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AI is highly educated. It's a different sort of artifact we're dealing with where it can't tell truth from fiction. What's going on is AI fatigue. We see it ev

AI is highly educated. It's a different sort of artifact we're dealing with where it can't tell truth from fiction. What's going on is AI fatigue. We see it everywhere, we use it all the time. It's becoming generic and annoying and we're getting bored of it EVEN though the accomplishment is through the fucking roof. If elon musk makes interstellar car that can reach the nearest star in 1 second and priced it at 1k, I guarantee within a year people will be bored of it and finding some angle to criticize it. So what happens is we get fatigued, and then we have such negative emotions about it that we can't possibly classify it as the same thing as human intelligence. We magnify the flaws and until it takes up all the space and we demand a redefinition of what agi is because it doesn't "feel" right. We already had a definition of AGI. We hit it. We moved the goal posts because we weren't satisfied. This cycle is endless. The definition of AGI will always be changing. Take LLMs as they exist now and only allow 10% of the population to access it. Then the opposite effect will happen. The good parts will be over magnified and the bad parts will be acknowledged and then subsequently dismissed. Think about it. All the AI slop we see on social media are freaking masterpieces works of art produced in minutes what most humans can't even hope to come close to. Yet we're annoyed and unimpressed by them. That's how it's always going to go down.

