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Dario Amodei says pre-training sits somewhere between learning and evolution. Humans inherit priors shaped over millions of years. LLMs start as random weights and distill trillions of tokens into those priors. We describe them using human learning metaphors. But the analogy only goes so far.

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45,540 görüntüleme • 5 ay önce •via X (Twitter)

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Geoffrey Hinton explains the coyote test for medical AI: radiology is already over the cliff, it just has not looked down yet "If you work as a radiologist, you're like the coyote that's already over the edge of the cliff, but hasn't yet looked down" "People should stop training radiologists now. It's just completely obvious that within 5 years deep learning is going to do better than radiologists" "It might be 10 years, but we got plenty of radiologists already" "There's going to be thousands of applications of the deep learning technology we currently have, especially stuff using fast chips" "Thousands of applications and hundreds of them are going to work, and those are the ones you're going to hear about" Most healthcare AI conversations still price the bottleneck as adoption. Hinton was pointing at a different bottleneck: medicine has specialties where the machine gets more examples than any human can. The brutal part is the error bar. If it is 5 years, training pipelines are already late. If it is 10, hospitals still have to redesign the workflow before the models arrive. - Geoffrey Hinton, Nobel laureate and pioneer of deep learning, at Creative Destruction Lab

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