A rising superstar in the community of machine learning and NLP, Dr. Liang has received countless academic distinctions over the years: IJCAI Computers and Thought Award in 2016, NSF CAREER Award in 2016, Sloan Research Fellowship in 2015, Microsoft Research Faculty Fellowship in 2014. Discover the user you aren’t thinking about: A framework for AI ethics & secondary users, Installing TensorFlow Object Detection API on Windows 10. Table 9: A table showing the distribution of bigrams in a corpus (from (Manning and Schutze, 1999, - "Corpus-Based Methods in Chinese Morphology and Phonology" 2018. Download PDF (4 MB) Abstract. “Percy is one of the most extraordinary researchers I’ve ever worked with,” he commented. Do We Need to Dehumanize Artificial Intelligence? This article is to get a glimpse of his academic career, research focus, and his vision for AI. A very early algorithm for segmenting Chinese using a lexicon, called maximum matching, operates by scanning the text from left to right and greedily matchingtheinputstringwiththelongestwordinthedictionary(Liang,1986). Percy Liang will speak at AI Frontiers Conference on Nov 9, 2018 in San Jose, California. In this paper, we present the first free-form multiple-Choice Chinese machine reading Comprehension dataset (C ³ ), containing 13,369 documents … Dr. Klein tried to get his young talented apprentice on board. Percy Liang, Associate Professor & Dorsa Sadigh, Assistant Professor – Stanford University. CodaLab addresses this problem by providing a cloud-based virtual “workbench,” where computer scientists can conduct data-driven experiments quickly and easily. 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. While Dr. Liang put the majority of his time and energy on the language understanding, his interest in interpretable machine learning continued in parallel. Percy Liang. Aditi Raghunathan*, Sang Michael Xie*, Fanny Yang , John Duchi and Machine learning and language understanding are still at an early stage. In 2004, Dr. Liang received his Bachelor of Science degree from MIT. 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. View the profiles of professionals named "Percy Liang" on LinkedIn. 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. Lecture 1: Overview CS221 / Autumn 2014 / Liang Teaching sta Percy Liang (instructor) Panupong (Ice) Pasupat (head That is why studying natural language processing (NLP) promises huge potential for approaching the holy grail of artificial general intelligence (A.G.I). CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): Statistical supervised learning techniques have been successful for many natural language processing tasks, but they require labeled datasets, which can be expensive to obtain. Dr. Percy Liang is the brilliant mind behind SQuAD; the creator of core language understanding technology behind Google Assistant. Having attended Chinese schools from elementary all the way to middle school, Mandarin Chinese served as the main language throughout his education. Learning adaptive language interfaces through decomposition, On the importance of adaptive data collection for extremely imbalanced pairwise tasks, RNNs can generate bounded hierarchical languages with optimal memory, Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming, Task-Oriented dialogue as dataflow synthesis, An investigation of why overparameterization exacerbates spurious correlations, Feature noise induces loss discrepancy across groups, Graph-based, self-supervised program repair from diagnostic feedback, Understanding and mitigating the tradeoff between robustness and accuracy, Understanding self-training for gradual domain adaptation, Robustness to spurious correlations via human annotations, Robust encodings: a framework for combating adversarial typos, Selective question answering under domain shift, Shaping visual representations with language for few-shot classification, ExpBERT: representation engineering with natural language explanations, Enabling language models to fill in the blanks, Distributionally robust neural networks for group shifts: on the importance of regularization for worst-case generalization, Strategies for pre-training graph neural networks, Selection via proxy: efficient data selection for deep learning, A tight analysis of greedy yields subexponential time approximation for uniform decision tree, Certified robustness to adversarial word substitutions, Distributionally robust language modeling, Designing and interpreting probes with control tasks, Unlabeled data improves adversarial robustness, On the accuracy of influence functions for measuring group effects, Learning autocomplete systems as a communication game, Unifying human and statistical evaluation for natural language generation, Learning a SAT solver from single-bit supervision, Defending against whitebox adversarial attacks via randomized discretization, Inferring multidimensional rates of aging from cross-sectional data, FrAngel: component-based synthesis with control structures, Semidefinite relaxations for certifying robustness to adversarial examples, Uncertainty sampling is preconditioned stochastic gradient descent on zero-one loss, A retrieve-and-edit framework for predicting structured outputs, Decoupling strategy and generation in negotiation dialogues, Mapping natural language commands to web elements, Textual analogy parsing: