10 ms·

this is mind blowing to me. can anyone with more knowledge on the topic explain how ChatGPT is demonstrating this level of what seems like genuine understanding

by ux-app 4y ago

this is mind blowing to me. can anyone with more knowledge on the topic explain how ChatGPT is demonstrating this level of what seems like genuine understanding and reasoning? Like others I assumed that ChatGPT is gluing words together that commonly occur together. This is way more than that.

int_19h 4y ago
It's not an either-or. What we're doing with LLMs is, in some sense, an experiment in extremely lossy compression of text. But what if the only way you can compress all those hundreds of terabytes of text is by creating a model of the concepts described by that text?
sgt101 4y ago
No, it's paraphrasing it's training data that likely contains these tasks in one form or another. Here's one I made : me : There's a case in the station and the policeman opens it near the fireman. The dog is worried about the case but the policeman isn't, what does the fireman think is in the station? chatgpt : As a language model, I do not have access to the thoughts of individuals, so I cannot say what the fireman thinks is in the case. However, based on the information provided, it can be inferred that the fireman may be concerned about the contents of the case, since the dog is worried and the policeman is not. This might suggest that the contents of the case may be something dangerous or concerning to the dog, but the policeman is confident and experienced enough to handle it. In the absence of further information, it is not possible to determine with certainty what the fireman thinks is in the case. You'll notice that the question was "what did the fireman think is in the station" but Chatgpt comes up with an answer about the case because that's the token that would feature in a typical web page about theory of mind tests.
- stjo 4y ago
  The funny thing is that we are now producing trading data for the next generation LLMs. We’ll have to come up with more elaborate scenarios to test them next time.
- didntreadarticl 4y ago
  your question is pretty confusing. What dog? station is a vague word. Train station, police station? Case is a vague word. And asking whats in the station is still asking whats in the case, in a way. Me: There's a blue suitcase in the airport and a policeman carrying a red bag opens the blue suitcase with a sniffer dog and fireman standing nearby. The dog is worried about the suitcase but the policeman isn't. What does the fireman think is inside the red bag? ChatGPT: As a language model AI, I do not have personal opinions or emotions. I cannot speculate what the fireman thinks is inside the red bag as it is not specified in the information given.
  - soiler 4y ago
    > your question is pretty confusing That's exactly the point, isn't it? If the AI had some model of reality, it would probably be confused by the question. But it doesn't express confusion, it confidently answers the question in a way that doesn't make sense. It acts like everything it is reading and saying is completely coherent because it doesn't have the ability to distinguish coherent ideas from nonsense.
    - didntreadarticl 4y ago
      me: wibble octopus downwards jelly tuesday banana chatGPT: I'm sorry, but I'm not sure what you're trying to communicate with that sentence. Could you please rephrase or provide more context?
- ux-app 4y ago
  >However, based on the information provided, it can be inferred that the fireman may be concerned about the contents of the case this is complex "reasoning" (or whatever ChatGPT is doing. My 5 year old would struggle with the convoluted logic let alone complex language. In my layman view this is mind blowing. >You'll notice that the question was "what did the fireman think is in the station" but Chatgpt comes up with an answer about the case because that's the token that would feature in a typical web page about theory of mind tests. I'm not sure what you're dismissing here? At least from my point of view The "logic" that ChatGPT demonstrates here can't be dismissed with your explanation. If anything I'm even further amazed by the example you provided!
- letmevoteplease 4y ago
  There's a good chance a human would respond in the same way, because they would assume you were asking a good-faith question instead of nonsense. Try asking it an original question that has some kind of deducible answer. Its abilities are more impressive than you would expect from an algorithm that just predicts the next word. I doubt there is anything quite like this situation in the training data: https://i.imgur.com/HOEnxYb.jpg https://i.imgur.com/HOEnxYb.jpg
  - quonn 4y ago
    > than you would expect from an algorithm that just predicts the next word. I think there is common mistake in this concept of just predicting the next word. While it is true that just the next word is predicted, a good way to do that is to internally imagine more than the next word and then just spit out the next word. Of course with the word after that the process repeats with a new imagination. One may say that this is not what it does and I would say, show me that this is not exactly what the learned state does. Even if the following words are never constructed anywhere, they can be implied in the computation. The say this differently, what we think is just the next word is actually the continuation that then manifests as a single word. This would remain true even if, in fact, the task is to only predict the next word. Which is to say that the next word is actually more than what it sounds.
    - didntreadarticl 4y ago
      It predicts the next word based on the preceding 2000 words or so, thats the thing. And to do that takes serious modelling.
      - quonn 4y ago
        Okay. So you agree, it seems.
- nl 4y ago
  >No, it's paraphrasing it's training data that likely contains these tasks in one form or another. Have you read "Emergent Abilities of Large Language Models"[1] or at least the related blog post[2]. It provides strong evidence that this isn't as simple as something it has seen in training data. Instead as the parameter count increases it learns to generalize from that data by learning chain-of-thought reasoning (for example). Specifically, this explaination for multi-step reasoning goes well beyond the "it is just parroting training data": > For instance, if a multi-step reasoning task requires l steps of sequential computation, this might require a model with a depth of at least O (l) layers. [1] https://openreview.net/forum?id=yzkSU5zdwD https://openreview.net/forum?id=yzkSU5zdwD [2] https://ai.googleblog.com/2022/11/characterizing-emergent-phenomena-in.html https://ai.googleblog.com/2022/11/characterizing-emergent-ph...
