This year at Cannes Lions, judges are recognizing work at the intersection of AI and advertising. The Next Rembrandt, a campaign by JWT Amsterdam for the Dutch bank ING, used "deep learning" algorithms to analyze more than 168,000 painting fragments by the 17th century master to create a digital image based on his existing work. Then, a 3D printer fabricated a painting based on the image, replicating the depth and texture of original Rembrandt works to a surprising degree. The campaign took home a Grand Prix at Cannes in the Cyber Lions category, which recognizes "ideas indigenous to, or enhanced by, the digital environment," and an additional Grand Prix in the Creative Data Lions category. This year's entries were more sophisticated than those in the past, said Cyber Lions jury president Chloe Gottlieb, SVP and executive creative director at R/GA. "The data is not an output from the creativity," said Gottlieb.

A group of researchers from Beth Israel Deaconess Medical Center (BIDMC) and Harvard Medical School (HMS) have developed a way to train artificial intelligence to read and interpret pathology images. Scientists tested the artificial intelligence (AI) during a competition at the annual International Symposium of Biomedical Imaging, where it was tasked to look for breast cancer in images of lymph nodes. It turns out it can detect breast cancer accurately 92 percent of the time and won in two separate categories during the contest. Andrew Beck from BIDMC says they used the deep learning method, which is commonly used to train AI to recognize speech, images and objects. They fed the machine with hundreds of slides marked to indicate which parts have cancerous cells and which have normal ones.

Just a few weeks back, scientists at Google's artificial intelligence division DeepMind announced they were developing a "kill switch" to ensure that intelligent machines do not go all Terminator on us. Now, it seems, Google's AI-related concerns …… Read More Google released a new paper on a highly controversial topic: safety rules for Artificial Intelligence. Artificial intelligence is either the bright shining future of technology or an insidious threat that could endanger all of mankind, depending on your point of view. The words Artificial Intelligence can bring to mind far-fetched, sci-fi ideas and a society where robots have replaced humans. Well, this idea may not be too far off given Google's recent innovations.

Washington (AFP) - If it has to make a choice, will your autonomous car kill you or pedestrians on the street? The looming arrival of self-driving vehicles is likely to vastly reduce traffic fatalities, but also poses difficult moral dilemmas, researchers said in a study Thursday. Autonomous driving systems will require programmers to develop algorithms to make critical decisions that are based more on ethics than technology, according to the study published in the journal Science. "Figuring out how to build ethical autonomous machines is one of the thorniest challenges in artificial intelligence today," said the study by Jean-Francois Bonnefon of the Toulouse School of Economics, Azim Shariff of the University of Oregon and Iyad Rahwan of the Massachusetts Institute of Technology. "For the time being, there seems to be no easy way to design algorithms that would reconcile moral values and personal self-interest -- let alone account for different cultures with various moral attitudes regarding life-life tradeoffs -- but public opinion and social pressure may very well shift as this conversation progresses."

Since 2009, Microsoft has engaged with academic pioneers of deep learning and has created industry-scale successes in speech recognition as well as in speech translation, object recognition, automatic image captioning, natural language, multimodal processing, semantic modeling, web search, contextual entity search, ad selection, and big data analytics. Much of these successes are attributed to the availability of big datasets for training deep models, the powerful general-purpose GPU computing, and the innovations in deep learning architectures and algorithms. In this talk, a selected overview will be given to highlight our center's work in some of these exciting applications, as well as the lessons we have learned along the way as to what tasks are best solved by deep learning methods.

So, again: this is the principle, below is only a discussion of the nature of the signals or representations!

The Mill's Blackbird is a fully adjustable car rig used to create photorealistic CG vehicles. VP of Advanced Manufacturing Technologies advises on budgeting, partnerships and common mistakes for SMES. BREAKING: What Does Brexit Mean for UK Manufacturing? Proposes Cloud Robotics for 3D Print Farm Automation Tesla Motor Company wants to purchase SolarCity. Here are some ways that such a merger could affect the solar industry.

Either take a new photo from within the app or import a pre-existing one (don't bother with anything aside from vertical shots) and pick from one of about 20 filters, then export to your social network of choice. Same goes for transforming into a The Scream-like brushstroke patterns. The development team tells TechCrunch that the goal is to add two or more new filters each day, and expects to have 40 within a month. The results are pretty impressive, and unlike Paper Camera on Android, your phone isn't doing any of the heavy lifting here. The processing is done via Prisma's remote servers, and the outfit claims that no photos are stored or viewed from its side of things.

As we're on the cusp of using machine learning for rendering basically all kinds of consequential decisions about human beings in domains such as education, employment, advertising, health care and policing, it is important to understand why machine learning is not, by default, fair or just in any meaningful way. This runs counter to the widespread misbelief that algorithmic decisions tend to be fair, because, y'know, math is about equations and not skin color. Examples of this misbelief are common and evident in a recent piece on data-driven crime fighting that appeared in the Financial Times, which Cathy O'Neil brought to my attention. Ironically, Gilian Tett is well known for reporting on the failure of such things as "multi-variable equations" in the wake of the financial crisis, but she is perplexingly quick to accept that multi-variable equations are neutral and therefore fair, because the "computer experts" (whatever that means) at the police station asserted them to be so. My goal is not to belabor this one example.

Machine-learning algorithms are ubiquitous these days. Technology giants like Netflix Inc., Amazon.com Inc. and Google Inc. use them to suggest items customers might like based on their past browsing. Scientists use them to identify gene mutations associated with treatment resistance or amenable to targeted drug therapy. And doctors use them for image classification, early disease detection and better treatment outcomes. These algorithms can improve quality of life and can even help save lives.