This article considers the issue of opacity as a problem for socially consequential mechanisms of classification and ranking, such as spam filters, credit card fraud detection, search engines, news trends, market segmentation and advertising, insurance or loan qualification, and credit scoring. These mechanisms of classification all frequently rely on computational algorithms, and in many cases on machine learning algorithms to do this work. In this article, I draw a distinction between three forms of opacity: (1) opacity as intentional corporate or state secrecy, (2) opacity as technical illiteracy, and (3) an opacity that arises from the characteristics of machine learning algorithms and the scale required to apply them usefully. The analysis in this article gets inside the algorithms themselves. I cite existing literatures in computer science, known industry practices (as they are publicly presented), and do some testing and manipulation of code as a form of lightweight code audit. I argue that recognizing the distinct forms of opacity that may be coming into play in a given application is a key to determining which of a variety of technical and non-technical solutions could help to prevent harm. This article considers the issue of opacity as a problem for socially consequential mechanisms of classification and ranking, such as spam filters, credit card fraud detection, search engines, news trends, market segmentation and advertising, insurance or loan qualification, and credit scoring. These are just some examples of mechanisms of classification that the personal and trace data we generate is subject to every day in network-connected, advanced capitalist societies. These mechanisms of classification all frequently rely on computational algorithms, and lately on machine learning algorithms to do this work. Opacity seems to be at the very heart of new concerns about'algorithms' among legal scholars and social scientists. The algorithms in question operate on data. Using this data as input, they produce an output; specifically, a classification (i.e. They are opaque in the sense that if one is a recipient of the output of the algorithm (the classification decision), rarely does one have any concrete sense of how or why a particular classification has been arrived at from inputs.

A career in law and extremely long hours tend to go hand in hand. Partly of course it's about proving your commitment, but being a lawyer also involves an awful lot of grunt work - spending hours and hours looking through past case law to help your firm determine how to fight a current case. It's this time consuming, labour intensive research aspect of the legal system that Andrew Arruda, co-founder and chief executive of tech start-up Ross Intelligence, believes its invention can address. The AI (or artificial intelligence) legal research system allows lawyers to type in a question - much in the same way they'd ask a colleague - and bring up relevant examples of what has happened in previous US legal cases, essentially at the touch of a button. "Lawyers may know the law and where it stands on a particular issue today but many cases come out and it can change that so they're always looking into the past to build the future. "The issue with that is there's just millions of cases.

Automatic modulation classification (AMC) is an important task for modern communication systems; however, it is a challenging problem when signal features and precise models for generating each modulation may be unknown. We present a new biologically-inspired AMC method without the need for models or manually specified features --- thus removing the requirement for expert prior knowledge. We accomplish this task using regularized stacked sparse denoising autoencoders (SSDAs). Our method selects efficient classification features directly from raw in-phase/quadrature (I/Q) radio signals in an unsupervised manner. These features are then used to construct higher-complexity abstract features which can be used for automatic modulation classification. We demonstrate this process using a dataset generated with a software defined radio, consisting of random input bits encoded in 100-sample segments of various common digital radio modulations. Our results show correct classification rates of > 99% at 7.5 dB signal-to-noise ratio (SNR) and > 92% at 0 dB SNR in a 6-way classification test. Our experiments demonstrate a dramatically new and broadly applicable mechanism for performing AMC and related tasks without the need for expert-defined or modulation-specific signal information.

After last year's surprising (but not completely unexpected) Mr. Robot season finale, hacker Elliot Alderson (Rami Malek) and the fsociety are back. But, if you're hoping the season two trailer will shed any light about what's happening with Alderson and his hactivist collective, you're out of luck.

Suzanne, a young woman in San Francisco, met a man--call him John--on the dating site OKCupid. John was attractive and charming. More notably, he indulged in the kind of profligate displays of affection which signal a definite eagerness to commit. He sneaked Suzanne's favorite snacks into her purse as a workday surprise and insisted early on that she keep a key to his apartment. He asked her to help him choose a couch and then spooned with her on all the floor models. He even accompanied her, unprompted, to the D.M.V.--an act roughly equivalent, in today's gallantry currency, to Perseus rescuing Andromeda from the sea monster. As we learn from the podcast "Reply All," which reported the tale, Suzanne was not the only woman on whom John had chosen to bestow his favor. Six months into their relationship, she discovered that he was seeing half a dozen other women, one of whom he'd been stringing along for two years. All of them had received the couch-spooning treatment.

