Microsoft researchers have already created technology that can do two difficult tasks about as well as a person: identify images and recognize words in a conversation. Now, the company's leading AI experts are working on systems that can do something even more complex: Read passages of text and answer questions about them. "We're trying to develop what we call a literate machine: A machine that can read text, understand text and then learn how to communicate, whether it's written or orally," said Kaheer Suleman, the co-founder of Maluuba, a Quebec-based deep learning startup that Microsoft acquired earlier this year. Machine reading systems also could help doctors, lawyers and other experts more quickly get through the drudgery of things like reading through documents for specific medical findings or rarified legal precedent. That would leave experts more time to focus on treating patients or formulating legal defenses.

The San Francisco-based ride-hailing giant has fired more than 20 people following a sexual harassment investigation, Bloomberg reported, citing an anonymous source familiar with the matter. The firm had investigated 215 claims of sexual harassment within the company. Rigetti, who now works at Stripe, called out Uber board member Arianna Huffington and Uber's Chief of Human Resources Liane Hornsey's repeated claims against such "systemic" problems of sexual harassment within the company. Arianna and Liane to press: there is no systemic sexual harassment, just Susan.

In 2013, a man named Eric L. Loomis was sentenced for eluding police and driving a car without the owner's consent. When the judge weighed Loomis' sentence, he considered an array of evidence, including the results of an automated risk assessment tool called COMPAS. Loomis' COMPAS score indicated he was at a "high risk" of committing new crimes. Considering this prediction, the judge sentenced him to seven years. Loomis challenged his sentence, arguing it was unfair to use the data-driven score against him.

It is challenging to develop stochastic gradient based scalable inference for deep discrete latent variable models (LVMs), due to the difficulties in not only computing the gradients, but also adapting the step sizes to different latent factors and hidden layers. For the Poisson gamma belief network (PGBN), a recently proposed deep discrete LVM, we derive an alternative representation that is referred to as deep latent Dirichlet allocation (DLDA). Exploiting data augmentation and marginalization techniques, we derive a block-diagonal Fisher information matrix and its inverse for the simplex-constrained global model parameters of DLDA. Exploiting that Fisher information matrix with stochastic gradient MCMC, we present topic-layer-adaptive stochastic gradient Riemannian (TLASGR) MCMC that jointly learns simplex-constrained global parameters across all layers and topics, with topic and layer specific learning rates. State-of-the-art results are demonstrated on big data sets.

Maybe it's unfair that some people think tax lawyers have the personality of a robot, but Benjamin Alarie considers that to be a plus. A Yale-trained lawyer himself, Mr. Alarie's Toronto firm, Blue J Legal, harnesses artificial (or augmented) intelligence (AI) to help lawyers and their clients work their way through the complications of tax law. We take hundreds of cases on different legal questions and train AI on how the courts make those decisions, so users can run predictions on how the courts might decide a new case," he says. Blue J Legal is at the cutting edge of a wave of new uses for AI. Robots, which have already taken over manual labour and factory work, are finding their way quickly into white-collar and professional jobs that require judgment and thinking. "I think the nature of most white-collar jobs will drastically change in the future because of AI," says Henry Kim, associate professor of operations management and information systems at York University's Schulich School of Business in Toronto. "It's not to say that all the professional jobs will go away, they'll just be different," he says. AI is not only worming its way into law, but also finance, medicine and complex areas such as the development of new pharmaceuticals. In finance, "Artificial intelligence can help people make faster, better and cheaper decisions.

The artificial intelligence revolution is gathering pace and no part of legal practice will be left untouched, writes legal commentator Morry Bailes. When South Korea's reigning Go master was beaten by artificial intelligence last year I published a column about the future of law. While a computer defeating a gaming champion seemed innocuous enough, the gravity of that event, given the complexity of Go, was significant. It has flow-on effects for all businesses, including law firms. Little over a year later, a Google-powered AI has just defeated China's best Go player, Ke Jie, who is also the world's No.1 player.

Much has been made about the coming effects of the GDPR -- from how organizations collect data to how they use that data and more. But as machine learning gains a more prominent role across organizations, a key question is emerging, and it's baffling lawyers, scholars and regulators alike: How does the GDPR affect machine learning in the enterprise? As in other areas, the GDPR is less than clear. And as a result, the idea that the GDPR mandates a "right to explanation" from machine learning models -- meaning that those significantly affected by such models are due an accounting of how the model made a particular decision -- has become a controversial subject. Some scholars, for example, have spoken out vehemently against the mere possibility that such a right exists.

Lately, I have been in the press discussing the need for more inclusive artificial intelligence and more representative data sets. Still, the valid points commenters have brought up that 1.) default camera settings do not properly expose dark skin and 2.) algorithms are not intentionally developed to be racist warrant further discussion. To address these questions and others related to bias in artificial intelligence, I am starting the Illuminate Series. The goal of the Illuminate Series is to broaden the public discourse on artificial intelligence and it's impact on everyday people. Please let me know if you would like to participate by submitting a question, guest posting, leading a discussion group, creating educational demos, or in some other capacity.

Artificial Intelligence (AI) does more than make our technology smarter, it also protects the planet. Consider the work of researchers in the field of'Computational Sustainability' – a field of AI research making us better stewards of life on Earth. Despite being a relatively new research field, Computational Sustainability has already helped fight wildlife poaching, reduce greenhouse gas emissions, understand poverty, manage wildlife populations, and protect biodiversity. Each of these contributions address one of the United Nations Sustainable Development Goals (SDGs). The collected progress of AI is addressing all SDGs, but I will highlight three specific cases.

One of the biggest civil liberties issues raised by technology today is whether, when, and how we allow computer algorithms to make decisions that affect people's lives. We're starting to see this in particular in the criminal justice system. For the past several years the ACLU of Idaho has been involved in a fascinating case that, so far as I can tell, has received very little if any national coverage, but which raises fascinating issues that are core to the new era of big data that we are entering. The case, K.W. v. Armstrong, is a class action lawsuit brought by the ACLU representing about 4,000 Idahoans with developmental and intellectual disabilities who receive assistance from the state's Medicaid program. It originally started because a bunch of people were contacting me and saying that that the amount of assistance that they were being given each year by the state Medicaid program was being suddenly cut by 20 or 30 percent.