As described in our recent announcement about AI pioneer Randy Goebel joining the ROSS team as an advisor, Goebel is a professor in the Department of Computing Science at the University of Alberta, a founder and researcher with the Alberta Machine Intelligence Institute (AMII) and is involved with the development of the University of Alberta Google DeepMind relationship, the group behind AlphaGo. Goebel's theoretical work on abduction, hypothetical reasoning and belief revision is internationally acclaimed and his recent application of practical belief revision and constraint programming to scheduling, layout, and web mining has had widespread impact across multiple industry verticals. More recently, Goebel has been working on the application of machine learning to visual explanation and natural language processing, with focus on legal reasoning. He has previously held faculty appointments at the University of Waterloo and the University of Tokyo, and is actively involved in academic and industrial collaborative research projects in Canada, Australia, Malaysia, Europe and Japan. Goebel is on the advisory boards of the German Research Centre for AI, the Japan Science and Technology Organization and the Japanese National Institute for Informatics.

Apple has been accused of being'racist' after a Chinese boy realised he could unlock his mum's iPhone X using the facial recognition software. A husband bought his wife the new smartphone, but she was then shocked to discover it could be unlocked by the couple's son. It seems the family, who live in the city of Shanghai are not the only Chinese users who have been able to open each other's phones. Increasingly iPhone users in China - a country of more than a billion people - are concerned about their iPhone X's security and privacy features. Apple has been accused of being'racist' after a Chinese boy (right) realised he could unlock his mum's iPhone X using the facial recognition software Face ID uses a TrueDepth front-facing camera on the iPhone X, which has multiple components.

In 1941, science-fiction writer Isaac Asimov stated "The Three Laws of Robotics," in his short story "Runaround." Law One: A robot may not injure a human being or, through inaction, allow a human being to come to harm. Law Two: A robot must obey orders given it by human beings except where such orders would conflict with the First Law. Law Three: A robot must protect its own existence as long as such protection does not conflict with the First or Second Laws. These laws come from the world of science fiction, but the real world is catching up.

The long search is over for Uber's chief operating officer. Former Orbitz CEO Barney Harford has formally accepted the position today. Looking fwd to working w @dkhos again to help @Uber achieve its full potential https://t.co/gdnXJfFrjS In early March, former CEO Travis Kalanick disclosed he would be searching for someone to fill the position amidst allegations he had fostered a culture of sexism and achievement at any cost. The company continued to suffer a number of setbacks in the months following, including Kalanick's resignation, a lawsuit from Alphabet's self-driving car company Waymo and a Department of Justice investigation.

One of the most important issues that Congress will face in 2018 is how and when to regulate our growing dependence on artificial intelligence (AI). During the U.S. National Governors Association summer meetings, Elon Musk urged the group to push forward with regulation "before it's too late," stating that AI was an "existential threat to humanity." Hyperbole aside, there are legitimate concerns about the technology and its use. But a rush to regulation could exacerbate current issues, or create new issues that we're not prepared to deal with along the way. To begin with, one of the biggest issues in the world of AI is the lack of clear definition for what the technology is -- and is not.

A way to combat the capricious and biased nature of human decisions. In courtrooms across the country, judges turn to computer algorithms when deciding whether defendants awaiting trial must pay bail or can be released without payment. The increasing use of such algorithms has prompted warnings about the dangers of artificial intelligence. But research shows that algorithms are powerful tools for combating the capricious and biased nature of human decisions. Bail decisions have traditionally been made by judges relying on intuition and personal preference, in a hasty process that often lasts just a few minutes. In New York City, the strictest judges are more than twice as likely to demand bail as the most lenient ones.

Increasingly, discrimination by algorithms is perceived as a societal and legal problem. As a response, a number of criteria for implementing algorithmic fairness in machine learning have been developed in the literature. This paper proposes the Continuous Fairness Algorithm (CFA$\theta$) which enables a continuous interpolation between different fairness definitions. More specifically, we make three main contributions to the existing literature. First, our approach allows the decision maker to continuously vary between concepts of individual and group fairness. As a consequence, the algorithm enables the decision maker to adopt intermediate "worldviews" on the degree of discrimination encoded in algorithmic processes, adding nuance to the extreme cases of "we're all equal" (WAE) and "what you see is what you get" (WYSIWYG) proposed so far in the literature. Second, we use optimal transport theory, and specifically the concept of the barycenter, to maximize decision maker utility under the chosen fairness constraints. Third, the algorithm is able to handle cases of intersectionality, i.e., of multi-dimensional discrimination of certain groups on grounds of several criteria. We discuss three main examples (college admissions; credit application; insurance contracts) and map out the policy implications of our approach. The explicit formalization of the trade-off between individual and group fairness allows this post-processing approach to be tailored to different situational contexts in which one or the other fairness criterion may take precedence.

Water pollution is a major global environmental problem, and it poses a great environmental risk to public health and biological diversity. This work is motivated by assessing the potential environmental threat of coal mining through increased sulfate concentrations in river networks, which do not belong to any simple parametric distribution. However, existing network models mainly focus on binary or discrete networks and weighted networks with known parametric weight distributions. We propose a principled nonparametric weighted network model based on exponential-family random graph models and local likelihood estimation and study its model-based clustering with application to large-scale water pollution network analysis. We do not require any parametric distribution assumption on network weights. The proposed method greatly extends the methodology and applicability of statistical network models. Furthermore, it is scalable to large and complex networks in large-scale environmental studies and geoscientific research. The power of our proposed methods is demonstrated in simulation studies.

Artificial intelligence is beginning to replace many of the workplace roles that men dominate. The parts of those jobs that will have staying power are those that rely more heavily on emotional intelligence -- skills in which women typically excel. Many researchers are reporting, and our research confirms, that artificial intelligence (AI) will reshape our economy -- and the roles of workers and leaders along with it. Jobs that don't disappear will see a significant shift as the tasks that are easily and inexpensively accomplished by robots become automated. The work that remains will very likely focus on relating.

As Greek philosopher, Heraclitus, said, "the only one constant in life is change". This is certainly true for anyone working in areas related to or based upon technology (and few don't these days). The pace of technological innovation is such that even the most fantastic of imagined futures seem like they could easily become reality. As existing technologies reach maturity, unforeseen developments arrive ever more quickly, and innovations make the leap from consumer applications to business (and vice versa) it's imperative that we constantly seek to find those that have the potential to add value to our own business and those of our customers. As we look ahead to 2018, I've been working with my colleagues to identify some trends that we think will have an impact on our business and industry.