

99.999% would be fantastic.
90% is not good enough to be a primary feature that discourages inspection (like a naive chatbot).
What we have now is like…I dunno, anywhere from <1% to maybe 80% depending on your use case and definition of accuracy, I guess?
I haven’t used Samsung’s stuff specifically. Some web search engines do cite their sources, and I find that to be a nice little time-saver. With the prevalence of SEO spam, most results have like one meaningful sentence buried in 10 paragraphs of nonsense. When the AI can effectively extract that tiny morsel of information, it’s great.
Ideally, I don’t ever want to hear an AI’s opinion, and I don’t ever want information that’s baked into the model from training. I want it to process text with an awareness of complex grammar, syntax, and vocabulary. That’s what LLMs are actually good at.
I don’t think that’s a good comparison in context. If Forbes replaced all their bloggers with ChatGPT, that might very well be a net gain. But that’s not the use case we’re talking about. Nobody goes to Forbes as their first step for information anyway (I mean…I sure hope not…).
Correct.
If we’re talking about an AI search summarizer, then the accuracy lies not in how correct the information is in regard to my query, but in how closely the AI summary matches the cited source material. Kagi does this pretty well. Last I checked, Bing and Google did it very badly. Not sure about Samsung.
On top of that, the UX is critically important. In a traditional search engine, the source comes before the content. I can implicitly ignore any results from Forbes blogs. Even Kagi shunts the sources into footnotes. That’s not a great UX because it elevates unvetted information above its source. In this context, I think it’s fair to consider the quality of the source material as part of the “accuracy”, the same way I would when reading Wikipedia. If Wikipedia replaced their editors with ChatGPT, it would most certainly NOT be a net gain.