Since the discovery of penicillin in the late 1920s, antibiotics have "revolutionized medicine and saved millions of lives." Unfortunately, the effectiveness of antibiotics is now threatened by the increase of antibiotic-resistant bacteria globally. Antibiotic-resistant infections cause the deaths of up to 1.2 million people annually, making them one of the leading causes of death. There are several factors contributing to this crisis of resistance to antibiotics. These include overusing and misusing antibiotics in treatments.

Tristan Glatard, co-director of Applied AI Institute at Concordia University (left) and Fenwick McKelvey, co-director of Applied AI Institute at Concordia University (right).Supplied It wasn't long ago when, for the average person, the words "artificial intelligence" conjured up visions of futuristic fiction – the androids of Blade Runner, say, or Steven Spielberg's A.I. In fact, artificial intelligence is very much here-and-now, and not only with a human façade (think of virtual assistants Siri and Alexa, those smartphone genies ready to do our bidding), but in a myriad of forms. From Google searches to medical diagnoses, Netflix's personalized "Top Picks" to self-driving cars, AI technology has become pervasive. It's predicted that the AI market will contribute as much as $15.7-trillion per year to the global economy by the end of this decade. Its applications will be increasingly integrated into our lives and play a role in shaping our society.

Concordia University in Irvine will discontinue its use of antigen testing for asymptomatic students and employees, after more than 50 false positives prompted unwarranted concern about a possible major coronavirus outbreak. As of Wednesday, university officials said there were six active cases -- four students and two employees -- on campus as opposed to the more than 60 infections reported two days ago. Testing in another six cases has not been confirmed, and 55 students and employees have been confirmed as negative for the virus, they said. Campus officials had canceled athletic practices and urged against out-of-state travel for Thanksgiving because of the erroneous test results, which were preliminary pending confirmation from an outside lab. The university previously had been posting only confirmed test results on its COVID-19 dashboard, but made an exception for the unconfirmed numbers because of the indication of a "potential outbreak."

BROOKLYN, New York, Wednesday, March 4, 2020 - Robots hold promise for a large number of people with neurological movement disorders severely affecting the quality of their lives. Now researchers have tapped artificial intelligence techniques to build an algorithmic model that will make the robots more accurate, faster, and safer when battling hand tremors. Their model, which is ready for others to deploy, appears this month in Scientific Reports, an online journal of Nature. The international team reports the most robust techniques to date to characterize pathological hand tremors symptomatic of the common and debilitating motor problems affecting a large number of aging adults. One million people throughout the world have been diagnosed with Parkinson's disease, just one of the neurodegenerative diseases that can cause hand tremors. While technology such as sophisticated wearable exoskeleton suits and neurorehabilitative robots could help people offset some involuntary movements, these robotic assistants need to precisely predict involuntary movements in real-time - a lag of merely 10 or 20 milliseconds can thwart effective compensation by the machine and in some cases may even jeopardize safety.

Pizza lunch will be provided at 12:15pm, with the talk following at 12:30pm. All students, alumni, staff and faculty are welcome to register for this free event using the registration link below. "Communication has been weaponized, used to provoke, mislead and influence the public in numerous insidious ways. Disinformation was just the first stage of an evolving trend of using information to subvert democracy, confuse rival states, define the narrative and control public opinion. Using large, unregulated, open environments that tech companies once promised would "empower" ordinary people, disinformation has spread rapidly across the globe.

How can artificial intelligence be oriented toward the common good? The belief in AI for good has widespread acceptance in the industry and among governments. Declarations from around the globe--Canada, China, South Korea, France, and more--call for the development of AI to have a social purpose. But what is that purpose? The workshop seeks to develop a vision for a commons-based approach to the future of AI.

Providing a broad but in-depth introduction to neural network and machine learning in a statistical framework, this book provides a single, comprehensive resource for study and further research. All the major popular neural network models and statistical learning approaches are covered with examples and exercises in every chapter to develop a practical working understanding of the content. Each of the twenty-five chapters includes state-of-the-art descriptions and important research results on the respective topics. The broad coverage includes the multilayer perceptron, the Hopfield network, associative memory models, clustering models and algorithms, the radial basis function network, recurrent neural networks, principal component analysis, nonnegative matrix factorization, independent component analysis, discriminant analysis, support vector machines, kernel methods, reinforcement learning, probabilistic and Bayesian networks, data fusion and ensemble learning, fuzzy sets and logic, neurofuzzy models, hardware implementations, and some machine learning topics. Applications to biometric/bioinformatics and data mining are also included.

Providing a broad but in-depth introduction to neural network and machine learning in a statistical framework, this book provides a single, comprehensive resource for study and further research. All the major popular neural network models and statistical learning approaches are covered with examples and exercises in every chapter to develop a practical working understanding of the content. Each of the twenty-five chapters includes state-of-the-art descriptions and important research results on the respective topics. The broad coverage includes the multilayer perceptron, the Hopfield network, associative memory models, clustering models and algorithms, the radial basis function network, recurrent neural networks, principal component analysis, nonnegative matrix factorization, independent component analysis, discriminant analysis, support vector machines, kernel methods, reinforcement learning, probabilistic and Bayesian networks, data fusion and ensemble learning, fuzzy sets and logic, neurofuzzy models, hardware implementations, and some machine learning topics. Applications to biometric/bioinformatics and data mining are also included.

This paper further introduces and formalizes a novel concept of self-forensics for automotive vehicles, specified in the Forensic Lucid language. We argue that self-forensics, with the forensics taken out of the cybercrime domain, is applicable to "self-dissection" of intelligent vehicles and hardware systems for automated incident and anomaly analysis and event reconstruction by the software with or without the aid of the engineering teams in a variety of forensic scenarios. We propose a formal design, requirements, and specification of the self-forensic enabled units (similar to blackboxes) in vehicles that will help investigation of incidents and also automated reasoning and verification of theories along with the events reconstruction in a formal model. We argue such an analysis is beneficial to improve the safety of the passengers and their vehicles, like the airline industry does for planes.