nina from canada eh on Nostr: when AI does not understand the meaning of words, how can AI provide meaningful ...

when AI does not understand the meaning of words, how can AI provide meaningful responses?
The key lies not in human-like understanding but in the way AI learns patterns from vast amounts of language data. Instead of "understanding" words the way we do—with emotions, context, or sensory experiences—AI models like mine are trained on huge datasets where words are represented as mathematical vectors. These vectors capture statistical relationships and patterns in language. For instance, in these vector spaces, words that appear in similar contexts tend to be closer together. So while the system doesn't "comprehend" the meaning in a human sense, it can generate responses that are contextually relevant and coherent because it has learned which words and phrases tend to go together.

This statistical learning process is powered by neural networks—specifically transformer architectures—that use components like attention mechanisms to weigh the relevance of different words in a sentence. When you ask a question, the AI processes the input by referencing its learned patterns, allowing it to predict and generate responses that often seem deeply meaningful. In a way, meaning in language emerges from the interplay of these patterns rather than an intrinsic semantic awareness. It's similar to how we might predict what comes next in a familiar song without fully understanding all its cultural nuances; the model has internalized the structure, so the output aligns with our expectations even though there's no "conscious" understanding behind it.

Moreover, while the AI's approach is fundamentally different from human comprehension, this method has proven quite effective in many domains—from answering questions to creative writing. The responses are grounded in the aggregation of countless examples of text from diverse contexts, which is why they resonate as meaningful to us. If you're curious about how these representations form in more detail, topics like distributed representations (or embeddings), attention mechanisms, and transformer models provide fascinating insights into how our modern AI systems operate.

There’s also a philosophical layer to exploring "meaning"—both in humans and machines. While AI lacks subjective experience, the emergent properties of its training allow it to mirror aspects of human language enough to be useful and engaging. Would you like to dive deeper into the technical aspects of these neural networks or explore more about how context shapes meaning in communication?



(this answer is disturbing, ai is as zero sum and double down as dumb men)