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by   -   October 17, 2016
MIT Media Lab Director Joi Ito (left), WIRED Editor-in-Chief Scott Dadich (center), and U.S. President Barack Obama confer in the Roosevelt Room of the White House. Photo: WIRED
MIT Media Lab Director Joi Ito (left), WIRED Editor-in-Chief Scott Dadich (center), and U.S. President Barack Obama confer in the Roosevelt Room of the White House. Photo: WIRED

When President Barack Obama agreed to guest-edit the November issue of WIRED, he selected MIT Media Lab Director Joi Ito for an exchange of ideas about artificial intelligence (AI). Their recent interview at the White House is featured in the latest online issue of WIRED, published on Oct. 12.

The one-on-one conversation, moderated by WIRED Editor-in-Chief Scott Dadich, ran the gamut of topics at the intersection of societal needs, ethics, and technology — from cybersecurity to self-driving cars; from the roles of government, industry, and academia to the lack of diversity in tech; from “moonshot” motivations to innovation at the margins; and from neurodiversity to Star Trek. All this was covered in the context of AI and extended intelligence (EI), which uses machine learning to augment human capabilities.

by   -   September 21, 2016
From L-R: PhD Fadel Adib, PhD Mingmin Zhao and Professor Dina Katabi demonstrating different 'emotions' like the picture. Credit: Jason Dorfman, MIT CSAIL
From L-R: PhD Fadel Adib, PhD Mingmin Zhao and Professor Dina Katabi demonstrating different ’emotions’ like the picture. Credit: Jason Dorfman, MIT CSAIL

By measuring your heartbeat and breath, this device from MIT’s Computer Science and Artificial Intelligence Lab can tell if you’re excited, happy, angry or sad
.

by   -   September 15, 2016
Images shows the aggregation behavior that the robots should learn ( final snapshot of an already aggregated system). Credit: Roderich Gross
Image shows the aggregation behavior that the robots should learn (final snapshot of an already aggregated system). Credit: Roderich Gross

We have developed a new machine learning method at the University of Sheffield called Turing Learning that allows machines to model natural or artificial systems.

by   -   July 27, 2016
Testing lead in water during the Flint water crisis. Image credit: CC0 Public Domain
Testing lead in water during the Flint water crisis.

In episode fourteen of season two, we discuss Perturb-and-MAP and answer a listener question about classic artificial intelligence ideas being used in modern machine learning. Plus, we speak with Jake Abernethy from the University of Michigan about municipal data and his work on the Flint water crisis.

by   -   June 23, 2016
Photo: Xi Jessie Yang
Photo: Xi Jessie Yang

MIT-SUTD researchers are creating improved interfaces to help machines and humans work together to complete tasks.

by   -   June 20, 2016
Generating faces with Torch. Photo source: torch.ch
Generating faces with Torch. Photo source: torch.ch

In episode twelve of season two, we discuss generative adversarial networks, take a listener question about using machine learning to improve or create products, and lastly, speak with Iain Murray from University of Edinburgh.

by   -   June 6, 2016
Source: Pexels/CC0
Source: Pexels/CCO

In episode eleven of season two, we talk about the machine learning toolkit Spark and answer a listener question about the difference between Neural Information Processing Systems (NIPS) and International Conference on Machine Learning (ICML). Plus, we speak with Sinead Williamson from The University of Texas at Austin.

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?

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The UK Royal Society would like to hear your thoughts on machine learning – you are invited to answer some or all of the questions set out in a Call for Evidence here. The closing date for evidence is 3 January.

In episode twenty four we talk with Ben Vigoda about his work in probabilistic programming (everything from his thesis, to his new company).

David Mimno, assistant professor of information science (ISP).
David Mimno, assistant professor of information science (ISP).

In episode 23 we talk with David Mimno of Cornell University about his work in the digital humanities, and explore what machine learning can tell us about lady zombie ghosts and huge bodies of literature. Ryan introduces us to probabilistic programming and we take a listener question about knowledge transfer between math and machine learning.

In episode twenty two we talk with Adam Kalai of Microsoft Research New England about his work using crowdsourcing in Machine Learning.

In episode twenty one we talk with Quaid Morris of the University of Toronto, who is using machine learning to find a better way to treat cancers.

by   -   October 1, 2015

Brain-Machine Interfaces (BMIs) — where brain waves captured by electrodes on the skin are used to control external devices such as a robotic prosthetic — are a promising tool for helping people who have lost motor control due to injury or illness. However, learning to operate a BMI can be very time consuming. In a paper published in Nature Scientific Reports, a group from CNBI, EPFL and NCCR Robotics show how their new feedback system can speed up the training process by detecting error messages from the brain and adapting accordingly.

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