The arguments about symbolic vs deep learning for AI reminds me a lot of arguments about numerical methods vs ML for solving physics problems. Seems like hybrid methods are the way to go in both cases.

hybrid methods are the way to go when it comes to symbolic vs deep learning for AI

Let me start by saying a few things that seem obvious,” Geoffrey Hinton, “Godfather” of deep learning, and one of the most celebrated scientists of our time, told a leading AI conference in Toronto in 2016. “If you work as a radiologist you’re like the coyote that’s already over the edge of the cliff but hasn’t looked down.” Deep learning is so well-suited to reading images from MRIs and CT scans, he reasoned, that people should “stop training radiologists now” and that it’s “just completely obvious within five years deep learning is going to do better.”

Fast forward to 2022, and not a single radiologist has been replaced. Rather, the consensus view nowadays is that machine learning for radiology is harder than it looks1; at least for now, humans and machines complement each other’s strengths.2

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Deep Learning Is Hitting a Wall

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Tag: "deep learning"

hybrid methods are the way to go when it comes to symbolic vs deep learning for AI

Deep Learning Is Hitting a Wall

"Deep Learning is hitting a Wall" — Gary Marcus