And everyone in tech who has worked on ML before collectively says “yeah that’s what we’ve been trying to tell you”. Don’t get me wrong, LLMs are a huge leap, but god did it show how greedy corporations are, just immediately jumping to “how quick can we lay people off?”. The tech is not to that spec. Yet. It will get there, but goddamn do we need to be demanding some regulations now
I was mostly posting this because the last time LLMs came up, people kept on going on and on about how much their thoughts are like ours and how they know so much information. But as this article makes clear, they have no thoughts and know no information.
In many ways they are simply a mathematical party trick; formulas trained on so much language, they can produce language themselves. But there is no “there” there.
False. There’s plenty of information stored in the models, and plenty of papers that delve into how it’s stored, or how to extract or modify it.
I guess you can nitpick over the work “know”, and what it means, but as someone else pointed out, we don’t actually know what that means in humans anyway. But LLMs do use the information stored in context, they don’t simply regurgitate it verbatim. For example (from this article):
If you ask an LLM what’s near the Eiffel Tower, it’ll list location in Paris. If you edit its stored information to think the Eiffel Tower is in Rome, it’ll actually start suggesting you sights in Rome instead.
They only use words in context, which is their problem. It doesn’t know what the words mean or what the context means; it’s glorified autocomplete.
I guess it depends on what you mean by “information.” Since all of the words it uses are meaningless to it (it doesn’t understand anything of what it either is asked or says), I would say it has no information and knows nothing. At least, nothing more than a calculator knows when it returns 7 + 8 = 15. It doesn’t know what those numbers mean or what it represents; it’s simply returning the result of a computation.
For example, I’ve used it to learn the basics of Galois theory, and it worked pretty well.
The information is stored in the model, do it can tell me the basics
The interactive nature of taking to LLM actually helped me learn better than just reading.
And I know enough general math so I can tell the rare occasions (and they indeed were rare) when it makes things up.
Asking it questions can be better than searching Google, because Google needs exact keywords to find the answer, and the LLM can be more flexible (of course, neither will answer if the answer isn’t in the index/training data).
So what if it doesn’t understand Galois theory - it could teach it to me well enough. Frankly if it did actually understand it, I’d be worried about slavery.
You obviously know some of what it’s telling you is inaccurate already. There is the possibility it’s all bullshit. Granted a lot of it probably isn’t, but it will tell you the bullshit with the exact same level of confidence as actual facts… because it doesn’t know Galois theory and it isn’t teaching it to you, it’s simply stringing sentences together in response to your queries.
If a human were doing this we would rightly proclaim the human a bad teacher that didn’t know their subject, and that you should go somewhere else to get your knowledge. That same critique should apply to the LLM as well.
That said it definitely can be a useful tool. I just would never fully trust knowledge I gained from an LLM. All of it needs to be reviewed for correctness by a human.
That same critique should apply to the LLM as well.
No, it shouldn’t. Instead, you should compare it to the alternatives you have on hand.
The fact is,
Using LLM was a better experience for me then reading a textbook.
And it was also a better experience for me then watching recorded video lectures.
So, if I have to learn something, I have enough background to spot hallucinations, and I don’t have a teacher (having graduated college, that’s always true), I would consider using it, because it’s better then the alternatives.
I just would never fully trust knowledge I gained from an LLM
There are plenty of cases where you shouldn’t fully trust knowledge you gained from a human, too.
And there are, actually, cases where you can trust the knowledge gained from an LLM. Not because it sounds confident, but because you know how it behaves.
Obviously you should do what you think is right, so I mean, I’m not telling you you’re living wrong. Do what you want.
The reason to not trust a human is different from the reasons not to trust an LLM. An LLM is not revealing to you knowledge it understands. Or even knowledge it doesn’t understand. It’s literally completing sentences based on word likelihood. It doesn’t understand any of what it’s saying, and none of it is rooted in any knowledge of the subject of any kind.
I find that concerning in terms of learning from it. But if it worked for you, then go for it.
Sadly we don’t even know what “knowing” is, considering human memory changes every time it is accessed. We might just need language and language only. Right now they’re testing if generating verbalized trains of thought helps (it might?). The question might change to: Does the sum total of human language have enough consistency to produce behavior we might call consciousness? Can we brute force the Chinese room with enough data?
And everyone in tech who has worked on ML before collectively says “yeah that’s what we’ve been trying to tell you”.
