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machine learning

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 16, 2016

System correlates recorded speech with images, could lead to fully automated speech recognition.

by   -   November 30, 2016

We need to do more than teach machines to learn. We need to overcome the barriers that separate machines from us – and us from them.

by   -   November 11, 2016

“Information extraction” system helps turn plain text into data for statistical analysis.

by   -   October 28, 2016

CSAIL’s new training technique would reveal the basis for machine-learning systems’ decisions.

by   -   October 21, 2016

westworld-promo-hbo

For a sci-fi fan like me, fascinated by the nature of human intelligence and the possibility of building life-like robots, it’s always interesting to find a new angle on these questions. As a re-imagining of the original 1970s science fiction film set in a cowboy-themed, hyper-real adult theme park populated by robots that look and act like people, Westworld does not disappoint.

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?





Ladybird
May 21, 2016


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