Google Overhauls Search Algorithm in Bid to Fight Fake News

The Telegraph, 4/25/2017

Google is changing their search algorithm to better distinguish websites that contain fake news, conspiracy theories, or extreme views from more reputable sources or websites. The search engine’s Autosuggest and Direct Answer features will also be modified, to allow users to report offensive suggestions or false information (respectively). This comes after months of growing public pressure for Google to make changes to combat the spread of misinformation on the Internet.

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.

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 Algorithms Can Predict Suicide Risk More Readily Than Clinicians, Study Finds

Newsweek, 2/27/2017

Human clinicians are known not to be very accurate at predicting suicides, so researchers are developing machine-learning algorithms that use multiple factors to identify short-term suicide risk. Data scientists trained the algorithm on data from thousands of clinical records, from both non-fatal suicide cases and random patients. Accuracy was significantly better than studying only one risk factor at a time. Using such a system could aid clinicians in targeting at-risk patients and treating them early.

Toyota Funds AI Research to Build Autonomous Cars

Network World, 2/7/2017

Toyota is investing 50 million dollars and partnering with Stanford University and the Massachusetts Institute of Technology (MIT) to develop artificial intelligence and robotics technology needed for advanced driver-assistance systems. While Toyota is not trying to develop a fully automated car, they note that incremental advances in such driver-aid systems could eventually lead to driverless smart cars.

Tech Companies Are Building Tiny, Personal AIs to Keep Your Messages Private

Quartz, 2/10/2017

Technology companies, such as Facebook and Google, are developing artificial intelligence systems (AI) for mobile devices to improve the privacy of messaging applications. New AI innovations allow algorithms that need less computing power, and can therefore be implemented locally on mobile devices. This means that information would not be sent to and from the cloud, reducing potential security issues.

New Algorithms by University of Toronto Researchers May Revolutionize Drug Discoveries

University of Toronto News, 2/6/2017

University of Toronto researchers have developed algorithms which can efficiently generate an accurate 3D image of a protein molecule from several 2D images in just a few minutes. This advance has far-reaching implications for the medical field. For example, drug researchers will be able to use these 3D protein models to analyze the structure of disease-specific proteins and predict the way experimental medications will bind to those proteins inside the body.

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