Sep 9–12, 2019

Understanding AI for the physical world

Haixun Wang (WeWork)
1:45pm2:25pm Wednesday, September 11, 2019
Location: 230 B

Who is this presentation for?

  • Industrial practitioners and academic researchers in the area of smart buildings, smart cities, IoT, human-computer interface (HCI), robotics, computer vision, natural language processing (NLP), computational economics, etc.

Level

Beginner

Description

Artificial intelligence and machine learning are making big strides in cyberspace. For example, Google’s Gmail is able to complete our sentences as we type and a technique known as generative adversarial network (GAN) is able to generate custom human faces that are hyperrealistic. Yet there has been limited progress with AI in the physical world. This is mostly due to the challenges around infrastructure—from the need to collect and analyze data in the physical world to the need for physical action complicating the ability to make a prediction. WeWork is at the cusp of changing this with a team of engineers and data scientists focused on using advanced techniques to make the physical world more intelligent.

As the cyber world becomes more intelligent, so too must the physical world. Haixun Wang details WeWork’s approach, showcases its techniques, and highlights several case studies to show how WeWork is approaching this.

With over 400 buildings around the world, WeWork has a fleet of spaces ripe for experimenting how to blend the physical and the digital. At this scale, every decision, from day-to-day ones such as how to schedule room cleaning to billion-dollar ones such as how to source its next building and location, becomes a nontrivial data science problem. While most research and development around smart buildings has focused on energy saving, WeWork sees an opportunity to broaden this area to focus on improving the quality of life in workspaces and making businesses more efficient. WeWork believes that intelligent environments help make space more efficient and the addition of human insight makes for a more engaging experience. The company is experimenting with using sensors to collect anonymized, aggregate data to train models to learn how people use space to optimize space for their needs. From using AI to inform interior design and space layout to using ML to reshuffle conference room booking to match guests with the perfect space for their meetings and predict the health of an organization based on engagement insights, WeWork is exploring a variety of ways to use cutting-edge, data science techniques in the real world.

Prerequisite knowledge

  • General knowledge of ML and AI (useful but not required)

What you'll learn

  • Understand the opportunities when the physical world becomes intelligent and the challenges in building and interacting with the intelligent physical world
  • Learn the recent advances industry and academia have made in IoT, speech, NLP, computer vision, HCI, robotics, and geospatial AI when it comes to interacting the physical world
Photo of Haixun Wang

Haixun Wang

WeWork

Haixun Wang is the vice president of Engineering and distinguished scientist at WeWork. He’s an IEEE fellow and editor in chief of the IEEE Data Engineering Bulletin. Previously, he was a director of natural language processing at Amazon; led the NLP organization in Facebook working on query and document understanding; worked on natural language processing with Google Research; led research in knowledge bases, graph systems, and text processing at Microsoft Research Asia; and he was a research staff member at IBM T. J. Watson Research Center, a technical assistant to Stuart Feldman (vice president of computer science of IBM Research), and a technical assistant to Mark Wegman (head of computer science of IBM Research). He’s published more than 200 research papers in international journals and conference proceedings. He served as PC chairs of many academic conferences, and he’s on the editorial board of journals such as IEEE Transactions of Knowledge and Data Engineering (TKDE) and Journal of Computer Science and Technology (JCST). He won the best paper award in ICDE 2015, 10-year best paper award in ICDM 2013, and best paper award of ER 2009. He earned his PhD in computer science from UCLA.

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