>> In the past, there were some ambitious projects aiming at this goal, though they all failed. So some people like to repeat. Yet, outside of the hand-picked examples in the article (the 5th generation computer project? Blast from the past!) there are a whole bunch of classic AI domains where real progress has been achieved in the last few decades. Here's a few: * Game-playing and adversarial search: from Deep Blue…
There seems to be only one broad paradigm which achieved basically all the AI big impact we see today: Deep learning. That is, machine learning with multi-layer neural networks with backpropagation and ReLU activation functions. Everything else seems to be mostly irrelevant or very small scale.
From a perspective that could be too local in time. But:
> ReLU activation functions
Why did you pick ReLU, of all? The sigmoid makes sense because of the aesthetic (with reference to the derivative), but ReLU in that perspective is an information cutoff. And in the perspective of the goal, I am not aware of a theory that defends it as "the activation function that makes sense" (beyond effectiveness). Are you saying that working applications overwhelmingly use ReLU? If so, which ones?