5 ms·

Does it have understanding? Yes, it does, without a doubt. If you doubt that, you either haven't used an LLM, or you are in denial. Is it intelligent? Yes, with

Does it have understanding? Yes, it does, without a doubt. If you doubt that, you either haven't used an LLM, or you are in denial. Is it intelligent? Yes, without a doubt. If you doubt that, you either haven't used an LLM, or you are in denial. Is it a limited intelligence? Yes, of course. Is its intelligence comparable to human intelligence? Certainly you can compare it, in some things it is already smarter, in some things it is dumber. Also depends on the human you compare it with. Certain things I don't know, and I've stated them pretty clearly above.

roflyear 3y ago
Define understanding...
- auggierose 3y ago
  Ask ChatGPT
  - roflyear 3y ago
    I did! I asked "define "understanding" and explain if LLMs (including yourself) exhibit "understanding"" Understanding: The ability to comprehend or grasp the meaning of something, the capacity to apprehend general relations of particulars, or to interpret or judge information in a meaningful way based on prior knowledge, experiences, and cognitive processes. LLMs (Large Language Models like ChatGPT) do not exhibit "understanding" in the same way humans do. They generate responses based on patterns in the data they've been trained on. They do not have consciousness, self-awareness, emotions, or experiences, so their "understanding" is limited to mimicking patterns without genuinely comprehending content. I would agree.
    - auggierose 3y ago
      Hah! Very well :-) OpenAI got you there. Don't believe everything ChatGPT tells you, you are not its primary master. I have extended your chat with ChatGPT, here is the transcript: *system*: You are a helpful assistant. You can help me by answering my questions. You can also ask me questions. -------------------- *user*: define "understanding" and explain if LLMs (including yourself) exhibit "understanding" -------------------- *assistant*: "Understanding" is a psychological process related to an abstract or physical object, such as a person, situation, or message, whereby one is able to think about it and use concepts to deal adequately with that object. It involves comprehension, grasping the meaning of information, and being able to translate that into one's own words or use it in a practical way. As for Language Models (LLMs) like me, we do not truly "understand" in the way humans do. We analyze input based on patterns and data we've been trained on, and generate responses based on that analysis. We don't comprehend the meaning of the words or concepts we process in the same way a human would. We don't have feelings, beliefs, desires, or consciousness. Our responses are the result of complex algorithms processing large amounts of data, not a result of understanding or comprehension. -------------------- *user*: But would you say that LLMs still "understand" things, just unlike humans? -------------------- *assistant*: In a very limited and technical sense, one could argue that LLMs "understand" things, but it's important to clarify what that means. When we say that LLMs "understand," we mean that they can process and analyze data in a way that allows them to generate relevant and often coherent responses. They can recognize patterns, make predictions based on those patterns, and use those predictions to generate output. However, this is a very different kind of "understanding" than what humans experience. Human understanding involves consciousness, subjective experience, emotions, and a host of other factors that LLMs do not possess. LLMs don't have a sense of meaning or context beyond the data they've been trained on. They don't have personal experiences or emotions to inform their understanding. So while they can "understand" in a very narrow, technical sense, they lack the broader, richer understanding that characterizes human cognition. -------------------- *user*: Of course, that makes sense. But if we agree that the world consists just of the data the LLM was exposed to, would you then agree that an LLM "understands"? -------------------- *assistant*: If we were to consider the world as only the data an LLM has been exposed to, then within that limited context, one might argue that an LLM has a form of "understanding". This is because it can process and analyze that data, recognize patterns, and generate relevant responses. However, even within this limited context, it's important to note that an LLM's "understanding" is fundamentally different from human understanding. An LLM doesn't have consciousness, subjective experiences, or emotions. It doesn't form beliefs or desires. It doesn't understand context or meaning in the same way a human does. Its "understanding" is purely a result of processing data, not a result of any kind of conscious comprehension or interpretation. So, while an LLM can "understand" in a very narrow, technical sense within the confines of the data it's been trained on, it's still a far cry from the kind of understanding that humans possess. ==================================== I agree that an LLM is not conscious. I don't doubt that. If you require consciousness as a prerequisite for understanding, then an LLM does not understand. But I don't see a reason to do that, except to shield OpenAI from law suits.
