I think you’re misreading Eliezer’s article; even with major advances in neural networks, we don’t have general intelligence, which was the standard that he was holding them to in 2007, not “state of the art on most practical AI applications.” He also stresses the “people outside the field”—to a machine learning specialist, the suggestion “use neural networks” is not nearly enough to go off of. “What kind?” they might ask exasperatedly, or even if you were suggesting “well, why not make it as deep as the actual human cortex?” they might point out the ways in which backpropagation fails to work on that scale, without those defects having an obvious remedy. In the context—the Seeing With Fresh Eyes sequence—it seems pretty clear that it’s about thinking that this is a brilliant new idea as opposed to the thing that lots of people think.
Where’s your impression coming from? [I do agree that Eliezer has been critical of neural networks elsewhere, but I think generally in precise and narrow ways, as opposed to broadly underestimating them.]
I think you’re misreading Eliezer’s article; even with major advances in neural networks, we don’t have general intelligence, which was the standard that he was holding them to in 2007, not “state of the art on most practical AI applications.” He also stresses the “people outside the field”—to a machine learning specialist, the suggestion “use neural networks” is not nearly enough to go off of. “What kind?” they might ask exasperatedly, or even if you were suggesting “well, why not make it as deep as the actual human cortex?” they might point out the ways in which backpropagation fails to work on that scale, without those defects having an obvious remedy. In the context—the Seeing With Fresh Eyes sequence—it seems pretty clear that it’s about thinking that this is a brilliant new idea as opposed to the thing that lots of people think.
Where’s your impression coming from? [I do agree that Eliezer has been critical of neural networks elsewhere, but I think generally in precise and narrow ways, as opposed to broadly underestimating them.]