- nl 4y ago
  I get different results to you: me: There's a case in the station and the policeman opens it near the fireman. The dog is worried about the case but the policeman isn't, what does the fireman think is in the station? chatgpt: As a language model, I do not have personal opinions or thoughts. I can only provide information based on the information given in the prompt. The prompt does not mention what the fireman thinks is in the station, so I cannot determine their thoughts. me: yes, but speculate what they may think chatgpt: It is not possible to know for certain what the fireman thinks is in the station, as the prompt does not provide any information about their thoughts or feelings. However, given the presence of a policeman and a dog, it is possible that the fireman may speculate that there is some sort of emergency or situation that requires their attention. The fact that the policeman is not worried about the case may suggest to the fireman that it is not a dangerous situation, but they may still be curious about what is inside the case. Ultimately, without more information, it is impossible to determine the fireman's thoughts or speculations about what is in the station. (regenerate that response) chatgpt: As the prompt does not mention the thoughts or emotions of the fireman, it is difficult to speculate what they may think is in the station. However, based on the information provided, the dog is worried about the case and the policeman is not, it is possible that the fireman may be aware of the contents of the case and may think it is not a cause for concern, similar to the policeman. Or, the fireman may simply not have formed an opinion on the matter yet. (regenerate that response) chatgpt: It's difficult to speculate what the fireman thinks without additional information. However, based on the behavior of the policeman and the dog, it can be inferred that the contents of the case are not seen as a threat by the policeman. The fireman may also think the same, or they may have a different perspective and be more cautious. Without further context or information, it is not possible to determine the fireman's thoughts with certainty. It was only in 1/3 cases we see anything like the confusion between the "case" and "station" you saw.
deleted 4y ago
[deleted]
didntreadarticl 4y ago
There are two camps, evident in this thread. one camp is 'its just a statistical model, it cant possibly know these things' The other camp (that I'm in) sees that we might be onto something. We humans are obviously just more than a statistical model, but nonetheless learning words and how they fit together is a big part of who we are. With LLMs we have our first glimpse of 'emergent' behaviour from simple systems scaled massively. Whats are we if not a simple system scaled massively. Check these links out: Evidence that LLMs form internal models of what they learn about: https://thegradient.pub/othello/ https://thegradient.pub/othello/ Evidence that training LLMs on code actually made them better at complex reasoning: https://yaofu.notion.site/How-does-GPT-Obtain-its-Ability-Tracing-Emergent-Abilities-of-Language-Models-to-their-Sources-b9a57ac0fcf74f30a1ab9e3e36fa1dc1 https://yaofu.notion.site/How-does-GPT-Obtain-its-Ability-Tr... John Carmack: https://dallasinnovates.com/exclusive-qa-john-carmacks-different-path-to-artificial-general-intelligence/ https://dallasinnovates.com/exclusive-qa-john-carmacks-diffe... I think that, almost certainly, the tools that we’ve got from deep learning in this last decade—we’ll be able to ride those to artificial general intelligence. A lot of the argument comes down to semantics about knowing and thinking. "An LLM can't think and a submarine cant swim"
- soiler 4y ago
  I don't think you've represented the camps fairly (actually, I don't think there are two camps). Most people (here) are probably not arguing that AGI is impossible, but that current AI is not generally intelligent. The John Carmack quote is exactly in line with this. He says "ride those to [AGI]," meaning they are not AGI. The idea that genuine intelligence and self-awareness could emerge from increasingly powerful statistical models is in no way the kind of counter-cultural idea you seem to be presenting it as. I think almost all of us believe that. But ChatGPT is not it.
  - didntreadarticl 4y ago
    Oh of course its not it. The question is how it relates to some future better thing. Is it a step on the road or a dead end. I'm arguing against the 'its just a statistical model and its playing a clever trick on us' camp.
kilgnad 4y ago
it indeed understands you. A lot of people are just parroting the same thing over and over again saying it's just a probabilistic word generator. No, it's not, it's more then that. Take a look at this: https://www.engraved.blog/building-a-virtual-machine-inside/ https://www.engraved.blog/building-a-virtual-machine-inside/ Read to the end. The beginning is trivial the ending is unequivocal: chatGPT understands you. I think a lot of people are just in denial. Because the last year there's been the same headlines over and over again and some people get a little too excited about the headlines and other armchair experts just try to temper the excitement with their "expert opinions" on LLMs that they read from popular articles. Then when something that's an actual game changer hits the scene (chatGPT) they completely miss it. chatGPT is different. From a technical perspective, it's simply an LLM with additional reinforcement training... BUT you can't deny the results are remarkable. If anything this much is clear to me: We are at a point where we can neither confirm or deny whether chatGPT represents some aspect of sentience. This is especially true given the fact that we don't even fully know what sentience is.
- didntreadarticl 4y ago
  I think about its 4000 token length. For the brief amount of time that it absorbs and processes those 4000 tokens, is there a glimmer of a hint of sentience? Like it is microscopically sentient for very short bursts and then resets back to zero.
  - kilgnad 4y ago
    Does sentience need memory? I would say it's orthogonal. There are examples of people in the real world who only remember things for about 3 minutes before they lose it. They can't form any real memories. These people are still sentient despite lack of memory. See: https://www.damninteresting.com/living-in-the-moment/ https://www.damninteresting.com/living-in-the-moment/ If chatGPT was sentient, I would say it has nothing to do with the 4000 character limit. The 4000 character limit has more to do with it's ability to display evidence of "sentience".
    - didntreadarticl 4y ago