In the 30 years since it was released in theaters on May 16,1986, it's almost unimaginable to think "Top Gun" -- the Tony Scott-directed action film starring Tom Cruise as a hot shot fighter pilot whose cocky attitude puts him at odds with other students at the Top Gun Naval Fighter Weapons School -- wouldn't be a hit. Despite mixed reviews -- the film has a 55 percent rating on Rotten Tomatoes -- "Top Gun" was a box-office smash, earning 177 million in the U.S. (and another 177 million internationally) during its initial theatrical release. The movie not only cemented Cruise's leading-man status, it also launched the careers of Val Kilmer, Meg Ryan and Anthony Edwards, who were all relative unknowns at the time. "Top Gun" went on to win an Academy Award for Best Original Song for Berlin's "Take My Breath Away," spawned numerous video game adaptations, earned a place in the Library of Congress' National Film Registry, and, ahead of its 30th anniversary, reportedly started moving forward with a highly anticipated sequel, marking Cruise's return as Maverick. "You don't make'Top Gun' without Tom Cruise," producer Jerry Bruckheimer told ET after he shared a photo of him and the actor in New Orleans, discussing "a little Top Gun 2." But Bruckheimer, who has since launched the "Bad Boys" and "Pirates of the Caribbean" franchises, is the first to admit the cards were stacked against their ace pilot.

When you're the resident Penny Dreadful evangelist in your office (or neighborhood, or corner pub, or book club), you find yourself trying to entice people using genre comparisons: "Oh, you like romance? The problem is, Showtime's drama doesn't really have a genre. People assume it's horror, which makes sense--there's a werewolf, and supernatural forces, and the opening credits have blood and spiders, and the show is admittedly gory--but it also draws from a number of a literary sources, and there's plenty of romance and even a little comedy. Showtime calls it a psychological thriller. Too bad none of those are right. Penny Dreadful is a gothic romance, and while it may be seasoned with a dash of thriller and pinch of horror, its genre recipe is more than 250 years old. Romance was seen as too unserious to be called literature, and literature was too strict to allow the supernatural, so Walpole subtitled his tale of forbidden love and haunted castles "A Gothic Story." Penny Dreadful is an unraveling, and re-spinning, of that yarn. Its characters live, like Walpole, in London in the late 18th century. Their stories are drawn from the books that would follow Castle of Otranto's lead: Mary Shelley's Frankenstein and Bram Stoker's Dracula. And they were imagined by a man, John Logan, who, like Walpole, sought to marry romance with horror and the supernatural. "Penny Dreadful came from reading a lot of romantic poetry, especially William Wordsworth," Logan says. "That led me to Byron and Keats, and eventually back to Mary Shelley and Frankenstein.

Artificial intelligence can control self-driving cars, beat the best humans at incredibly complex board games, and fight cancer; but one thing it can't do perfectly is communicate. To help solve the problem, Google has been feeding it's AI with more than 11,000 unpublished books, including 3,000 steamy romance titles. "come with me," she said. "talk to me," she said. "don't worry about it," she said.

In Little Britain, the world-weary character Carol Beer responds to all customer enquiries by clacking into her PC keyboard, glancing at the results shown on screen, and nasally intoning: "Computer says, 'no'". It does however severely underplay the current real-world situation. In reality the computer often says'no' these days without any need for a Carol Beer to input data or to even speak to the customer. Small computer programmes are used to cut out the middle-human. The chances are that if you were to apply for a loan or a credit card online today, behind the scenes a computer is working its way through an algorithm, whirring away in a split-second to decide whether your application will be successful by classifying you as'low risk' (computer says, 'yes') or as'high risk' (computer says, 'no').

First Posted: May 06, 2016 10:50 AM EDT Tags Deep Learning, contextual deep learning, artificial intelligence, AI, RAGE Frameworks Contextual deep learning allows artificial intelligence machines to react in a more natural and intelligent way to the real-world auditory, visual or other type of data. Information for and about the digital publishing industry from the leading trade association dedicated to representing the interests of high-quality digital publishers before the advertising community, the press, the government and the public. Includes recent news and latest research on the industry, as well as membership information. Or you might imagine the disembodied voice of HAL, the recalcitrant computer that wouldn't open the pod-bay doors in '2001: A Space Odyssey,' the ... For those betting on the 142nd Kentucky Derby on Saturday, there are several ways to approach the strategy. Last year, Jimmy Fallon's puppies took a stab at it-and correctly predicted the winner, American Pharoah.