symposium


AI Compute Symposium Charts Path from Emerging to Pervasive AI

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Together with the IEEE Circuits and Systems Society and Electron Device Society, IBM Research organized the 2nd AI Compute Symposium at the IBM T.J. Watson Research Center THINKLab in Yorktown Heights, N.Y., on Oct 17. More than 200 distinguished academics, renowned thinkers, students, and innovators from across industry and academia assembled for the one-day symposium, which showcased leadership and advancement in research addressing AI compute from pervasive to general AI. The free event featured three keynotes, three invited talks, a student poster session, and a panel discussion. The keynoters were Dr. Luis Lastras, a researcher with IBM; Professor Wen-mei Hwu of the University of Illinois at Urbana-Champaign (UIUC); and Harvard University/Samsung Fellow Donhee Ham. Lastras provided an exciting overview of research projects from IBM related to natural language processing and its evolution.


AI and the Police State AI Now 2019 Symposium

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Sign in to report inappropriate content. An examination of AI systems used by border patrol and police, looking at the ways in which these systems expand the power and reach of law enforcement and the carceral state, and how researchers, organizers, and scholars are pushing back.


Whose Insurance Pays in a Driverless Vehicle Crash?

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Who is liable for a car accident when no one's driving the car? That was one of several questions explored by experts in Arizona's autonomous vehicle industry during a special symposium last Thursday as representatives from public and private sectors hashed out what the near future looks like for driverless cars. As more self-driving cars move around the East Valley, stakeholders and policymakers gathered in Chandler to discuss how Arizona will adapt to the rapidly-developing technology. "A lot of work needs to be done," said Jill Sciarappo, senior marketing director for Intel, "and we need to come together to solve a lot of problems to make that happen." A reoccurring theme of the symposium, organized by the Chandler Chamber of Commerce, involved the liability factors involving self-driving cars.


Privacy Preserving Gaze Estimation using Synthetic Images via a Randomized Encoding Based Framework

arXiv.org Machine Learning

Eye tracking is handled as one of the key technologies for applications which assess and evaluate human attention, behavior and biometrics, especially using gaze, pupillary and blink behaviors. One of the main challenges with regard to the social acceptance of eye-tracking technology is however the preserving of sensitive and personal information. To tackle this challenge, we employed a privacy-preserving framework based on randomized encoding to train a Support Vector Regression model on synthetic eye images privately to estimate human gaze. During the computation, none of the parties learns about the data or the result that any other party has. Furthermore, the party that trains the model cannot reconstruct pupil, blink or visual scanpath. The experimental results showed that our privacy preserving framework is also capable of working in real-time, as accurate as a non-private version of it and could be extended to other eye-tracking related problems.



Littler Co-Hosts AI & Robotics Symposium

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World-class thought leaders discuss how emerging technologies are reshaping business and the future of work. With advanced technologies now at the forefront of nearly every major industry, stakeholders from a range of perspectives will discuss the latest innovations in robotics and AI, the pressing questions posed by their adoption and how these trends are transforming the world of work. The symposium will feature executives from some of the world's leading robotics manufacturers, system integrators, end users, technologists, corporate leaders and academics in a series of engaging panel discussions. Robotics manufacturers will also participate in speed networking with University of Michigan students prior to the symposium and will have robots on-site to showcase their capabilities. The following forward-thinking organizations are participating in the speed networking program: ABB; AMT Applied Manufacturing; the Association for Advancing Automation; ATI Industrial Automation; Comau; FANUC America Corporation; Ford Advanced Manufacturing; Honeywell Intelligrated; JR Automation; Littler; Kawasaki Robotics; KUKA Robotics; Universal Robots; and the University of Michigan Robotics Institute.


'People fix things. Tech doesn't fix things.' – TechCrunch

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Veena Dubal is an unlikely star in the tech world. A scholar of labor practices regarding the taxi and ride-hailing industries and an Associate Professor at San Francisco's U.C. Hastings College of the Law, her work on the ethics of the gig economy has been covered by the New York Times, NBC News, New York Magazine, and other publications. She's been in public dialogue with Naomi Klein and other famous authors, and penned a prominent op-ed on facial recognition tech in San Francisco -- all while winning awards for her contributions to legal scholarship in her area of specialization, labor and employment law. At the annual symposium of the AI Now Institute, an interdisciplinary research center at New York University, Dubal was a featured speaker. The symposium is the largest annual public gathering of the NYU-affiliated research group that examines AI's social implications.


Machine Learning for Text Analytics is Getting a Boost

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BLOOMINGTON, Ind., Oct. 22, 2019 (GLOBE NEWSWIRE) -- Megaputer Intelligence, Inc. will share an innovative new tool for building training datasets for use in machine learning during a presentation at the Text Analytics Forum '19 held in Washington, DC on November 7. Dr. Sergei Ananyan, CEO of Megaputer Intelligence, Inc., will present a cutting-edge topic entitled, "NLP & Rule-Based Approach for Fact Extraction: Launchpad for Machine Learning Techniques" on Thursday, November 7 at 11:15 AM EST. The Text Analytics Forum will host the presentation at the JW Marriott in Washington, DC as part of its comprehensive programming, running from Nov 4-7. The content of the presentation is designed for people interested in discovering how to achieve higher accuracy from machine learning, relieve the burden of needing experts to manually create a gold standard training dataset, and illuminate the black box surrounding machine learning as much as possible with insight into today's latest technological advances. Professionals such as text analysts, data scientists, DBAs, information knowledge architects, knowledge organizers, taxonomists, ontologists, CIOs, CKOs, research scientists, and data quality managers will benefit greatly from this technique to overcome well-known challenges of machine learning. One fundamental obstacle for using machine learning (ML) to accurately extract facts from free-text documents is that it requires huge quantities of pre-categorized data for training a model.


"People fix things. Tech doesn't fix things." – TechCrunch

#artificialintelligence

Veena Dubal is an unlikely star in the tech world. A scholar of labor practices regarding the taxi and ride-hailing industries and an Associate Professor at San Francisco's U.C. Hastings College of the Law, her work on the ethics of the gig economy has been covered by the New York Times, NBC News, New York Magazine, and other publications. She's been in public dialogue with Naomi Klein and other famous authors, and penned a prominent op-ed on facial recognition tech in San Francisco -- all while winning awards for her contributions to legal scholarship in her area of specialization, labor and employment law. At the annual symposium of the AI Now Institute, an interdisciplinary research center at New York University, Dubal was a featured speaker. The symposium is the largest annual public gathering of the NYU-affiliated research group that examines AI's social implications.