By brain-like I mostly just meant neuromorphic, so the statement is almost a tautology. DL models are all ready naturally somewhat ‘brain-like’, in the space of all ML models, as DL is a form of vague brain reverse engineering. But most of the remaining key differences ultimately stem from the low level circuit differences between von neuman and neuromorphic architectures. As just one example—DL currently uses large-batch GD style training because that is what is actually efficient on VN architecture, but will necessarily shift to brain-style small batch techniques on neuromorphic/PIM architecture as that is what efficiency dictates.
Almost a tauutology = carries very little useful information.
In this case most of the information is carried by the definition of “Neuromorphic”. A researcher proposes a new learning algorithm. You claim that if its not neuromorphic then it can’t be efficient. How do you tell if the algorithm is neuromorphic?
Brain-like != human brain.
By brain-like I mostly just meant neuromorphic, so the statement is almost a tautology. DL models are all ready naturally somewhat ‘brain-like’, in the space of all ML models, as DL is a form of vague brain reverse engineering. But most of the remaining key differences ultimately stem from the low level circuit differences between von neuman and neuromorphic architectures. As just one example—DL currently uses large-batch GD style training because that is what is actually efficient on VN architecture, but will necessarily shift to brain-style small batch techniques on neuromorphic/PIM architecture as that is what efficiency dictates.
Almost a tauutology = carries very little useful information.
In this case most of the information is carried by the definition of “Neuromorphic”. A researcher proposes a new learning algorithm. You claim that if its not neuromorphic then it can’t be efficient. How do you tell if the algorithm is neuromorphic?