AI can detect signals that are informative about mental health from questionnaires and brain scans. A study published today by an interdisciplinary collaboration, directed by Denis Engemann from Inria, demonstrates that machine learning from large population cohorts can yield "proxy measures" for brain-related health issues without the need for a specialist's assessment. The researchers took advantage of the UK Biobank, one of the world's largest and most comprehensive biomedical databases, that contains detailed and secure health-related data on the UK population. This work is published in the open access journal GigaScience. Mental health issues have been increasing worldwide, with the WHO determining that there has been a 13% increase in mental health conditions and substance abuse disorders between 2007 and 2017.
A new artificial intelligence system could assess tipping points in the world's ecosystems, and act as an early warning system to help stop "runaway climate change", researchers have said. Climate tipping points are a particular threat to life on Earth, as when they are reached, they can set off chain reactions of climate-altering processes, supercharging global heating and rapidly exacerbating the existing climate crisis. Examples include the melting of the Arctic permafrost, which could release massive amounts of the potent greenhouse gas methane, which would generate further rapid heating; the breakdown of ocean current systems, which would cause almost immediate major changes to global weather patterns; and ice sheet disintegration, which could lead to rapid sea-level rises. Using a "deep-learning" algorithm, the researchers examined thresholds beyond which rapid or irreversible change happens in a system. Chris Bauch, professor of applied mathematics at the University of Waterloo ...
A number of experts believe the Earth is rapidly approaching its'tipping point' for reversing climate change, but researchers at Canada's University of Waterloo are creating artificial intelligence that could act as an'early warning system' against a runway threat to the planet. The deep learning algorithm was created to better predict the tipping points, while also understanding what happens after they have been reached, the study's co-author, Chris Bauch, a professor of applied mathematics at the University of Waterloo, said. 'Many of these tipping points are undesirable, and we'd like to prevent them if we can,' Bauch said in a statement. Canadian researchers are creating AI that could act as an'early warning system' against runway climate change. In May, scientists said there was a 40 percent chance that annual temperature rises would go beyond the the 1.5C (2.7F) set by the 2015 Paris Agreement.
We study the problem of efficiently scaling ensemble-based deep neural networks for time series (TS) forecasting on a large set of time series. Current state-of-the-art deep ensemble models have high memory and computational requirements, hampering their use to forecast millions of TS in practical scenarios. We propose N-BEATS(P), a global multivariate variant of the N-BEATS model designed to allow simultaneous training of multiple univariate TS forecasting models. Our model addresses the practical limitations of related models, reducing the training time by half and memory requirement by a factor of 5, while keeping the same level of accuracy. We have performed multiple experiments detailing the various ways to train our model and have obtained results that demonstrate its capacity to support zero-shot TS forecasting, i.e., to train a neural network on a source TS dataset and deploy it on a different target TS dataset without retraining, which provides an efficient and reliable solution to forecast at scale even in difficult forecasting conditions.
Pandemics and natural disasters over the years have changed the behavior of people, which has had a tremendous impact on all life aspects. With the technologies available in each era, governments, organizations, and companies have used these technologies to track, control, and influence the behavior of individuals for a benefit. Nowadays, the use of the Internet of Things (IoT), cloud computing, and artificial intelligence (AI) have made it easier to track and change the behavior of users through changing IoT behavior. This article introduces and discusses the concept of the Internet of Behavior (IoB) and its integration with Explainable AI (XAI) techniques to provide trusted and evident experience in the process of changing IoT behavior to ultimately improving users' behavior. Therefore, a system based on IoB and XAI has been proposed in a use case scenario of electrical power consumption that aims to influence user consuming behavior to reduce power consumption and cost. The scenario results showed a decrease of 522.2 kW of active power when compared to original consumption over a 200-hours period. It also showed a total power cost saving of 95.04 Euro for the same period. Moreover, decreasing the global active power will reduce the power intensity through the positive correlation.
But what if that second opinion could be generated by a computer, using artificial intelligence? Would it come up with better treatment recommendations than your professional proposes? A pair of Canadian mental-health researchers believe it can. In a study published in the Journal of Applied Behavior Analysis, Marc Lanovaz of Université de Montréal and Kieva Hranchuk of St. Lawrence College, in Ontario, make a case for using AI in treating behavioral problems. To find a better way, Lanovaz and Hranchuk, a professor of behavioral science and behavioral psychology at St. Lawrence, compiled simulated data from 1,024 individuals receiving treatment for behavioral issues.
But what if that second opinion could be generated by a computer, using artificial intelligence? Would it come up with better treatment recommendations than your professional proposes? A pair of Canadian mental-health researchers believe it can. In a study published in the Journal of Applied Behavior Analysis, Marc Lanovaz of Université de Montréal and Kieva Hranchuk of St. Lawrence College, in Ontario, make a case for using AI in treating behavioral problems. "Medical and educational professionals frequently disagree on the effectiveness of behavioral interventions, which may cause people to receive inadequate treatment," said Lanovaz, an associate professor who heads the Applied Behavioral Research Lab at UdeM's School of Psychoeducation.
Researchers in the life sciences who use machine learning for their studies should adopt standards that allow other researchers to reproduce their results, according to a comment article published today in the journal Nature Methods. The authors explain that the standards are key to advancing scientific breakthroughs, making advances in knowledge, and ensuring research findings are reproducible from one group of scientists to the next. The standards would allow other groups of scientists to focus on the next breakthrough rather than spending time recreating the wheel built by the authors of the original study. Casey S. Greene, Ph.D., director of the University of Colorado School of Medicine's Center for Health AI, is a corresponding author of the article, which he co-authored with first author Benjamin J. Heil, a member of Greene's research team, and researchers from the United States, Canada, and Europe. "Ultimately all science requires trust--no scientist can reproduce the results from every paper they read," Greene and his co-authors write.
Cleveland Clinic researchers have engineered a "first-of-its-kind bionic arm" for patients with upper-limb amputations that allows wearers to think, behave and function like a person without an amputation, according to new findings published in Science Robotics. The Cleveland Clinic-led international research team developed the bionic system that combines three important functions – intuitive motor control, touch and grip kinesthesia, the intuitive feeling of opening and closing the hand. Collaborators included University of Alberta and University of New Brunswick. "We modified a standard-of-care prosthetic with this complex bionic system which enables wearers to move their prosthetic arm more intuitively and feel sensations of touch and movement at the same time," said lead investigator Paul Marasco, PhD, associate professor in Cleveland Clinic Lerner Research Institute's Department of Biomedical Engineering. "These findings are an important step towards providing people with amputation with complete restoration of natural arm function."
If you're irritated by the mere sight of people fidgeting, a new scientific study suggests you're not alone. Researchers in Canada recruited 4,100 participants who were asked to self-report whether they have sensitivities to seeing people fidget. They found that almost one in three people experienced the psychological phenomenon known as'misokinesia, or a'hatred of movements'. Misokinesia is psychological response to the sight of someone else's small but repetitive movements, the experts say, and it can seriously affect daily living. Misokinesia - the'hatred of movements' - is a psychological response to the sight of someone else's small and repetitive movements (concept image) Misokinesia - or the'hatred of movements' - is a psychological phenomenon that is defined as a strong negative affective or emotional response to the sight of someone else's small and repetitive movements.