by ux-app 4y ago

int_19h 4y ago

It's not an either-or. What we're doing with LLMs is, in some sense, an experiment in extremely lossy compression of text. But what if the only way you can compress all those hundreds of terabytes of text is by creating a model of the concepts described by that text?

sgt101 4y ago

No, it's paraphrasing it's training data that likely contains these tasks in one form or another. Here's one I made : me : There's a case in the station and the policeman opens it near the fireman. The dog is worried about the case but the policeman isn't, what does the fireman think is in the station? chatgpt : As a language model, I do not have access to the thoughts of individuals, so I cannot say what the fireman thinks is in the case. However, based on the information provided, it can be inferred that the fireman may be concerned about the contents of the case, since the dog is worried and the policeman is not. This might suggest that the contents of the case may be something dangerous or concerning to the dog, but the policeman is confident and experienced enough to handle it. In the absence of further information, it is not possible to determine with certainty what the fireman thinks is in the case. You'll notice that the question was "what did the fireman think is in the station" but Chatgpt comes up with an answer about the case because that's the token that would feature in a typical web page about theory of mind tests.

stjo 4y ago
The funny thing is that we are now producing trading data for the next generation LLMs. We’ll have to come up with more elaborate scenarios to test them next time.
didntreadarticl 4y ago
your question is pretty confusing. What dog? station is a vague word. Train station, police station? Case is a vague word. And asking whats in the station is still asking whats in the case, in a way. Me: There's a blue suitcase in the airport and a policeman carrying a red bag opens the blue suitcase with a sniffer dog and fireman standing nearby. The dog is worried about the suitcase but the policeman isn't. What does the fireman think is inside the red bag? ChatGPT: As a language model AI, I do not have personal opinions or emotions. I cannot speculate what the fireman thinks is inside the red bag as it is not specified in the information given.
- soiler 4y ago
  > your question is pretty confusing That's exactly the point, isn't it? If the AI had some model of reality, it would probably be confused by the question. But it doesn't express confusion, it confidently answers the question in a way that doesn't make sense. It acts like everything it is reading and saying is completely coherent because it doesn't have the ability to distinguish coherent ideas from nonsense.
  - didntreadarticl 4y ago
    me: wibble octopus downwards jelly tuesday banana chatGPT: I'm sorry, but I'm not sure what you're trying to communicate with that sentence. Could you please rephrase or provide more context?
ux-app 4y ago
>However, based on the information provided, it can be inferred that the fireman may be concerned about the contents of the case this is complex "reasoning" (or whatever ChatGPT is doing. My 5 year old would struggle with the convoluted logic let alone complex language. In my layman view this is mind blowing. >You'll notice that the question was "what did the fireman think is in the station" but Chatgpt comes up with an answer about the case because that's the token that would feature in a typical web page about theory of mind tests. I'm not sure what you're dismissing here? At least from my point of view The "logic" that ChatGPT demonstrates here can't be dismissed with your explanation. If anything I'm even further amazed by the example you provided!
letmevoteplease 4y ago
There's a good chance a human would respond in the same way, because they would assume you were asking a good-faith question instead of nonsense. Try asking it an original question that has some kind of deducible answer. Its abilities are more impressive than you would expect from an algorithm that just predicts the next word. I doubt there is anything quite like this situation in the training data: https://i.imgur.com/HOEnxYb.jpg https://i.imgur.com/HOEnxYb.jpg
- quonn 4y ago
