Machine Learning Shows Exactly When to Zap Brain to Boost Memory

New Scientist, 4/20/2017

Michael Kahana and other researchers at the University of Pennsylvania have used machine learning to analyze data on brain function and brain wave patterns. Electrodes implanted in the subjects’ brains measured brain activity while the subjects attempted to memorize and recall information. The electrodes could also transmit electric shocks to the brain. Results showed that carefully timed shocks made people 13 percent more likely to recall material.

Startup Matroid Uses AI to Pluck Images From Streams of Video

MSN/Bloomberg News, 3/26/2017

Reza Zadeh, a professor at Stanford University, has begun an artificial intelligence (AI) startup called Matroid whose software can detect specific people or objects in video streams. The user specifies what they are looking for, using example images and video or preset options, and the algorithms find corresponding people or objects in different videos. This approach could prove useful for businesses, law enforcement, and political and social science.

Offline AI Revolution Awaits Smartphones, 2/27/2017

Phone manufacturers are attempting to make handsets operate offline by using artificial intelligence (AI) and faster processors. This could let handsets use data already stored to operate. Some companies are focusing on AI technologies that let handsets perform tasks before the user does. For example, Neura, a startup from┬áCalifornia uses an AI that takes data from user’s routine behaviors and then makes predictions on what the users next steps are. Handsets that operate offline and perform tasks before the user does become more helpful and user interactive.

AncestryAI Algorithm Traces Your Family Tree Back More Than 300 Years

Aalto University, 3/16/2017

An Aalto University graduate student is developing a new family-tree algorithm called AncestryAI that uses Finnish parish registers to generate a probable family tree. Register data comes from HisKi, an open genealogical database supported by volunteer data entry. AncestryAI users can leave feedback on the accuracy of their tree, which is used to further train the algorithm.

Machine Learning Reveals Lack of Female Screen Time in Top Films

New Scientist, 3/8/2017

Shri Narayanan, from the University of Southern California, has developed a new program that runs facial recognition software and voice recognition software simultaneously to analyze gender bias in high-grossing box-office films. This program has analyzed 300 films, and the study shows that women are underrepresented on the big screen. Narayanan is also using machine learning and natural language processing to evaluate film scripts, to explore further biases.

AI Predicts Autism From Infant Brain Scans

IEEE Spectrum, 2/15/2017

Researchers at the University of North Carolina, Chapel Hill have applied deep-learning algorithms to brain scans of children with a high risk of autism. Algorithms used three indicators, brain surface area, volume, and the gender of the child, to determine if 6- to 12-month-old infants were likely to develop autism. The results were 81% accurate at predicting later diagnosis. This improves over a 50% prediction rate from behavioral questionnaires.

Twitter Data Could Improve Subway Operations During Big Events

University at Buffalo News Center, 1/26/2017

Research performed at the University at Buffalo has suggested that the swelling of subway usage during large events correlates closely with increases in Twitter activity. The Twitter data, which can be filtered by location and content, could potentially become a cost-effective aid to event planning and transit scheduling for crowded occasions.

AI Spots Skin Cancer as Well as Human Doctor

Newsweek, 1/26/2017

A team of researchers at Stanford University has developed an artificial intelligence (AI) algorithm that can identify early symptoms of skin cancer. The researchers trained the algorithm with a large data set of images that had already been identified as cancerous or benign. The algorithm identifies skin lesions with the same accuracy as board-certified dermatologists.

Big Data Analytics — Nostradamus of the 21st Century

Griffith University, 11/30/2016

Researchers at Griffith University successfully predicted the winner of the 2016 presidential election, including the outcomes in 49 out of 50 states, using data collected from social media interactions. The prediction ran contrary to general expectations based on polling, suggesting that more accurate election predictions can be obtained by analyzing social media interactions — which requires large-scale data analytics.

Meeting of the Minds for Machine Intelligence

MIT News Office, 11/22/2016

Industry leaders and computer scientists are pushing for more use of machine intelligence so that machines can aid doctors, business corporations, investors and many more entities in decision making. The article discusses the potential rewards of using machine intelligence to solve real-world problems, for example, whether machine learning can help to better quantify uncertainty when trying to predict outcomes in various fields.

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