看看Hinton十年前怎么吹深度学习的，现在回过头看，是预言还是打脸？
2016年，Geoff Hinton在Creative Destruction Lab上表示，深度学习将在5年内超越放射科医生，建议停止培训放射科医生。他提出医院需要重新设计工作流程来适应模型。9年后，Gary Marcus在X上翻出此言论，认为Hinton当时过度夸大。这条推文引发关于AI在医疗领域落地速度的讨论。
ha ha Geoff Hinton in 2016 completely overhyping where deep learning was at then
ha ha Geoff Hinton in 2016 completely overhyping where deep learning was at then Karl Mehta @karlmehta 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 Your browser does not support the video tag. 🔗 View on Twitter 🔗 View Quoted Tweet 💬 5 🔄 6 ❤️ 63 👀 9695 📊 11 ⚡