@froztbyte Given that I am currently working with GenAI every day and have been for a while, I’m going to have to disagree with you about “failed to deliver on promises” and “worthless.”
There are definitely serious problems with GenAI, but actually being useful isn’t one of them.
Consider traditional databases which let you search for strings. Vector databases let you search the meaning.
For one client, someone could search for “videos about cats”. With stemming and stop words, that becomes “cat” and the results might be lists of videos about house cats and maybe the unix “cat” command. Tigers, lions, cheetahs? Nope.
Vector database will return tigers/lions/cheetahs because it “knows” they are cats. A much smarter search. I’ve built that for a client.
I realize it’s probably a toy example but specifically for “cats” you could achieve the similar results by running a thesaurus/synonym-set on your stem words. With the added benefit that a client could add custom synonyms, for more domain-specific stuff that the LLM would probably not know, and not reliably learn through in-prompt or with fine-tuning. (Although i’d argue that if i’m looking for cats, I don’t want to also see videos of tigers, or based on the “understanding” of the LLM of what a cat might be)
For the labeling of videos itself, the most valuable labels would be added by humans, and/or full-text search on the transcript of the video if applicable, speech-to-text being more in the realm of traditional ML than in the realm of GenAI.
As a minor quibble your use case of GenAI is not really “Generative” which is the main thing it’s being sold as.
fosstodon is the programming dot dev of mastodon and I mean that in every negative way you can imagine
your posts all give me slimy SEO vibes and you haven’t shown any upward trajectory since claiming that only generative AI lacks a separation between code and data (fucking what? seriously, think on this) so you’re getting trimmed
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 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.
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.
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.”
@froztbyte Given that I am currently working with GenAI every day and have been for a while, I’m going to have to disagree with you about “failed to deliver on promises” and “worthless.”
There are definitely serious problems with GenAI, but actually being useful isn’t one of them.
for those who can’t be bothered tracing down the thread, Curtis’ slam dunk example of GenAI usefulness turns out to be a searchish engine
god I just read that comment (been busy with other stuff this morning after my last post)
I … I think I sprained my eyes
You know what? I’d have to agree, actually being useful isn’t one of the problems of GenAI. Not being useful very well might be.
@zogwarg OK, my grammar may have been awkward, but you know what I meant.
Meanwhile, those of us working with AI and providing real value will continue to do so.
I wish people would start focusing on the REAL problems with AI and not keep pretending it’s just a Markov Chain on steroids.
On a less sneerious note, I would draw distinctions between:
And so far i’ve really not been convinced of the latter.
@zogwarg
Consider traditional databases which let you search for strings. Vector databases let you search the meaning.
For one client, someone could search for “videos about cats”. With stemming and stop words, that becomes “cat” and the results might be lists of videos about house cats and maybe the unix “cat” command. Tigers, lions, cheetahs? Nope.
Vector database will return tigers/lions/cheetahs because it “knows” they are cats. A much smarter search. I’ve built that for a client.
Removed by mod
I realize it’s probably a toy example but specifically for “cats” you could achieve the similar results by running a thesaurus/synonym-set on your stem words. With the added benefit that a client could add custom synonyms, for more domain-specific stuff that the LLM would probably not know, and not reliably learn through in-prompt or with fine-tuning. (Although i’d argue that if i’m looking for cats, I don’t want to also see videos of tigers, or based on the “understanding” of the LLM of what a cat might be)
For the labeling of videos itself, the most valuable labels would be added by humans, and/or full-text search on the transcript of the video if applicable, speech-to-text being more in the realm of traditional ML than in the realm of GenAI.
As a minor quibble your use case of GenAI is not really “Generative” which is the main thing it’s being sold as.
Removed by mod
fosstodon is the programming dot dev of mastodon and I mean that in every negative way you can imagine
your posts all give me slimy SEO vibes and you haven’t shown any upward trajectory since claiming that only generative AI lacks a separation between code and data (fucking what? seriously, think on this) so you’re getting trimmed
(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. 😃
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.
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.
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.”