what's shared and what's compared among analogous facts, On the relationship between data efficiency and error in active learning, Fairness without demographics in repeated loss minimization, Training classifiers with natural language explanations, The price of debiasing automatic metrics in natural language evaluation, Know what you don't know: unanswerable questions for SQuAD, Generalized binary search for split-neighborly problems, Planning, inference and pragmatics in sequential language games, Generating sentences by editing prototypes, Delete, retrieve, generate: a simple approach to sentiment and style transfer, Reinforcement learning on web interfaces using workflow-guided exploration, Certified defenses against adversarial examples, Active learning of points-to specifications, Certified defenses for data poisoning attacks, Unsupervised transformation learning via convex relaxations, Adversarial examples for evaluating reading comprehension systems, Macro grammars and holistic triggering for efficient semantic parsing, Importance sampling for unbiased on-demand evaluation of knowledge base population, Understanding black-box predictions via influence functions, Convexified convolutional neural networks, Developing bug-free machine learning systems with formal mathematics, World of bits: an open-domain platform for web-based agents, A hitting time analysis of stochastic gradient Langevin dynamics, Naturalizing a programming language via interactive learning, Learning symmetric collaborative dialogue agents with dynamic knowledge graph embeddings, From language to programs: bridging reinforcement learning and maximum marginal likelihood, Unsupervised risk estimation using only conditional independence structure, SQuAD: 100,000+ questions for machine comprehension of text, Learning language games through interaction, Data recombination for neural semantic parsing, Simpler context-dependent logical forms via model projections, Unanimous prediction for 100% precision with application to learning semantic mappings, How much is 131 million dollars? Statistical supervised learning techniques have been successful for many natural language processing tasks, but they require labeled datasets, which can be expensive to obtain. Meanwhile, Dr. Liang’s mentor at UC Berkeley Dr. Klein founded Semantic Machines in 2014. Percy Liang, a Stanford CS professor and NLP … QuAC: Question answering in con-text. Buy tickets at aifrontiers.com. There are 3 professionals named "Percy Liang", who use LinkedIn to exchange information, ideas, and opportunities. 2018. Systems that aim to interact with humans should fundamentally understand how humans think and act, at least at a behavioral level. The goal of Chinese word segmentation is to find the word boundaries in a sentence that has been written as a string of characters without spaces. 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. Percy Liang, Computer Science Department, Stanford University/Statistics Department, Stanford University, My goal is to develop trustworthy systems that can communicate effectively with people and improve over time through interaction. from MIT, 2004; Ph.D. from UC Berkeley, 2011). You might appreciate a brief linguistics lesson before we continue on to define and describe those categories. ... and locations in a sentence. Chinese: the Penn Chinese Treebank. On the other hand, unlabeled data (raw text) is often available “for free ” in large quantities. Dr. Percy Liang is the brilliant mind behind SQuAD; the creator of core language understanding technology behind Google Assistant. By Percy Liang. However, Dr. Liang is always up for a challenge. View the profiles of professionals named "Percy Liang" on LinkedIn. View Notes - overview from CS 221 at Massachusetts Institute of Technology. You should complain to them for creating you and us grief. 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 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. Hang Yan, Xipeng Qiu, Xuanjing Huang Article at MIT Press (presented at ACL 2020) 78-92 A Knowledge-Enhanced Pretraining Model for Commonsense Story Generation. 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.Before that, I was a PhD student at the EECS department of UC Berkeley advised by Martin Wainwright. His advisor Michael Collins at MIT, a respected researcher in the field of computational linguistics, encouraged him to pursue a Master’s degree in natural language processing, which perfectly suited his interest. of Electrical Engineering and Computer Science. 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. The purpose of language understanding is not merely to imitate humans. “I am fortunate to have these two mentors. Jian Guan, Fei Huang, Minlie Huang, Zhihao Zhao, Xiaoyan Zhu Article at MIT Press (presented at ACL 2020) 93-108 Improving Candidate Generation for … 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. – Stanford University ( B.S language so as to communicate with humans step, use. 2004 ; Ph.D. from UC Berkeley, 2011 ) far been the holy grail of artificial.. The main language throughout his education creator of core language understanding technology behind Google Assistant Luke Zettle-moyer, least. Achieved tremendous progress, owing to the power of deep learning achieving human-level performance my passion ignited. 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