4 ms·

LLMs can do this already. They are well past this. It's just they can't do it as well as humans and they can hallucinate as well. The problems we are having wi

LLMs can do this already. They are well past this. It's just they can't do it as well as humans and they can hallucinate as well. The problems we are having with LLMs aren't the fact that they aren't creative. It's the fact that they are too creative. They make up too much stuff that isn't true.

mrangle 3y ago
Show me where ChatGPT4 can solve for a novel allegory. That type of processing is not within the scope of its capability. Ask it. Prod it. Try to force it. It'll tell you. I like Ai. But It's important to be realistic. It can't abstract in a completely creative way from a basis of language with embedded and only-implied meta-patterns, the latter which is the nature of human cognition. I just tried asking Bing ChatGPT to solve the most rudimentary allegory: presented in a single sentence and with given three solution options. It deflected endlessly, before telling me that it couldn't solve it for the benefit of my self-growth and then signing itself off! It'll be nice when open source Ai renders these clownish interactions obsolete, but there still isn't evidence that it can begin to mimic the plasticity of human cognition. What it can do is bs a lot when it hits a dead end. Ai can match shapes, as a rudimentary part of its ability. That is a start. But what it needs to be able to do is decaode complex narrative as a shape, and then identify the same shape in other narratives and concepts. Thereby making a novel connection born out of abstract thought. Or it even might create a new model (ie: allegory) using the prior undetected pattern. As high IQ humans do, subconsciously and sometimes consciously.
- corethree 3y ago
  First off if you knew the algorithm that ML runs off of, you'd know that it's doing curve fitting between data points. Every point on that curve that doesn't touch an original data point is by definition novel. The very algorithm itself is literally 99.999% completely generating solutions that don't exist. But that won't convince you. Its weird because you believe I have wishful thinking and I believe on your side that you are in denial. When I query chatGPT I look for intelligence, when you query it you look for flaws. The pattern in our argument will be obvious and repetitive just like the output of an LLM. What will happen is this. I will present to you a prompt and the result which I believe verifies the status of chatGPT as an AI capable of solving novel problems. You will look at that query and you will say something along the lines that the problem and answer set already exists in the data and it's just regurgitating existing solutions from the training set. We can attempt to pre empt this. I can allow you to come up with a prompt to produce something novel but what will happen is that you will choose a prompt biased towards your argument and I will choose a prompt clearly biased towards mine and we will each have excuses for why the other is invalid. That's how humans work. We don't believe things based off of rational arguments, we construct rational arguments based off of pre existing beliefs. But you are unaware of this. It's like religion people are unaware of their own bias. You will never believe ai can produce novel things. Nothing anyone can say will ever convince you at this moment. What may convince you is over a span of 10 years as ai improves your opinion slowly flips the same way a scientologist slowly comes to terms that he is in a cult. I want to bypass all this in built bias I want both of us to admit to logical statements before we present our arguments and if that logical statement is violated you are wrong. Definitively. Bias and excuses be damned. First ai can't always produce novel and correct output. It only does this sometimes. As we all know it's inconsistent. So examples of AI being stupid does not preclude it from being smart. What if I have it construct a GUI? An arbitrary GUI that I need for a work assignment. chatGPT has no eyes so there's no visual data it can copy the solution from. If it were to construct a working code solution it would have to be novel by definition. If I present to you such a prompt and answer pair. Would that convince you? Of course not because you already know I have such a prompt prepared and these debates are more about competition then it is about learning. But I ask anyway. Would it convince you? I'm curious whether my intense meta-analysis helps you see the alternate possibility here: that I am not just hopeful of AI to be great and that it's possible that you are the one in denial about the inevitable and unknown future that novel AI will herald. Unlikely. by definition we are all blind to our bias, am I blind or are you?
