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> do many of the researchers and engineers working with deep learning systems actually think this way? No, this has been a straw-man argument, made repeatedly

> do many of the researchers and engineers working with deep learning systems actually think this way? No, this has been a straw-man argument, made repeatedly in popular press for public recognition, for years. Even the NYT article to which Marcus responded in 2012 [1] does not make the claims to which it responds! Here is the sentence in Marcus' New Yorker post where it transitions to attacking a claim nobody made (hint - look at where the quote ends): > While the Times reports that “advances in an artificial intelligence technology that can recognize patterns offer the possibility of machines that perform human activities like seeing, listening and thinking,” deep learning takes us, at best, only a small step toward the creation of truly intelligent machines. As a rule, the academic deep learning community avoids discussions and claims about what constitutes a "truly intelligent machine", instead focusing on results in specific applications which can be objectively measured. I'm sure you can find both academics and public press that claim deep learning is a panacea. But it's disingenuous punching up to continually imply that leaders in the field are slow to acknowledge its limits. [1] https://www.nytimes.com/2012/11/24/science/scientists-see-advances-in-deep-learning-a-part-of-artificial-intelligence.html https://www.nytimes.com/2012/11/24/science/scientists-see-ad...

AndrewKemendo 8y ago
Very eloquently said. A trend in the DL/ML research community is the strong and vocal distancing from discussing or approaching the problem of General Intelligence or anything which might look like sci-fi AI. You can see this with aggressive railing against silly robots like Sophia and others, which have gotten press. I think this is warranted, and understandable as the field is so scared of another AI winter. On the other hand it leaves the field without a real progress vector. Especially given that broadly, ML research isn't scientific as such. In other words, the vast majority of "research" is not around hypothesis testing and using computing to test questions about intelligence. Rather it's demonstrating increments of improvement within very narrow computing tasks. I make that point not to diminish the work or results, as they've obviously been astounding. Rather, to point out that unlike basic or social sciences research it's not attempting to surface fundamental truths (theories, laws etc...) about some natural or emergent phenomena, but rather continuously benchmarking on narrow tasks - often without prior reference. Now that ILSVRC is gone, I'm wondering what kind of benchmarking is going to emerge as the new vector for the field.

AndrewKemendo 8y ago

Very eloquently said. A trend in the DL/ML research community is the strong and vocal distancing from discussing or approaching the problem of General Intelligence or anything which might look like sci-fi AI. You can see this with aggressive railing against silly robots like Sophia and others, which have gotten press. I think this is warranted, and understandable as the field is so scared of another AI winter. On the other hand it leaves the field without a real progress vector. Especially given that broadly, ML research isn't scientific as such. In other words, the vast majority of "research" is not around hypothesis testing and using computing to test questions about intelligence. Rather it's demonstrating increments of improvement within very narrow computing tasks. I make that point not to diminish the work or results, as they've obviously been astounding. Rather, to point out that unlike basic or social sciences research it's not attempting to surface fundamental truths (theories, laws etc...) about some natural or emergent phenomena, but rather continuously benchmarking on narrow tasks - often without prior reference. Now that ILSVRC is gone, I'm wondering what kind of benchmarking is going to emerge as the new vector for the field.