Collaborating Authors


Data vs. Disaster: 5 Ways Analytics Is Helping Tackle Climate Change - DATAVERSITY


With the recent Intergovernmental Panel on Climate Change (IPPC) report painting a worrying picture of our battle against climate change, we will explore five ways analytics can help turn the tide. The UN Secretary-General, Antonio Guterres, called the report "a code red for humanity," adding that "the alarm bells are deafening and evidence irrefutable." U.S. President Joe Biden said about it, "The cost of inaction is mounting." In summary, without immediate action, the damage we've done may be irreversible. For this to change, we're going to have to rely on the latest tools and technologies, including big data, advanced analytics, modeling, and simulation techniques.

Haskayne scholars use artificial intelligence to help detect fraudulent websites


The University of Calgary acknowledges the traditional territories of the people of the Treaty 7 region in Southern Alberta, which includes the Blackfoot Confederacy (comprised of the Siksika, Piikani, and Kainai First Nations), as well as the Tsuut'ina First Nation, and the Stoney Nakoda (including the Chiniki, Bearspaw and Wesley First Nations). The City of Calgary is also home to Metis Nation of Alberta, Region 3. The University of Calgary acknowledges the impact of colonization on Indigenous peoples in Canada and is committed to our collective journey towards reconciliation to create a welcome and inclusive campus that encourages Indigenous ways of knowing, doing, connecting and being.

Provinces order Clearview AI to stop using facial recognition without consent


Three provincial privacy watchdogs have ordered facial recognition company Clearview AI to stop collecting, using and disclosing images of people without consent. The privacy authorities of British Columbia, Alberta and Quebec are also requiring the U.S. firm to delete images and biometric data collected without permission from individuals. The binding orders made public Tuesday follow a joint investigation by the three provincial authorities with the office of federal privacy commissioner Daniel Therrien. The watchdogs found in February that Clearview AI's facial recognition technology resulted in mass surveillance of Canadians and violated federal and provincial laws governing personal information. They said the New York-based company's scraping of billions of images of people from across the internet to help police forces, financial institutions and other clients identify people was a clear breach of Canadians' privacy rights.

Researchers train computers to predict the next designer drugs: Global law enforcement agencies are already using the new method


Law enforcement agencies are in a race to identify and regulate new versions of dangerous psychoactive drugs such as bath salts and synthetic opioids, even as clandestine chemists work to synthesize and distribute new molecules with the same psychoactive effects as classical drugs of abuse. Identifying these so-called "legal highs" within seized pills or powders can take months, during which time thousands of people may have already used a new designer drug. But new research is already helping law enforcement agencies around the world to cut identification time down from months to days, crucial in the race to identify and regulate new versions of dangerous psychoactive drugs. "The vast majority of these designer drugs have never been tested in humans and are completely unregulated. They are a major public health concern to emergency departments across the world," says UBC medical student Dr. Michael Skinnider, who completed the research as a doctoral student at UBC's Michael Smith Laboratories.

Artificial Intelligence Can Predict New Designer Drugs With 90% Accuracy


New drugs are created all the time. And many are extremely dangerous. This is why researchers trained computers to predict what designer drugs will emerge onto the scene before they hit the market, according to a recent study published in the journal Nature Machine Intelligence. With highly-addictive drugs flooding regions throughout the U.S., this program could save countless lives. But it could also unlock an entire "dark matter" world of unknown psychoactive possibilities.

TAG: Toward Accurate Social Media Content Tagging with a Concept Graph Artificial Intelligence

Although conceptualization has been widely studied in semantics and knowledge representation, it is still challenging to find the most accurate concept phrases to characterize the main idea of a text snippet on the fast-growing social media. This is partly attributed to the fact that most knowledge bases contain general terms of the world, such as trees and cars, which do not have the defining power or are not interesting enough to social media app users. Another reason is that the intricacy of natural language allows the use of tense, negation and grammar to change the logic or emphasis of language, thus conveying completely different meanings. In this paper, we present TAG, a high-quality concept matching dataset consisting of 10,000 labeled pairs of fine-grained concepts and web-styled natural language sentences, mined from the open-domain social media. The concepts we consider represent the trending interests of online users. Associated with TAG is a concept graph of these fine-grained concepts and entities to provide the structural context information. We evaluate a wide range of popular neural text matching models as well as pre-trained language models on TAG, and point out their insufficiency to tag social media content with the most appropriate concept. We further propose a novel graph-graph matching method that demonstrates superior abstraction and generalization performance by better utilizing both the structural context in the concept graph and logic interactions between semantic units in the sentence via syntactic dependency parsing. We open-source both the TAG dataset and the proposed methods to facilitate further research.

