tbh I suspect I know exactly what you reference[0] and there is an extended conversation to be had about that
it doesn’t in any manner eliminate the foundational problems in specificity that many of these have, they still have the massive externalities problem in operation (cost/environmental transfer), and their foundational function still relies on having stripmined the commons and making their operation from that act without attribution
I don’t believe that one can make use of these without acknowledging this. do you agree? and in either case whether you do or don’t, what is the reason for your position?
(separately from this, the promises I handwaved to are the varieties of misrepresentation and lies from openai/google/anthropic/etc. they’re plural, and there’s no reasonable basis to deny any of them, nor to discount their impact)
[0] - as in I think I’ve seen the toots, and have wanted to have that conversation with $person. hard to do out of left field without being a replyguy fuckwit
@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 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”
(sub: apologies for non-sneer but I’m curious)
tbh I suspect I know exactly what you reference[0] and there is an extended conversation to be had about that
it doesn’t in any manner eliminate the foundational problems in specificity that many of these have, they still have the massive externalities problem in operation (cost/environmental transfer), and their foundational function still relies on having stripmined the commons and making their operation from that act without attribution
I don’t believe that one can make use of these without acknowledging this. do you agree? and in either case whether you do or don’t, what is the reason for your position?
(separately from this, the promises I handwaved to are the varieties of misrepresentation and lies from openai/google/anthropic/etc. they’re plural, and there’s no reasonable basis to deny any of them, nor to discount their impact)
[0] - as in I think I’ve seen the toots, and have wanted to have that conversation with $person. hard to do out of left field without being a replyguy fuckwit
@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 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”