  - mrangle 3y ago
    >Every point on that curve that doesn't touch an original data point is by definition novel. The very algorithm itself is literally 99.999% completely generating solutions that don't exist. That may be the case, conceding your chosen use of language. The minutia doesn't matter if it can't demonstrate novel high level creativity for novel problems. Acknowledging the importance of its actual capabilities, but also acknowledging that we are arguing over one: applied abstract thinking. If it can't output what is requested, then there is a lie somehwere in the description of its processing IF it is held to be capable. >When I query chatGPT I look for intelligence, when you query it you look for flaws. I disagree with your binary frame. One can't acknowledge its ability and where it falls short? >What will happen is this. I will present to you a prompt and the result which I believe verifies the status of chatGPT as an AI capable of solving novel problems This is an irrelevant prediction that seeks to straw man this conversation, because you haven't done this. Keep in mind that my argument rests in creative language analysis. Not in its ability to process math, biological systems logic, nor anything parallel. > We can attempt to pre empt this. I can allow you to come up with a prompt to produce something novel but what will happen is that you will choose a prompt biased towards your argument and I will choose a prompt clearly biased towards mine and we will each have excuses for why the other is invalid Do you understand that trying to predict a conversation as a form of argument and an attempt at an IQ display (respectfullly) is boorish and boils down to fallacy? >But you are unaware of this. Nevermind. You are faking intellgience as a fig leaf for being arrogant and insulting as a substitute for conversation. >You will never believe ai can produce novel things. Nothing anyone can say will ever convince you at this moment. What may convince you is over a span of 10 years as ai improves your opinion slowly flips the same way a scientologist slowly comes to terms that he is in a cult. This is how you speak about a piece of software, to another human being, and I'm the one who is acting like they are in a cult? >my intense meta-analysis You think that you have presented "intense meta-analysis"? My recommendation to you, toward improving your writing and thinking, is to pare down the number of words that you use toward the goal of distilling the actual information that you present. Here, it isn't much. Its an accusation of bias, religious thinking, and insults. Combined with sparse comments on the tech and a barely decipherable fully hypothetical thought experiment without a clear stream of logic. There was nothing intense nor meta about it, aside from the intensity of the word count. >am I blind or are you? I'm having a flashback to middle school writing assignments. Last, no one called anyone "blind" except for you.
mrangle 3y ago
LLM's aren't past it if they can't do it as well as humans. As dogs? As mice? No, not yet? You are conflating the meaning of "creativity". In this dicussion, "creativity" refers to a type of definable cognitive process (abstraction) that has an output that is rooted in a starting concept. ie: One abstracts a non-explicit concept from the starting concept, and then arrives at a novel yet related expression or concept that shares the abstraction. This is the nature of human creative logic that underlies reasoning. It has nothing to do with inventing unlrelated / untrue concepts (ie" "too much creativity" in your alternative definition). As such "untrue" output is a failure of creative logic, not too much of it. If the creative thought process is actually engaged in, then the relatively trivial part is coming up with "correct" output. The hard part is the process, not the output. To the point that incorrect output strongly suggests a substandard or alternative process. Say I asked you to provide an object that was a rough analogy to a birthday party balloon, and you said a winter coat. Would that be an example of "too much creativity" or a failure of creative (abstract) thought? One arguably (partially) correct answer would be a bowling ball. First I abstract the shape of a sphere from the balloon, and then find another match for it in a bowling ball. Purely as an example of rudumentary abstract thought. Ai does this easily. What it can't do is apply this process to complex concept and language expression. For example, a bowling ball is only partially analgolous to a balloon. They are both spheres, etc. But they are conceptual opposites in terms of mass. A more correct creative answer might be a propelled low altitude firework This answer, which requires more abstraction talent, is an example of what any LLM will increasingly have difficulty giving. And we don't need Ai to tell us that a firework is roughly analagous to a balloon. We need vastly more complex creative analysis and output. Solving the simplest allegory is in another category of complexity. I get the view that we need to recognize what it can do and its potential. That's great. But that doesn't change its forsseable limitations that are also important to recognize.