U.S. Midstream Energy Leader Adopts CIM Machine Learning Solution to Augment Its Pipeline Asset Management System


Edmonton, Alberta, Canada (September 20, 2021) – OneSoft Solutions Inc. (TSX-V:OSS; OTCQB:OSSIF) (the "Company" or "OneSoft") pleased to announce that a large U.S. pipeline operator (the "Client") has entered into a multi-year agreement with OneSoft's wholly owned subsidiary, OneBridge Solutions Inc. ("OneBridge") to integrate Cognitive Integrity ManagementTM ("CIM") software-as-a-service solution into its asset and integrity management practices for its pipeline operations. The Client is a midstream energy leader that transports approximately 30% of natural gas and crude oil in the U.S.A. and has operations in Canada and other countries. Company operations span numerous U.S. states and include facilities for natural gas midstream, intrastate and interstate transportation and storage; crude oil; natural gas liquids and fractionation; refined product transportation; terminal assets; and ownership stakes in other oil and gas operations. The Client currently operates approximately 90,000 miles of pipelines and is actively seeking acquisition of additional energy assets to continue its business growth. The agreement reflects a plan to initially onboard CIM for the Client's piggable pipelines over several years, which currently comprise approximately 45% of its infrastructure, with potential opportunity to subsequently incorporate probabilistic risk, direct assessment and other new CIM functionality enhancements for the majority of its pipeline assets in the future.

Defying the odds!


The phrase "overcoming the odds" is an understatement for 24-year-old Joshua Burgess. Though born with congenital rubella syndrome, which has caused him to suffer from a number of health challenges over the years, he continues to break barriers. On September 28, Burgess participated in the prestigious UNESCO Information for All Programme's (IFAP) Second Artificial Intelligence for Information Accessibility (AI4IA) Conference, where he spoke about'Openness and Inclusivity for the Disabled Community in a New Era'. "My presentation reflected my views as a young, blind Jamaican also living with chronic hearing loss. It was important for me to note that, while I have benefited from artificial intelligence's (AI) ability to help me integrate into society, it is also important for us to recognise that it is not a one-size-fits-all. We must collaborate with key stakeholders to ensure openness, inclusivity, fairness, and accessibility for everyone," said Burgess.

Beethoven's Unfinished 10th Symphony Brought to Life by Artificial Intelligence


Teresa Carey: This is Scientific American's 60-Second Science. Every morning at five o'clock, composer Walter Werzowa would sit down at his computer to anticipate a particular daily e-mail. It came from six time zones away, where a team had been working all night (or day, rather) to draft Beethoven's unfinished 10th Symphony--almost two centuries after his death. The e-mail contained hundreds of variations, and Werzowa listened to them all. Carey: Werzowa was listening for the perfect tune--a sound that was unmistakably Beethoven.

Hindsight Network Credit Assignment: Efficient Credit Assignment in Networks of Discrete Stochastic Units Artificial Intelligence

Training neural networks with discrete stochastic variables presents a unique challenge. Backpropagation is not directly applicable, nor are the reparameterization tricks used in networks with continuous stochastic variables. To address this challenge, we present Hindsight Network Credit Assignment (HNCA), a novel learning algorithm for networks of discrete stochastic units. HNCA works by assigning credit to each unit based on the degree to which its output influences its immediate children in the network. We prove that HNCA produces unbiased gradient estimates with reduced variance compared to the REINFORCE estimator, while the computational cost is similar to that of backpropagation. We first apply HNCA in a contextual bandit setting to optimize a reward function that is unknown to the agent. In this setting, we empirically demonstrate that HNCA significantly outperforms REINFORCE, indicating that the variance reduction implied by our theoretical analysis is significant and impactful. We then show how HNCA can be extended to optimize a more general function of the outputs of a network of stochastic units, where the function is known to the agent. We apply this extended version of HNCA to train a discrete variational auto-encoder and empirically show it compares favourably to other strong methods. We believe that the ideas underlying HNCA can help stimulate new ways of thinking about efficient credit assignment in stochastic compute graphs.