Artificial intelligence (AI) is one of the fast-growing industries in the world today, and there are several Canadian AI stocks already making a name for themselves in the sector. The industry has attracted attention from all sectors, and most companies are investing significantly in AI owing to the promise of bug returns going forward. Almost every company is now adopting AI, with over 80% of enterprises believing that AI will help them in sustaining or obtaining a competitive advantage. AI is becoming the tech everybody wants to adopt to help them grow profits and compete. Some Canadian AI stocks have already shown potential and taken the lead.
Researchers from U of T Engineering and Carnegie Mellon University are using electrolyzers like this one to convert waste CO2 into commercially valuable chemicals. Their latest catalyst, designed in part through the use of AI, is the most efficient in its class. Researchers at University of Toronto Engineering and Carnegie Mellon University are using artificial intelligence (AI) to accelerate progress in transforming waste carbon into a commercially valuable product with record efficiency. They leveraged AI to speed up the search for the key material in a new catalyst that converts carbon dioxide (CO2) into ethylene -- a chemical precursor to a wide range of products, from plastics to dish detergent. The resulting electrocatalyst is the most efficient in its class.
A team of researchers hailing from Harvard and Université de Montréal today launched Epitopes.world, It's built atop an algorithm -- CAMAP -- that generates predictions for potential vaccine targets, enabling researchers to identify which parts of the virus are more likely to be exposed at the surface (epitopes) of infected cells. Project lead Dr. Tariq Daouda, who worked alongside doctorates in machine learning, immunobiologists, and bioinformaticians to build Epitopes.world, Fewer than 12% of all drugs entering clinical trials end up in pharmacies, and it takes at least 10 years for medicines to complete the journey from discovery to the marketplace. Clinical trials alone take six to seven years, on average, putting the cost of R&D at roughly $2.6 billion, according to the Pharmaceutical Research and Manufacturers of America.
Before and even more so now during the pandemic, CIOs and IT leaders are managing new capacity increases, security demands, and, in some cases critical, life-saving applications. It is essential how optimized technological performance enables the digital applications that power daily lives. AppDynamics, a Cisco company, helps companies around the world power their complex multi-cloud environments, through application performance management (APM) and Artificial Intelligence for IT operations (AIOps). I asked Luke Rogers, Area VP, Canada, AppDynamics, how COVID-19 has impacted businesses. "The COVID-19 pandemic has transformed our everyday interactions and how companies operate," replied Rogers.
On New Year's Eve of last year, the artificial intelligence platform BlueDot picked up an anomaly. It registered a cluster of unusual pneumonia cases in Wuhan, China. BlueDot, based in Toronto, Canada, uses natural language processing and machine learning to track, locate, and report on infectious disease spread. It sends out its alerts to a variety of clients, including health care, government, business, and public health bodies. It had spotted what would come to be known as Covid-19, nine days before the World Health Organization released its statement alerting people to the emergence of a novel coronavirus.
DarwinAI, the explainable AI company located in Waterloo, Canada, announced a strategic collaboration with global aerospace leader Lockheed Martin that seeks to improve Lockheed Martin's customers' understanding of AI solutions. Explainable AI (XAI) or "explainability" attempts to illuminate how neural networks – complex constructions that mimic the human brain – reach their decisions. The lack of understanding around AI's decision-making process has hampered the widespread adoption of AI. In response to this industry-wide impasse, DarwinAI created an explainability platform for deep learning development powered by its proprietary technology, GenSynth Explain. In addition to improving neural network efficiencies, the platform can dramatically reduce the time it takes to produce robust and accurate models through the insights it generates.
At the University of Toronto, Ted Sargent runs a test kitchen of sorts. His team, composed of researchers and students, develops recipes, measures and mixes ingredients carefully, and then evaluates the aftermath. The concoctions mostly--if not always--turn out to be inedible. Fortunately, though, flavor is not the point. Their goal is to invent recipes to "upgrade" the greenhouse gas into useful materials, says Sargent, an electrical engineer.
By Equipment the market for lab automation is segmented into automated liquid handlers, automated plate handlers, robotic arm, automated storage and retrieval systems. By software the lab automation market is segmented into laboratory information management system, laboratory information system, chromatography data system, electronic lab notebook, scientific data management system. On the basis of analyzer the market is segmented into biochemistry analyzers, immuno-based analyzers, hematology analyzers segments. By application the segmentation of the market is drug discovery, genomics, proteomics, protein engineering, bio analysis, analytical chemistry, system biology, clinical diagnostics, lyophilization. Based on end user the lab automation market is segmented into biotechnology & pharmaceuticals, hospitals, research institutions, academics, private labs. On the basis of geography, lab automation market report covers data points for 28 countries across multiple geographies such as North America & South America, Europe, Asia-Pacific, and Middle East & Africa. Some of the major countries covered in this report are U.S., Canada, Germany, France, U.K., Netherlands, Switzerland, Turkey, Russia, China, India, South Korea, Japan, Australia, Singapore, Saudi Arabia, South Africa, and Brazil among others. In 2017, North America is expected to dominate the market.
Researchers at the University of British Columbia are compiling CT scans and chest X-rays from around the world to create a global dataset aimed at helping physicians determine the best treatment courses for people with COVID-19. Thanks to a partnership with Amazon Web Services, the UBC team is sharing its data online for free, with the goal of helping in the battle against the novel coronavirus by using predictive modelling to better diagnose the severity of the disease and improve treatment. Radiology resident Dr. William Parker and his research partner Dr. Savvas Nicolaou, a professor of radiology at UBC and the director of emergency and trauma radiology at Vancouver General Hospital, began collecting CT images from colleagues in multiple countries in January. They developed an artificial intelligence algorithm to better identify the percentage of lung tissue involvement and the subtle patterns of infection documented in the CT scans and what that indicates about how a patient may fare in the long run. Developing a better understanding of how the virus presents in CT images will help doctors identify which patients "will do better to go home and self-isolate and which ones may need more support, like ventilation and ICU admission," Parker told CTV's Your Morning on Friday.
He may be more productive. We roll with it, don't we? Most every organization has been thrust into the future of work faster than prognosticators dared imagine. What will determine failure or success in this brave new world of work? We blithely call it the new normal and find new ways to make jokes about it.