3 ms·

It's possible to understand how something works at a low level, but not at a high level. Imagine an electronic machine designed by someone else, with millions

It's possible to understand how something works at a low level, but not at a high level. Imagine an electronic machine designed by someone else, with millions of components, completely undocumented. You might understand how the resistors and transistors and capacitors and whatnot each work, but not understand how the machine itself does what it does. You can see that you give it some input, and get some output, but if someone asked you to point at the paths in the machine that cause that to happen, you would not know. You would need a long time to study it before you could understand. We know how computers work because every part of a computer, every bit of software that runs on it, was at some point designed by a human. There's no single human that understands everything in a computer at once, but if you ask a good developer why some bit of software is not working, or what they would expect it to do given certain inputs, they can generally figure things out. With AI, we're creating systems to design other systems automatically. We understand the process works, but the systems being designed are complex enough that we don't really understand much about them, except that they work. They are just massive arrays of numbers that feed into equations and then spit out some output. They're not like systems designed by humans, which are usually intended to be understandable by other humans -- they're weird and naturally incomprehensible. The only way to know what ChatGPT will output, at this point, is to actually run it, even if you were to take the randomness part out of it. Emergent behavior isn't pseudoscience, it's a real thing. Sometimes when a system gets complex enough, it starts to behave in surprising ways. Maybe we can make a brain, using a computer, and this is how you do it.

YeGoblynQueenne 4y ago
What I think you're trying to say is that because we can't trace the behaviour of ChatGPT or whatever, because they are black boxes, we might as well assume they work like a brain because we don't really know they're not; and that makes no sense at all. If you don't understand how something works, you wait until you can understand how it works. You don't just start coming up with fanciful theories that have no basis in fact. If you want to say that a machine works like a brain, then you should explain how it does that. If you can't do that, then there's no reason for anyone to believe it, and there's no obvious reason for you to assume it. So at least explain why you think that ChatGPT works like a brain, if that's what you really think.
- Mike_12345 4y ago
  ChatGPT is a deep neural network. We know that such neural networks construct a hierarchical model of the data. The fact that it is recognizing abstract "meaning" relations in that model doesn't seem particularly controversial to me. We build deep neural networks specifically for pattern recognition. It is similar to a brain in some aspects but obviously not the same. I asked GPT-4: "Does the neural network in GPT-4 build a hierarchical model of the data? Does it derive metaphorical "meaning" from the symbolic data?" Answer: As a transformer-based neural network, GPT-4 does build a hierarchical model of the data to some extent. Although the hierarchy in transformer models like GPT-4 is not as explicitly structured as it is in convolutional neural networks (CNNs) used for image processing, the model does learn to encode complex patterns and relationships in the input text data through its multiple layers and self-attention mechanism. The transformer architecture, which GPT-4 is based on, is designed to capture long-range dependencies and relationships in the input data. The self-attention mechanism allows the model to weigh the importance of different parts of the input in relation to each other, which helps it learn and capture semantic and syntactic structures in the data. Regarding deriving metaphorical "meaning" from symbolic data, GPT-4 can identify and understand metaphors, idiomatic expressions, and abstract concepts to some extent, as it has been trained on a diverse range of text data. However, it is crucial to note that GPT-4 does not "understand" meaning in the same way humans do. It is exceptionally good at identifying patterns and generating text that resembles human-like understanding, but its "understanding" of meaning is more akin to a sophisticated pattern-matching process rather than true comprehension. In summary, GPT-4 does build a hierarchical model of data to some extent and can generate text that exhibits an understanding of metaphorical meaning. However, this understanding is based on the model's ability to recognize and generate patterns in the data rather than genuine comprehension of the underlying meaning.

YeGoblynQueenne 4y ago

What I think you're trying to say is that because we can't trace the behaviour of ChatGPT or whatever, because they are black boxes, we might as well assume they work like a brain because we don't really know they're not; and that makes no sense at all. If you don't understand how something works, you wait until you can understand how it works. You don't just start coming up with fanciful theories that have no basis in fact. If you want to say that a machine works like a brain, then you should explain how it does that. If you can't do that, then there's no reason for anyone to believe it, and there's no obvious reason for you to assume it. So at least explain why you think that ChatGPT works like a brain, if that's what you really think.

Mike_12345 4y ago
ChatGPT is a deep neural network. We know that such neural networks construct a hierarchical model of the data. The fact that it is recognizing abstract "meaning" relations in that model doesn't seem particularly controversial to me. We build deep neural networks specifically for pattern recognition. It is similar to a brain in some aspects but obviously not the same. I asked GPT-4: "Does the neural network in GPT-4 build a hierarchical model of the data? Does it derive metaphorical "meaning" from the symbolic data?" Answer: As a transformer-based neural network, GPT-4 does build a hierarchical model of the data to some extent. Although the hierarchy in transformer models like GPT-4 is not as explicitly structured as it is in convolutional neural networks (CNNs) used for image processing, the model does learn to encode complex patterns and relationships in the input text data through its multiple layers and self-attention mechanism. The transformer architecture, which GPT-4 is based on, is designed to capture long-range dependencies and relationships in the input data. The self-attention mechanism allows the model to weigh the importance of different parts of the input in relation to each other, which helps it learn and capture semantic and syntactic structures in the data. Regarding deriving metaphorical "meaning" from symbolic data, GPT-4 can identify and understand metaphors, idiomatic expressions, and abstract concepts to some extent, as it has been trained on a diverse range of text data. However, it is crucial to note that GPT-4 does not "understand" meaning in the same way humans do. It is exceptionally good at identifying patterns and generating text that resembles human-like understanding, but its "understanding" of meaning is more akin to a sophisticated pattern-matching process rather than true comprehension. In summary, GPT-4 does build a hierarchical model of data to some extent and can generate text that exhibits an understanding of metaphorical meaning. However, this understanding is based on the model's ability to recognize and generate patterns in the data rather than genuine comprehension of the underlying meaning.