I find that debatable. You’ll have to read a lot of irrelevant stuff on Wikipedia first to get the answer you’re looking for. Furthermore, getting educated on related, but important topics in order to read and understand the article is much more effort then getting a tailored answer by an LLM. It also tunes to your specific level of understanding whereas the difficulty in understanding Wikipedia articles varies strongly with topic and your current understanding.
It also tunes to your specific level of understanding whereas the difficulty in understanding Wikipedia articles varies strongly with topic and your current understanding.
How fucking stupid do you have to be to believe this? Who told you this horse shit and how the fuck were you oblivious enough to eat it? Jesus fucking christ. Holy shit. That is a monumental level of naivety and gullibility. That is so fucking stupid.
Furthermore, getting educated on related, but important topics in order to read and understand the article is much more effort then getting a tailored answer by an LLM.
Ah, that’s the issue. I don’t want information spoon-fed to me and I live for learning how the world works. I learn better if I put in more effort, so why would I want to save it? Besides, I can usually skim and find what I’m looking for if I just want quick facts.
That’s cool, I like to do so as well. But it highly depends on the topic. If you have no interest in the topic and only need to deal with some stuff temporarily then reading multiple related encyclopedia entries thoroughly is much more time intensive than getting it ‘spoon fed’. Especially when you look at the outcomes this might just not be worth the effort.
And that’s one of the uses where I find LLMs neat: get the answer tailored to what you want and what you need, skipping a lot of intellectual bloat that you have no use or interest for anyway.
But I fundamentally don’t trust it in a way that is different than any real source. It just isn’t a good tool for accurate information and never will be. Even if it’s using accurate sources, the summaries can easily mislead by virtue of not understanding which details are most important. It can’t reliably figure out what is relevant though language analysis alone.
Yeah you’re right about the accuracy issue. Even if an LLM is 90% accurate, after 1 million requests, you still get 100 thousand erroneous replies. This is significant. Which is why caution is important. I suppose there is a middle way between risks of errors and the outcome one would like to achieve.
The best use of machine learning is to do “human like” pattern recognition at large scale. Like with any statistical analysis, it can have predictable error rates that can be accounted for. The key is to give it narrowly defined tasks that are then followed up by manual review.
A big part of the problem with using it to summarize information is that different topics require different perspectives to analyze properly. A scientist might excel at analyzing and understanding research papers written in their field, but be unable to parse the technical jargon and format of articles from different fields. This is also true with understanding fandoms or cultural subgroups. A specific LLM might be fantastic at analyzing certain types of articles, but suck at others, and because you aren’t able to tweak parameters, you can’t really train it to specialize.
In a sense, the major LLM companies are not only selling a questionable product, but actively holding back more productive uses of AI. They buy up all the computer hardware and waste it trying to build an all purpose tool. Meanwhile, the most fruitful uses of AI cost more to use. Training and running specialized models can theoretically be done on local hardware, but even most prosumers have been priced out. More ambitious uses for scientific research spend more on renting out supercomputers, all because techbros want a monopoly without offering a better product.
And on top of all of that, how much time are you actually saving by asking it to summarize for you? If you need to double check it anyways because it keeps getting stuff wrong, wouldn’t it have been better to do it yourself from the start? You aren’t doing something that would be impossible otherwise, you’re just trying to save time. Machine learning can help scientists run simulations that would take millions of years to run otherwise. Comparing that to the time you spend using and then double checking the LLM, what would your gains be? Would it save orders of magnitude, or would you only save a few minutes off a half hour task? Is the risk of being misinformed worth even that?
I find that debatable. You’ll have to read a lot of irrelevant stuff on Wikipedia first to get the answer you’re looking for. Furthermore, getting educated on related, but important topics in order to read and understand the article is much more effort then getting a tailored answer by an LLM. It also tunes to your specific level of understanding whereas the difficulty in understanding Wikipedia articles varies strongly with topic and your current understanding.
“I don’t want to have to think and learn, it’s too hard, I like when computer tell how think.” - you
Sorry, sorry, I had to come back to this.
How fucking stupid do you have to be to believe this? Who told you this horse shit and how the fuck were you oblivious enough to eat it? Jesus fucking christ. Holy shit. That is a monumental level of naivety and gullibility. That is so fucking stupid.
Ah, that’s the issue. I don’t want information spoon-fed to me and I live for learning how the world works. I learn better if I put in more effort, so why would I want to save it? Besides, I can usually skim and find what I’m looking for if I just want quick facts.
That’s cool, I like to do so as well. But it highly depends on the topic. If you have no interest in the topic and only need to deal with some stuff temporarily then reading multiple related encyclopedia entries thoroughly is much more time intensive than getting it ‘spoon fed’. Especially when you look at the outcomes this might just not be worth the effort.
And that’s one of the uses where I find LLMs neat: get the answer tailored to what you want and what you need, skipping a lot of intellectual bloat that you have no use or interest for anyway.
But I fundamentally don’t trust it in a way that is different than any real source. It just isn’t a good tool for accurate information and never will be. Even if it’s using accurate sources, the summaries can easily mislead by virtue of not understanding which details are most important. It can’t reliably figure out what is relevant though language analysis alone.
Yeah you’re right about the accuracy issue. Even if an LLM is 90% accurate, after 1 million requests, you still get 100 thousand erroneous replies. This is significant. Which is why caution is important. I suppose there is a middle way between risks of errors and the outcome one would like to achieve.
The best use of machine learning is to do “human like” pattern recognition at large scale. Like with any statistical analysis, it can have predictable error rates that can be accounted for. The key is to give it narrowly defined tasks that are then followed up by manual review.
A big part of the problem with using it to summarize information is that different topics require different perspectives to analyze properly. A scientist might excel at analyzing and understanding research papers written in their field, but be unable to parse the technical jargon and format of articles from different fields. This is also true with understanding fandoms or cultural subgroups. A specific LLM might be fantastic at analyzing certain types of articles, but suck at others, and because you aren’t able to tweak parameters, you can’t really train it to specialize.
In a sense, the major LLM companies are not only selling a questionable product, but actively holding back more productive uses of AI. They buy up all the computer hardware and waste it trying to build an all purpose tool. Meanwhile, the most fruitful uses of AI cost more to use. Training and running specialized models can theoretically be done on local hardware, but even most prosumers have been priced out. More ambitious uses for scientific research spend more on renting out supercomputers, all because techbros want a monopoly without offering a better product.
And on top of all of that, how much time are you actually saving by asking it to summarize for you? If you need to double check it anyways because it keeps getting stuff wrong, wouldn’t it have been better to do it yourself from the start? You aren’t doing something that would be impossible otherwise, you’re just trying to save time. Machine learning can help scientists run simulations that would take millions of years to run otherwise. Comparing that to the time you spend using and then double checking the LLM, what would your gains be? Would it save orders of magnitude, or would you only save a few minutes off a half hour task? Is the risk of being misinformed worth even that?