Everybody in tech would even have a passing understanding of the technology was collectively saying that. We understand the limits of technology and can feel out the bounds easily. But, too many of these dumbasses with dollar signs in their eyes are all “to the moon!”, and tripping and failing on implementing the tech in unreasonable ways.
It was never a factoid machine, like some people wanted to believe. It was always about creatively writing something, and only one with so much attention.
Funny tidbit about the word “factoid”: its original meaning was “an item of unreliable information that is reported and repeated so often that it becomes accepted as fact”, but the modern usage is “a brief or trivial item of news or information”.
This means that the modern usage of “factoid” is in itself a factoid, and that in the old sense LLMs sort of are factoid machines.
Note that I’m not saying the modern use is wrong. Languages evolve, and words taking on new meanings doesn’t mean the new meanings are “wrong” (and surprisingly words changing to mean the opposite of what they used to mean isn’t all that uncommon either.)
I’ve been unemployed for 7 months. Every online job I see that’s been posted for at least 6 hours has over 200 applications. I’m a senior Dev with 30 years experience, and I can’t find work.
I’d say generative AI is an existential threat as bad as offshoring was for steel in the early 80s. I’m now left with the prospect of spending the last 20 years of my work life at or near minimum wage.
After all, I can’t afford to spend $250,000 on a new bachelor’s degree, and a community college degree might get me to $25/hr, and still costs thousands. This is causing impoverishment on a massive scale.
I’m a senior dev too, and at first I thought the same, but really it’s a market downturn. Companies are just afraid to hire right now. I’d look into generative AI, try to understand how it works. That’s how I’ve been spending my time, and yeah, it’s intuitive the way they do it but the more you understand how it works the more you realize that it’s not ready to take our jobs. Yet. Again maybe someday, but there is a lot of work that needs to be done to get something semi up and running, and the models that Google uses are not going to be usable for every company. (Take a look at all the specialized models already).
Our job never goes away, but it does constantly evolve. This is just another point where we have to learn new skills, and that may be that we all need to be model tuners some day. At the end of the day the user still needs to correctly describe what they want to have happen on the screen, and there are currently no ways to take what they describe into a full piece of software.
Hard to believe a senior dev can’t find work. Those positions are the most needed. Also 25 an hour is 50k a year. No where in the US are senior devs paid that little. I suppose you may not be US based, but your cost for college seems to imply US, albeit at an expensive school.
And everyone in tech who has worked on ML before collectively says “yeah that’s what we’ve been trying to tell you”. Don’t get me wrong, LLMs are a huge leap, but god did it show how greedy corporations are, just immediately jumping to “how quick can we lay people off?”. The tech is not to that spec. Yet. It will get there, but goddamn do we need to be demanding some regulations now
I was mostly posting this because the last time LLMs came up, people kept on going on and on about how much their thoughts are like ours and how they know so much information. But as this article makes clear, they have no thoughts and know no information.
In many ways they are simply a mathematical party trick; formulas trained on so much language, they can produce language themselves. But there is no “there” there.
True
False. There’s plenty of information stored in the models, and plenty of papers that delve into how it’s stored, or how to extract or modify it.
I guess you can nitpick over the work “know”, and what it means, but as someone else pointed out, we don’t actually know what that means in humans anyway. But LLMs do use the information stored in context, they don’t simply regurgitate it verbatim. For example (from this article):
If you ask an LLM what’s near the Eiffel Tower, it’ll list location in Paris. If you edit its stored information to think the Eiffel Tower is in Rome, it’ll actually start suggesting you sights in Rome instead.
They only use words in context, which is their problem. It doesn’t know what the words mean or what the context means; it’s glorified autocomplete.
I guess it depends on what you mean by “information.” Since all of the words it uses are meaningless to it (it doesn’t understand anything of what it either is asked or says), I would say it has no information and knows nothing. At least, nothing more than a calculator knows when it returns 7 + 8 = 15. It doesn’t know what those numbers mean or what it represents; it’s simply returning the result of a computation.
So too LLMs responding to language.
Why is that a problem?
For example, I’ve used it to learn the basics of Galois theory, and it worked pretty well.
So what if it doesn’t understand Galois theory - it could teach it to me well enough. Frankly if it did actually understand it, I’d be worried about slavery.
Basically the problem is point 3.
