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Sports

AI-Powered Technology to Capture Sports Tracking Data via Broadcast Video

STATS, the worldwide leader in sports data and intelligence, announced the official launch of AutoSTATS, the first AI and computer vision technology to deliver comprehensive player-tracking data through any broadcast. AutoSTATS harnesses the latest capabilities in AI and machine learning to automate tracking with X/Y coordinates, differentiating players and the ball and coordinating player movements around the court or playing field. The technology also has the ability to track player and team performance data directly from video. AutoSTATS has already begun collecting tracking data for select college basketball games during the 2018-19 season, where optical tracking solutions are limited. STATS has also begun adding a new layer to AutoSTATS using OpenPose, a product manufactured under license from Carnegie Mellon University. OpenPose unlocks new layers of body-pose information, providing a deeper quality of player tracking data like body position, shot form, torque, and other aspects of the game. Read More

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Education

AI to Transform Learning Methodology in Schools

Alef Education, an Abu Dhabi- based company is using AI to create a new learning experience. The platform focuses on core subjects like Math, Science and English. It helps students by using AI to tailor the curriculum to individual demands. Machine-learning algorithms identify struggling concepts from millions of data points and provides students, teachers and parents real-time feedback and adjusts accordingly. For students, that could mean a concept is repeated in a more personalized manner to help them understand it. The platform can also identify students' strengths and creates an individual learning path. The company has closely worked with the UAE government to introduce the platform to 25,000 students at 57 public schools. Alef Education aims to expand into more countries and looks to introduce its platform to more than 300 schools this year. Read More

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Healthcare

Machine-Learning Models to Help Detect Sepsis in Newborns Earlier

Sepsis, the result of a bacterial infection in the circulatory system, is a major cause of infant mortality even in developed nations. Now, researchers at the Children’s Hospital of Philidelphia (CHOP) have found that by feeding machine-learning models regularly collected clinical data, they could identify cases of sepsis in newborns hours before they usually would. To develop machine-learning models capable of detecting sepsis, the research team trained algorithms on retroactive sets of data with the goal of identifying sepsis at least four hours before clinicians had suspected the illness. Using electronic health record data, such as vital signs like blood pressure and temperature, from 618 infants in the CHOP neonatal intensive care unit from 2014 to 2017, the team trained eight machine-learning models to compare vital signs to 36 potential indicators of infant sepsis. Because the data was retroactive, the research team was able to compare the machine-learning models’ accuracy to clinical findings. Of the eight models, six were able to accurately identify cases of sepsis up to four hours earlier than clinicians had. Read More

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Surveillance

AI-enabled Software Can Spot Shoplifters Even Before They Steal

Vaak, a Japanese startup, has developed an AI-enabled software that hunts for potential shoplifters, using footage from security cameras for fidgeting, restlessness and other potentially suspicious body language. The algorithms analyse security-camera footage and alert staff about potential thieves via a smartphone app. What makes AI-based shoplifting detection a straightforward proposition is the fact that most of the hardware — security cameras — is usually already in place. Vaak made headlines last year when it helped to nab a shoplifter at a convenience store in Yokohama. Vaak had set up its software in the shop as a test case, which picked up on previously undetected shoplifting activity. The perpetrator was arrested a few days later. Vaak is currently testing in a few dozen stores in the Tokyo area. The company began selling a market-ready version of its shoplifting-detection software this month, and is aiming to be in 100,000 stores across Japan in three years. Read More

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Human Resources

AI Tool Identifies Work Styles of Individuals

Bunch.ai, a Berlin startup has built an AI-powered Chrome extension called Emma, that uses machine learning and a natural language processing algorithm to analyse an individual’s LinkedIn profile, posts, and recommendations. The software then fits the person into one of 14 behavioural types based on a model developed by Charles O’Reilly, PhD, an organizational behaviour professor at the Stanford Graduate School of Business. First, Emma identifies the type such as “achievers,” “catalysts,” and “customer advocates,” among other descriptors. Next, she describes the individual with adjectives such as “results-oriented” and “collaborative.” Then Emma points out strengths, weaknesses, ideal team type, and motivators. Finally, Emma tells if her predictions should be fairly or highly accurate, based on the amount of information in the profile. Emma’s dataset includes the LinkedIn profiles of thousands of job applicants as well as self-assessments they completed on the Bunch.ai platform., and an ever-expanding vocabulary derived from O’Reilly’s original work, the Harvard University dictionary, and expanded through user input. Bunch.ai tests and adjusts Emma’s algorithms each month to mitigate biases introduced by the dataset and continuous machine learning. Read More

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Psychology

AI to Help Diagnose Mental Illnesses

Researchers from India and Canada have developed a machine learning-based tool that can diagnose schizophrenia with high accuracy. Researchers at the National Institute of Mental Health and Neurosciences used functional MRI (fMRI), a method in which magnetic field is used to map and measure brain activity. Brain information was obtained from fMRI during the resting stage. Researchers divided the whole brain into different regions or parcels. This was done in 14 different ways based on similarities in volume, surface, connectivity etc. From each method of dividing the brain, information was derived on three features based on the region and three features based on connectivity of the brain. This helped researchers collate 84 points of data (from 14 brain division schemes, and 6 features extracted from each scheme) from each subject. Using these data points from healthy and schizophrenic patients, the group has built a model that could predict schizophrenia with an accuracy of 87%. The model has been named “EMPaSchiz” or ‘Ensemble algorithm with Multiple Parcellations for Schizophrenia prediction’. Read More

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“Quantum Computing, a revolution in technology, has shown great promise towards solving complex computing problems currently outside the capabilities of current computers. Though in its infancy, we at Decimal Point Analytics strongly believe that it is going to grow exponentially in the near future. It holds the potential not only to boost the AI revolution but also transform the way data is synthesized. The articles on Quantum computing will give an insight into the recent developments in this space.”

 
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Quantum Computing

A (Theoretical) Quantum Refrigerator

In an article published by American Association for the Advancement of Science, the researchers have proposed an intriguing scheme for making a tiny refrigerator that exploits the laws of quantum mechanics. Their refrigerator consists of two qubits that were initially in contact with reservoirs at different temperatures. In a refrigeration cycle, the qubits first underwent a quantum measurement, which changed their state and caused them to exchange energy with the measurement apparatus; the cycle was closed by putting the qubits back in contact with their respective reservoirs, giving away more heat than they received. The researchers predict that the procedure is robust with respect to experimental noise and may be realizable with superconducting qubits. Read More

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