poopiokaka 1y ago
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Forgeties79 1y ago
> EVEN though the accomplishment is through the fucking roof. I agree with this but also, the output is almost entirely worthless if you can’t vet it with your own knowledge and experience because it routinely gives you large swaths of incorrect info. Enough that you can’t really use the output unless you can find the inevitable issues. If I had to put a number to it, I would say 30% of what an LLM spits out at any given time to me is completely bullshit or at best irrelevant. 70% is very impressive, but still, it presents major issues. That’s not boredom, that’s just acknowledging the limitations. It’s like designing an engine or power source that has incredible efficiency but doesn’t actually move or affect anything (not saying LLM’s are worthless but bear with me). It just outputs with no productive result. I can be impressed with the achievement while also acknowledging it has severe limitations
- ninetyninenine 1y ago
  Not all content needs to be real. A huge portion of what humans appreciate is fiction. There's a huge amount of that content and hallucination is the name of the game in these contexts.
  - Forgeties79 1y ago
    > Not all content needs to be real. A huge portion of what humans appreciate is fiction. Yes but that’s deliberately fiction and I know I am reading fiction. AI hallucinations are not comparable to deliberately created works of fiction. I don’t “appreciate” the fiction LLM’s serve me. They are explicitly sold as a utility, as a source of accurate information. They are literally being used to replaced google search. Put another way: When someone claims something is true but speaks fiction it’s called a lie. If they think it’s true but are wrong it’s called a mistake. If I ask someone what is wrong with my car I don’t want 30% fiction. If I ask an LLM for a recipe I don’t want 70% accuracy.
flkiwi 1y ago
(1) AI isn't educated. It has access to a lot of information. That's two different things. (2) I was rebutting the paper's standard that AGI should be achieving the status of a well-educated adult, which is probably far, far too high a standard. Even something measured to a much lower standard--which we aren't at yet--would change the world. Or, going back to my example, an AI that was as intelligent as a labrador in terms of its ability to synthesize and act on information would be truly extraordinary.
- arthurcolle 1y ago
  It has access to a compressed representation of some subset of the information it was trained on, depending on training regime. By this, what I mean is. Take an image of this: https://en.wikipedia.org/wiki/Traitorous_eight#/media/File:The_Traitorous_Eight.jpg https://en.wikipedia.org/wiki/Traitorous_eight#/media/File:T..., change the file name to something like image.jpg and pass it into Qwen 3 4B, 8B, 30B and look at the responses you get: It has no idea who these guys are. It thinks they are the beatles, the doors. If you probe enough, it'll say it's IBM cofounders. In a way, it kinda sees that these are mid-1900s folks with cool haircuts, but it doesn't recognize anything. If you probe on the F the model in question becomes convinced it's the Ford racing team with a detailed explanation of two brothers in the photo, etc. The creation of autoregressive next token predictors is very cool and clearly has and will continue to have many valuable applications, but I think we're missing something that makes interactions with users actually shape the trajectory of its own experience. Maybe scaffolding + qlora solves this. Maybe it doesn't
PopePompus 1y ago
> If elon musk makes interstellar car that can reach the nearest star in 1 second and priced it at 1k, I guarantee within a year people will be bored of it and finding some angle to criticize it. Americans were glued to their seats watching Apollo 11 land. Most were back to watching I Dream of Jeanie reruns when Apollo 17 touched down.
- card_zero 1y ago
  Well yes, but if this actually happened it would open up a new frontier. We'd have an entire galaxy of unspoilt ecosystems* to shit in. Climate anxiety would go from being existential dread to mere sentimental indignation, and everybody would be interested in the latest news from the various interstellar colonies and planning when to emigrate. Mental illness epidemics would clear up, politics would look like an old-fashioned activity, the global mood would lift, and people would say "global" much less often. * Ecosystems may require self-assembly
  - ogogmad 1y ago
    It always seems like whenever something close to this happens, people's lives carry on, don't get easier, and they keep finding new things to complain about.
buu700 1y ago
Pretty much. Capabilities we now consider mundane were science fiction just three years ago, as far as anyone not employed by OpenAI was concerned. We already had a definition of AGI. We hit it. Are you sure about that? Which definition are you referring to? From what I can tell with Google and Grok, every proposed definition has been that AGI strictly matches or exceeds human cognitive capabilities across the board. Generative AI is great, but it's not like you could just assign an arbitrary job to a present-day LLM, give it access to an expense account, and check in quarterly with reasonable expectations of useful progress.
- wild_egg 1y ago
  You generally can't just have a quarterly check-in with humans either. There's a significant fraction of humanity that would not clear the bar to meet current AGI definitions. The distribution of human cognitive abilities is vast and current AI systems definitely exceed the capabilities of a surprising number of people.
  - buu700 1y ago
    That's fair. I suppose it gets at the heart of the question of what the specific threshold for AGI should be. Is it matching or exceeding all cognitive capabilities of any given human, an average 100-IQ adult, a severely learning-disabled child, or something else entirely? Maybe AGI is in the eye of the beholder, and a given person just has to decide by using their own experience of human intelligence as a benchmark. In other words, AGI is AGI whenever majority consensus decides it's a universal replacement for ordinary human intelligence.
- ninetyninenine 1y ago
  >We already had a definition of AGI. We hit it. The turing test.
  - rmunn 1y ago
    The Turing test was never a test of thinking: Turing said that thinking was difficult to define and so he decided to "replace the question by another, which is closely related to it" (I disagree with him there) "and is expressed in relatively unambiguous words," i.e. the question of whether a chatbot can fool a text-only observer into thinking it's human. Clearly, current LLMs have passed the Turing test, as witnessed by the difficulty many schools have in enforcing "do not use LLMs to do your homework" rules. But even Turing didn't say his test was a test of intelligence, just a test "closely related" to intelligence. And if he had seen today's LLMs, I think he would have revised that opinion, because today's LLMs generate text with no underlying fact model, no fundamental understanding of the truth behind the words they're saying. (No understanding, even, of the concepts of truth or falsehood). I think today's LLMs have demonstrated that being able to string words together in coherent sentences is not "closely related" to intelligence at all.
parineum 1y ago
