> Why does language understand have to be analogous to training an ML model rather than using an ML model for inference?
Why would you look at ML model inferences in particular? There is no compression or decompression going on during inferences, you're just running data through the existing weights.
Creating an ML model on the other hand is lossy compression. You reduce the size of the data (Training set -> model) in exchange for reduced accuracy (100% -> 90-95% or whatever).
NLU is decompression because you are extracting information that doesn't exist in the text.
I see ML as ahead-of-time compression (Creating a model), whereas NLU is just-in-time decompression (Extracting information from current context). Looking specifically at inference-time doesn't make sense to me because all the work for ML is done during training, not inference.
Why would you look at ML model inferences in particular? There is no compression or decompression going on during inferences, you're just running data through the existing weights.
Creating an ML model on the other hand is lossy compression. You reduce the size of the data (Training set -> model) in exchange for reduced accuracy (100% -> 90-95% or whatever).
NLU is decompression because you are extracting information that doesn't exist in the text.
I see ML as ahead-of-time compression (Creating a model), whereas NLU is just-in-time decompression (Extracting information from current context). Looking specifically at inference-time doesn't make sense to me because all the work for ML is done during training, not inference.