> Nobody knows how to represent meaning and how to train a neural network on examples of it. We represent meaning with language models, and GPT has been traine

> Nobody knows how to represent meaning and how to train a neural network on examples of it. We represent meaning with language models, and GPT has been trained on examples of it (semantic similarity). It learns how to represent meaning through the training process. The meaning relations are an emergent property of the trained neural network, not explicitly programmed. The meaning relations are automatically learned by the machine. This is the magic of machine learning and neural networks. (I don't mean literal magic. These large language models are simply too large and opaque. You can't easily identify the specific neural activation pattern that encodes meaning of any given concept within a 100 layer deep 175 billion parameter neural network.) "In essence, the "meaning" referred to in LLMs is not the same as the meaning humans typically ascribe to language, which is derived from direct experiences and real-world knowledge. Instead, it is a form of abstract, statistical understanding that emerges from the relationships between words, their contexts, and the patterns in the data. The model essentially learns a high-dimensional representation of these relationships, which can be thought of as an approximation of meaning within the context of the model's training data." This might help get you on the right track. Question: Do neural networks have emergent properties? Answer (from GPT-4): Yes, neural networks can exhibit emergent properties. Emergent properties are characteristics or behaviors that arise from the collective interactions of the individual components in a system, which are not directly attributable to any single component. In the case of neural networks, these individual components are the neurons (or nodes) and their connections (or weights). As neural networks learn from data through a training process, they adjust the weights between neurons, which can lead to complex patterns of activation across the network. The network's ability to recognize patterns, make predictions, and perform tasks can be seen as an emergent property that arises from the collective behavior of its interconnected neurons. For example, in the case of language models like GPT-3, the ability to generate coherent and contextually relevant text can be considered an emergent property. This capability is not explicitly programmed into the model but arises from the learned relationships between words and phrases through the training process. It's important to note that while neural networks can exhibit emergent properties, these properties might not always be easily explainable or interpretable, as they result from the complex interactions within the network.