SECTION: Features
Understanding Human-building Interactions Using Computing
Reconstructing the network of life from molecular data is a complicated task. How can computational algebraic geometry play a role?
By Bowen Du, May 2024
Articles Tagged: Machine learning
Articles & Features
Unveiling Patterns of the Earth through Machine Learning and Geospatial Analysis
Konstantin Klemmer is a researcher at Microsoft Research New England, where he works on the representation of geospatial phenomena in machine learning methods.
By Jiayi Li, Konstantin Klemmer, May 2024
COLUMN: INIT
Exploring the Horizon of Computation for Creativity
By Sam Bourgault, Jane E, June 2023
SECTION: Features
Machine learning fairness in big tech
Auriel Wright talks about her work on advancing fairness and equity in computer vision at Google.
By Adinawa Adjagbodjou, July 2022
SECTION: Features
AI for conservation
How deep neural networks can process millions of weather radar data points to help researchers monitor continental-scale bird migration.
By Zezhou Cheng, Subhransu Maji, Daniel Sheldon, June 2021
OpenGPT-2
When OpenAI released its billion-parameter language model GPT-2, their attempts to withhold the model inspired two researchers to use open research practices to combat the misuse of machine learning.
By Vanya Cohen, Aaron Gokaslan, September 2020
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
COLUMN: INIT
Interpreting AI and its place in our worlds
By Christine T. Wolf, Ezinne Nwankwo, April 2019
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
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
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
That's not fair!
Why we need to study machine learning fairness, even in an increasingly unfair world.
By Deborah Raji, April 2019
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
COLUMN: Advice
Navigating through the hype that surrounds machine learning
By Parang Saraf, January 2019
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
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
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
DEPARTMENT: Hello world
Convolutional neural networks: an illustration in TensorFlow
By Abhineet Saxena, June 2016
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
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
Tracking how we read
Using activity recognition for cognitive tasks can provide new insights about reading and learning habits.
By Kai Kunze, December 2013
The sensorium
Research teams from around the world reflect on their brain sensing setups.
By Evan M. Peck, Erin T. Solovey, September 2011
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
Detecting steganography on a large scale
By William Ella, December 2008
Superhuman speech by 2010
By Paula Bach, September 2007
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
Voice activity detection
By Deepti Singh, Frank Boland, September 2007
Prostate ultrasound image processing
By Deian Stefan, March 2007
Introduction
By William Stevenson, December 2004
Learning how to tell ham from spam
By George Sakkis, December 2004
Identifying spam without peeking at the contents
By Shlomo Hershkop, Salvatore J. Stolfo, December 2004
Peer-to-peer collaborative spam detection
By Nathan Dimmock, Ian Maddison, December 2004
Evolutionary learning in mobile robot navigation
By Cory Quammen, December 2001