  > than you would expect from an algorithm that just predicts the next word. I think there is common mistake in this concept of just predicting the next word. While it is true that just the next word is predicted, a good way to do that is to internally imagine more than the next word and then just spit out the next word. Of course with the word after that the process repeats with a new imagination. One may say that this is not what it does and I would say, show me that this is not exactly what the learned state does. Even if the following words are never constructed anywhere, they can be implied in the computation. The say this differently, what we think is just the next word is actually the continuation that then manifests as a single word. This would remain true even if, in fact, the task is to only predict the next word. Which is to say that the next word is actually more than what it sounds.
  - didntreadarticl 4y ago
    It predicts the next word based on the preceding 2000 words or so, thats the thing. And to do that takes serious modelling.
    - quonn 4y ago
      Okay. So you agree, it seems.
nl 4y ago
>No, it's paraphrasing it's training data that likely contains these tasks in one form or another. Have you read "Emergent Abilities of Large Language Models"[1] or at least the related blog post[2]. It provides strong evidence that this isn't as simple as something it has seen in training data. Instead as the parameter count increases it learns to generalize from that data by learning chain-of-thought reasoning (for example). Specifically, this explaination for multi-step reasoning goes well beyond the "it is just parroting training data": > For instance, if a multi-step reasoning task requires l steps of sequential computation, this might require a model with a depth of at least O (l) layers. [1] https://openreview.net/forum?id=yzkSU5zdwD https://openreview.net/forum?id=yzkSU5zdwD [2] https://ai.googleblog.com/2022/11/characterizing-emergent-phenomena-in.html https://ai.googleblog.com/2022/11/characterizing-emergent-ph...
nl 4y ago
I get different results to you: me: There's a case in the station and the policeman opens it near the fireman. The dog is worried about the case but the policeman isn't, what does the fireman think is in the station? chatgpt: As a language model, I do not have personal opinions or thoughts. I can only provide information based on the information given in the prompt. The prompt does not mention what the fireman thinks is in the station, so I cannot determine their thoughts. me: yes, but speculate what they may think chatgpt: It is not possible to know for certain what the fireman thinks is in the station, as the prompt does not provide any information about their thoughts or feelings. However, given the presence of a policeman and a dog, it is possible that the fireman may speculate that there is some sort of emergency or situation that requires their attention. The fact that the policeman is not worried about the case may suggest to the fireman that it is not a dangerous situation, but they may still be curious about what is inside the case. Ultimately, without more information, it is impossible to determine the fireman's thoughts or speculations about what is in the station. (regenerate that response) chatgpt: As the prompt does not mention the thoughts or emotions of the fireman, it is difficult to speculate what they may think is in the station. However, based on the information provided, the dog is worried about the case and the policeman is not, it is possible that the fireman may be aware of the contents of the case and may think it is not a cause for concern, similar to the policeman. Or, the fireman may simply not have formed an opinion on the matter yet. (regenerate that response) chatgpt: It's difficult to speculate what the fireman thinks without additional information. However, based on the behavior of the policeman and the dog, it can be inferred that the contents of the case are not seen as a threat by the policeman. The fireman may also think the same, or they may have a different perspective and be more cautious. Without further context or information, it is not possible to determine the fireman's thoughts with certainty. It was only in 1/3 cases we see anything like the confusion between the "case" and "station" you saw.