You obviously know some of what it’s telling you is inaccurate already. There is the possibility it’s all bullshit. Granted a lot of it probably isn’t, but it will tell you the bullshit with the exact same level of confidence as actual facts… because it doesn’t know Galois theory and it isn’t teaching it to you, it’s simply stringing sentences together in response to your queries.
If a human were doing this we would rightly proclaim the human a bad teacher that didn’t know their subject, and that you should go somewhere else to get your knowledge. That same critique should apply to the LLM as well.
That said it definitely can be a useful tool. I just would never fully trust knowledge I gained from an LLM. All of it needs to be reviewed for correctness by a human.
No, it shouldn’t. Instead, you should compare it to the alternatives you have on hand.
The fact is,
So, if I have to learn something, I have enough background to spot hallucinations, and I don’t have a teacher (having graduated college, that’s always true), I would consider using it, because it’s better then the alternatives.
There are plenty of cases where you shouldn’t fully trust knowledge you gained from a human, too.
And there are, actually, cases where you can trust the knowledge gained from an LLM. Not because it sounds confident, but because you know how it behaves.
Obviously you should do what you think is right, so I mean, I’m not telling you you’re living wrong. Do what you want.
The reason to not trust a human is different from the reasons not to trust an LLM. An LLM is not revealing to you knowledge it understands. Or even knowledge it doesn’t understand. It’s literally completing sentences based on word likelihood. It doesn’t understand any of what it’s saying, and none of it is rooted in any knowledge of the subject of any kind.
I find that concerning in terms of learning from it. But if it worked for you, then go for it.
Sadly we don’t even know what “knowing” is, considering human memory changes every time it is accessed. We might just need language and language only. Right now they’re testing if generating verbalized trains of thought helps (it might?). The question might change to: Does the sum total of human language have enough consistency to produce behavior we might call consciousness? Can we brute force the Chinese room with enough data?
They are the perfect embodiment of the internet.
They know everything, but understand nothing
Everybody in tech would even have a passing understanding of the technology was collectively saying that. We understand the limits of technology and can feel out the bounds easily. But, too many of these dumbasses with dollar signs in their eyes are all “to the moon!”, and tripping and failing on implementing the tech in unreasonable ways.
It was never a factoid machine, like some people wanted to believe. It was always about creatively writing something, and only one with so much attention.
Funny tidbit about the word “factoid”: its original meaning was “an item of unreliable information that is reported and repeated so often that it becomes accepted as fact”, but the modern usage is “a brief or trivial item of news or information”.
This means that the modern usage of “factoid” is in itself a factoid, and that in the old sense LLMs sort of are factoid machines.
Note that I’m not saying the modern use is wrong. Languages evolve, and words taking on new meanings doesn’t mean the new meanings are “wrong” (and surprisingly words changing to mean the opposite of what they used to mean isn’t all that uncommon either.)
I’ve been unemployed for 7 months. Every online job I see that’s been posted for at least 6 hours has over 200 applications. I’m a senior Dev with 30 years experience, and I can’t find work.
I’d say generative AI is an existential threat as bad as offshoring was for steel in the early 80s. I’m now left with the prospect of spending the last 20 years of my work life at or near minimum wage.
After all, I can’t afford to spend $250,000 on a new bachelor’s degree, and a community college degree might get me to $25/hr, and still costs thousands. This is causing impoverishment on a massive scale.
Ignore this threat at your peril.
I’m a senior dev too, and at first I thought the same, but really it’s a market downturn. Companies are just afraid to hire right now. I’d look into generative AI, try to understand how it works. That’s how I’ve been spending my time, and yeah, it’s intuitive the way they do it but the more you understand how it works the more you realize that it’s not ready to take our jobs. Yet. Again maybe someday, but there is a lot of work that needs to be done to get something semi up and running, and the models that Google uses are not going to be usable for every company. (Take a look at all the specialized models already).
Our job never goes away, but it does constantly evolve. This is just another point where we have to learn new skills, and that may be that we all need to be model tuners some day. At the end of the day the user still needs to correctly describe what they want to have happen on the screen, and there are currently no ways to take what they describe into a full piece of software.
Hard to believe a senior dev can’t find work. Those positions are the most needed. Also 25 an hour is 50k a year. No where in the US are senior devs paid that little. I suppose you may not be US based, but your cost for college seems to imply US, albeit at an expensive school.
I was not saying 25 for a dev job, i was saying that for other kinds of work i might be able to get without getting a new degree.
I disagree, a lot of white collar work is simply writing bullshit.