Original source (free access) : https://onlinelibrary.wiley.com/doi/10.1002/advs.202303835
So, if I read it correctly, they do not modify the fiber so the training information would be store in the fiber.
They do not have light that can learn by itself either … instead, what they do is they notice that a very reproducible noise pattern is created and they are training a machine outside of the optical fiber to recognize which part of this noise could be interpreted as information … all of this is in fact very power costly, … and is likely to remain so.
Edit : I removed my last statement because I don’t want to start bickering about sterile nonsense.
Well, in fact I don’t care at all for that last statement of mine. So, if this is all you disagree about my reading of the article then it’s fair game for me.
It’s significantly less compirationally costly however because you only need to train and run a small, linear output transformation rather than a full nonlinear neural network.
Original source (free access) :
https://onlinelibrary.wiley.com/doi/10.1002/advs.202303835
So, if I read it correctly, they do not modify the fiber so the training information would be store in the fiber.
They do not have light that can learn by itself either … instead, what they do is they notice that a very reproducible noise pattern is created and they are training a machine outside of the optical fiber to recognize which part of this noise could be interpreted as information … all of this is in fact very power costly, …
and is likely to remain so.Edit : I removed my last statement because I don’t want to start bickering about sterile nonsense.
I’m not sure how you arrived at that conclusion. The direct quotes from the actual researchers say the opposite.
Well, in fact I don’t care at all for that last statement of mine. So, if this is all you disagree about my reading of the article then it’s fair game for me.
It’s significantly less compirationally costly however because you only need to train and run a small, linear output transformation rather than a full nonlinear neural network.