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Algorithm AI-Cognition

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

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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.

interview by   -   August 6, 2016

Black-rhino-Yoki-WC

In this episode, Audrow Nash interviews Fredrik Gustafsson, Professor in Sensor Informatics at Department of Electrical Engineering in Linköping University, about an initiative to reduce poaching in a rhino sanctuary in Ngulia, Kenya. Gustafsson discusses how he first became involved in this project, how he has worked with the rangers to develop solutions, and the future of this work.

by and   -   January 27, 2016

The idea of connecting brain-inspired models of computation to robots is probably as old as the discipline of robotics itself. Today, researchers are connecting robotics with neuroscience in order to both build intelligent robots and to better understand the brain. The workshop Advances in Biologically Inspired Brain-Like Cognition and Control for Learning Robots at IROS (Hamburg) brought together experts from diverse fields in brain-based robotics, neurorobotics, artificial neural networks and machine learning to discuss the state of the art.

Two years after the small London-based startup DeepMind published their pioneering work on “Playing Atari with Deep Reinforcement Learning”, they have become one of the leaders in the chase for Artificial General Intelligence. In our previous article, we took a thorough peek inside their technology. In our latest research, we ask: what happens when multiple AI agents are competing or collaborating in the same environment?

by   -   July 31, 2015

aerial_view_football_field_stadiumIn episode sixteen we chat with Danny Tarlow of Microsoft Research Cambridge (in the UK not MA). Danny (along with Chris Maddison and Tom Minka) won best paper at NIPS 2014 for his paper A* Sampling. We talk with him about his work in applying machine learning to sports and politics.

by   -   June 4, 2015
Source: http://cs.stanford.edu/people/ang/
Source: Stanford University  

In episode twelve we talk with Andrew Ng, Chief Scientist at Baidu, about how speech recognition is going to explode the way we use mobile devices and his approach to working on the problem.

by   -   May 22, 2015

data_eye_privacyIn episode eleven we chat with Neil Lawrence from the University of Sheffield. We talk about the problems of privacy in the age of machine learning, and the responsibilities that come with using machine learning tools and making data more open. We learn about the Markov decision process (and what happens when you use it in the real world and it becomes a partially observable Markov decision process) and take a listener question about finding insights into features in the black boxes of deep learning.

by   -   May 7, 2015

Talking Machines

In Episode 10 we talk with David Blei of Columbia University. We talk about his work on latent dirichlet allocation, topic models, the PhD program in data that he’s helping to create at Columbia and why exploring data is inherently multidisciplinary. We learn about Markov Chain Monte Carlo and take a listener question about how machine learning can make humans more creative.

interview by   -   May 1, 2015

In this episode, Audrow Nash interviews Todd Hylton, Senior Vice President at Brain Corporation, about neuromorphic computers. They discuss the robotics development board bStem, which approximates a neuromorphic computer, as well as the eyeRover: a small balancing robot that demonstrates how the bStem can be used in mobile robots.





Nylon fishing line actuator
October 31, 2014


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