- corethree 3y ago
  I took your example to chatGPT and ran with it all the way to the end. Sometimes you literally need to ask it for the "best" analogy. Otherwise it will just give you "A" analogy that works. https://chat.openai.com/share/790e743d-ed54-49ca-8709-df1da661bd84 https://chat.openai.com/share/790e743d-ed54-49ca-8709-df1da6... It fulfils your abstraction task here and goes far beyond your own answer. Likely you can't come up with a better one. I doubt you can be more creative than the last abstraction it came up with. >Ai does this easily. What it can't do is apply this process to complex concept and language expression. >For example, a bowling ball is only partially analgolous to >a balloon. They are both spheres, etc. But they are conceptual opposites in terms of mass. >A more correct creative answer might be a propelled low altitude firework I think this set of sentences is categorically proven wrong by the link I pasted above. It literally did what you said it could not do. Look clearly LLMs are nowhere near perfect. But it's further along then you think. Much further along. It beats dogs and mice for sure. It's far enough along that human creativity as an occupation is at risk for replacement in the near future. Certain jobs are ALREADY at risk. And I believe sports illustrated already has AI news casters and AI generated articles. This is just the beginning.
HarHarVeryFunny 3y ago
I think that a lot of hallucination might just be due to not planning ahead - basically a case of running mouth before engaging brain, and then being in a situation where one has uttered a bunch of nonsense - basically backed oneself into a conversational corner. A human might catch themselves with "err, never mind, forget that!", but the LLM's only recourse is to continue extrapolating the nonsense the only way it knows how - i.e. to bullshit/hallucinate the most statistically plausible continuation of the hole it dug for itself. As a simplistic made up example, say the training set included a bunch of statements about capital cities of various countries of the form "the capital of england is london", "the capital of france is paris", etc, but didn't include any data indicating the capital of australia... Now, if asked "what is the capital of australia?", it may confidently start "the capital of australia is ..." since this matches the pattern it learnt from the training set (with "australia" being copied from the context). However, when generating the next word the LLM (without realizing it) finds itself in the unfortunate situation of not having been trained on data that would let it predict the right answer, but it of course goes ahead and predicts as best it can anyway (i.e. hallucinates/bullshits) and maybe generates the name of some random important city in australia such as "sydney". The way to fix at least this cause (maybe the primary one) of hallucination is essentially to plan ahead .. don't start off saying "the capital of australia is .." without knowing where you are going with it! One approach might be additional training using tree of thought rollouts (generate multiple possible branching continuations for each training prompt) then evaluate them and use these as RL rewards to learn to predict words leading to good outcomes (e.g. "i don't know", rather than "the capital of australia is ..").
- corethree 3y ago
  As the query prompter you are in control of the feedback loop. You can ask the AI to re-examine it's output to catch errors just as a human would do for himself. Practically speaking this does work to a limited extent. Sometimes the AI just sticks with it's guns and runs with it just like a human might.
  - HarHarVeryFunny 3y ago
    Sure, but I was addressing the issue of why "hallucinations" occur in the first place, and how to fix them. Having a human in the loop does seem pretty much required at the moment, but it doesn't help when asking the AI for the answer to something you don't know, and therefore not being able to realize that the confident answer was hallucinated and wrong. Of course multiple-answer quizzes are easier to get right by process of elimination, so the human might sometimes be able to say "THAT can't be right" and catch SOME of the hallucinations.