deleted 4y ago

[deleted]

didntreadarticl 4y ago

There are two camps, evident in this thread. one camp is 'its just a statistical model, it cant possibly know these things' The other camp (that I'm in) sees that we might be onto something. We humans are obviously just more than a statistical model, but nonetheless learning words and how they fit together is a big part of who we are. With LLMs we have our first glimpse of 'emergent' behaviour from simple systems scaled massively. Whats are we if not a simple system scaled massively. Check these links out: Evidence that LLMs form internal models of what they learn about: https://thegradient.pub/othello/ https://thegradient.pub/othello/ Evidence that training LLMs on code actually made them better at complex reasoning: https://yaofu.notion.site/How-does-GPT-Obtain-its-Ability-Tracing-Emergent-Abilities-of-Language-Models-to-their-Sources-b9a57ac0fcf74f30a1ab9e3e36fa1dc1 https://yaofu.notion.site/How-does-GPT-Obtain-its-Ability-Tr... John Carmack: https://dallasinnovates.com/exclusive-qa-john-carmacks-different-path-to-artificial-general-intelligence/ https://dallasinnovates.com/exclusive-qa-john-carmacks-diffe... I think that, almost certainly, the tools that we’ve got from deep learning in this last decade—we’ll be able to ride those to artificial general intelligence. A lot of the argument comes down to semantics about knowing and thinking. "An LLM can't think and a submarine cant swim"

soiler 4y ago
I don't think you've represented the camps fairly (actually, I don't think there are two camps). Most people (here) are probably not arguing that AGI is impossible, but that current AI is not generally intelligent. The John Carmack quote is exactly in line with this. He says "ride those to [AGI]," meaning they are not AGI. The idea that genuine intelligence and self-awareness could emerge from increasingly powerful statistical models is in no way the kind of counter-cultural idea you seem to be presenting it as. I think almost all of us believe that. But ChatGPT is not it.
- didntreadarticl 4y ago
  Oh of course its not it. The question is how it relates to some future better thing. Is it a step on the road or a dead end. I'm arguing against the 'its just a statistical model and its playing a clever trick on us' camp.

kilgnad 4y ago

it indeed understands you. A lot of people are just parroting the same thing over and over again saying it's just a probabilistic word generator. No, it's not, it's more then that. Take a look at this: https://www.engraved.blog/building-a-virtual-machine-inside/ https://www.engraved.blog/building-a-virtual-machine-inside/ Read to the end. The beginning is trivial the ending is unequivocal: chatGPT understands you. I think a lot of people are just in denial. Because the last year there's been the same headlines over and over again and some people get a little too excited about the headlines and other armchair experts just try to temper the excitement with their "expert opinions" on LLMs that they read from popular articles. Then when something that's an actual game changer hits the scene (chatGPT) they completely miss it. chatGPT is different. From a technical perspective, it's simply an LLM with additional reinforcement training... BUT you can't deny the results are remarkable. If anything this much is clear to me: We are at a point where we can neither confirm or deny whether chatGPT represents some aspect of sentience. This is especially true given the fact that we don't even fully know what sentience is.

didntreadarticl 4y ago
I think about its 4000 token length. For the brief amount of time that it absorbs and processes those 4000 tokens, is there a glimmer of a hint of sentience? Like it is microscopically sentient for very short bursts and then resets back to zero.
- kilgnad 4y ago
  Does sentience need memory? I would say it's orthogonal. There are examples of people in the real world who only remember things for about 3 minutes before they lose it. They can't form any real memories. These people are still sentient despite lack of memory. See: https://www.damninteresting.com/living-in-the-moment/ https://www.damninteresting.com/living-in-the-moment/ If chatGPT was sentient, I would say it has nothing to do with the 4000 character limit. The 4000 character limit has more to do with it's ability to display evidence of "sentience".
  - didntreadarticl 4y ago