In order to foster interest in machine learning among young people, presented are simple and effective ways to engage kids using sensors on their own bodies.
By Abigail Zimmermann-Niefield, R. Benjamin Shapiro, Shaun Kane, July 2019
By Christine T. Wolf, Ezinne Nwankwo, April 2019
How good are you at explaining your decisions? Are you better than a machine? Today, AI systems are being asked to explain their decisions. This article explores the challenges in solving this problem and approaches researchers are pursuing.
By Michael Hind, April 2019
How can we add the most important ingredient to our relationship with machine learning?
By Kush R. Varshney, April 2019
What sociology and ethnography can teach us about designing the workplace technologies of tomorrow.
By Christine T. Wolf, April 2019
Why we need to study machine learning fairness, even in an increasingly unfair world.
By Deborah Raji, April 2019
It's dangerous, racializing, and has few legitimate uses; facial recognition needs regulation and control on par with nuclear waste.
By Luke Stark, April 2019
By Parang Saraf, January 2019
Computers help us understand art. Art helps us teach computers.
By Shiry Ginosar, Xi Shen, Karan Dwivedi, Elizabeth Honig, Mathieu Aubry, April 2018
Computer-generated art has long challenged traditional notions of the role of the artist and the curator in the creative process. In the age of machine learning these philosophical conceptions require even further consideration.
By Emily L. Spratt, April 2018
Quantum computing and machine learning are two technologies that have generated unparalleled amounts of hype among the scientific community and popular press. Both are mysterious, immensely powerful, and on a collision course with each other.
By Bingjie Wang, September 2016
By Abhineet Saxena, June 2016
Web and semantic technologies will form the foundation for ecosystems of machines that interact with each other and with people as never before.
By Florian Michahelles, Simon Mayer, December 2015
Babbel's Director of Didactics, Miriam Plieninger, weighs in on how mobile apps are rapidly changing the way we approach language learning.
By Daniel Bauer, Billy Rathje, October 2014
Using activity recognition for cognitive tasks can provide new insights about reading and learning habits.
By Kai Kunze, December 2013
Research teams from around the world reflect on their brain sensing setups.
By Evan M. Peck, Erin T. Solovey, September 2011
Pondering the brain with the help of machine learning expert Andrew Ng and researcher-turned-author-turned-entrepreneur Jeff Hawkins.
By Jonathan Laserson, September 2011
By William Ella, December 2008
By Paula Bach, September 2007
In this study, we developed an algorithmic method to analyze late contrast-enhanced (CE) magnetic resonance (MR) images, revealing the so-called hibernating myocardium. The algorithm is based on an efficient and robust image registration algorithm. Using our method, we are able to integrate the static late CE MR image with its corresponding cardiac cine MR images, constructing cardiac motion CE MR images, which are referred to as cardiac cine CE MR images. This method appears promising as an improved cardiac viability assessment tool
By Gang Gao, Paul Cockshott, September 2007
By Deepti Singh, Frank Boland, September 2007
By Deian Stefan, March 2007
By William Stevenson, December 2004
By George Sakkis, December 2004
By Shlomo Hershkop, Salvatore J. Stolfo, December 2004
By Nathan Dimmock, Ian Maddison, December 2004
By Cory Quammen, December 2001
