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One Giant Step for a Chess-Playing Machine

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When AlphaZero was first unveiled, some observers complained that Stockfish had been lobotomized by not giving it access to its book of memorized openings. This time around, even with its book, it got crushed again. And when AlphaZero handicapped itself by giving Stockfish ten times more time to think, it still destroyed the brute.

Tellingly, AlphaZero won by thinking smarter, not faster; it examined only 60 thousand positions a second, compared to 60 million for Stockfish. It was wiser, knowing what to think about and what to ignore. By discovering the principles of chess on its own, AlphaZero developed a style of play that “reflects the truth” about the game rather than “the priorities and prejudices of programmers,” Mr. Kasparov wrote in a commentary accompanying the Science article.

The question now is whether machine learning can help humans discover similar truths about the things we really care about: the great unsolved problems of science and medicine, such as cancer and consciousness; the riddles of the immune system, the mysteries of the genome.

The early signs are encouraging. Last August, two articles in Nature Medicine explored how machine learning could be applied to medical diagnosis. In one, researchers at DeepMind teamed up with clinicians at Moorfields Eye Hospital in London to develop a deep-learning algorithm that could classify a wide range of retinal pathologies as accurately as human experts can. (Ophthalmology suffers from a severe shortage of experts who can interpret the millions of diagnostic eye scans performed each year; artificially intelligent assistants could help enormously.)

The other article concerned a machine-learning algorithm that decides whether a CT scan of an emergency-room patient shows signs of a stroke, an intracranial hemorrhage or other critical neurological event. For stroke victims, every minute matters; the longer treatment is delayed, the worse the outcome tends to be. (Neurologists have a grim saying: “Time is brain.”) The new algorithm flagged these and other critical events with an accuracy comparable to human experts — but it did so 150 times faster. A faster diagnostician could allow the most urgent cases to be triaged sooner, with review by a human radiologist.

What is frustrating about machine learning, however, is that the algorithms can’t articulate what they’re thinking. We don’t know why they work, so we don’t know if they can be trusted. AlphaZero gives every appearance of having discovered some important principles about chess, but it can’t share that understanding with us. Not yet, at least. As human beings, we want more than answers. We want insight. This is going to be a source of tension in our interactions with computers from now on.

In fact, in mathematics, it’s been happening for years already. Consider the longstanding math problem called the four-color map theorem. It proposes that, under certain reasonable constraints, any map of contiguous countries can always be colored with just four colors such that no two neighboring countries are colored the same.

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More groups join in support of women in STEM program at Carleton

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OTTAWA — Major companies and government partners are lending their support to Carleton University’s newly established Women in Engineering and Information Technology Program.

The list of supporters includes Mississauga-based construction company EllisDon.

The latest to announce their support for the program also include BlackBerry QNX, CIRA (Canadian Internet Registration Authority), Ericsson, Nokia, Solace, Trend Micro, the Canadian Nuclear Safety Commission, CGI, Gastops, Leonardo DRS, Lockheed Martin Canada, Amdocs and Ross.

The program is officially set to launch this September.

It is being led by Carleton’s Faculty of Engineering and Design with the goal of establishing meaningful partnerships in support of women in STEM.  

The program will host events for women students to build relationships with industry and government partners, create mentorship opportunities, as well as establish a special fund to support allies at Carleton in meeting equity, diversity and inclusion goals.

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VR tech to revolutionize commercial driver training

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Serious Labs seems to have found a way from tragedy to triumph? The Edmonton-based firm designs and manufactures virtual reality simulators to standardize training programs for operators of heavy equipment such as aerial lifts, cranes, forklifts, and commercial trucks. These simulators enable operators to acquire and practice operational skills for the job safety and efficiency in a risk-free virtual environment so they can work more safely and efficiently.

The 2018 Humboldt bus catastrophe sent shock waves across the industry. The tragedy highlighted the need for standardized commercial driver training and testing. It also contributed to the acceleration of the federal government implementing a Mandatory Entry-Level Training (MELT) program for Class 1 & 2 drivers currently being adopted across Canada. MELT is a much more rigorous standard that promotes safety and in-depth practice for new drivers.

Enter Serious Labs. By proposing to harness the power of virtual reality (VR), Serious Labs has earned considerable funding to develop a VR commercial truck driving simulator.

The Government of Alberta has awarded $1 million, and Emissions Reduction Alberta (ERA) is contributing an additional $2 million for the simulator development. Commercial deployment is estimated to begin in 2024, with the simulator to be made available across Canada and the United States, and with the Alberta Motor Transport Association (AMTA) helping to provide simulator tests to certify that driver trainees have attained the appropriate standard. West Tech Report recently took the opportunity to chat with Serious Labs CEO, Jim Colvin, about the environmental and labour benefits of VR Driver Training, as well as the unique way that Colvin went from angel investor to CEO of the company.

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Next-Gen Tech Company Pops on New Cover Detection Test

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While the world comes out of the initial stages of the pandemic, COVID-19 will be continue to be a threat for some time to come. Companies, such as Zen Graphene, are working on ways to detect the virus and its variants and are on the forefronts of technology.

Nanotechnology firm ZEN Graphene Solutions Ltd. (TSX-Venture:ZEN) (OTCPK:ZENYF), is working to develop technology to help detect the COVID-19 virus and its variants. The firm signed an exclusive agreement with McMaster University to be the global commercializing partner for a newly developed aptamer-based, SARS-CoV-2 rapid detection technology.

This patent-pending technology uses clinical samples from patients and was funded by the Canadian Institutes of Health Research. The test is considered extremely accurate, scalable, saliva-based, affordable, and provides results in under 10 minutes.

Shares were trading up over 5% to $3.07 in early afternoon trade.

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