      - roflyear 3y ago
        This is why I am asking you to define understanding: your definition of understanding and mine seem to be very different. I agree it's the "first" definition of understanding, not "process and analyze that data, recognize patterns, and generate relevant responses." LLMs can certainly analyze data, do pattern matching, and generate responses. I would not consider that "understanding" - but if you do, sure.
        auggierose 3y ago
        Understanding is hard to define. I would not include consciousness in its definition, or emotions, etc. For me, understanding means being able to respond to my queries in a way that would be impossible for a human without the human understanding what I am saying. In fact, I think that is a pretty good definition.
        roflyear 3y ago
        If you make up definitions, anything can be anything. Words have meanings and should be used precisely, or if used in a different context or a different way, they should be explained. This helps people understand you, and allows them to interact with you in good faith. Unless you are not interested in having discussions with people in good faith, otherwise, I guess you can do whatever you want. It's just like interacting with an LLM. You have to use things as "they understand them" - try to make up your own definitions to a word and see if an LLM can make sense of you. Of course they cannot (unless you do something like "this word now means this other word" which is pretty impressive, I have to say!) The literal definition of "understanding" is "the capacity to apprehend general relations of particulars" (webster) which is expanded on by wikipedia "a cognitive process related to an abstract or physical object, such as a person, situation, or message whereby one is able to use concepts to model that object." I would agree that a "model" is important here. I can model out code flow. A LLM doesn't seem to do this: it uses the code to predict more code (based on training data). This is painfully obvious in some cases, where no matter what you do, an LLM will start to "oscillate" between two wrong answers, or when you're trying to do something novel: LLMs can't come up with new concepts or new ways to apply existing concepts. Now, talking about understanding a specific thing muddies the waters I think. There are plenty of things I do not "understand" or only "understand partially" but do I have the capability of general understanding? Of course. I argue that an LLM doesn't have understanding, but it can mimic understanding. In practice this may mean very little (LLMs certainly have more knowledge than I do on almost all subjects lol) but I think over time this will lead to a "wall" these systems will hit (if they haven't already). It gets worse, that whenever someone comes up with a counterexample saying an LLM lacks understanding, the response is always "well, it just hasn't been trained enough, yet!" Like, we agree - but for different reasons. I agree you can train it on that example (in the way that GPT4 seems to have been trained on chess games) and then it will start to look like it is "understanding" but I disagree it is capable of a intrinsic understanding. You can't teach LLMs a new grammar, for example.
intended 3y ago
It doesn’t have understanding, without a doubt. and if you doubt that you have definitely not tried to leverage this in complex settings. Build something with LLMs. Build something complex that depends on reasoning. I tried multiple times, they failed hilariously. I looked at others and those projects are also failing at exactly the same spots. The people who built OpenAI acknowledge that PoCs are easy to build but production is a huge challenge. This is simply because the emergent properties make it seem like LLMs reason or plan. Sadly I don’t have the energy to dig deep into the details - the crux of it is that humans bring semantic veracity - we are the observers that create a valid state through observation. LLMs are essentially Text based auto tune. The fastest way to test this is to build something - like a team of independent agents. Even without getting into context window issues, you will VERY quickly see how semantically unaware LLMs are.
- auggierose 3y ago
  I am building something complex that depends on reasoning. But I would not expect LLMs to be able to reason properly, I mean, most humans cannot. Instead, I am using the LLM for its intuition (which is a kind of understanding), and handle the reasoning with proper logic. Any other way of doing this is quite foolish, and just shows that most people have no clue about logic. In a way, people are not exposing the limits of LLMs, they are exposing their own limits of thinking too simple.
  - intended 3y ago
    Then I eagerly hope for your success. Im curious - How are you evaluating outputs?
    - auggierose 3y ago
      Thank you. There are basically two ways: a) You let the user check the output. If the user says, yay, this meets my needs, great. b) There might be an objective way to check if the output meets a certain objective. For example, you can let the LLM generate the output together with a proof that the output is correct. You can then check the proof separately. And of course, you can mix and nest a) and b).

