An unsupervised machine learning study presented at the 2019 meeting of Association for Computational Linguistics--which examined 3.5M books published between 1900 and 2008--indicates that men are described based on their behavior, where women are described based on appearance. In specific, words like "beautiful" and "sexy" are two of the adjectives most frequently used to describe women, while common descriptors for men were "brave," "rational," and "righteous." The books, which amounted to approximately 11B words in sum, included a mix of fiction and non-fiction. "We are clearly able to see that the words used for women refer much more to their appearances than the words used to describe men," said University of Copenhagen computer scientist and assistant professor Isabelle Augenstein in a statement. "Thus, we have been able to confirm a widespread perception, only now at a statistical level."

Surveillance watchdogs in the United Kingdom are worried that the government will deploy CCTV cameras equipped with lip-reading and gait-analyzing artificial intelligence. Tony Porter, who serves as the Surveillance Camera Commissioner, invoked the dystopian society of George Orwell's "1984." He predicts residents will soon start to cover their mouths when speaking, lest the government listen in on whatever they're saying. "The capability to run lip-sync technology to determine what people are saying would have a very suppressive effect. It would change the nature of our society," Porter told The Evening Standard. While the cameras haven't been deployed yet, scientists have been developing lip-reading artificial intelligence for years, meaning it could soon hit the streets of the already highly-surveiled U.K. Porter argued that just because the lip-reading technology exists and could feasibly be used to help law enforcement doesn't mean that using it is a good idea, and that leaders ought to be more thoughtful about compromising people's freedom and privacy.

The Radiological Society of North America (RSNA) is organizing a challenge intended to show the application of machine learning and artificial intelligence on medical imaging and the ways in which these emerging tools and methodologies may improve diagnostic care. The RSNA Pediatric Bone Age Machine Learning Challenge addresses a familiar image analysis activity for pediatric radiologists: assessment of bone age from hand radiographs of pediatric patients used to evaluate growth and diagnose developmental disorders. The Challenge uses a dataset of hand radiographs provided by a consortium of leading research institutions -- Stanford University, the University of California, Los Angeles and the University of Colorado -- that have associated bone age assessments provided by multiple expert observers. Participants in the challenge will be judged by how well the bone age evaluations produced by their algorithms accord with the expert observers' evaluations. Participants will have the opportunity to directly compare their algorithms in a structured way using this carefully curated dataset.

Superpowers across the world are putting great emphasis on strengthening their knowledge and application of artificial intelligence across the country. As it stands, China may soon become the biggest threat with its army being heavily integrated with artificially intelligent technology. Meanwhile, the United States is falling behind and concerns are growing as to how the US will cope with China's increasingly dominant AI technology. Artificial intelligence is not just restricted to consumer and industrial purposes, it also extends beyond that and plays a large role in the use of military weapons. Artificial intelligence is used to maximise efficiency and produce fast outcomes.

I wrote about recommender systems last week, but there is so much discussion around their effects right now in the mainstream tech press that they deserve a second issue. As a recap, I said that there were two things that made recommender systems super ineffective, and that YouTube, one of the premier companies tech using recommendations, suffers from both a lot of the first and a lot of the second. Recommender systems today have two huge problems that are leading companies (sometimes at enormous pressure from the public) to rethink how they're being used: technical bias, and business bias. The real problem is YouTube's business model. YouTube is THIRSTY for advertising money, at all times.

Robots must be smarter if they're going to pack boxes in warehouses, scan inventory in stores, and even care for the elderly. The rise of machine learning in recent years is making that possible. Steady innovation has led to robots that can independently "learn" to navigate tight corridors and grasp delicate objects without crushing them. Some of the leading American and Japanese robotics companies and investors recently gathered in Menlo Park, Calif. to discuss artificial intelligence in robotics and its impact on business. But it may require some cooperation between the U.S. and an important overseas ally.

The global Artificial Intelligence in Agriculture Market is valued at USD 432.2 million in 2016, and is expected to grow with a CAGR of 22.5% by 2025. The forecast period considered for market analysis and compiling detailed market research report is 2017-2025. Global Artificial Intelligence in Agriculture Market Research Report by Report Ocean offers competitive landscape, data, trends, information, and exclusive vital statistics of the market. The global Artificial Intelligence in Agriculture Market report is an in-depth study and analysis of various market parameters. This market research report studies market provides detailed analysis of various regions such as North America, Europe, Asia-Pacific, Latin America and Rest of the World.

Robots and artificial intelligence has long been touted as a replacement for humans carrying out jobs around the world. However, robots are rarely thought of as pole dancers in nightclubs - but that is exactly what is happening in Nantes. Two robot dancers, wearing high heels and topped with a CCTV camera for a head, will debut at the SC-Club in the French city to celebrate its fifth anniversary next week. The bots were the brainchild of British artist Giles Walker, who has overlaid their metal bodies with parts from plastic mannequins. Referring to their CCTV camera heads, he said the robots aimed to "play with the notion of voyeurism", posing the question of "who has the power between the voyeur and the observed person".

It was quite unlike any other acceptance speech of the UEFA President's award. In a rather philosophical address before the Champion's League draw in Monaco, former soccer player and actor Eric Cantona claimed: "Soon the science will not only be able to slow down the aging of the cells, soon the science will fix the cells to the state, and so we become eternal." But what was he actually talking about and does it hold up? In the context, the statement seemed out of place, perhaps even slightly mad. There's pathos in seeing aged sportsmen too--once sublime athletes now reduced to a snail's pace and going gray.

A team of researchers at Yonsei University and École Polytechnique Fédérale de Lausanne (EPFL) has recently developed a new technique that can recognize emotions by analyzing people's faces in images along with contextual features. They presented and outlined their deep learning-based architecture, called CAER-Net, in a paper pre-published on arXiv. For several years, researchers worldwide have been trying to develop tools for automatically detecting human emotions by analyzing images, videos or audio clips. These tools could have numerous applications, for instance, improving robot-human interactions or helping doctors to identify signs of mental or neural disorders (e.g.,, based on atypical speech patterns, facial features, etc.). So far, the majority of techniques for recognizing emotions in images have been based on the analysis of people's facial expressions, essentially assuming that these expressions best convey humans' emotional responses. As a result, most datasets for training and evaluating emotion recognition tools (e.g., the AFEW and FER2013 datasets) only contain cropped images of human faces.