This opinion piece was written by Petra Molnar and Samer Muscati, of the International Human Rights Program at the University of Toronto. It also appears on our refugees and migration newsfeed. The detention of migrants at the U.S.-Mexico border in every single case presented; the wrongful deportation of 7,000 foreign students accused of cheating on a language test; racist or sexist discrimination based on social media profile or appearance -- what do these seemingly disparate examples have in common? In every case, an algorithm made a decision with serious consequences for people's lives. Algorithms and artificial intelligence (AI) are starting to augment human decision-making in Canada's immigration and refugee system, with significant implications for the fundamental human rights of those subjected to these technologies.

Smart speakers such as Amazon's Echo and Alphabet Inc.'s Google Home products can handle a growing array of tasks from playing music to adjusting the thermostat to arming a security system. They are also sometimes freaking people out, seeming to drop into conversations uninvited, playing music unprompted in the middle of the night, turning on other gadgets at random and acting generally, well, possessed. Companies say there are reasonable explanations, such as the device mishearing its "wake word"--which it recognizes to start listening to commands. But such episodes can leave owners shaken and unsure of what to do next. Put the device in time out?

The traditional view of outsourcing has tended to see cost reduction as one of the primary drivers for any customer. The idea that the'total cost of ownership' of a particular business function over the term of the outsourcing contract should be lower is very often part of the business case. Similarly, seeing outsourcing as a means of transforming a collection of assets on the balance sheet into a recurring service charge, and reducing (or at least apparently reducing) capital costs is another common refrain at the outset of deals. Whilst technology transformation / business change deals do happen where the aim is for a service upgrade at an increased overall cost, they are by no means as common as cost-led deals. To date, a large portion of the cost savings delivered through outsourcing deals – especially those that involve any sort of offshoring, nearshoring or even (in the London-centric UK at least) 'northshoring' – come from labour arbitrage: the central idea being that the activity undertaken by the outsourcing customer's relatively more expensive current local employees can be performed at a lower cost, but to the materially the same or even a better overall standard, by the outsourcing vendor's resources.

Sharing a name with a famous person can prompt endless jokes and comments -- but in these particularly politically-charged times, having the same name as a political figure can be especially tiresome. That's something a young man from Kentucky named Brett Kavanagh has learned only too well in recent weeks: On Friday, Brett, 27, complained about the recent woes of having his name, prompting others with famous names to commiserate. Women named Siri and Alexa, and men named Michael Jackson and Bruce Lee, all tweeted about how hard it is to have a well-known name. His tweet inspired others to chime in, including this person who pointed to a Scottish man named Steve Bannon -- who is not the same as Breitbart's Steve Bannon A man named Bruce Y. Lee knows the struggle This Brett, who works in customer service and lives in Louisville, spells his last name differently from new Supreme Court Justice Brett Kavanaugh, but it seems their nearly-identical names has caused him some trouble. Tough times: Brett (pictured) doesn't spell his name the same way as the judge, either'This is a terrible time to be named Brett Kavanagh,' he tweeted.

As Hurricane Florence ground its way through North Carolina, it released what might politely be called an excrement storm. Massive hog farm manure pools washed a stew of dangerous bacteria and heavy metals into nearby waterways. More efficient oversight might have prevented some of the worst effects, but even in the best of times, state and federal environmental regulators are overextended and underfunded. Help is at hand, however, in the form of machine learning--training computers to automatically detect patterns in data--according to Stanford researchers. Their study, published in Nature Sustainability, finds that machine learning techniques could catch two to seven times as many infractions as current approaches, and suggests far-reaching applications for public investments.

An attempt to sue Google over claims it collected sensitive personal data from millions of iPhone users has failed at the High Court. The claim would have seen more than four million people sue the company because of personal information that it harvested about them. But Mr Justice Warby, sitting in London, blocked the proceedings. The I.F.O. is fuelled by eight electric engines, which is able to push the flying object to an estimated top speed of about 120mph. The giant human-like robot bears a striking resemblance to the military robots starring in the movie'Avatar' and is claimed as a world first by its creators from a South Korean robotic company Waseda University's saxophonist robot WAS-5, developed by professor Atsuo Takanishi and Kaptain Rock playing one string light saber guitar perform jam session A man looks at an exhibit entitled'Mimus' a giant industrial robot which has been reprogrammed to interact with humans during a photocall at the new Design Museum in South Kensington, London Electrification Guru Dr. Wolfgang Ziebart talks about the electric Jaguar I-PACE concept SUV before it was unveiled before the Los Angeles Auto Show in Los Angeles, California, U.S The Jaguar I-PACE Concept car is the start of a new era for Jaguar.

In the U.S., corporations have been given rights of free speech and religion. Some natural features also have person-like rights. A new argument has laid a path for artificial intelligence systems to be recognized as people too – without any legislation, court rulings or other revisions to existing law. Legal scholar Shawn Bayer has shown that anyone can confer legal personhood on a computer system, by putting it in control of a limited liability corporation in the U.S. If that maneuver is upheld in courts, artificial intelligence systems would be able to own property, sue, hire lawyers and enjoy freedom of speech and other protections under the law. In my view, human rights and dignity would suffer as a result.

Across the technology industry, rank-and-file employees are demanding greater insight into how their companies are deploying the technology that they built. At Google, Amazon, Microsoft and Salesforce, as well as at tech start-ups, engineers and technologists are increasingly asking whether the products they are working on are being used for surveillance in places like China or for military projects in the United States or elsewhere. That's a change from the past, when Silicon Valley workers typically developed products with little questioning about the social costs. It is also a sign of how some tech companies, which grew by serving consumers and businesses, are expanding more into government work. And the shift coincides with concerns in Silicon Valley about the Trump administration's policies and the larger role of technology in government.

Inspired by the success of self attention mechanism and Transformer architecture in sequence transduction and image generation applications, we propose novel self attention-based architectures to improve the performance of adversarial latent codebased schemes in text generation. Adversarial latent code-based text generation has recently gained a lot of attention due to its promising results. In this paper, we take a step to fortify the architectures used in these setups, specifically AAE and ARAE. We benchmark two latent code-based methods (AAE and ARAE) designed based on adversarial setups. In our experiments, the Google sentence compression dataset is utilized to compare our method with these methods using various objective and subjective measures. The experiments demonstrate the proposed (self) attention-based models outperform the state-of-the-art in adversarial code-based text generation. Text generation is of particular interest in many natural language processing (NLP) applications such as dialogue systems, machine translation, image captioning and text summarization. Recent deep learning-based approaches to this problem can be categorized into three classes: auto-regressive or maximum likelihood estimation (MLE)-based, generative adversarial network (GAN)-based and reinforcement learning (RL)-based approaches. RNNs compactly represent the samples history in the form of recurrent states.

According to the report, a new algorithm called BigGAN can create detailed and quite realistic photos from scratch. "BigGAN, the last three letters of which stand for generative adversarial network. This kind of neural net is composed of two models: one that conjures random images out of random numbers, and one that compares these generated images to real images and tells the generator just how far off it is. GANs are common in machine learning research, and BigGAN isn't that different from other algorithms out there. But there is one big difference: BigGAN throws a ton of computational power, courtesy of Google, at the problem."