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Machine learning applications need less data than has been assumed


A combined team of researchers from the University of British Columbia and the University of Alberta has found that at least some machine learning applications can learn from far fewer examples than has been assumed. In their paper published in the journal Nature Machine Intelligence, the group describes testing they carried out with machine learning applications created to predict certain types of molecular structures. Machine learning can be used in a wide variety of applications--one of the most well-known is learning to spot people or objects in photographs. Such applications typically require huge amounts of data for training. In this new effort, the researchers have found that in some instances, machine learning applications do not need such huge amounts of data to be useful.

Quorum receives research funding for Machine Learning project


CALGARY, Alberta, July 06, 2021 (GLOBE NEWSWIRE) -- Quorum Information Technologies Inc. (TSX Venture: QIS) (Quorum) announced today that it is receiving advisory services and funding of up to $724,746 from the National Research Council of Canada Industrial Research Assistance Program (NRC IRAP) to support a research and development project to consolidate Quorum's dealership data and add machine learning capabilities to its Cloud-based applications. The NRC IRAP support is the next step in a process started in 2020 when Quorum launched QAnalytics – an enterprise reporting tool for the Quorum suite of products powered by Microsoft Power BI. QAnalytics is now utilized by 30% of Quorum's XSellerator Dealership Management System (DMS) customers. "QAnalytics has changed how we manage our 11 franchised dealerships in our auto group," stated Tim Davis, CEO of Davis Auto Group. "The real time metrics that QAnalytics provides for all aspects of our dealership's operations allow our management team to make confident, data-driven decisions." Quorum's next step is to strategically consolidate dealership data from its 1,025 customers on Microsoft Azure Synapse, enabling QAnalytics to deliver enhanced critical Business Intelligence insights into dealership operations and provide a consolidated dataset for Machine Learning projects.

Mydecine Unveils Artificial Intelligence Drug Discovery Program


DENVER, June 16, 2021 (GLOBE NEWSWIRE) -- Mydecine Innovations Group (NEO: MYCO) (OTC: MYCOF) (FSE: 0NFA) ("Mydecine" or the "Company"), an emerging biopharma and life sciences company committed to the research, development, and acceptance of alternative nature-sourced medicine for mainstream use, announced the launch of its in-silico drug discovery program in conjunction with researchers at the University of Alberta. Led by top computer-aided drug development expert, Dr. Khaled Barakat, the program is focused on developing artificial intelligence/machine learning (AI/ML) supported drug screenings, including both the ability to build drugs from the receptor up and assess drugs around the receptors of Mydecine's choosing. With its broader R&D capacity in drug development up and running, the in-silico program will enable the Company to more rapidly screen hundreds of thousands of new molecules without the need to produce them, allowing Mydecine to focus on the strongest potential therapeutics for its chemical and natural development programs. Mydecine will also be able to more efficiently screen its proprietary library of novel compounds designed by Chief Science Officer Rob Roscow and Advisory Board member, Dr. Denton Hoyer. "Years of research have shown that the chemical components of psychoactive and non-psychoactive mushrooms can be extremely powerful in a therapeutic setting and yet, there is still so much that we don't understand about how these molecules can affect biological systems. As the next evolution of drug discovery progresses forward, we strongly believe that this new age will be fully led by artificial intelligence and machine learning," said Josh Bartch, CEO of Mydecine.

Congratulations to the #IJCAI2021 award winners


The winners of three IJCAI awards have been announced. These three distinctions are: the Award for Research Excellence, the John McCarthy Award and the Computers and Thought Award. The Research Excellence award is given to a scientist who has carried out a program of research of consistently high quality throughout an entire career yielding several substantial results. The winner of the 2021 Award for Research Excellence is Richard Sutton (University of Alberta). Richard is recognized for his fundamental contributions to the foundation of reinforcement learning.

New AI-powered deep learning model to support medical diagnostics


A new deep-learning model can learn to identify diseases from medical scans faster and more accurately, according to new research by a team of University of Alberta computing scientists and the U of A spinoff company MEDO. The breakthrough model is the work of a team of researchers in the Faculty of Science--including the contributions of Pouneh Gorji, a graduate student lost in Flight PS752. Deep learning is a type of machine learning--a subfield of artificial intelligence; deep learning techniques are computer algorithms that find patterns in large sets of data, producing models that can then be used to make predictions.These models work best when they learn from hundreds of thousands or even millions of examples. But the field of medical diagnostics presents a unique challenge, where researchers typically only have access to a few hundred medical scan images for reasons of privacy. "When a deep-learning model is trained with so few instances, its performance tends to be poor," said Roberto Vega, lead author of the study and graduate student in the Department of Computing Science.

Digitization in the energy industry - the machine learning revolution


In researching for this blog, I reached out to Brendan Bennett, a Reinforcement Learning Researcher at the University of Alberta, for his thoughts on how emerging digital technologies may be deployed in the energy industry. Brendan and I discussed how some recent landmark accomplishments in artificial intelligence might soon make their way into the energy industry. Digital innovation in commercial spheres has largely been a story of improving efficiency and reliability while reducing costs. In the energy sector, these innovations have been a result of oil and gas companies doing what they do best: relying on talented engineers to improve on existing solutions. Improvements have quickly spread across the industry, bringing down costs and making processes more efficient.

Data Science is Where to Find the Most AI Jobs and Highest Salaries - AI Trends


Jobs in data science grew nearly 46% in 2020, with salaries in the range of $100,000 to $130,000 annually, according to a recent account in TechRepublic based on information from LinkedIn and LHH, formerly Lee Hecht Harrison, a global provider of talent and leadership development. Related job titles include data science specialist and data management analyst. Novacoast, which helps organizations build a cybersecurity posture through engineering, development, and managed services. Founded in 1996 in Santa Barbara, the company has many remote employees and a presence in the UK, Canada, Mexico, and Guatemala. The company offers a security operations center (SOC) cloud offering called novaSOC, that analyzes emerging challenges.

'I was terrible at crosswords so I built an AI to do them'

BBC News

Michael Bowling, senior research scientist at DeepMind and professor of computing science at the University of Alberta, said of the win: "Congratulations to Dr Ginsburg and the Berkeley team. It's a terrific achievement and an inspiring collaboration, both seeing leading AI researchers combining forces, and seeing powerful AI building blocks of search and learning being employed together.

Attabotics Partners With AltaML and Amii to Bolster Artificial Intelligence and Machine Learning …


New Collaboration Will Support the Growth of Calgary as an Innovation Hub for Emerging Technologies. Attabotics, the 3D robotics supply chain …

AI Used to Predict Early Symptoms of Schizophrenia in Relatives of Patients - Neuroscience News


Summary: Combining brain scans with AI technology, researchers were able to accurately predict the likelihood of a person developing schizophrenia in those with a family history of the psychiatric disorder. University of Alberta researchers have taken another step forward in developing an artificial intelligence tool to predict schizophrenia by analyzing brain scans. In recently published research, the tool was used to analyze functional magnetic resonance images of 57 healthy first-degree relatives (siblings or children) of schizophrenia patients. It accurately identified the 14 individuals who scored highest on a self-reported schizotypal personality trait scale. Schizophrenia, which affects 300,000 Canadians, can cause delusions, hallucinations, disorganized speech, trouble with thinking and lack of motivation, and is usually treated with a combination of drugs, psychotherapy and brain stimulation.