Neural networks are natural imitators, learning patterns of language by scouring vast amounts of text. The article is to get a glimpse of his academic career, research focus, and his vision for AI. I am an assistant professor of computer science and statistics at Stanford. Hi! Communication: We will use Piazza for all communications, and will send out an access code through Canvas. Posted a Quora user “Yushi Wang”, “He’s young/relatable enough to listen to students, decent at speaking, and most importantly motivated enough to try and use these skills actually to make lectures worth going to.”. While SQuAD is designed for reading comprehension, Dr. Liang believes it has greater impacts: the dataset encourages researchers to develop new generic models — neural machine translation produces an attention-based model, which is now one of the most common models in the field of machine learning; models trained on one dataset are valuable to other tasks. Use of this site constitutes acceptance of our User Agreement (updated as of 1/1/21) and Privacy Policy and Cookie Statement (updated as of 1/1/21) and Your California Privacy Rights. If the question is classified as ”Policy”, we use regular expressions to match the policy question to a specific subcategory and return an appropriate pre-written Slav Petrov; Aria Haghighi; Percy Liang Percy Liang is an Associate Professor of Computer Science at Stanford University (B.S. However, Dr. Liang is always up for a challenge. Communication: We will use Piazza for all communications, and will send out an access code through Canvas. A well-crafted joke teeters at the edge of coherency without wading into nonsense, He says, and neural networks simply don’t have the sense to strike that balance. Patricia … Blame the machines. Natural Language, Dialog and Speech Symposium November 13 2020 New York Academy of Sciences KEYNOTE ADDRESS 3: 2:20pm EST - 3:15pm EST Percy Liang, PhD Teaching staff Percy Liang (instructor) Panupong (Ice) Pasupat (head CA) Arijit Banerjee Greg Bodwin Diego Canales Adam Goldberg Ilan Goodman Will Harvey Jiaji Hu Billy Jun Aparna Krishnan Janice Lan Amrit Saxena Jiawei Yao CS221 / Autumn 2014 / Liang 1 His two research goals are (i) to make machine learning more robust, fair, and interpretable; and (ii) to make computers easier to communicate with through natural language. Percy Liang’s group meetings, advice, and teaching set a wonderful standard for doing theoretically challenging machine learning work. Evan Liu, Ramtin Keramati, Kelvin Guu, Sudarshan Seshadri, Panupong Pasupat, Percy Liang and Emma Brunskill. Percy Liang. Siddharth Karamcheti and Dorsa Sadigh and Percy Liang Computer Science Department, Stanford University fskaramcheti, pliang, dorsag@cs.stanford.edu Abstract Our goal is to create an interactive natural language interface that efficiently and reli-ably learns from users to complete tasks in simulated robotics settings. Sections Teaching Effectiveness Instructors; CS188 Fall 2008 Not only did I learn a lot from them, but what I learned is complementary, and not just in the field of research (machine learning and NLP),” said Dr. Liang in an interview with Chinese media. If coherency is your aim, that approach works well---so well, in fact, that recent advances have sparked an ethical debate about whether people could abuse AI to generate convincing fake news. Dr. Klein tried to get his young talented apprentice on board. Four years ago, Levy described a computational approach to predicting whether a pun is funny---work that would eventually become the foundation for He’s joke-generation method. For a pun to work, He decided it needs to be surprising in a local context (“stopped to get a hare cut” makes little sense on its own) but also have an “aha” factor that ties it all together (in this case, thanks to the word “greyhound”). Output: for each word type, its cluster (see output.txt for an example). The best way to contact me is email: sidawxyz [at] gmail.com. Papers (by Topic) / Teaching & Service About. Plus, the puns were stuck in a rather rudimentary structure (and struggled at times with grammar). Ad Choices, The Comedian Is in the Machine. Association for Computational Linguistics (ACL), 2020. In the Summer of 2019, I was the head instructor for CS221, ... Stanford's introductory artificial intelligence class, taught by Percy Liang. Jonathan Berant, Percy Liang. Percy Liang Computer Science Department Stanford University pliang@cs.stanford.edu Abstract Modeling crisp logical regularities is cru-cial in semantic parsing, making it difficult for neural models with no task-specific prior knowledge to achieve good results. To anyone who’s dared craft a pun, the intuition will sound familiar. This year, the company was acquired by Microsoft. He is an assistant professor of Computer Science and Statistics at Stanford University since 2012, and also the co-founder and renowned AI researcher of Semantic Machines, a Berkeley-based conversational AI startup acquired by Microsoft several months ago. 2014. Chris Donahue, Mina Lee, and Percy Liang. I obtained my PhD from Stanford, working with Stefano Ermon, Serafim Batzoglou, Michael Snyder, Christopher Re, and I am a fourth year PhD student in Computer Science at Stanford advised by Princeton Engineering Commendation List for Outstanding Teaching. Dr. Liang is also exploring agents that learn language interactively, or can engage in a collaborative dialogue with humans. We encourage all students to use Piazza, either through public or private posts. Interpretability is now a hot topic since the