> We already had a definition of AGI. We hit it. I'm curious when and what you consider to have been the moment. To me, the general in AGI means I should be able to teach it something it's never seen before. I don't think I can even teach an LLM something it's seen a million times before. Long division, for example. I don't think a model that is solid state until it's "trained" again has a very good chance of being AGI (unless that training is built into it and the model can decide to train itself).
- ninetyninenine 1y ago
  The turing test.
  - root_axis 1y ago
    Why do you believe that passing the turing test was previously the definition of AGI? LLMs haven't actually passed the turing test since you can trivially determine if an LLM is on the other side of a conversation by using a silly prompt (e.g. what is your system prompt).
    - ninetyninenine 1y ago
      The Turing test was proposed as an operational criterion for machine intelligence: if a judge cannot reliably tell machine from human in unrestricted dialogue, the machine has achieved functional equivalence to human general intelligence. That is exactly the property people now label with the word general. The test does not ask what parts the system has, it asks what it can do across open domains, with shifting goals, and under the pressure of follow up questions. That is a benchmark for AGI in any plain sense of the words. On teachability. The Turing setup already allows the judge to teach during the conversation. If the machine can be instructed, corrected, and pushed into new tasks on the fly, it shows generality. Modern language models exhibit in context learning. Give a new convention, a new format, or a new rule set and they adopt it within the session. That is teaching. Long division is a red herring. A person can be generally intelligent while rusty at a hand algorithm. What matters is the ability to follow a described procedure, apply it to fresh cases, and recover from mistakes when corrected. Current models can do that when the task is specified clearly. Failure cases exist, but isolated lapses do not collapse the definition of intelligence any more than a human slip does. On the claim that a model is solid state unless retrained. Human brains also split learning into fast context dependent adaptation and slow consolidation. Within a session, a model updates its working state through the prompt and can bind facts, rules, and goals it was never trained on. With tools and memory, it can write notes, retrieve information, and modify plans. Whether weights move is irrelevant to the criterion. The question is competence under interaction, not the biological or computational substrate of that competence. On the idea that LLMs have not passed the test because you can ask for a system prompt. That misunderstands the test. The imitation game assumes the judge does not have oracle access to the machinery and does not play gotcha with implementation details. Asking for a system prompt is like asking a human for a dump of their synapses. It is outside the rules because it bypasses behavior in favor of backstage trivia. If you keep to ordinary conversation about the world, language, plans, and reasoning, the relevant question is whether you can reliably tell. In many settings you cannot. And if you can, you can also tell many humans apart from other humans by writing style tics. That does not disqualify them from being generally intelligent. So the logic is simple. Turing gave a sufficient behavioral bar for general intelligence. The bar is open ended dialogue with sustained competence across topics, including the ability to be instructed midstream. Modern systems meet that in many practical contexts. If someone wants a different bar, the burden is to define a new operational test and show why Turing’s is not sufficient. Pointing to a contrived prompt about internal configuration or to a single brittle task does not do that.
dns_snek 1y ago
> We already had a definition of AGI. We hit it. Any definition of AGI that allows for this is utterly useless: > Me: Does adding salt and yeast together in pizza dough kill the yeast? > ChatGPT: No, adding salt and yeast together in pizza dough doesn't kill the yeast. (new chat) > Me: My pizza dough didn't rise. Did adding salt and yeast together kill the yeast? > ChatGPT: It's possible, what order did you add them in? > Me: Water, yeast, salt, flour > ChatGPT: Okay, that explains it! Adding the salt right after the yeast is definitely the issue. (It is not the issue)
- ninetyninenine 1y ago
  You picked one trivial failure and built an entire worldview around it while ignoring the tidal wave of success stories that define what these models can already do. ChatGPT can draft legal documents, debug code in multiple languages, generate functional architectures, summarize thousand page reports, compose music, write poetry, design marketing campaigns, and tutor students in real time. It can hold domain specific conversations with doctors, engineers, and lawyers and produce coherent, context aware reasoning that would have been considered impossible five years ago. And you’re pointing to a single pizza dough error as if that somehow invalidates all of it. If that’s your bar, then every human who ever made a mistake in a kitchen is disqualified from being intelligent too. You’re cherry picking the single dumbest moment and pretending it defines the whole picture. It doesn’t. The real story is that these models already demonstrate reasoning and generalization across virtually every intellectual domain. They write, argue, and problem solve with flexibility and intent. They’re not perfect, but perfection was never the standard. The Turing test was passed the moment you could no longer draw a clear line between where imitation ends and understanding begins. You can sneer about yeast all you want, but the irony is that while you mock, the machines are already doing useful work coding, researching, analyzing, and creating, quietly exceeding every benchmark that once defined general intelligence.
  - dns_snek 1y ago
    > You’re cherry picking the single dumbest moment and pretending it defines the whole picture. It doesn’t. Yes, it's just one example that betrays their lack of conceptual understanding but it's not a cherry-picked example like you keep repeating. It's an entire failure mode that I discovered by myself because while the average ChatGPT user enjoys sycophancy and quick, simple answer, I value correctness and "I don't know". > The Turing test was passed the moment you could no longer draw a clear line between where imitation ends and understanding begins. Where? By whom? How was the test designed? Citation needed. > You can sneer about yeast all you want, but the irony is that while you mock, the machines are already doing useful work coding, researching, analyzing, and creating, quietly exceeding every benchmark that once defined general intelligence. Did you have an AI write this entire sales pitch? I've seen all the "useful" work LLMs are doing, especially with coding. It's not going to be pretty when all this puffery is forced to confront reality in a few months - maybe a few years. It's hard to tell how long they can keep up the act.
criddell 1y ago
> We already had a definition of AGI. I'm not an expert, but my layman's understanding of AI was that AGI meant the ability to learn in an abstract way. Give me a dumb robot that can learn and I should be able to teach it how to drive, argue in court, write poetry, pull weeds in a field, or fold laundry the same way I could teach a person to do those things.

