Over the past few years, the Canada Revenue Agency has been using data analytics and AI, such as machine-learning algorithms that predict tax non-compliance and detect activity in the underground economy. Since 2018, the Department of Justice Canada has licensed the use of Tax Foresight, AI software developed by Blue J Legal Inc. in Toronto, which employs machine learning to predict – with about 90% accuracy, according to the company – how a court might rule on a particular tax scenario. "It's not just about speeding up [analysis] that would otherwise happen," says Benjamin Alarie, co-founder and CEO of Blue J Legal and Osler Chair of Business Law at the University of Toronto. "It's about making [widely] available a really good prediction that would otherwise be the domain of an experienced [lawyer]." AI technology could bring more certainty to the interpretation of tax law, Alarie adds: "Everyone benefits from that."

British Prime Minister Boris Johnson introduced a new fast-track visa to attract more of the world's best scientists to the U.K. in hopes of creating a global science "superpower." Johnson paired the announcement of the Global Talent route program with a pledge of 300 million pounds ($392 million) for research into advanced mathematics. The money will help fund researchers and doctoral students whose work in math underpins myriad developments such as safer air travel, smart phone technology and artificial intelligence. The new visa route will have no cap on the number of people able to come to the U.K. under the program. "The UK has a proud history of scientific discovery, but to lead the field and face the challenges of the future we need to continue to invest in talent and cutting edge research,'' Johnson said in a statement.

A lack of proper data is hurting the use of machine learning to develop drugs, which could put U.S. drugmakers at a competitive disadvantage compared to other countries, according to a report from the U.S. Government Accountability Office and the National Academy of Medicine. Machine learning is a type of artificial intelligence that involves using data to train computers to make decisions and learn from experiences, according to Pharmaphorum. It has the potential to cut costs of research and development for drugmakers by helping researchers to predict what will and won't work in clinical trials. However, the report says a lot of the data being used in drug development is not suitable for machine learning purposes. There is a phenomenon known as "garbage in, garbage out," where a machine learning system can't produce credible results because of poor data, according to Pharmaphorum.

Garry Kasparov has warned that any attempts by the Government to regulate artificial intelligence (AI) could "stifle" its development and give Russia and China an advantage. The former world chess champion has become an advocate for AI development following his resignation from professional chess in 2005. He told The Telegraph that "the government should be involved" in helping researchers and private firms to develop AI in order to "pave the road" for the technology. However, he cautioned against governments attempting to regulate the technology too closely. "It's too early for the government to interfere," he said.

Apply for a position in our exciting research on "Materials for Neuromorphic Circuits" (MANIC), and become part of the next generation of neuromorphic experts! Funded by the European Commission through the Horizon 2020 Marie Sklodowska-Curie ITN Programme, the MANIC network offers 15 high level fellowships for joint research on new materials for cognitive applications. The most talented and motivated students will be selected for advanced multidisciplinary research training, preferably starting July 2020. The scientific aim of MANIC is to synthesize materials that can function as networks of neurons and synapses by integrating conductivity, plasticity and self-organization. Successes in deep learning show that the paradigm of neuromorphic computing is very attractive.

Artificial intelligence is growing up fast, as are robots whose facial expressions can elicit empathy and make your mirror neurons quiver.

Artificial intelligence is growing up fast, as are robots whose facial expressions can elicit empathy and make your mirror neurons quiver.

A team of University of Illinois researchers estimated the mortality costs associated with air pollution in the U.S. by developing and applying a novel machine learning-based method to estimate the life-years lost and cost associated with air pollution exposure. Scholars from the Gies College of Business at Illinois studied the causal effects of acute fine particulate matter exposure on mortality, health care use and medical costs among older Americans through Medicare data and a unique way of measuring air pollution via changes in local wind direction. The researchers - Tatyana Deryugina, Nolan Miller, David Molitor and Julian Reif - calculated that the reduction in particulate matter experienced between 1999-2013 resulted in elderly mortality reductions worth $24 billion annually by the end of that period. Garth Heutel of Georgia State University and the National Bureau of Economic Research was a co-author of the paper. "Our goal with this paper was to quantify the costs of air pollution on mortality in a particularly vulnerable population: the elderly," said Deryugina, a professor of finance who studies the health effects and distributional impact of air pollution.

Does your company have an AI ethics officer? In 2014, Stephen Hawking said that AI would be humankind's best or last invention. Six years later, as we welcome 2020, companies are looking at how to use Artificial Intelligence (AI) in their business to stay competitive. The question they are facing is how to evaluate whether the AI products they use will do more harm than good. Many public and private leaders worldwide are thinking about how to address these questions around the safety, privacy, accountability transparency and bias in algorithms.

The majority of Information Security teams' cybersecurity analysts are overwhelmed today analyzing security logs, thwarting breach attempts, investigating potential fraud incidents and more. The following graphic compares the percentage of organizations by industry who are relying on AI to improve their cybersecurity. The bottom line is all organizations have an urgent need to improve endpoint security and resilience, protect privileged access credentials, reduce fraudulent transactions, and secure every mobile device applying Zero Trust principles. Many are relying on AI and machine learning to determine if login and resource requests are legitimate or not based on past behavioral and system use patterns. Several of the top ten companies to watch take into account a diverse series of indicators to determine if a login attempt, transaction, or system resource request is legitimate or not.