My hope stays with continual learning, specifically class level continual learning. Being more energy efficient is always nice, while at the same time emulate how humans learn.
Need more hopium for a chance at better approach than backprop to change the paradigm altogether for neural networks.
These are pretty general theoretical concepts, but can be applied better in many domains. Aiding discoveries in natural science seems most interesting.
Intrepid-Learner t1_j1kx19e wrote
Reply to [D] What are some applied domains where academic ML researchers are hoping to produce impressive results soon? by [deleted]
My hope stays with continual learning, specifically class level continual learning. Being more energy efficient is always nice, while at the same time emulate how humans learn.
Need more hopium for a chance at better approach than backprop to change the paradigm altogether for neural networks.
These are pretty general theoretical concepts, but can be applied better in many domains. Aiding discoveries in natural science seems most interesting.