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Articles Tagged: Machine learning

Articles & Features

Sports and machine learning: How young people can use data from their own bodies to learn about machine learning

SECTION: Features

Sports and machine learning: How young people can use data from their own bodies to learn about machine learning

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

PDF | HTML | In the Digital Library

Interpreting AI and its place in our worlds

COLUMN: INIT

Interpreting AI and its place in our worlds

By Christine T. Wolf, Ezinne Nwankwo, April 2019

PDF | HTML | In the Digital Library

Explaining explainable AI

SECTION: Features

Explaining explainable AI

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

PDF | HTML | In the Digital Library

Trustworthy machine learning and artificial intelligence

Trustworthy machine learning and artificial intelligence

How can we add the most important ingredient to our relationship with machine learning?

By Kush R. Varshney, April 2019

PDF | HTML | In the Digital Library

Co-creating the future of work: Lessons from workplace automation

Co-creating the future of work: Lessons from workplace automation

What sociology and ethnography can teach us about designing the workplace technologies of tomorrow.

By Christine T. Wolf, April 2019

PDF | HTML | In the Digital Library

That's not fair!

That's not fair!

Why we need to study machine learning fairness, even in an increasingly unfair world.

By Deborah Raji, April 2019

PDF | HTML | In the Digital Library

Facial recognition is the plutonium of AI

Facial recognition is the plutonium of AI

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

PDF | HTML | In the Digital Library

Navigating through the hype that surrounds machine learning

Finding the edge: Art and automation

COLUMN: Letter from the editors

Finding the edge: Art and automation

By Jennifer Jacobs, April 2018

PDF | HTML | In the Digital Library

Computers and art in the age of machine learning

COLUMN: INIT

Computers and art in the age of machine learning

By Emily L. Spratt, April 2018

PDF | HTML | In the Digital Library

The burgeoning computer-art symbiosis

SECTION: Features

The burgeoning computer-art symbiosis

Computers help us understand art. Art helps us teach computers.

By Shiry Ginosar, Xi Shen, Karan Dwivedi, Elizabeth Honig, Mathieu Aubry, April 2018

PDF | HTML | In the Digital Library

Creation, curation, and classification: Mario Klingemann and Emily L. Spratt in conversation

Creation, curation, and classification: Mario Klingemann and Emily L. Spratt in conversation

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

PDF | HTML | In the Digital Library

Visualizing high-dimensional data

DEPARTMENT: Hello world

Visualizing high-dimensional data

By Tejas Khot, December 2016

PDF | HTML | In the Digital Library

Quantum algorithms for machine learning

SECTION: Features

Quantum algorithms for machine learning

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

PDF | HTML | In the Digital Library

Convolutional neural networks: an illustration in TensorFlow

DEPARTMENT: Hello world

Convolutional neural networks: an illustration in TensorFlow

By Abhineet Saxena, June 2016

PDF | HTML | In the Digital Library

Toward a web of systems

FEATURE: Features

Toward a web of systems

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

PDF | HTML | In the Digital Library

Miriam Plieninger on language learning with Babbel

SECTION: Features

Miriam Plieninger on language learning with Babbel

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

PDF | HTML | In the Digital Library

Tracking how we read

Tracking how we read

Using activity recognition for cognitive tasks can provide new insights about reading and learning habits.

By Kai Kunze, December 2013

PDF | HTML | In the Digital Library

The sensorium

The sensorium

Research teams from around the world reflect on their brain sensing setups.

By Evan M. Peck, Erin T. Solovey, September 2011

PDF | HTML | In the Digital Library

From Neural Networks to Deep Learning

From Neural Networks to Deep Learning

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

PDF | HTML | In the Digital Library

Superhuman speech by 2010

By Paula Bach, September 2007

PDF | HTML | In the Digital Library

Use of motion field warping to generate cardiac images

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

PDF | HTML | In the Digital Library

Voice activity detection

By Deepti Singh, Frank Boland, September 2007

PDF | HTML | In the Digital Library

Introduction

By William Stevenson, December 2004

PDF | HTML | In the Digital Library

Identifying spam without peeking at the contents

By Shlomo Hershkop, Salvatore J. Stolfo, December 2004

PDF | HTML | In the Digital Library

Peer-to-peer collaborative spam detection

By Nathan Dimmock, Ian Maddison, December 2004

PDF | HTML | In the Digital Library