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Retail

AI To Help Monitor Stores in Real Time

Walmart has unveiled a new “store of the future” and test grounds for emerging technologies, including AI-enabled cameras and interactive displays. The store, a working concept called the Intelligent Retail Lab (IRL) operates out of a Walmart Neighborhood Market in Levittown, NY. Thousands of cameras suspended from the ceiling, combined with other technology like sensors on shelves, will monitor the store in real time, so workers can quickly replenish products or fix other problems. The technology will also be able to spot spills, track when shelves need to be restocked and know when shopping carts are running low. Cameras, for example, can determine how ripe bananas are from their colour and workers will get an alert on their phone if they need to be replaced. The idea is that the AI will help the store associates know more precisely where and when to restock products and this, in turn, means customers will have more information about the produce and that the meat is always fresh and in stock when they arrive. Read More

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Neuroscience

AI Translates Brain Signals into Speech

Researchers at the University of California have now taken crucial steps towards someday restoring the voices of people who have lost the ability to speak. In their study, they worked with five participants who had electrodes on the surface of their motor cortex as a part of their treatment for epilepsy. These people were asked to read 101 English sentences, while the team recorded the signals sent from the motor cortex during speech. The team trained an algorithm to reproduce the sound of a spoken word from the collection of signals sent to the lips, jaw and tongue. Once they had generated audio files based on the signals, the team asked hundreds of native English speakers to listen to the output sentences and identify the words from a set of 10, 25 or 50 choices. The team stated that “robust performance” was possible when training the device on just 25 minutes of speech, but the decoder improved with more data. Read More

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Psychiatry

AI To Help Diagnose PTSD

Researchers at the NYU School of Medicine have developed an AI-based computer programme that can help diagnose post-traumatic stress disorder (PTSD) in veterans by analysing their voices. The study, published in the journal, Depression and Anxiety, found that the AI tool can distinguish with 89.0% accuracy between the voices of those with or without PTSD. For the study, the research team used a statistical/Machine Learning (ML) technique, called random forests, that has the ability to “learn” how to classify individuals based on examples. The team involved 53 participants with PTSD and 78 veterans without the disease. The random forest programme linked patterns of specific voice features with PTSD, including less clear speech and a lifeless, metallic tone, both of which had long been reported anecdotally as helpful in diagnosis. While the current study did not explore the disease mechanisms behind PTSD, the theory is that traumatic events change brain circuits that process emotion and muscle tone, which affects a person's voice. Read More

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Physical Fitness

AI Can Recommend Workouts Based on Fitness Tracker Data

Scientists at the University of California have developed an AI tool that can make recommendations for workouts based on data from fitness trackers. The tool, called FitRec, was trained on a dataset of more than 250,000 workout records for more than 1,000 runners. This allowed computer scientists to build a model that analysed past performance to predict speed and heart rate, given specific future workout times and routes. The tool makes use of a deep learning architecture called long short-term memory networks, which the researchers adapted to capture the individual dynamic behaviour of each user in the dataset. Researchers fed the network a subset of a public dataset from endomondo.com, an app and website that functions as a workout diary. After cleaning up the data, researchers wound up with more than 100,000 workout records to train the network. They validated FitRec's predictions by comparing them with existing workout records that were not part of the training dataset. In the future, FitRec could be trained to include other data, such as the way users' fitness levels evolve over time, to make its predictions. Read More

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Geoscience

Could AI be the Key to Earthquake Prediction?

A group of researchers have used a machine learning process involving decision trees to uncover fresh insights into the earth’s slipping faults that often trigger catastrophic earthquakes. Decision trees establish a set of questions about the statistical aspects of the information contained in an earthquake’s acoustic signal. Based on one decision, the machine learning program branches to another decision, and so on—a diagram of the process ends up looking like a tree. The team then analysed the acoustic signals from a laboratory earthquake machine at Penn State University. Between artificial earthquake events, the machine learning discovered a continuous signal that had previously been dismissed as useless noise and ignored for years by all. The same machine learning technique was then used to study real-world seismic data from “slow slip” events in major earthquake zones. The geologically active areas continuously broadcast the same signal through a slow-slip cycle. The signal diagnoses precisely, at any instant in time, the fault movement in that area and how soon it might slip. That information allows to determine how and when a slow slip leads to a mega-earthquake. Read More

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Energy

AI to Help Predict Electricity Demand

Researchers at Thammasat University, Bangkok have discovered how a neural network can be trained with a genetic algorithm to forecast short-term demands on electricity load. It is critical for electricity producers to be able to estimate how much demand there will be on their systems in the next 48 hours. Without such predictions, there will inevitably be shortfalls in power generation when demand is higher than estimated or energy and resources wasted if demand is lower than expected. The team used data from the electricity generating authority of Thailand, to train a neural network via a genetic algorithm. The results are compared with the more conventional back-propagation approach to prediction and show that the system is much better and predict the rise and falls in electricity demand. The genetic algorithm neural network approach takes about 30 minutes to train for prediction compared with 1 minute for back-propagation training of a neural network. However, the added value of much more accurate predictions far outweighs this additional time and effort. Read More

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Miscellaneous

AI To Enhance Safety at Petrol Pumps

Neuroplex, a startup that emerged from Future Technologies Lab, an initiative by Kerala Startup Mission, has signed an agreement with Indian Oil Corporation (IOC) to use AI to qualitatively improve customer interaction and safety in their petrol pumps across the country. Their signature product 'Eyes Age' uses deep learning to empower video surveillance with AI capabilities. It creates visual relationships between objects in the video which allows for a logical analysis of video data. The AI-powered video content analysis modules developed by Neuroplex can enhance security at petrol pumps, which must follow several protocols to avoid fire hazard. Petrol pumps would be able to manage entry and exit of vehicles using automatic number plate recognition, keep count of attendees at venues and send out alerts in the event of protocol violations like use of mobile phones. Read More

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