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Algorithm AI-Cognition

by   -   February 21, 2017

Current legal AI systems do not think like human lawyers. But, as their capabilities improve, the temptation grows to use such systems not only to supplement but to eliminate the need for some personnel. Ron Yu examines how this might affect the legal profession and the future development of legal AI.

by   -   February 17, 2017
MIT Professor Regina Barzilay has struck up new research collaborations, drawn in MIT students, launched projects with local doctors, and begun empowering cancer treatment with the machine-learning insight that has already transformed many areas of modern life.
Photo: Lillie Paquette/School of Engineering

Computer scientist Regina Barzilay is working with MIT students and medical doctors in an ambitious bid to revolutionize cancer care. She is relying on a tool largely unrecognized in the oncology world but deeply familiar to hers: machine learning.

by   -   February 10, 2017

There’s a great deal of concern over artificial intelligence; what it means for our jobs, whether robots will one day replace us in the workplace, whether it will one day lead to robot wars. But current research projects show that artificial intelligence (AI) can also be used for the greater good. Here are five global problems that machine learning could help us solve.

by   -   February 8, 2017

In this interview, Dr. Yueh-Hsuan Weng invites Prof. Ronald C. Arkin, Executive Committee Member of IEEE Global Initiative for Ethical Considerations in Artificial Intelligence and Autonomous Systems, to share his insights on roboethics, with a focus on its technical aspects, military and caregiver applications..

by   -   February 3, 2017

A recent Conversation piece pointed out that the British electricity mix in 2016 was the cleanest in 60 years, with record capacity from renewable energy, mainly from wind and solar power. But one problem with this great expansion in renewables is they are intermittent, meaning they depend on weather conditions such as the wind blowing or sun shining. Unlike conventional power, this means they can’t necessarily meet surges in demand. Hence many press headlines in recent years about the “lights going out”.

by   -   February 1, 2017

Coupled with audio and vital-sign data, this deep-learning, wearable system could someday serve as a “social coach” for people with anxiety or Asperger’s

by   -   February 1, 2017

Brad Knox talks bots_alive and a new form of character AI. Much like motion capture for scripted animation, this new technique may revolutionize how interactive characters are created, through observation of authentic human-generated behavior.

by   -   January 26, 2017

With machine intelligence emerging as an essential tool in many aspects of modern life, Alan Winfield discusses autonomous sytems, safety and regulation.

by   -   January 16, 2017

machine-learning-2

In this fascinating animation from Oxford Sparks, we take a look at how statistics and computer science can be used to make machines that learn for themselves, without being explicitly programmed.

by   -   January 11, 2017

Join Professor Brian Cox as he brings together experts on AI and machine learning (including RoboHub’s own Sabine Hauert) to discuss key issues that will shape our technological future

by   -   December 1, 2016
Tomaso Poggio, a professor of brain and cognitive sciences at MIT and director of the Center for Brains, Minds, and Machines, has long thought that the brain must produce “invariant” representations of faces and other objects, meaning representations that are indifferent to objects’ orientation in space, their distance from the viewer, or their location in the visual field. Image Credit: Massachusetts Institute of Technology
Image: Massachusetts Institute of Technology

MIT researchers and their colleagues have developed a new computational model of the human brain’s face-recognition mechanism that seems to capture aspects of human neurology that previous models have missed.

by   -   October 26, 2016

mroberto-title-view-miniature

Developed by a team at the University of Toronto, mROBerTO (milli-ROBot TORonto) is designed for swarm-robotics researchers who might wish to test their collective-behavior algorithms with real physical robots. With just a 16 mm x 16 mm footprint, mROBerTO can be used in a multitude of other miniature robot projects too—its modular design allowing for easy addition or removal of components.

by   -   October 19, 2016

Algorithms are prone to errors, biases and predictable malfunctions, writes Frank Pasquale.

interview by   -   October 16, 2016

In this episode, Audrow Nash interviews Edson Prestes, Professor at Federal University of Rio Grande do Sul and an organizer of the Humanitarian Robotics and Automation Technology Challenge (HRATC) 2016 competition. The HRATC competition challenges teams around the world to develop methods of controlling robots to detect land mines in large open environments.

interview by   -   October 3, 2016

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In this episode, Audrow Nash interviews several researchers presenting their work at the Robotics Science and Systems (RSS) 2016 conference in Ann Arbor, Michigan.





The Wambots Team
November 30, 2012


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