> They are all based on learning to recognize patterns to infer which things look the same as whatever was in their training set, but they have no semantic capabilities beyond simple classification
Deep learning is more than just imagenet classification or object detection.
There are many approaches that require more understanding, such as future video prediction, captioning, question answering, reinforcement learning requiring an implicitly learned model of how the environment works beyond mere appearances, image generation, structure extraction, anomaly detection, 3d reasoning, external memory, few/one/zero shot learning, meta-learning, etc etc.
The field is huge and whatever "obvious shortcomings of deep learning" non-specialists come up with after reading popular articles are probably being tackled already in many groups and have several lines of approaches and papers already.
Deep learning is more than just imagenet classification or object detection.
There are many approaches that require more understanding, such as future video prediction, captioning, question answering, reinforcement learning requiring an implicitly learned model of how the environment works beyond mere appearances, image generation, structure extraction, anomaly detection, 3d reasoning, external memory, few/one/zero shot learning, meta-learning, etc etc.
The field is huge and whatever "obvious shortcomings of deep learning" non-specialists come up with after reading popular articles are probably being tackled already in many groups and have several lines of approaches and papers already.