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Algorithm AI-Cognition

As AI surpasses human abilities in Go and poker – two decades after Deep Blue trounced chess grandmaster Garry Kasparov – it is seeping into our lives in ever more profound ways. It affects the way we search the web, receive medical advice and whether we receive finance from our banks.

We are only in the earliest stages of so-called algorithmic regulation – intelligent machines deploying big data, machine learning and artificial intelligence (AI) to regulate human behaviour and enforce laws – but it already has profound implications for the relationship between private citizens and the state.

by   -   July 20, 2017

Given a still image of a dish filled with food, CSAIL team’s deep-learning algorithm recommends ingredients and recipes.

By Christoph Salge, Marie Curie Global Fellow, University of Hertfordshire

How do you stop a robot from hurting people? Many existing robots, such as those assembling cars in factories, shut down immediately when a human comes near. But this quick fix wouldn’t work for something like a self-driving car that might have to move to avoid a collision, or a care robot that might need to catch an old person if they fall. With robots set to become our servants, companions and co-workers, we need to deal with the increasingly complex situations this will create and the ethical and safety questions this will raise.

interview by   -   July 8, 2017

In this episode, MeiXing Dong conducts interviews at the 2017 Midwest Speech and Language Days workshop in Chicago. She talks with Michael White of Ohio State University about question interpretation in a dialogue system; Dmitriy Dligach of Loyola University Chicago about extracting patient timelines from doctor’s notes; and Denis Newman-Griffiths of Ohio State University about connecting words and phrases to relevant medical topics.

interview by   -   June 24, 2017

In this episode, Audrow Nash interviews Sergey Levine, assistant professor at UC Berkeley, about deep learning on robotics. Levine explains what deep learning is and he discusses the challenges of using deep learning in robotics. Lastly, Levine speaks about his collaboration with Google and some of the surprising behavior that emerged from his deep learning approach (how the system grasps soft objects).

In addition to the main interview, Audrow interviewed Levine about his professional path. They spoke about what questions motivate him, why his PhD experience was different to what he had expected, the value of self-directed learning,  work-life balance, and what he wishes he’d known in graduate school.

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A subject plays a computer game as part of a neural security experiment at the University of Washington.
Patrick Bennett, CC BY-ND

By Eran Klein, University of Washington and Katherine Pratt, University of Washington


In the 1995 film “Batman Forever,” the Riddler used 3-D television to secretly access viewers’ most personal thoughts in his hunt for Batman’s true identity. By 2011, the metrics company Nielsen had acquired Neurofocus and had created a “consumer neuroscience” division that uses integrated conscious and unconscious data to track customer decision-making habits. What was once a nefarious scheme in a Hollywood blockbuster seems poised to become a reality.

by   -   May 30, 2017

In episode two of season three Neil takes us through the basics on dropout, we chat about the definition of inference (It’s more about context than you think!) and hear an interview with Jennifer Chayes of Microsoft.

Dig below the surface of some of today’s biggest tech controversies and you are likely to find an algorithm misfiring. These errors are not primarily caused by problems in the data that can make algorithms discriminatory, or their inability to improvise creatively. No, they stem from something more fundamental: the fact that algorithms, even when they are generating routine predictions based on non-biased data, will make errors. To err is algorithm.

by   -   May 10, 2017

MIT CSAIL approach allows robots to learn a wider range of tasks using some basic knowledge and a single demo.

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In this article, we explain in plain language machine learning.

Last week I had the pleasure of debating the question “does AI pose a threat to society?” with friends and colleagues Christian List, Maja Pantic and Samantha Payne. The event was organised by the British Academy and brilliantly chaired by the Royal Society’s director of science policy Claire Craig. Here follows my opening statement:

As the last in our series of blog posts on machine learning in research, we spoke to Dr Nathan Griffiths to find out more about machine learning in transport. Nathan is a Reader in the Department of Computer Science at the University of Warwick, whose research into the application of machine learning for autonomous vehicles (or “driverless cars”) has been supported by a Royal Society University Research Fellowship.

Heavy Water Components Test Reactor decommissioning.

How can we create robots that can carry out important tasks in dangerous environments? Machine learning is supporting advances in the field of robotics. To find out more, we talked to Dr Rustam Stolkin, Royal Society Industry Fellow for Nuclear Robotics, Professor of Robotics at the University of Birmingham, and Director at A.R.M Robotics Ltd, about his work combining machine learning and robotics to create practical solutions to nuclear problems.

Northwestern University mechanical engineering professor Todd Murphey and his team are engineering robots that one might say could make robotic assistance as seamless as “humanly” possible. With support from the National Science Foundation (NSF), the team is using novel algorithmic tools, such as a drawing robot, to develop the algorithms, or rules of behavior, that would greatly enhance a robot’s ability to adapt to human unpredictability.

Disney Robotics
September 17, 2017

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