The fact that Catherine - who's learned that her ex-husband Theodore has taken up with Samantha, a honey-voiced Operating System who screens his emails, entertains his fantasies and sends his writing off to publishers - comes off as judgmental is testament to Jonze's filmmaking skills. But it's also proof of how deeply we've internalised the notion that artificial intelligence is an extension of male desires and that, really, few things may be hotter than the hard-to-nail promise of female servitude. As Laurie Penny writes in an April 2016 article in The New Statesman, the issue of whether or not robots are slaves designed to serve their masters or sentient beings with inner lives and autonomous instincts has long paralleled the questions we ask of women in the world. READ MORE: * New Zealand could become first country to use Domino's pizza delivery robot * Drones, self-drive cars and'car butler' in our near future * Professor hopes robots will take over the rehabilitation world * Robots could threaten up to half New Zealand's jobs in next 20 years * Robots fooling humans they love something that can't love them back: AI expert * Self-learning robot escapes Russian facility, disrupts traffic * New robot from Google shows off human-like qualities Robots may take on domestic tasks and give working mothers more time. From Metropolis, the 1927 Fritz Lang classic in which Maria, a cyborg whose sultry ways plunge the city and its workers into chaos (she's later burned at a stake) to Austin Powers: International Man of Mystery, the hit 1997 spy film whose comely fembots are programmed to ensnare the bumbling Powers with his own libido, female robots are often cast as temptresses or destroyers, coincidentally enough, the same roles reserved for flesh-and-blood women.

Massachusetts … of the robotics institute at Carnegie Mellon University in Pittsburgh, who was not involved in the MIT study, called it "an important work." "Some argue that prediction is a central part of (artificial) intelligence," Hebert …… Read More MIT's artificial intelligence passes key Turing test An MIT algorithm has managed to produce sounds able to fool human listeners and beat Turing's sound test for artificial intelligence. Researchers from the Massachusetts Institute of Technology are using Alan Turing's tests, developed in the 1950's … According to a new paper, researchers at the University of Georgia and the Massachusetts Institute of Technology's Computer Science and Artificial Intelligence Laboratory have developed a piece of software capable of turning almost any smartphone into … Massachusetts … of the robotics institute at Carnegie Mellon University in Pittsburgh, who was not involved in the MIT study, called it "an important work." "Some argue that prediction is a central part of (artificial) intelligence," Hebert … Researchers from the Computer Science and Artificial Intelligence Laboratory (CSAIL) at Massachusetts Institute of Technology (MIT) demonstrated an algorithm that was able to view silent video clips and accurately predict realistic sounds that might appear.

That's one of the questions animating the field of computational creativity, which seeks to design artificial intelligence that can replicate human creativity. We wrote recently about a Google effort to create algorithms that make original music. But what if artificial intelligence could design and make 3D objects you could actually hold in your hand? That was the challenge that Joel Lehman, an assistant professor at the IT University of Copenhagen, set out to tackle. Lehman wondered if he could somehow leverage the remarkable image recognition power of deep neural networks (DNN) to create new artifacts without human input.

Research shows that machine learning algorithms may learn and incorporate bias into their decision-making processes. They help make better decisions, in part, because of the large amounts of data that can be extracted – from content provided around decisions that were made in the past to the outcomes that resulted. Each transaction provides a historical map from which better decisions can be made. But the truth is, when it comes to people at work and many human capital decisions, there is no cookie cutter approach. And those past decisions may not form the best map to read from when informing future outcomes.

This week, Forrester Research, Inc. named Tamr a Strong Performer in "The Forrester Wave: Data Quality Solutions, Q4 2015" report. The report evaluated 13 vendors based on 30 criteria, including current offering, strategy and market presence. In addition to receiving the highest scores in the validation, cleansing and standardization, as well as the cloud, criteria, Tamr scored a 4.00 out of a possible 5.00 in data governance and stewardship and in data link, match and survivorship. Tamr also received the highest score possible (5.00) for product strategy. We believe that for a young company like Tamr to be named a Strong Performer in "The Forrester Wave: Data Quality Solutions, Q4 2015" report, it takes unnatural amounts of work by a team that, like ours, is fully on the same page.

"Two recent accomplishments are near and dear to my heart. First, I've worked on role discovery in complex networks. A complex network is an ecosystem with the nodes (i.e., entities) playing multiple functional roles over time. Automatically discovering the underlying roles of nodes in a network is a challenging problem. Given a network, intuitively two nodes belong to the same role (or behavioral cluster) if they have similar structural behavior. Examples of roles that can be automatically determined from data include'broker,' 'clique-member,' 'periphery-node,' etc. Roles enable numerous novel and useful tasks, such as transfer learning, sense making, and anomaly detection. "In 2012, together with colleagues at LLNL and CMU, I introduced RolX (short for Role eXtraction).

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Many of these titles probably sound familiar to you. For a long time now, the concept of artificial intelligence has provided the masses with novels and blockbuster movies of science fiction, drama, comedy, and even unexpected stories of friendship. Hollywood and all of its fans have enjoyed these stories over the years – appreciating them for what they are: Entertainment. Today, artificial intelligence (AI) has a very different meaning. In fact, as AI has moved from the silver screen to the screens of modern computers used by virtually every segment of society, it has a remarkably different purpose.

Continuous mobile vision is limited by the inability to efficiently capture image frames and process vision features. This is largely due to the energy burden of analog readout circuitry, data traffic, and intensive computation. To promote efficiency, we shift early vision processing into the analog domain. This results in RedEye, an analog convolutional image sensor that performs layers of a convolutional neural network in the analog domain before quantization. We design RedEye to mitigate analog design complexity, using a modular column-parallel design to promote physical design reuse and algorithmic cyclic reuse.

These days, sensors are everywhere: Phones know where their users have been, what music they've listened to and which events they've agreed to attend. And while the data that results is a powerful tool for businesses, they aren't always connected to material generated on laptops, tablets and wearable devices. Maybe they should be, because, already, 70 percent of Americans own two or more connected devices. A percentage like that tells us that the days of one-screen users are numbered: Already, 88 percent of millennials are engaging in second-screen behaviors while watching videos online. Clearly, businesses must keep up with the times.