Genesis: “So God created mankind in his own image, in the image of God he created them; male and female he created them.”
The Monster Inside ChatGPT
141–150 of 152 posts
Re: The Monster Inside ChatGPT
#142Earlier quoted context omitted.
This isn't suggesting no one understands how these models are architected, nor is anyone saying that SDPA / matrix multiplication isn't understood by those who create these systems. What's being said is that the result of training and the way in which information is processed in latent space is opaque. There are strategies to dissect a models inner workings, but this is an active field of research and incomplete.
Whatever comes out of any LLM will directly depend upon the data you fed it and which answers your reinforced as correct. There is nothing unknown or mystical about it.
The same could be said of people, revealing the emptiness of this idea. Knowing the process at a mechanism level says nothing about the outcome. Some people output German, some English. It’s sub-mechanisms are plastic and emergent
Re: The Monster Inside ChatGPT
#143If you put lemons in a blender and add water it'll produce lemon juice. If you put your hand in a blender however, you'll get a mangled hand. Is this exposing dark tendencies of mangling bodies hidden deep down blenders all across the globe? Or is it just doing what's supposed to be doing? My point is, we can add all sorts of security measures but at the end of the day nothing is a replacement for user education and…
How much power and control do we assume we have in determining the ultimate purpose or "end goal" (telos) of large language models? Assuming teleological essentialism is real, where does the telos come from? How much of it comes from the creators? If there are other sources, what are they and what's the mechanism of transfer?
LLMs by themselves are pretty useless, it's only during inference where they produce potentially valuable output. So the analysis of causality isn't just exclusive to the LLM, but the combination of the model and prompt.
So there is some cause and influence by the models biases, or its essence if you must, but the prompt takes an important role too. I believe it's important for companies to figure this out, but for me personally I'm not interested at all in this balance.
What I'm interested in is how I can use these models as an extension of myself. And I'm also interested in showing people around me how they could do the same.
Re: The Monster Inside ChatGPT
#144If you put lemons in a blender and add water it'll produce lemon juice. If you put your hand in a blender however, you'll get a mangled hand. Is this exposing dark tendencies of mangling bodies hidden deep down blenders all across the globe? Or is it just doing what's supposed to be doing? My point is, we can add all sorts of security measures but at the end of the day nothing is a replacement for user education and…
The scary part is that no one put their hand in the blender. They put a rotten fruit in and got mangled hand bits out. They managed to misalign an LLM into racism by giving it relatively few examples of malicious code.
That's an interesting point. I have to admit I haven't taken a look at the examples. So you say there is a problem of proportion? You put relatively little effort and get a lot of garbage out?
In any case, this might be interesting for companies making tons of money, but for us general public I think it's much more important to talk about education.
Re: The Monster Inside ChatGPT
#145If you put lemons in a blender and add water it'll produce lemon juice. If you put your hand in a blender however, you'll get a mangled hand. Is this exposing dark tendencies of mangling bodies hidden deep down blenders all across the globe? Or is it just doing what's supposed to be doing? My point is, we can add all sorts of security measures but at the end of the day nothing is a replacement for user education and…
The industry sells the devices as "intelligent" which brings the expectation of maturity and wisdom-- dependability. So the analogy is more like a cabin door on a 737. Some yahoo could try to open it in flight, but that doesn't justify it spontaneously blowing out at altitude. But the elephant in the room is why are we persevering over these silly dichotomies? If you've got a problem with an AI, why not just ask the…
Yeah and that's a problem for the industry. At most it's exposing a problem in society. These companies are not interested in smart LLMs. They are interested in smart LLMs just as long as they make them obscenely rich.
For the regular user it's just a matter of changing the prompt to get a better output using a capable model. So it's a matter of education.
Of course model bias takes a role. If you train a model on racist posts you'll get a racist model. But as long as you have a fairly capable model for the average use, these edge cases aren't of interest for the user who can just adjust their prompts.
Re: The Monster Inside ChatGPT
#146If you put lemons in a blender and add water it'll produce lemon juice. If you put your hand in a blender however, you'll get a mangled hand. Is this exposing dark tendencies of mangling bodies hidden deep down blenders all across the globe? Or is it just doing what's supposed to be doing? My point is, we can add all sorts of security measures but at the end of the day nothing is a replacement for user education and…
I disagree. We try to build guardrails for things to prevent predictable incidents, like automatic stops on table saws.
As we should, but if the automatic table saw stopping mechanism breaks and you just bypass it, it's on you not the table saw.
So if you make the LLM spit malware by crafting a prompt in order to do it, it's not the fault of the model. It's important maybe for companies profiting on selling inference time for users to moderate output, but for us regular users it's completely tangential.
Re: The Monster Inside ChatGPT
#147Earlier quoted context omitted.
How much power and control do we assume we have in determining the ultimate purpose or "end goal" (telos) of large language models? Assuming teleological essentialism is real, where does the telos come from? How much of it comes from the creators? If there are other sources, what are they and what's the mechanism of transfer?
LLMs by themselves are pretty useless, it's only during inference where they produce potentially valuable output. So the analysis of causality isn't just exclusive to the LLM, but the combination of the model and prompt. So there is some cause and influence by the models biases, or its essence if you must, but the prompt takes an important role too. I believe it's important for companies to figure this out, but for m…
It's a bit difficult to understand how your comment responds to the teleology of LLMs. Can you please clarify?
Re: The Monster Inside ChatGPT
#148Earlier quoted context omitted.
LLMs by themselves are pretty useless, it's only during inference where they produce potentially valuable output. So the analysis of causality isn't just exclusive to the LLM, but the combination of the model and prompt. So there is some cause and influence by the models biases, or its essence if you must, but the prompt takes an important role too. I believe it's important for companies to figure this out, but for m…
It's a bit difficult to understand how your comment responds to the teleology of LLMs. Can you please clarify?
I said it's important for companies, but for me I'm more interest in how it functions for me personally, for the average consumer.
Re: The Monster Inside ChatGPT
#149Earlier quoted context omitted.
It's a bit difficult to understand how your comment responds to the teleology of LLMs. Can you please clarify?
I said it's important for companies, but for me I'm more interest in how it functions for me personally, for the average consumer.
It sounds like you're trying to respond to another comment thread.
Re: The Monster Inside ChatGPT
#150Earlier quoted context omitted.
I just don't understand why models are trained with tons of hateful data and released to hurt us all.
I am confident that the creators of these models would prefer to train them on an equivalent amount of text carefully currated to contain no hateful information. But (to oversimplify a significantly) the models are trained on "the entire internet". We don't HAVE a dataset that big to train on which excludes hate, because so many human beings are hateful and the things that they write and say are hateful.
Then... Stop. Stop doing this shit. Stop poisoning the well. Stop cultivating and amplifying and spreading everywherethis shit. Stop it.