Spartan Controls Ltd. and AltaML forge a formal partnership and strategic alliance to build applications for the process industries in Western Canada. They are combining their respective strengths in automation and applied artificial intelligence/machine learning (AI/ML) to modernize process industries in the region. Modern industrial processes are employing an increasing number of industrial sensors, complex process control systems, and a plethora of other industrial data systems in the safe and efficient operation of these facilities. These processes generate large volumes of data which creates the opportunity to leverage modern AI/ML technologies in a wide variety of applications. According to the partners, conceptualizing, developing, and commercializing AI/ML solutions will generate significant business value for process industries by leveraging AltaML's AI/ML development capability with Spartan's domain knowledge and expertise around industrial processes, automation, optimization, equipment, and process reliability.
Experts at University of British Columbia in Canada who are building an Artificial Intelligence-powered Covid-19 diagnosis tool with the help of resources from Amazon Web Services (AWS) are seeking lung scan images from India to refine their open source model, a researcher involved with the project said. This tool is important because it becomes easier for doctors the world over to treat a patient if they know what disease they are suffering from and how badly that disease has infected that person. The same goes with Covid-19 patients. Knowing that a person is Covid-19 positive can help, but this is not all that doctors want to know. They would do better if they knew how deep the infections were and how the patients were likely to respond to the treatments.
At the American Association of Physicists in Medicine (AAPM) 2019 meeting, new artificial intelligence (AI) software to assist with radiotherapy treatment planning systems was highlighted. The goal of the AI-based systems is to save staff time, while still allowing clinicians to do the final patient review. RaySearch demonstrated a new U.S. Food and Drug Administration (FDA)-cleared machine learning treatment planning system. The RaySearch RayStation machine learning algorithm is being used clinically by University Health Network, Princess Margaret Cancer Center, Toronto, Canada, where it was rolled out over several months in late-2019. Medical physicist Leigh Conroy, Ph.D., was involved in this rollout and helped conduct a study, showing the automated plans and traditionally made plans to radiation oncologists to get valuable feedback.
In the middle of June, I'd discussed why it is a smart move for investors to get in on artificial intelligence (AI). The development of AI has the potential to dramatically reshape our economy and society for decades to come. Today, I want to discuss why Canadians should seek exposure to this space. Moreover, I want to look at two stocks that are betting big on AI to propel their growth going forward. AI development is occurring in a broad array of sectors.
I'd lost almost $200 million in October. It was 2008, after the Lehman Brothers bankruptcy. Banks were failing left and right. I worked at a major investment bank, and while I didn't think the disastrous deal I'd done would cause its collapse, my losses were quickly decimating its commodities profits for the year, along with the potential pay of my more profitable colleagues. I thought my career could be over. I'd already started to feel those other traders and salespeople keeping their distance, as if I'd contracted a disease. My eyes started to fill from a sudden wash of gratitude and relief that came, I think, from no longer being alone. I landed in London on the morning of November 4, having flown overnight from New York. I was a derivatives trader, but also the supervisor of the bank's oil options trading team, about a dozen guys split between Singapore, London, and New York.
Artificial intelligence has a terrible carbon footprint. Researchers at Stanford University, Facebook AI Research, and Canada's McGill University have developed a tool to measure the hidden cost of machine learning. The "experiment impact tracker" quantifies how much electricity a machine learning project will consume, and its cost in carbon emissions. The team first measured the energy cost of a specific artificial intelligence (AI) model--a challenge because a single machine often trains several models concurrently, while each session also draws power for shared overhead functions like data storage and cooling. The researchers then translated power consumption into carbon emissions, whose blend of renewable and fossil fuels varies by location and time of day, by tapping into public sources about this energy mix.
Clearview AI will no longer sell its facial recognition software in Canada, according to government privacy officials investigating the company. The end of Clearview AI operations in Canada will also mean the end of the company's contract with the Royal Canadian Mounted Police, according to an announcement released today by the Office of the Privacy Commissioner of Canada. Canadian privacy officials started investigating Clearview AI in February following media reports about the company's practice of scraping billions of images from social media and the web without consent from the people in photos in order to create its facial recognition system. Critics say Clearview's approach could mean the end of privacy. Government officials from Quebec, British Columbia, and Alberta provinces continue to investigate Clearview AI and Royal Canadian Mounted Police use of its facial recognition software despite Clearview's exit.
A study led by University of Toronto alumna Kimberly Ren is among the first to quantify predictors that could lead women towards, or away from, pursuing careers in machine learning and artificial intelligence, or AI. Women currently make up 22 per cent of global AI professionals, with that proportion oscillating between 21 per cent and 23 per cent over a four-year trend, according to a 2018 report by the World Economic Forum. "The talent gap isn't closing," says Ren, who recently graduated from the Faculty of Applied Science & Engineering and was awarded the Best Paper Award at the American Society for Engineering Education Conference for her fourth-year thesis project. She led the study under the supervision of Alison Olechowski, an assistant professor in the department of mechanical and industrial engineering. "What I hope this research does is find some reasoning behind this gap, so that we can increase the persistence of women in the field going forward."
By now, it's almost old news that artificial intelligence (AI) will have a transformative role in medicine. Algorithms have the potential to work tirelessly, at faster rates and now with potentially greater accuracy than clinicians. In 2016, it was predicted that'machine learning will displace much of the work of radiologists and anatomical pathologists'. In the same year, a University of Toronto professor controversially announced that'we should stop training radiologists now'. But is it really the beginning of the end for some medical specialties?