I saw that a lot of people are confused by “what does Yudkowsky mean by this difference between deep causes and surface analogies?”. I didn’t have this problem, with no delay I had interpretation what he means.
I thought that it’s difference between deep and surface regarding to black box metaphor. Difference between searching correlation between similar inputs and outputs and building a structure of hidden nodes and checking the predictions with rewarding correct ones and dividing that all by complexity of internal structure.
Difference between making step from inputs to outputs and having a model. Looking only at visible things and thinking about invisible ones. Looking only at experiment results and building theories from that.
Just like difference between deep neural networks and neural networks with no hidden layers, the first ones are much more powerful.
I am really unsure that it is right, because if it was so, why he just didn’t say that? But I write it here just in case.
I saw that a lot of people are confused by “what does Yudkowsky mean by this difference between deep causes and surface analogies?”. I didn’t have this problem, with no delay I had interpretation what he means.
I thought that it’s difference between deep and surface regarding to black box metaphor. Difference between searching correlation between similar inputs and outputs and building a structure of hidden nodes and checking the predictions with rewarding correct ones and dividing that all by complexity of internal structure.
Difference between making step from inputs to outputs and having a model. Looking only at visible things and thinking about invisible ones. Looking only at experiment results and building theories from that.
Just like difference between deep neural networks and neural networks with no hidden layers, the first ones are much more powerful.
I am really unsure that it is right, because if it was so, why he just didn’t say that? But I write it here just in case.