Stanford CS221: Artificial Intelligence: Principles and Techniques | Autumn 2019 - Conclusion | Stanford CS221: AI (Autumn 2019)

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3bbEpQ3 Topics: Future of AI Percy Liang, Associate Professor & Dorsa Sadigh, Assistant Professor - Stanford University http://onlinehub.stanford.edu/ Associate Professor Percy Liang Associate Professor of Computer Science and Statistics (courtesy) https://profiles.stanford.edu/percy-liang Assistant Professor Dorsa Sadigh Assistant Professor in the Computer Science Department & Electrical Engineering Department https://profiles.stanford.edu/dorsa-sadigh To follow along with the course schedule and syllabus, visit: https://stanford-cs221.github.io/autumn2019/#schedule 0:00 Introduction 0:47 Roadmap 2:55 Course plan 4:50 Reflex-based models 5:44 State-based models 7:00 Variable-based models 8:07 Logic-based models 9:14 Tools 10:21 Other Al-related courses 11:26 Probabilistic graphical models (CS228) 12:30 Machine learning (CS229) 13:41 Statistical learning theory (CS229T) 17:56 Language (CS224N, CS224U) 19:16 Cognitive science 20:14 Neuroscience 24:03 underwhelming results 26:01 Implications of early era 27:11 Knowledge-based systems (70-80s) 30:19 The Complexity Barrier 32:10 A melting pot 34:16 Google Machine Translation (2016) 36:48 Image classification 37:23 Adversaries 41:21 Reading comprehension 42:32 Optimizing for clicks 44:44 How to model human objectives? 47:32 Generating fake content 51:03 Fairness in criminal risk assessment 53:09 Are algorithms neutral? 55:20 Privacy: Randomized response 56:23 Causalty 57:33 Interpretability versus accuracy