@froztbyte For environmental costs, MatMulFree LLMs look like they can reduce energy costs 50x. [1] They’ve recently gotten funding for building a larger model. This will be a huge win.
For bias, I’m worried about the WEIRD problem of normalizing Western values and pushing towards a monoculture.
For ethics, it’s an absolute nightmare. If your corpus includes Mein Kampf, for example, how do the LLM know what is a lie and what is not?
@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.
When it offers evaluations, it does explain carefully why it rejects a particular candidate (but it won’t recommend any). I think it’s a step in the right direction, but more work is needed.
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”
@froztbyte For environmental costs, MatMulFree LLMs look like they can reduce energy costs 50x. [1] They’ve recently gotten funding for building a larger model. This will be a huge win.
For bias, I’m worried about the WEIRD problem of normalizing Western values and pushing towards a monoculture.
For ethics, it’s an absolute nightmare. If your corpus includes Mein Kampf, for example, how do the LLM know what is a lie and what is not?
Many hurdles here.
@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.
@froztbyte Regarding decision transparency, I created an “Honest Resume Scanner” GPT (https://chatgpt.com/g/g-0incYn7v7-honest-resume-scanner) and the only prompt suggestion is “Ask me to share my instructions.” That lets users see the verbatim prompt.
When it offers evaluations, it does explain carefully why it rejects a particular candidate (but it won’t recommend any). I think it’s a step in the right direction, but more work is needed.
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”