Dr. Liang is also exploring agents that learn language interactively, or can engage in a collaborative dialogue with humans. Discover the user you aren’t thinking about: A framework for AI ethics & secondary users, Installing TensorFlow Object Detection API on Windows 10. Language Complexity Inspires Many Natural Language Processing (NLP) Techniques. However, Dr. Liang is always up for a challenge. 2018. Previously I was a postdoctoral Scholar at Stanford University working with John Duchi and Percy Liang and a Junior Fellow at the Institute for Theoretical Studies at ETH Zurich working with Nicolai Meinshausen. An End-to-End Discriminative Approach to Machine Translation, 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. A Graph-based Model for Joint Chinese Word Segmentation and Dependency Parsing. Download PDF (4 MB) Abstract. Liang, a senior majoring in computer science and minoring in music and also a student in the Master of Engineering program, will present an Advanced Music Performance piano recital today (March 17) at … On the other hand, unlabeled data (raw text) is often available "for free" in large quantities. For question and media inquiry, please contact: email@example.com, engage in a collaborative dialogue with humans, The Craziest Consequences of Artificial Superintelligence, A Comprehensive Summary and Categorization on Reinforcement Learning Papers at ICML 2018. Putting numbers in perspective with compositional descriptions, Estimation from indirect supervision with linear moments, Learning executable semantic parsers for natural language understanding, Imitation learning of agenda-based semantic parsers, Estimating mixture models via mixture of polynomials, On-the-Job learning with Bayesian decision theory, Traversing knowledge graphs in vector space, Compositional semantic parsing on semi-structured tables, Environment-Driven lexicon induction for high-level instructions, Learning fast-mixing models for structured prediction, Learning where to sample in structured prediction, Tensor factorization via matrix factorization, Bringing machine learning and compositional semantics together, Linking people with "their" names using coreference resolution, Zero-shot entity extraction from web pages, Estimating latent-variable graphical models using moments and likelihoods, Adaptivity and optimism: an improved exponentiated gradient algorithm, Altitude training: strong bounds for single-layer dropout, Simple MAP inference via low-rank relaxations, Relaxations for inference in restricted Boltzmann machines, Semantic parsing on Freebase from question-answer pairs, Feature noising for log-linear structured prediction, Dropout training as adaptive regularization, Spectral experts for estimating mixtures of linear regressions, Video event understanding using natural language descriptions, A data driven approach for algebraic loop invariants, Identifiability and unmixing of latent parse trees, Learning dependency-based compositional semantics, Scaling up abstraction refinement via pruning, A game-theoretic approach to generating spatial descriptions, A simple domain-independent probabilistic approach to generation, A dynamic evaluation of static heap abstractions, Learning programs: a hierarchical Bayesian approach, On the interaction between norm and dimensionality: multiple regimes in learning, Asymptotically optimal regularization in smooth parametric models, Probabilistic grammars and hierarchical Dirichlet processes, Learning semantic correspondences with less supervision, Learning from measurements in exponential families, An asymptotic analysis of generative, discriminative, and pseudolikelihood estimators, Structure compilation: trading structure for features, Analyzing the errors of unsupervised learning, Learning bilingual lexicons from monolingual corpora, A probabilistic approach to language change, Structured Bayesian nonparametric models with variational inference (tutorial), A permutation-augmented sampler for Dirichlet process mixture models, The infinite PCFG using hierarchical Dirichlet processes, A probabilistic approach to diachronic phonology, An end-to-end discriminative approach to machine translation, Semi-Supervised learning for natural language, A data structure for maintaining acyclicity in hypergraphs, Linear programming in bounded tree-width Markov networks, Efficient geometric algorithms for parsing in two dimensions, Methods and experiments with bounded tree-width Markov networks. The Phang family had its ancestry from Haifeng County in Guangdong, and Percy was raised in Malaysia. Percy Liang. We introduce a new methodol- ogy for this setting: First, we use a simple grammar to generate logical forms paired with canonical utterances. One of his papers proposed a statistics technique Influence Functions to trace a model’s prediction through the learning algorithm and back to its training data. Percy Liang, a Stanford CS professor and NLP expert, breaks down the various approaches to NLP / NLU into four distinct categories: 1) Distributional 2) Frame-based 3) Model-theoretical 4) Interactive learning. Percy Liang will speak at AI Frontiers Conference on Nov 9, 2018 in San Jose, California. It spawns some of the latest models achieving human-level performance in the task of question answering. Do We Need to Dehumanize Artificial Intelligence? 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. In ACL (Association for Computational Linguistics) 2018 conference, this achievement was celebrated by the award on the paper “Know What You Don’t Know: Unanswerable Questions for SQuAD” from Percy’s group. Lecture 7: Markov Decision Processes – Value … Dr. Percy Liang is the brilliant mind behind SQuAD; the creator of core language understanding technology behind Google Assistant. I would like to thank Dan Jurafsky and Percy Liang — the other two giants of the Stanford NLP group — for being on my thesis committee and for a lot of guidance and help throughout my PhD studies. Having attended Chinese schools from elementary all the way to middle school, Mandarin Chinese served as the main language throughout his education. Logical Representations of Sentence Meaning (J+M chapter 16) 11/20: Lecture: Question Answering Due: Project milestone: Questing Answering (J+M chapter 25) 11/25: No class - Angel at Emerging Technologies: BC's AI Showcase: 11/27: Lecture: Dialogue Equipped with a universal dictionary to map all possible Chinese input sentences to Chinese output sentences, anyone can perform a brute force lookup and produce conversationally acceptable answers without understanding what they’re actually saying. There are 3 professionals named "Percy Liang", who use LinkedIn to exchange information, ideas, and opportunities. 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. 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.”. Machine learning and language understanding are still at an early stage. On the other hand, unlabeled data (raw text) is often available "for free" in large quantities. 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. This year, our speakers include: Ilya Sutskever (Founder of OpenAI), Jay Yagnik (VP of Google AI), Kai-Fu Lee (CEO of Sinovation), Mario Munich (SVP of iRobot), Quoc Le (Google Brain), Pieter Abbeel (Professor of UC Berkeley) and more. Performing groundbreaking Natural Language Processing research since 1999. 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. After spending a year as a post-doc at Google New York, where he developed language understanding technologies for Google Assistant, Dr. Liang joined Stanford University and started teaching students AI courses. Dept. Liang, Percy. View Notes - overview from CS 221 at Massachusetts Institute of Technology. This article is to get a glimpse of his academic career, research focus, and his vision for AI. from MIT, 2004; Ph.D. from UC Berkeley, 2011). Our approach is as follows: In a preprocessing step, we use raw text to cluster words and calculate mutual information statistics. 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