepts, creating lucid lies.

A lucid lie is a lie that, unlike a hallucination, can be traced directly to content in training data uncritically absorbed by a large language model. MDN’s AI Help is the perfect example.

Originally posted on seirdy.one: see original. #MDN #AI #LLM #LucidLies

MDN’s AI Help and lucid lies

MDN’s AI Help can’t critically examine training data’s gaps, biases, and unrelated topics. It’s a useful demonstration of LLMs’ uncorrectable lucid lies.

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