Yejin Choi is a Professor at the School of Computer Science & Engineering at the University of Washington and also a senior research director at AI2. She received her doctorate in Computer Science at Cornell University and bachelor's in Computer Science and Engineering at Seoul National University in Korea. Her research investigates a wide variety problems across NLP and AI including commonsense knowledge and reasoning, neural language (de-)generation, language grounding with vision and experience, and AI for social good. She is a MacArthur Fellow and a recipient of many best paper awards, such as NAACL, I C ML, NeurIPS, I C C V, and triple A I. She is the recipient of ACL and CVPR Test of Time awards. She won the inaugural Alexa Prize and was one of IEEE AI's 10 to Watch. GUEST LINKS: Yejin's Website: https://homes.cs.washington.edu/~yejin/ Yejin's Linkedin: https://www.linkedin.com/in/yejin-choi-0b900b1/ Yejin's Twitter: https://twitter.com/YejinChoinka PODCAST INFO: Podcast Website: https://www.yasppodcast.com/ Podcast Twitter: https://twitter.com/yasp_podcast Podcast Discord: https://discord.gg/JkRMnC5frj Timestamps: 0:00 Intro 1:58 What is Common Sense? Why is it so hard for Computers and AI? 7:05 Common sense in a logical, social, and cultural lens 15:40 How does AI leverage various forms of reasoning? 30:30 Unconscious Reasoning 32:50 Large Language Models and "Emergent Phenomena" 41:08 How did you find this problem? What made you want to study AI/NLP? 44:30 Who inspired you? 48:20 What is Natural Language Processing (NLP)? 50:10 Seq2Seq, Attentiion, and Transformers 54:20 Semi Supervised, Unsupervised, and Information Theory in NLP 1:05:40 Computer Vision and Language Grounding 1:12:40 Physical Common Sense in AI 1:14:30 Social Common Sense in AI 1:19:30 Logic Solvers and MaxSAT in NLP 1:26:10 Sapir-Whorf Hypothesis 1:29:10 Is NLP Engineering or Science? 1:31:45 Language, Reasoning, AI, and Consciousness 1:37:00 Can Machines Learn Morals? 1:46:30 AI for Social Good 1:54:45 Post Turing Test Society 2:00:40 AI, Engagement, Consumerism, and Surveillance Capitalism 2:08:15 Could AI eliminate the working class? 2:13:40 Optimism, and Positive Impacts of AI

Timestamps 0:00 Intro 1:04 What if a Computer Could Program Itself? 7:50 Automatically Programming with Different Forms of Reasoning 12:48 Neruosymbolic Computing, Continuous vs. Discrete AI 19:28 How did you find this Problem? 23:43 Program Synthesis 31:38 Who Inspired You? 35:41 Overview of Sketch 45:21 Counterexample-guided Abstraction Refinement 55:28 Examples and Applications of Program Synthesis 1:03:29 Synthesis with Machine Learning vs Solvers; Relationship with Neruosymbolic AI 1:07:15 The Holy Grail of AI - Neurosymbolic Computing 1:12:08 Socio-Economic Impacts of Software 1:22:31 Self-Replicating and Self-Programming AI 1:27:33 Fear of AI, Building Systems with Safe AI 1:33:39 Philosophy of Language and AI, Language as a Glue for Reasoning 1:41:43 Could Deductive Reasoning Emerge from Inductive/Abductive Reasoning? 1:47:31 Applications that you find Exciting/Utopian 1:50:00 Applications that you find Scary/Utopian

GUEST LINKS: Clark's Website: https://theory.stanford.edu/~barrett/Clark's Linkedin: https://www.linkedin.com/in/clark-bar... PODCAST INFO: Podcast Website: https://www.yasppodcast.com/ Podcast Twitter: https://twitter.com/yasp_podcast

Moshe Vardi is an Israeli mathematician and computer scientist. He is a Professor of Computer Science at Rice University, United States. His interests focus on applications of logic to computer science, including database theory, finite model theory, knowledge in multi-agent systems, computer-aided verification and reasoning, and teaching logic across the curriculum. He is an expert in model checking, constraint satisfaction and database theory, common knowledge (logic), and theoretical computer science.