> Previous generations of neural nets were kind of useless. Spotify ended up replacing their machine learning recommender with a simple system that would just recommend tracks that power listeners had already discovered.
“Previous generations of cars were useless because one guy rode a bike to work.” Pre-transformer neural nets were obviously useful. CNNs and RNNs were SOTA in most vision and audio processing tasks.
Language translation, object detection and segmentation for autonomous driving, surveillance, medical imaging... Indeed plenty fields where NNs are indispensable
Yeah, give 'em small constrained jobs where the lack of coherent internal representation is not a problem.
I was involved in ANN and equivalent based face recognition (not on the computational side, on the psychophysics side) briefly. Face recognition is one of these bigger more difficult jobs, but still more constrained than the things ANNs are useful for.
As far as I understand none of the face recognition algorithms in use these days are ANN based, but are instead computationally efficient versions of the brute force the maths implementations instead.
“Previous generations of cars were useless because one guy rode a bike to work.” Pre-transformer neural nets were obviously useful. CNNs and RNNs were SOTA in most vision and audio processing tasks.