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The World Wide Voice Web (WWvW)

Time / Place:

⏱️ 10/6 (Wed.) 20:25-20:50 at Online Track 1


We envision a world-wide voice web where everybody, including the illiterate speaking in rare languages, can easily use voice to ask for information, transact business, control IoTs, and automate compositional web-based tasks.

At the core of WWvW is the technical challenge of natural language understanding.

Our approach is to combine deep learning with formal programming languages. We use a neural contextual semantic parser to map dialogues to their executable formal semantics, expressed in our new ThingTalk programming language.

The technology is demonstrated with an open-source privacy-protecting virtual assistant that can control hundreds of IoT devices and perform other popular skills.

Our open-source Genie Toolkit embodying this methodology lets non AI experts create multilingual dialogue agents cost-effectively.

We invite participation in building the wwVw through contributions to the Genie Toolkit and our open, crowdsourced voice skill repository, Thingpedia.

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  • 林倩玲 Monica Lam Website:
  • Computer Science, Stanford Univesity / Professor
  • Monica Lam is a Professor in the Computer Science Department at Stanford University since 1988. She is the faculty director of the Open Virtual Assistant Lab (OVAL). She received a B.Sc. from University of British Columbia in 1980 and a Ph.D. in Computer Science from Carnegie Mellon University in 1987.

    Monica is a Member of the National Academy of Engineering and Association of Computing Machinery (ACM) Fellow. She is a co-author of the popular text Compilers, Principles, Techniques, and Tools (2nd Edition), also known as the Dragon book.

    Professor Lam's current research is on conversational virtual assistants with an emphasis on privacy protection. Her research uses deep learning to map task-oriented natural language dialogues into formal semantics, represented by a new executable programming language called ThingTalk. Her Almond virtual assistant, trained on open knowledge graphs and IoT API standards, can be easily customized to perform new tasks.

    She is leading an Open Virtual Assistant Initiative to create the largest, open, crowdsourced language semantics model to promote open access in all languages. Her decentralized Almond virtual assistant that supports fine-grain sharing with privacy has received Popular Science's Best of What's New Award in Security in 2019. Prof. Lam is also an expert in compilers for high-performance machines. Her pioneering work of affine partitioning provides a unifying theory to the field of loop transformations for parallelism and locality. Her software pipelining algorithm is used in commercial systems for instruction level parallelism. Her research team created the first, widely adopted research compiler, SUIF. Her contributions in computer architecture include the CMU Warp Systolic Array and the Stanford DASH Distributed Memory Multiprocessor.

    She was on the founding team of Tensilica, now a part of Cadence. She received an NSF Young Investigator award in 1992, the ACM Most Influential Programming Language Design and Implementation Paper Award in 2001, an ACM SIGSOFT Distinguished Paper Award in 2002, and the ACM Programming Language Design and Implementation Best Paper Award in 2004.

    She was the author of two of the papers in "20 Years of PLDI--a Selection (1979-1999)", and one paper in the "25 Years of the International Symposia on Computer Architecture". She received the University of British Columbia Computer Science 50th Anniversary Research Award in 2018, and an ASPLOS Influential Paper Award in 2021.

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