University of Alberta PhD student develops AI to identify depression


Our voices may convey subtle clues about our mood and psychological state. Now, scientists are using artificial intelligence to pick up these clues, with the aim of building voice-analyzing technologies that can identify individuals in need of mental-health care. But others caution they could do more harm than good. At the University of Alberta, computing science PhD student Mashrura Tasnim has developed a machine-learning model that can recognize the speech qualities of people with depression. Her goal is to create a smartphone application that would monitor users' conversations and alert their emergency contacts or mental-health professionals when it detects depression.

Chowbotics is Sending Sally the Salad Making Robot Off to College(s)


Chowbotics is packing up Sally the salad making robot and sending it off to college. Well, many colleges actually, as the food robotics startup is set to announce next week a bigger push into the higher education market. Chowbotics told us that this school year, students at multiple colleges and universities in the U.S. will be able to buy salads and breakfast bowls from Sally the robot. Those schools include: Case Western Reserve University in Cleveland, OH; College of the Holy Cross in Worcester, MA; the University of Guelph in Ontario, Canada; Elmira College in Elmira, NY; the University of Memphis in Memphis, TN; and Wichita State University in Wichita, KS. These schools join Marshall University in Huntington, WV, which installed Sally in 2018.

The Ethics of Artificial Intelligence


Road Watch 2.0 Vision Zero Pedestrian Deaths Project: Learn how an award-winning Richmond Hill and York Regional Police road safety Road Watch program is the base for a space age approach to make Toronto roads safer, as kicked off on the Global News 640 AM John Oakley Show. Hear a plan to make roads safer while mitigating climate through earth and Space LiDAR technology. Learn how road safety and climate change mitigation is combined in the Ethical AI Energy Cloud City master plan, a UN 17 Sustainable Development Goals Emerging Technology Framework to Unite Society. Dave D'Silva founded Intelligent Market Solutions Group (IMSG) to make good on a University of Waterloo pact with Bill Gates. IMSG is a socio-economic emerging technology project management firm creating Star Trek inspired Ethical AI systems.

A.I. can say when neurosurgeons are ready to operate - Futurity


You are free to share this article under the Attribution 4.0 International license. Machine learning algorithms can accurately assess the capabilities of neurosurgeons during virtual surgery before they step into an actual operating room, a new study shows. Researchers recruited fifty participants from four stages of neurosurgical training; neurosurgeons, fellows and senior residents, junior residents, and medical students. The participants performed 250 complex tumor resections using NeuroVR, a virtual reality surgical simulator. The National Research Council of Canada developed the system; CAE recorded all instrument movements in 20 millisecond intervals.

Machine learning-guided virtual reality simulators can be powerful tools in surgeon training


Machine learning-guided virtual reality simulators can help neurosurgeons develop the skills they need before they step in the operating room, according to a new study. Research from the Neurosurgical Simulation and Artificial Intelligence Learning Centre at The Neuro (Montreal Neurological Institute and Hospital) and McGill University shows that machine learning algorithms can accurately assess the capabilities of neurosurgeons during virtual surgery, demonstrating that virtual reality simulators using artificial intelligence can be powerful tools in surgeon training. Fifty participants were recruited from four stages of neurosurgical training; neurosurgeons, fellows and senior residents, junior residents, and medical students. They performed 250 complex tumor resections using NeuroVR, a virtual reality surgical simulator developed by the National Research Council of Canada and distributed by CAE, which recorded all instrument movements in 20 millisecond intervals. Using this raw data, a machine learning algorithm developed performance measures such as instrument position and force applied, as well as outcomes such as amount of tumor removed and blood loss, which could predict the level of expertise of each participant with 90 per-cent accuracy.

Skills evaluation, tailored feedback: McGill AI project could change the way brain surgeons are trained


Alexander Winkler-Schwartz, a neurosurgery resident and PhD candidate, poses in the lab with a NeuroVR neurosurgical simulator at McGill University, on July 31, 2019. Alexander Winkler-Schwartz focuses on the computer-generated brain on the screen while, below, his hands gently remove the virtual brain tumour inside the mannequin's head. An artificial intelligence algorithm tracks the neurosurgery resident's every movement – ready to classify his performance as part of a research project at McGill University, where intelligent machines are learning to rank people based on how deftly they take away the tumour. It's part of a wider effort to harness the power of technology to improve medicine. Artificial intelligence is already helping monitor the vital signs of babies in intensive-care, and robots are a fixture in operating rooms.

One insurance role that could be wiped out by artificial intelligence


Artificial intelligence is going to have a big impact on adjusters, actuaries and insurance agents, but it won't necessarily kill their jobs, an AI expert suggests. But lower-skilled jobs may be in jeopardy. "Jobs at call centres are going to disappear slowly with chatbots," predicts Eli Fathi, co-founder and CEO of Ottawa-based MindBridge Analytics Inc., which makes financial analysis products that use AI. Fathi is a recently-announced finalist in EY Canada's Entrepreneur Of The Year award program. Ernst & Young released the list of its Ontario finalists July 10; it plans to announce the winners at a gala this October.

Might artificial intelligence be able to tell you why your baby is crying?


American researchers have developed an artificial intelligence tool capable of detecting whether a baby's cries mean they are hungry, they need changing, are tired, uncomfortable or that they want a cuddle. Over the long term, if exposed to a greater amount of more varied data, the algorithm could become a tool for interpreting a baby's cries. Parents who rack their brains trying to understand what their baby's cries might mean could one day be able to rely on artificial intelligence for help. Researchers from the University of Northern Illinois and the College of New Jersey in the United States have developed an algorithm which, they say, is able to identify the reason behind a young child's cries, reports the Quebec version of The Huffington Post. Of course, every infant is different, including how it cries.

Deep Learning for Time Series Forecasting: The Electric Load Case Machine Learning

Management and efficient operations in critical infrastructure such as Smart Grids take huge advantage of accurate power load forecasting which, due to its nonlinear nature, remains a challenging task. Recently, deep learning has emerged in the machine learning field achieving impressive performance in a vast range of tasks, from image classification to machine translation. Applications of deep learning models to the electric load forecasting problem are gaining interest among researchers as well as the industry, but a comprehensive and sound comparison among different architectures is not yet available in the literature. This work aims at filling the gap by reviewing and experimentally evaluating on two real-world datasets the most recent trends in electric load forecasting, by contrasting deep learning architectures on short term forecast (one day ahead prediction). Specifically, we focus on feedforward and recurrent neural networks, sequence to sequence models and temporal convolutional neural networks along with architectural variants, which are known in the signal processing community but are novel to the load forecasting one.