alt Hacker NewsThe rate of progress can be high and they can also be dumb next token predictors. Not sure why that is hard to understand.
These models can do a lot of things but they also can't do a lot of things. In order to use these models effectively you have to understand that they are next token predictors and how that allows it to do what they do.
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It can be a token predictor and still tell me exactly how my life will proceed from now until the indefinite future, or be the most intelligent conversational entity you have ever witnessed.
The issue is of course with using the word "dumb": they are next token predictors, no doubt about it, but whether LLms as a class of system are smart or dumb is entirely unknown and entirely variable in time.
To interact with them effectively you must know how they behave, just like you have to know how humans behave to interact with them effectively. If you disagree, find someone with autism and have a conversation with them.
Much of an LLM's capability comes from the structure encoded in its learned representations. The probabilistic outputs are primarily a way of expressing uncertainty and generating fluent text, while compression during training is what forces the model to discover that underlying structure.
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Are they useful or not? Will they continue changing the world or not? People who choose one way or the other for describing them typically fall on one side or the other in these questions imo. What do you think? Will these next token predictors change the world or not?