@froztbyte Yeah, having in-depth discussions are hard with Mastodon. I keep wanting to write a long post about this topic. For me, the big issues are environmental, bias, and ethics.
Transparency is different. I see it in two categories: how it made its decisions and where it got its data. Both are hard problems and I don’t want to deny them. I just like to push back on the idea that AI is not providing value. 😃
@froztbyte As for the issue of transparency, it’s ridiculously hard in real life. For example, for my website, I used a format I created called “blogdown”, which is Markdown combined with a template language to make it easy to write articles. I never cited my sources, nor do I think I could. From decades of programming, how can I cite everything I’ve ever learned from?
As for how AI is transparent for arriving at decisions, this falls into a separate category and requires different thinking.
You’re not just confident that asking chatGPT to explain it’s inner workings works exactly like a --verbose flag, you’re so sure that’s what happening that it apparently does not occur to you to explain why you think the output is not just more plausible text prediction based on its training weights with no particular insight into the chatGPT black box.
Is this confidence from an intimate knowledge of how LLMs work, or because the output you saw from doing this looks really really plausible? Try and give an explanation without projecting agency onto the LLM, as you did with “explain carefully why it rejects”
@earthquake You’re correct that projecting agency to the LLM is problematic, but in doing so, we get better quality results. I’ve argued that we need new words for LLMs instead of “think,” “understand,” “learn,” etc. We’re anthropomorphizing them and this makes people less critical and gradually shifts their attitudes in incorrect directions.
Unfortunately, I don’t think we’ll ever develop new words which more accurately reflect what is going on.
Got it, because the output you saw from doing this looks really really plausible. Disappointing, but what other answer could it have been?
Here’s a story for you: a scientist cannot get his papers published. In frustration, he complains to his co-worker, “I have detailed charts on the different type and amount of offerings to the idol, and the correlations to results on prayers answered. I think this is a really valuable contribution to understanding how to beseech the gods for intervention in our lives, this will help people! Why won’t they publish my work?”
His co-worker replies, “Certainly! As a large language model I can see how that would be a frustrating experience. Here are five common reasons that research papers are rejected for publication.”
Seriously, what kind of reply is this, you ignore everything I said except the literal last thing, and even then it’s weasel words. “Using agential language for LLMs is wrong, but it works.”
Yes, Curtis, prompting the LLM with language more similar to its training data results in more plausible text prediction in the output, why is that? Because it’s more natural, there’s not a lot of training data on querying a program on its inner workings, so the response is less like natural language.
But you’re not actually getting any insight. You’re just improving the verisimilitude of the text prediction.
@froztbyte Yeah, having in-depth discussions are hard with Mastodon. I keep wanting to write a long post about this topic. For me, the big issues are environmental, bias, and ethics.
Transparency is different. I see it in two categories: how it made its decisions and where it got its data. Both are hard problems and I don’t want to deny them. I just like to push back on the idea that AI is not providing value. 😃
Removed by mod
@froztbyte As for the issue of transparency, it’s ridiculously hard in real life. For example, for my website, I used a format I created called “blogdown”, which is Markdown combined with a template language to make it easy to write articles. I never cited my sources, nor do I think I could. From decades of programming, how can I cite everything I’ve ever learned from?
As for how AI is transparent for arriving at decisions, this falls into a separate category and requires different thinking.
Removed by mod
You’re not just confident that asking chatGPT to explain it’s inner workings works exactly like a --verbose flag, you’re so sure that’s what happening that it apparently does not occur to you to explain why you think the output is not just more plausible text prediction based on its training weights with no particular insight into the chatGPT black box.
Is this confidence from an intimate knowledge of how LLMs work, or because the output you saw from doing this looks really really plausible? Try and give an explanation without projecting agency onto the LLM, as you did with “explain carefully why it rejects”
@earthquake You’re correct that projecting agency to the LLM is problematic, but in doing so, we get better quality results. I’ve argued that we need new words for LLMs instead of “think,” “understand,” “learn,” etc. We’re anthropomorphizing them and this makes people less critical and gradually shifts their attitudes in incorrect directions.
Unfortunately, I don’t think we’ll ever develop new words which more accurately reflect what is going on.
Got it, because the output you saw from doing this looks really really plausible. Disappointing, but what other answer could it have been?
Here’s a story for you: a scientist cannot get his papers published. In frustration, he complains to his co-worker, “I have detailed charts on the different type and amount of offerings to the idol, and the correlations to results on prayers answered. I think this is a really valuable contribution to understanding how to beseech the gods for intervention in our lives, this will help people! Why won’t they publish my work?”
His co-worker replies, “Certainly! As a large language model I can see how that would be a frustrating experience. Here are five common reasons that research papers are rejected for publication.”
Seriously, what kind of reply is this, you ignore everything I said except the literal last thing, and even then it’s weasel words. “Using agential language for LLMs is wrong, but it works.”
Yes, Curtis, prompting the LLM with language more similar to its training data results in more plausible text prediction in the output, why is that? Because it’s more natural, there’s not a lot of training data on querying a program on its inner workings, so the response is less like natural language.
But you’re not actually getting any insight. You’re just improving the verisimilitude of the text prediction.