public is increasingly worried about the safety of AI applications — autonomous driving, healthcare, facial recognition for criminals. This year, the research team led by Dr. Liang released SQuAD 2.0, which combines the SQuAD1.0 questions with over 50,000 new, unanswerable questions written adversarially by crowd workers to seem similar to answerable questions. Stanford University. I was a section leader for Stanford's CS106A (Introduction to Programming) class in the Winter of 2012. Percy Liang I certify that I have read this dissertation and that, in my opinion, it is fully adequate in scope and quality as a dissertation for the degree of Doctor of Philosophy. While Dr. Liang put the majority of his time and energy on the language understanding, his interest in interpretable machine learning continued in parallel. Teaching. Chris Donahue, Mina Lee, and Percy Liang. Implementation of the Brown hierarchical word clustering algorithm. Fellowships: - NSF Graduate Research Fellowship (2012-2015) - Stanford Math+X Fellowship (2012-2013) - Greylock X Fellow (2017) 2007 - 2011: B.S. The idea, she says, is to let the neural network do what it’s good at and then edit the result with human intelligence. Performing groundbreaking Natural Language Processing research since 1999. Journal Dynamics of the nanoneedle probe in trolling mode AFM . In the Summer of 2020, I will be graduating from Stanford to work with the Lex Team at Amazon Research, and I will be joining the PRIOR team at Allen Institute for AI (AI2) as a Pre-doctoral Young Investigator in the Fall of 2020. Researchers are using artificial intelligence techniques to create puns. That is the question that puzzled Dr. Liang when he was still at the high school. For your convenience, you can access these recordings by logging into the course Canvas site. Definitely not meant to be used as an actual CV. Neural networks, in other words, are rule-abiding to a fault, and that makes them terrible jokers. Shining a spotlight on the latest research progress of language understanding, the Association for Computational Linguistics (ACL) conference this year honored Know What You Don’t Know: Unanswerable Questions for SQuAD as its best short paper. View overview-6pp from CS 221 at Stanford University. from MIT, 2004; Ph.D. from UC Berkeley, 2011). “Even if we had a long list of puns it could learn from, that would miss the point,” she says. He’s aim is to build AI that’s natural and fun to talk to---bots that don’t just read us the news or tell us the weather, but can crack jokes or compose a poem, even tell a compelling story. - Fei-Fei Li & Yuval Noah Harari in Conversation with Nicholas Thompson. “I am fortunate to have these two mentors. WIRED25: Sebastian Thrun & Sam Altman Talk Flying Vehicles and Artificial Intelligence, ✨ Optimize your home life with our Gear team's best picks, from. This is my attempt to keep all the information and records at one place. Teaching staff Percy Liang (instructor) Panupong (Ice) Pasupat (head CA) Arijit Banerjee Greg Bodwin Diego Canales Adam Goldberg Ilan Goodman Will Harvey Jiaji Hu Billy Jun Aparna Krishnan Janice Lan Amrit Saxena Jiawei Yao CS221 / Autumn 2014 / Liang 1 Gill Bejerano. Adjusting for those differences, he says, could be the key to designing AI with more humanlike behavior. / February 8, 2019; Bill Freeman (Google/MIT) / Learning from Sight and Sound / February 1, 2019 ; Igor Labutov (LAER AI) / Teaching Machines like we Teach People / October 26, 2018; Dan Roth (U Penn) / Natural Language Understanding with Incidental Supervision / September 21, 2018 A teacher must enjoy teaching. More broadly, the humor research highlights the need to bring more human intelligence to neural nets, Levy says. We see growing interest in machine reading comprehension (MRC) due to potential industrial applications as well as technological advances, especially in deep learning and the availability of various MRC datasets that can benchmark different MRC systems. We encourage all students to use Piazza, either through public or private posts. I am an MS student in the Computer Science department at Stanford University, where I work with Percy Liang's group on distribution shift in NLP. In the meantime, He says she hopes to apply her general pun approach to more difficult creative tasks, like storytelling. In this paper, we introduce data recombination, a novel framework for injecting such prior knowledge into a model. The WIRED conversation illuminates how technology is changing every aspect of our lives—from culture to business, science to design. Showcasing the frontier technologies and people in artificial intelligence. Recently his research team has achieved some progress in explaining the black-box machine learning models. AI Is Now Learning Puns. Rishi Bommasani. It is worth mentioning that many AI figures today — Andrew Ng, Yoshua Bengio, Eric Xing — are Dr. Jordan’s students. In Empirical Methods in Natural Language Processing (EMNLP), pages 1533–1544. ¯\_(ツ)_/¯ Publications Enabling Language Models to Fill in the Blanks. Percy Liang I certify that I have read this dissertation and that, in my opinion, it is fully adequate in scope and quality as a dissertation for the degree of Doctor of Philosophy. Hi! “It’s really cool to see that actually pan out,” he says. in … Parameter estimation/Optimization techniques © 2020 Condé Nast. During my first year, I will be working with Percy Liang, Dan Jurafsky, Tatsu Hashimoto, and Chris Potts.. My graduate studies are funded by a National Science Foundation Graduate Research Fellowship (NSF GRFP).