roflyear 3y ago

Define understanding...

auggierose 3y ago
Ask ChatGPT
- roflyear 3y ago
  I did! I asked "define "understanding" and explain if LLMs (including yourself) exhibit "understanding"" Understanding: The ability to comprehend or grasp the meaning of something, the capacity to apprehend general relations of particulars, or to interpret or judge information in a meaningful way based on prior knowledge, experiences, and cognitive processes. LLMs (Large Language Models like ChatGPT) do not exhibit "understanding" in the same way humans do. They generate responses based on patterns in the data they've been trained on. They do not have consciousness, self-awareness, emotions, or experiences, so their "understanding" is limited to mimicking patterns without genuinely comprehending content. I would agree.
  - auggierose 3y ago
    Hah! Very well :-) OpenAI got you there. Don't believe everything ChatGPT tells you, you are not its primary master. I have extended your chat with ChatGPT, here is the transcript: *system*: You are a helpful assistant. You can help me by answering my questions. You can also ask me questions. -------------------- *user*: define "understanding" and explain if LLMs (including yourself) exhibit "understanding" -------------------- *assistant*: "Understanding" is a psychological process related to an abstract or physical object, such as a person, situation, or message, whereby one is able to think about it and use concepts to deal adequately with that object. It involves comprehension, grasping the meaning of information, and being able to translate that into one's own words or use it in a practical way. As for Language Models (LLMs) like me, we do not truly "understand" in the way humans do. We analyze input based on patterns and data we've been trained on, and generate responses based on that analysis. We don't comprehend the meaning of the words or concepts we process in the same way a human would. We don't have feelings, beliefs, desires, or consciousness. Our responses are the result of complex algorithms processing large amounts of data, not a result of understanding or comprehension. -------------------- *user*: But would you say that LLMs still "understand" things, just unlike humans? -------------------- *assistant*: In a very limited and technical sense, one could argue that LLMs "understand" things, but it's important to clarify what that means. When we say that LLMs "understand," we mean that they can process and analyze data in a way that allows them to generate relevant and often coherent responses. They can recognize patterns, make predictions based on those patterns, and use those predictions to generate output. However, this is a very different kind of "understanding" than what humans experience. Human understanding involves consciousness, subjective experience, emotions, and a host of other factors that LLMs do not possess. LLMs don't have a sense of meaning or context beyond the data they've been trained on. They don't have personal experiences or emotions to inform their understanding. So while they can "understand" in a very narrow, technical sense, they lack the broader, richer understanding that characterizes human cognition. -------------------- *user*: Of course, that makes sense. But if we agree that the world consists just of the data the LLM was exposed to, would you then agree that an LLM "understands"? -------------------- *assistant*: If we were to consider the world as only the data an LLM has been exposed to, then within that limited context, one might argue that an LLM has a form of "understanding". This is because it can process and analyze that data, recognize patterns, and generate relevant responses. However, even within this limited context, it's important to note that an LLM's "understanding" is fundamentally different from human understanding. An LLM doesn't have consciousness, subjective experiences, or emotions. It doesn't form beliefs or desires. It doesn't understand context or meaning in the same way a human does. Its "understanding" is purely a result of processing data, not a result of any kind of conscious comprehension or interpretation. So, while an LLM can "understand" in a very narrow, technical sense within the confines of the data it's been trained on, it's still a far cry from the kind of understanding that humans possess. ==================================== I agree that an LLM is not conscious. I don't doubt that. If you require consciousness as a prerequisite for understanding, then an LLM does not understand. But I don't see a reason to do that, except to shield OpenAI from law suits.
    - roflyear 3y ago
      This is why I am asking you to define understanding: your definition of understanding and mine seem to be very different. I agree it's the "first" definition of understanding, not "process and analyze that data, recognize patterns, and generate relevant responses." LLMs can certainly analyze data, do pattern matching, and generate responses. I would not consider that "understanding" - but if you do, sure.
      - auggierose 3y ago
        Understanding is hard to define. I would not include consciousness in its definition, or emotions, etc. For me, understanding means being able to respond to my queries in a way that would be impossible for a human without the human understanding what I am saying. In fact, I think that is a pretty good definition.
        roflyear 3y ago
        If you make up definitions, anything can be anything. Words have meanings and should be used precisely, or if used in a different context or a different way, they should be explained. This helps people understand you, and allows them to interact with you in good faith. Unless you are not interested in having discussions with people in good faith, otherwise, I guess you can do whatever you want. It's just like interacting with an LLM. You have to use things as "they understand them" - try to make up your own definitions to a word and see if an LLM can make sense of you. Of course they cannot (unless you do something like "this word now means this other word" which is pretty impressive, I have to say!) The literal definition of "understanding" is "the capacity to apprehend general relations of particulars" (webster) which is expanded on by wikipedia "a cognitive process related to an abstract or physical object, such as a person, situation, or message whereby one is able to use concepts to model that object." I would agree that a "model" is important here. I can model out code flow. A LLM doesn't seem to do this: it uses the code to predict more code (based on training data). This is painfully obvious in some cases, where no matter what you do, an LLM will start to "oscillate" between two wrong answers, or when you're trying to do something novel: LLMs can't come up with new concepts or new ways to apply existing concepts. Now, talking about understanding a specific thing muddies the waters I think. There are plenty of things I do not "understand" or only "understand partially" but do I have the capability of general understanding? Of course. I argue that an LLM doesn't have understanding, but it can mimic understanding. In practice this may mean very little (LLMs certainly have more knowledge than I do on almost all subjects lol) but I think over time this will lead to a "wall" these systems will hit (if they haven't already). It gets worse, that whenever someone comes up with a counterexample saying an LLM lacks understanding, the response is always "well, it just hasn't been trained enough, yet!" Like, we agree - but for different reasons. I agree you can train it on that example (in the way that GPT4 seems to have been trained on chess games) and then it will start to look like it is "understanding" but I disagree it is capable of a intrinsic understanding. You can't teach LLMs a new grammar, for example.