poopiokaka 1y ago

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Forgeties79 1y ago

> EVEN though the accomplishment is through the fucking roof. I agree with this but also, the output is almost entirely worthless if you can’t vet it with your own knowledge and experience because it routinely gives you large swaths of incorrect info. Enough that you can’t really use the output unless you can find the inevitable issues. If I had to put a number to it, I would say 30% of what an LLM spits out at any given time to me is completely bullshit or at best irrelevant. 70% is very impressive, but still, it presents major issues. That’s not boredom, that’s just acknowledging the limitations. It’s like designing an engine or power source that has incredible efficiency but doesn’t actually move or affect anything (not saying LLM’s are worthless but bear with me). It just outputs with no productive result. I can be impressed with the achievement while also acknowledging it has severe limitations

ninetyninenine 1y ago
Not all content needs to be real. A huge portion of what humans appreciate is fiction. There's a huge amount of that content and hallucination is the name of the game in these contexts.
- Forgeties79 1y ago
  > Not all content needs to be real. A huge portion of what humans appreciate is fiction. Yes but that’s deliberately fiction and I know I am reading fiction. AI hallucinations are not comparable to deliberately created works of fiction. I don’t “appreciate” the fiction LLM’s serve me. They are explicitly sold as a utility, as a source of accurate information. They are literally being used to replaced google search. Put another way: When someone claims something is true but speaks fiction it’s called a lie. If they think it’s true but are wrong it’s called a mistake. If I ask someone what is wrong with my car I don’t want 30% fiction. If I ask an LLM for a recipe I don’t want 70% accuracy.

flkiwi 1y ago

(1) AI isn't educated. It has access to a lot of information. That's two different things. (2) I was rebutting the paper's standard that AGI should be achieving the status of a well-educated adult, which is probably far, far too high a standard. Even something measured to a much lower standard--which we aren't at yet--would change the world. Or, going back to my example, an AI that was as intelligent as a labrador in terms of its ability to synthesize and act on information would be truly extraordinary.

arthurcolle 1y ago
It has access to a compressed representation of some subset of the information it was trained on, depending on training regime. By this, what I mean is. Take an image of this: https://en.wikipedia.org/wiki/Traitorous_eight#/media/File:The_Traitorous_Eight.jpg https://en.wikipedia.org/wiki/Traitorous_eight#/media/File:T..., change the file name to something like image.jpg and pass it into Qwen 3 4B, 8B, 30B and look at the responses you get: It has no idea who these guys are. It thinks they are the beatles, the doors. If you probe enough, it'll say it's IBM cofounders. In a way, it kinda sees that these are mid-1900s folks with cool haircuts, but it doesn't recognize anything. If you probe on the F the model in question becomes convinced it's the Ford racing team with a detailed explanation of two brothers in the photo, etc. The creation of autoregressive next token predictors is very cool and clearly has and will continue to have many valuable applications, but I think we're missing something that makes interactions with users actually shape the trajectory of its own experience. Maybe scaffolding + qlora solves this. Maybe it doesn't

PopePompus 1y ago

> If elon musk makes interstellar car that can reach the nearest star in 1 second and priced it at 1k, I guarantee within a year people will be bored of it and finding some angle to criticize it. Americans were glued to their seats watching Apollo 11 land. Most were back to watching I Dream of Jeanie reruns when Apollo 17 touched down.

