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Contrasting deep learning with optics is an interesting idea; and one that should be taken far more literally. E.g. coherent optical processing can be used to perform feature extraction for image classificationpic.twitter.com/LCGEHbwitg
Intuitively, differentiable layered representation learning (deep learning) is in some ways closer to classical signal processing than it is to other branches of AI and ML, due to its substrate (stack of learned linear filters with a hardcoded differentiable non-linearity)
I wonder how differently the field would have evolved if we had chosen to use terminology derived from the world of signal processing instead of neuropsychology
And regressing the optimal coefficients of the filters.
Says a lot about the shortcomings of intuition on these topics IMO
AI is a huge field and ours is that of mathematics, statistics and signal processing. Confusion arises as there's other AI subfields that include wet biology and philosophy
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