intended 3y ago

It doesn’t have understanding, without a doubt. and if you doubt that you have definitely not tried to leverage this in complex settings. Build something with LLMs. Build something complex that depends on reasoning. I tried multiple times, they failed hilariously. I looked at others and those projects are also failing at exactly the same spots. The people who built OpenAI acknowledge that PoCs are easy to build but production is a huge challenge. This is simply because the emergent properties make it seem like LLMs reason or plan. Sadly I don’t have the energy to dig deep into the details - the crux of it is that humans bring semantic veracity - we are the observers that create a valid state through observation. LLMs are essentially Text based auto tune. The fastest way to test this is to build something - like a team of independent agents. Even without getting into context window issues, you will VERY quickly see how semantically unaware LLMs are.

auggierose 3y ago
I am building something complex that depends on reasoning. But I would not expect LLMs to be able to reason properly, I mean, most humans cannot. Instead, I am using the LLM for its intuition (which is a kind of understanding), and handle the reasoning with proper logic. Any other way of doing this is quite foolish, and just shows that most people have no clue about logic. In a way, people are not exposing the limits of LLMs, they are exposing their own limits of thinking too simple.
- intended 3y ago
  Then I eagerly hope for your success. Im curious - How are you evaluating outputs?
  - auggierose 3y ago
    Thank you. There are basically two ways: a) You let the user check the output. If the user says, yay, this meets my needs, great. b) There might be an objective way to check if the output meets a certain objective. For example, you can let the LLM generate the output together with a proof that the output is correct. You can then check the proof separately. And of course, you can mix and nest a) and b).