card_zero 1y ago
Well yes, but if this actually happened it would open up a new frontier. We'd have an entire galaxy of unspoilt ecosystems* to shit in. Climate anxiety would go from being existential dread to mere sentimental indignation, and everybody would be interested in the latest news from the various interstellar colonies and planning when to emigrate. Mental illness epidemics would clear up, politics would look like an old-fashioned activity, the global mood would lift, and people would say "global" much less often. * Ecosystems may require self-assembly
- ogogmad 1y ago
  It always seems like whenever something close to this happens, people's lives carry on, don't get easier, and they keep finding new things to complain about.

buu700 1y ago

Pretty much. Capabilities we now consider mundane were science fiction just three years ago, as far as anyone not employed by OpenAI was concerned. We already had a definition of AGI. We hit it. Are you sure about that? Which definition are you referring to? From what I can tell with Google and Grok, every proposed definition has been that AGI strictly matches or exceeds human cognitive capabilities across the board. Generative AI is great, but it's not like you could just assign an arbitrary job to a present-day LLM, give it access to an expense account, and check in quarterly with reasonable expectations of useful progress.

wild_egg 1y ago
You generally can't just have a quarterly check-in with humans either. There's a significant fraction of humanity that would not clear the bar to meet current AGI definitions. The distribution of human cognitive abilities is vast and current AI systems definitely exceed the capabilities of a surprising number of people.
- buu700 1y ago
  That's fair. I suppose it gets at the heart of the question of what the specific threshold for AGI should be. Is it matching or exceeding all cognitive capabilities of any given human, an average 100-IQ adult, a severely learning-disabled child, or something else entirely? Maybe AGI is in the eye of the beholder, and a given person just has to decide by using their own experience of human intelligence as a benchmark. In other words, AGI is AGI whenever majority consensus decides it's a universal replacement for ordinary human intelligence.
ninetyninenine 1y ago
>We already had a definition of AGI. We hit it. The turing test.
- rmunn 1y ago
  The Turing test was never a test of thinking: Turing said that thinking was difficult to define and so he decided to "replace the question by another, which is closely related to it" (I disagree with him there) "and is expressed in relatively unambiguous words," i.e. the question of whether a chatbot can fool a text-only observer into thinking it's human. Clearly, current LLMs have passed the Turing test, as witnessed by the difficulty many schools have in enforcing "do not use LLMs to do your homework" rules. But even Turing didn't say his test was a test of intelligence, just a test "closely related" to intelligence. And if he had seen today's LLMs, I think he would have revised that opinion, because today's LLMs generate text with no underlying fact model, no fundamental understanding of the truth behind the words they're saying. (No understanding, even, of the concepts of truth or falsehood). I think today's LLMs have demonstrated that being able to string words together in coherent sentences is not "closely related" to intelligence at all.

parineum 1y ago

> We already had a definition of AGI. We hit it. I'm curious when and what you consider to have been the moment. To me, the general in AGI means I should be able to teach it something it's never seen before. I don't think I can even teach an LLM something it's seen a million times before. Long division, for example. I don't think a model that is solid state until it's "trained" again has a very good chance of being AGI (unless that training is built into it and the model can decide to train itself).

ninetyninenine 1y ago
The turing test.
- root_axis 1y ago
  Why do you believe that passing the turing test was previously the definition of AGI? LLMs haven't actually passed the turing test since you can trivially determine if an LLM is on the other side of a conversation by using a silly prompt (e.g. what is your system prompt).
  - ninetyninenine 1y ago
    The Turing test was proposed as an operational criterion for machine intelligence: if a judge cannot reliably tell machine from human in unrestricted dialogue, the machine has achieved functional equivalence to human general intelligence. That is exactly the property people now label with the word general. The test does not ask what parts the system has, it asks what it can do across open domains, with shifting goals, and under the pressure of follow up questions. That is a benchmark for AGI in any plain sense of the words. On teachability. The Turing setup already allows the judge to teach during the conversation. If the machine can be instructed, corrected, and pushed into new tasks on the fly, it shows generality. Modern language models exhibit in context learning. Give a new convention, a new format, or a new rule set and they adopt it within the session. That is teaching. Long division is a red herring. A person can be generally intelligent while rusty at a hand algorithm. What matters is the ability to follow a described procedure, apply it to fresh cases, and recover from mistakes when corrected. Current models can do that when the task is specified clearly. Failure cases exist, but isolated lapses do not collapse the definition of intelligence any more than a human slip does. On the claim that a model is solid state unless retrained. Human brains also split learning into fast context dependent adaptation and slow consolidation. Within a session, a model updates its working state through the prompt and can bind facts, rules, and goals it was never trained on. With tools and memory, it can write notes, retrieve information, and modify plans. Whether weights move is irrelevant to the criterion. The question is competence under interaction, not the biological or computational substrate of that competence. On the idea that LLMs have not passed the test because you can ask for a system prompt. That misunderstands the test. The imitation game assumes the judge does not have oracle access to the machinery and does not play gotcha with implementation details. Asking for a system prompt is like asking a human for a dump of their synapses. It is outside the rules because it bypasses behavior in favor of backstage trivia. If you keep to ordinary conversation about the world, language, plans, and reasoning, the relevant question is whether you can reliably tell. In many settings you cannot. And if you can, you can also tell many humans apart from other humans by writing style tics. That does not disqualify them from being generally intelligent. So the logic is simple. Turing gave a sufficient behavioral bar for general intelligence. The bar is open ended dialogue with sustained competence across topics, including the ability to be instructed midstream. Modern systems meet that in many practical contexts. If someone wants a different bar, the burden is to define a new operational test and show why Turing’s is not sufficient. Pointing to a contrived prompt about internal configuration or to a single brittle task does not do that.

