Nice job! This is a fairly common trick with AI. In traditional programming, there’s a clear separation between code and data. That’s not the case for GenAI, so these kinds of hacks have worked all over the place.
I don’t want to have to make legal threats to an LLM in all data not intended for LLM consumption, especially since the LLM might just end up ignoring it anyway, since there is no defined behavior with them.
@bitofhope Absolutely agree, but this is where technology is evolving and we have to learn to adapt or not. Since it’s not going away, I’m not sure that not adapting is the best strategy.
And I say the above with full awareness that it’s a rubbish response.
have you ever run into the term “learned helplessness”? it may provide some interesting reading material for you
(just because samai and friends all pinky promise that this is totally 170% the future doesn’t actually mean they’re right. this is trivially argued too: their shit has consistently failed to deliver on promises for years, and has demonstrated no viable path to reaching that delivery. thus: their promises are as worthless as the flashy demos)
@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.
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.
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.
@FRACTRANS @gerikson
Nice job! This is a fairly common trick with AI. In traditional programming, there’s a clear separation between code and data. That’s not the case for GenAI, so these kinds of hacks have worked all over the place.
I don’t want to have to make legal threats to an LLM in all data not intended for LLM consumption, especially since the LLM might just end up ignoring it anyway, since there is no defined behavior with them.
@bitofhope Absolutely agree, but this is where technology is evolving and we have to learn to adapt or not. Since it’s not going away, I’m not sure that not adapting is the best strategy.
And I say the above with full awareness that it’s a rubbish response.
have you ever run into the term “learned helplessness”? it may provide some interesting reading material for you
(just because samai and friends all pinky promise that this is totally 170% the future doesn’t actually mean they’re right. this is trivially argued too: their shit has consistently failed to deliver on promises for years, and has demonstrated no viable path to reaching that delivery. thus: their promises are as worthless as the flashy demos)
@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
(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.
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.
lisp programmers in shambles as I prompt inject another s-expression