MIT 6.S191: Introduction to Deep Learning - MIT 6.S191 (2020): Neurosymbolic AI

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deep learning mit artificial intelligence neural networks machine learning 6s191 6.s191 mit deep learning ava soleimany soleimany alexander amini amini

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MIT Introduction to Deep Learning 6.S191: Lecture 7 Neurosymbolic Hybrid Artificial Intelligence Lecturer: David Cox January 2020 For all lectures, slides, and lab materials: http://introtodeeplearning.com Lecture Outline 0:00 - Introduction 1:25 - Evolution of AI 7:32 - MIT-IBM Watson AI Lab 10:10 - Why is AI today "narrow"? 19:17 - Out-of-distribution performance 21:07 - ObjectNet 23:04 - Adversarial examples 25:24 - When does deep learning struggle? 27:00 - Neural networks vs symbolic AI 28:38 - Neurosymbolic AI 34:20 - Advantages of combining symbolic AI 39:01 - CLEVERER and more 40:40 - Summary Subscribe to stay up to date with new deep learning lectures at MIT, or follow us @MITDeepLearning on Twitter and Instagram to stay fully-connected!!

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