I didn’t mean your topics of interest are trivial, I meant that LLMs reproduce information devoid of human perspective which turns it into just a bunch of trivia, facts without substance. A blog post about tungsten’s use in lightbulbs by a lightbulb fanatic is a fascinating insight into the subject built on their years of fanaticism, every word is meaningful. An LLM’s words mean nothing, it is bland, it is soulless, an LLM’s summarization of a blog post is not a more efficient way to consume the blog post, it is something altogether different.
That the answers of an LLM are “sharp” and “specific” is not a good thing. You are only experiencing the subject through your own lens, through your own questions, through your own sycophantic robot. The imperfect way that humans communicate is so important to broadening our horizons. Some of the most influential learnings I’ve experienced came from tangential lines in books and blogs. And when I’m reading, sitting, thinking, letting the words bounce around my head, I am forced to reconcile ideas myself, to consider, to think. Just saying “tell me more about x” or “why is x?” every time I have a thought leaves no room to really learn and grow.
Anyway, we are all different, if your curiosity can be satisfied by LLMs summarizing, great :)
Could you define what you mean by human perspective? If LLM models are trained on data that's generated by humans and the resultant weights represents a gestalt of that data, isn't the resulting output going to be token patterns that represent the human perspective? For code, I could get that argument so I suppose it depends on the data you're trying to get.