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Algorithm AI-Cognition

interview by   -   May 29, 2018

In this episode, Abate interviews Andrew Stein from Anki. At Anki they developed an engaging robot called Cozmo which packs sophisticated robotic software inside a lifelike, palm sized, robot. Cozmo recognizes people and objects around him and plays games with them. Cozmo is unique in that a large amount of development has been implemented to make his animations and behavior feel natural, in addition to focusing on classical robotic elements such as computer vision and object manipulation.

interview by   -   April 14, 2018
Toyota HSR Trained with DART to Make a Bed.

In this episode, Audrow Nash speaks with Michael Laskey, PhD student at UC Berkeley, about a method for robust imitation learning, called DART. Laskey discusses how DART relates to previous imitation learning methods, how this approach has been used for folding bed sheets, and on the importance of robotics leveraging theory in other disciplines.

interview by   -   March 31, 2018



In this interview, Audrow speaks with Andrea Bajcsy and Dylan P. Losey about a method that allows robots to infer a human’s objective through physical interaction. They discuss their approach, the challenges of learning complex tasks, and their experience collaborating between different universities.

interview by   -   March 19, 2018



In this episode, Audrow Nash speaks with Maja Matarić, a professor at the University of Southern California and the Chief Science Officer of Embodied, about socially assistive robotics. Socially assistive robotics aims to endow robots with the ability to help people through individual non-contact assistance in convalescence, rehabilitation, training, and education. For example, a robot could help a child on the autism spectrum to connect to more neurotypical children and could help to motivate a stroke victim to follow their exercise routine for rehabilitation (see the videos below). In this interview, Matarić discusses the care gap in health care, how her work leverages research in psychology to make robots engaging, and opportunities in socially assistive robotics for entrepreneurship.

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.

File 20170609 4841 73vkw2
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.

File 20170426 2838 xiwppt

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



ICRA 2018 Exhibition
June 23, 2018


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