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I agree that the biggest difference is the missing ability of an artificial neuronal network to adapt.


Lack of adaption is mainly a feature, we choose not to train them in real-time and instead make available fixed models with repeatable behaviour. We could, if we wanted to, update the model weights continuously in response to feedback.

I think the biggest difference is that they need far more examples than we need, to learn anything.




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