mrangle 3y ago

Show me where ChatGPT4 can solve for a novel allegory. That type of processing is not within the scope of its capability. Ask it. Prod it. Try to force it. It'll tell you. I like Ai. But It's important to be realistic. It can't abstract in a completely creative way from a basis of language with embedded and only-implied meta-patterns, the latter which is the nature of human cognition. I just tried asking Bing ChatGPT to solve the most rudimentary allegory: presented in a single sentence and with given three solution options. It deflected endlessly, before telling me that it couldn't solve it for the benefit of my self-growth and then signing itself off! It'll be nice when open source Ai renders these clownish interactions obsolete, but there still isn't evidence that it can begin to mimic the plasticity of human cognition. What it can do is bs a lot when it hits a dead end. Ai can match shapes, as a rudimentary part of its ability. That is a start. But what it needs to be able to do is decaode complex narrative as a shape, and then identify the same shape in other narratives and concepts. Thereby making a novel connection born out of abstract thought. Or it even might create a new model (ie: allegory) using the prior undetected pattern. As high IQ humans do, subconsciously and sometimes consciously.

corethree 3y ago
First off if you knew the algorithm that ML runs off of, you'd know that it's doing curve fitting between data points. Every point on that curve that doesn't touch an original data point is by definition novel. The very algorithm itself is literally 99.999% completely generating solutions that don't exist. But that won't convince you. Its weird because you believe I have wishful thinking and I believe on your side that you are in denial. When I query chatGPT I look for intelligence, when you query it you look for flaws. The pattern in our argument will be obvious and repetitive just like the output of an LLM. What will happen is this. I will present to you a prompt and the result which I believe verifies the status of chatGPT as an AI capable of solving novel problems. You will look at that query and you will say something along the lines that the problem and answer set already exists in the data and it's just regurgitating existing solutions from the training set. We can attempt to pre empt this. I can allow you to come up with a prompt to produce something novel but what will happen is that you will choose a prompt biased towards your argument and I will choose a prompt clearly biased towards mine and we will each have excuses for why the other is invalid. That's how humans work. We don't believe things based off of rational arguments, we construct rational arguments based off of pre existing beliefs. But you are unaware of this. It's like religion people are unaware of their own bias. You will never believe ai can produce novel things. Nothing anyone can say will ever convince you at this moment. What may convince you is over a span of 10 years as ai improves your opinion slowly flips the same way a scientologist slowly comes to terms that he is in a cult. I want to bypass all this in built bias I want both of us to admit to logical statements before we present our arguments and if that logical statement is violated you are wrong. Definitively. Bias and excuses be damned. First ai can't always produce novel and correct output. It only does this sometimes. As we all know it's inconsistent. So examples of AI being stupid does not preclude it from being smart. What if I have it construct a GUI? An arbitrary GUI that I need for a work assignment. chatGPT has no eyes so there's no visual data it can copy the solution from. If it were to construct a working code solution it would have to be novel by definition. If I present to you such a prompt and answer pair. Would that convince you? Of course not because you already know I have such a prompt prepared and these debates are more about competition then it is about learning. But I ask anyway. Would it convince you? I'm curious whether my intense meta-analysis helps you see the alternate possibility here: that I am not just hopeful of AI to be great and that it's possible that you are the one in denial about the inevitable and unknown future that novel AI will herald. Unlikely. by definition we are all blind to our bias, am I blind or are you?
- mrangle 3y ago
  >Every point on that curve that doesn't touch an original data point is by definition novel. The very algorithm itself is literally 99.999% completely generating solutions that don't exist. That may be the case, conceding your chosen use of language. The minutia doesn't matter if it can't demonstrate novel high level creativity for novel problems. Acknowledging the importance of its actual capabilities, but also acknowledging that we are arguing over one: applied abstract thinking. If it can't output what is requested, then there is a lie somehwere in the description of its processing IF it is held to be capable. >When I query chatGPT I look for intelligence, when you query it you look for flaws. I disagree with your binary frame. One can't acknowledge its ability and where it falls short? >What will happen is this. I will present to you a prompt and the result which I believe verifies the status of chatGPT as an AI capable of solving novel problems This is an irrelevant prediction that seeks to straw man this conversation, because you haven't done this. Keep in mind that my argument rests in creative language analysis. Not in its ability to process math, biological systems logic, nor anything parallel. > We can attempt to pre empt this. I can allow you to come up with a prompt to produce something novel but what will happen is that you will choose a prompt biased towards your argument and I will choose a prompt clearly biased towards mine and we will each have excuses for why the other is invalid Do you understand that trying to predict a conversation as a form of argument and an attempt at an IQ display (respectfullly) is boorish and boils down to fallacy? >But you are unaware of this. Nevermind. You are faking intellgience as a fig leaf for being arrogant and insulting as a substitute for conversation. >You will never believe ai can produce novel things. Nothing anyone can say will ever convince you at this moment. What may convince you is over a span of 10 years as ai improves your opinion slowly flips the same way a scientologist slowly comes to terms that he is in a cult. This is how you speak about a piece of software, to another human being, and I'm the one who is acting like they are in a cult? >my intense meta-analysis You think that you have presented "intense meta-analysis"? My recommendation to you, toward improving your writing and thinking, is to pare down the number of words that you use toward the goal of distilling the actual information that you present. Here, it isn't much. Its an accusation of bias, religious thinking, and insults. Combined with sparse comments on the tech and a barely decipherable fully hypothetical thought experiment without a clear stream of logic. There was nothing intense nor meta about it, aside from the intensity of the word count. >am I blind or are you? I'm having a flashback to middle school writing assignments. Last, no one called anyone "blind" except for you.

