I mean you still gotta understand some shit for Ctrl+F to be helpful. If you’ve ever taken an open book quiz without prior study you’ll learn pretty quick that open book does NOT = easy A (depending on the class / prof I guess, but you get the gist).
So, open book Ctrl-F’able bar exam, I could probably get an okay score just on key word matching, not knowing jack shit about law; but it’d be far from a perfect score. Current state of machine learning appears to be in a comparable boat.
your post shows a serious lack of comprehension. just because so many of the posters in this thread are idiots didn’t mean you have to participate too.
(CPU time extremely counts, and resource-wise with these things it’s really quite a lot)
Steelmanning what this person said, I think the issue is that your ability to CTRL+F through a book during a time-limited exam is not as strong as even a single computer clocked at GHz doing the same thing. You can CTRL+F through a single book in the same time it takes it to CTRL+F through the entire body of knowledge.
But the AI isn’t “recalling” in the same way you do, it doesn’t “remember” what it “read”, it “reads” on demand and has instant access to essentially all of the information available online, from which it collects the information if and when it needs it.
So yes, it is literally “sat” there with all the books open in front of it, and the ability to pinpoint a bit of information in any one of all the books in milliseconds.
Yes, it does, from the information it was trained on (or - stored), which like you say, requires a lot of hardware power so it can be accessed on demand. It isn’t just manifesting the information out of thin air, and it definitely doesn’t “remember” in the same way we do.
It’s definitely not indexed, we use RAG architectures to add indexing to data stores that we want the model to have direct access to, the relevant information is injected directly in the context (prompt). This can somewhat be equated to short term memory
The rest of the information is approximated in the weights of the neural network which gives the model general knowledge and intuition…akin to long term memory
but rofl holy shit at “glad to see someone else knows how they work” given the … depth of understanding, shall we say? that was demonstrated in this thread
or it can be equated to a shitty database and lossy compression (with artifacts in the form of “hallucinations”), but that doesn’t make the tech sound particularly smart, does it?
but half the posts in your history are in this thread and that’s too many already
Or when you’re singing karaoke and suddenly break into a chorus of the phone number and email address of a random author of a scholarly paper whom you’ve never met whose paper has nothing to do with the lyrics of “Free Bird“?
These models have so many parameters that, while insufficient to memorize all text it has ever seen, it can end up memorizing some of the content. It is the difference between being able to recall a random passage versus recalling the exact thing you need. Both allow you to spill content verbatim, but one is problematic while the other can be helpful.
There are techniques to allow it it ‘read on demand’, but they are not part of the core model (i.e. the autocmpletion model / LLM) and are tacked on top of it. For example, you can tie it search engine, which Microsoft’s copilot does, and is something which I don’t think is enabled for ChatGPT by default. Or allow it to query a external data bank (Retrieval Augmented Generation).
Except it’s not, because they can’t perfectly recall everything.
It’s more like reading every book in the world, and someone asking you what comes next after “And I…”.
I mean you still gotta understand some shit for Ctrl+F to be helpful. If you’ve ever taken an open book quiz without prior study you’ll learn pretty quick that open book does NOT = easy A (depending on the class / prof I guess, but you get the gist).
So, open book Ctrl-F’able bar exam, I could probably get an okay score just on key word matching, not knowing jack shit about law; but it’d be far from a perfect score. Current state of machine learning appears to be in a comparable boat.
This is a computer. Time (in this aspect) isn’t an issue.
your post shows a serious lack of comprehension. just because so many of the posters in this thread are idiots didn’t mean you have to participate too.
(CPU time extremely counts, and resource-wise with these things it’s really quite a lot)
Steelmanning what this person said, I think the issue is that your ability to CTRL+F through a book during a time-limited exam is not as strong as even a single computer clocked at GHz doing the same thing. You can CTRL+F through a single book in the same time it takes it to CTRL+F through the entire body of knowledge.
But the AI isn’t “recalling” in the same way you do, it doesn’t “remember” what it “read”, it “reads” on demand and has instant access to essentially all of the information available online, from which it collects the information if and when it needs it.
So yes, it is literally “sat” there with all the books open in front of it, and the ability to pinpoint a bit of information in any one of all the books in milliseconds.
It doesn’t read on demand, it reads once when it’s being trained, and it later recalls what it learnt from that training.
Training LLMs takes a very long time and a lot of hardware power.
Yes, it does, from the information it was trained on (or - stored), which like you say, requires a lot of hardware power so it can be accessed on demand. It isn’t just manifesting the information out of thin air, and it definitely doesn’t “remember” in the same way we do.
It’s definitely not indexed, we use RAG architectures to add indexing to data stores that we want the model to have direct access to, the relevant information is injected directly in the context (prompt). This can somewhat be equated to short term memory
The rest of the information is approximated in the weights of the neural network which gives the model general knowledge and intuition…akin to long term memory
People have such crazy misconceptions about AI. Glad to see someone else knows how it works at least.
oh do fuck off
awww, I just got another bowl of popcorn!
but rofl holy shit at “glad to see someone else knows how they work” given the … depth of understanding, shall we say? that was demonstrated in this thread
or it can be equated to a shitty database and lossy compression (with artifacts in the form of “hallucinations”), but that doesn’t make the tech sound particularly smart, does it?
but half the posts in your history are in this thread and that’s too many already
If it doesn’t read it on demand, how does it sometimes spill its training data verbatim then?
The trained model shouldn’t have that, right? But it does?
https://m.slashdot.org/story/422185
Do you read a song on demand when you are singing the lyrics verbatim?
Or when you’re singing karaoke and suddenly break into a chorus of the phone number and email address of a random author of a scholarly paper whom you’ve never met whose paper has nothing to do with the lyrics of “Free Bird“?
These models have so many parameters that, while insufficient to memorize all text it has ever seen, it can end up memorizing some of the content. It is the difference between being able to recall a random passage versus recalling the exact thing you need. Both allow you to spill content verbatim, but one is problematic while the other can be helpful.
There are techniques to allow it it ‘read on demand’, but they are not part of the core model (i.e. the autocmpletion model / LLM) and are tacked on top of it. For example, you can tie it search engine, which Microsoft’s copilot does, and is something which I don’t think is enabled for ChatGPT by default. Or allow it to query a external data bank (Retrieval Augmented Generation).
and in conclusion an AI is very like an elephant, particularly the back end
“will alwaaays love you…”
Easy. No other answer.