dns_snek 1y ago

> We already had a definition of AGI. We hit it. Any definition of AGI that allows for this is utterly useless: > Me: Does adding salt and yeast together in pizza dough kill the yeast? > ChatGPT: No, adding salt and yeast together in pizza dough doesn't kill the yeast. (new chat) > Me: My pizza dough didn't rise. Did adding salt and yeast together kill the yeast? > ChatGPT: It's possible, what order did you add them in? > Me: Water, yeast, salt, flour > ChatGPT: Okay, that explains it! Adding the salt right after the yeast is definitely the issue. (It is not the issue)

ninetyninenine 1y ago
You picked one trivial failure and built an entire worldview around it while ignoring the tidal wave of success stories that define what these models can already do. ChatGPT can draft legal documents, debug code in multiple languages, generate functional architectures, summarize thousand page reports, compose music, write poetry, design marketing campaigns, and tutor students in real time. It can hold domain specific conversations with doctors, engineers, and lawyers and produce coherent, context aware reasoning that would have been considered impossible five years ago. And you’re pointing to a single pizza dough error as if that somehow invalidates all of it. If that’s your bar, then every human who ever made a mistake in a kitchen is disqualified from being intelligent too. You’re cherry picking the single dumbest moment and pretending it defines the whole picture. It doesn’t. The real story is that these models already demonstrate reasoning and generalization across virtually every intellectual domain. They write, argue, and problem solve with flexibility and intent. They’re not perfect, but perfection was never the standard. The Turing test was passed the moment you could no longer draw a clear line between where imitation ends and understanding begins. You can sneer about yeast all you want, but the irony is that while you mock, the machines are already doing useful work coding, researching, analyzing, and creating, quietly exceeding every benchmark that once defined general intelligence.
- dns_snek 1y ago
  > You’re cherry picking the single dumbest moment and pretending it defines the whole picture. It doesn’t. Yes, it's just one example that betrays their lack of conceptual understanding but it's not a cherry-picked example like you keep repeating. It's an entire failure mode that I discovered by myself because while the average ChatGPT user enjoys sycophancy and quick, simple answer, I value correctness and "I don't know". > The Turing test was passed the moment you could no longer draw a clear line between where imitation ends and understanding begins. Where? By whom? How was the test designed? Citation needed. > You can sneer about yeast all you want, but the irony is that while you mock, the machines are already doing useful work coding, researching, analyzing, and creating, quietly exceeding every benchmark that once defined general intelligence. Did you have an AI write this entire sales pitch? I've seen all the "useful" work LLMs are doing, especially with coding. It's not going to be pretty when all this puffery is forced to confront reality in a few months - maybe a few years. It's hard to tell how long they can keep up the act.

criddell 1y ago

> We already had a definition of AGI. I'm not an expert, but my layman's understanding of AI was that AGI meant the ability to learn in an abstract way. Give me a dumb robot that can learn and I should be able to teach it how to drive, argue in court, write poetry, pull weeds in a field, or fold laundry the same way I could teach a person to do those things.