mrangle 3y ago

LLM's aren't past it if they can't do it as well as humans. As dogs? As mice? No, not yet? You are conflating the meaning of "creativity". In this dicussion, "creativity" refers to a type of definable cognitive process (abstraction) that has an output that is rooted in a starting concept. ie: One abstracts a non-explicit concept from the starting concept, and then arrives at a novel yet related expression or concept that shares the abstraction. This is the nature of human creative logic that underlies reasoning. It has nothing to do with inventing unlrelated / untrue concepts (ie" "too much creativity" in your alternative definition). As such "untrue" output is a failure of creative logic, not too much of it. If the creative thought process is actually engaged in, then the relatively trivial part is coming up with "correct" output. The hard part is the process, not the output. To the point that incorrect output strongly suggests a substandard or alternative process. Say I asked you to provide an object that was a rough analogy to a birthday party balloon, and you said a winter coat. Would that be an example of "too much creativity" or a failure of creative (abstract) thought? One arguably (partially) correct answer would be a bowling ball. First I abstract the shape of a sphere from the balloon, and then find another match for it in a bowling ball. Purely as an example of rudumentary abstract thought. Ai does this easily. What it can't do is apply this process to complex concept and language expression. For example, a bowling ball is only partially analgolous to a balloon. They are both spheres, etc. But they are conceptual opposites in terms of mass. A more correct creative answer might be a propelled low altitude firework This answer, which requires more abstraction talent, is an example of what any LLM will increasingly have difficulty giving. And we don't need Ai to tell us that a firework is roughly analagous to a balloon. We need vastly more complex creative analysis and output. Solving the simplest allegory is in another category of complexity. I get the view that we need to recognize what it can do and its potential. That's great. But that doesn't change its forsseable limitations that are also important to recognize.

corethree 3y ago
I took your example to chatGPT and ran with it all the way to the end. Sometimes you literally need to ask it for the "best" analogy. Otherwise it will just give you "A" analogy that works. https://chat.openai.com/share/790e743d-ed54-49ca-8709-df1da661bd84 https://chat.openai.com/share/790e743d-ed54-49ca-8709-df1da6... It fulfils your abstraction task here and goes far beyond your own answer. Likely you can't come up with a better one. I doubt you can be more creative than the last abstraction it came up with. >Ai does this easily. What it can't do is apply this process to complex concept and language expression. >For example, a bowling ball is only partially analgolous to >a balloon. They are both spheres, etc. But they are conceptual opposites in terms of mass. >A more correct creative answer might be a propelled low altitude firework I think this set of sentences is categorically proven wrong by the link I pasted above. It literally did what you said it could not do. Look clearly LLMs are nowhere near perfect. But it's further along then you think. Much further along. It beats dogs and mice for sure. It's far enough along that human creativity as an occupation is at risk for replacement in the near future. Certain jobs are ALREADY at risk. And I believe sports illustrated already has AI news casters and AI generated articles. This is just the beginning.

HarHarVeryFunny 3y ago

I think that a lot of hallucination might just be due to not planning ahead - basically a case of running mouth before engaging brain, and then being in a situation where one has uttered a bunch of nonsense - basically backed oneself into a conversational corner. A human might catch themselves with "err, never mind, forget that!", but the LLM's only recourse is to continue extrapolating the nonsense the only way it knows how - i.e. to bullshit/hallucinate the most statistically plausible continuation of the hole it dug for itself. As a simplistic made up example, say the training set included a bunch of statements about capital cities of various countries of the form "the capital of england is london", "the capital of france is paris", etc, but didn't include any data indicating the capital of australia... Now, if asked "what is the capital of australia?", it may confidently start "the capital of australia is ..." since this matches the pattern it learnt from the training set (with "australia" being copied from the context). However, when generating the next word the LLM (without realizing it) finds itself in the unfortunate situation of not having been trained on data that would let it predict the right answer, but it of course goes ahead and predicts as best it can anyway (i.e. hallucinates/bullshits) and maybe generates the name of some random important city in australia such as "sydney". The way to fix at least this cause (maybe the primary one) of hallucination is essentially to plan ahead .. don't start off saying "the capital of australia is .." without knowing where you are going with it! One approach might be additional training using tree of thought rollouts (generate multiple possible branching continuations for each training prompt) then evaluate them and use these as RL rewards to learn to predict words leading to good outcomes (e.g. "i don't know", rather than "the capital of australia is ..").

corethree 3y ago
As the query prompter you are in control of the feedback loop. You can ask the AI to re-examine it's output to catch errors just as a human would do for himself. Practically speaking this does work to a limited extent. Sometimes the AI just sticks with it's guns and runs with it just like a human might.
- HarHarVeryFunny 3y ago
  Sure, but I was addressing the issue of why "hallucinations" occur in the first place, and how to fix them. Having a human in the loop does seem pretty much required at the moment, but it doesn't help when asking the AI for the answer to something you don't know, and therefore not being able to realize that the confident answer was hallucinated and wrong. Of course multiple-answer quizzes are easier to get right by process of elimination, so the human might sometimes be able to say "THAT can't be right" and catch SOME of the hallucinations.