In r/MachineLearning, long time practitioners complain that non experts assume models learn live via feedback, forcing repeated explanations that most systems keep the underlying model fixed and only add context at inference time.
The general public has a flawed understanding of the concept of training.
Almost everyone I have spoken to outside the field thinks that all ML (I guess you have to use the term AI with them) is Reinforcement Learning.
this is done by gathering the memory and inserting it as context at inference time, but the underlying model is the same.
This finding is one of many signals tracked across Artificial Intelligence. The live feed updates every few hours with new expert voices, debates, and emerging ideas.
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