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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 the

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.

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.