Sometimes recognition software is excellent at correctly categorizing certain types of images but totally fails with others. Some image recognition engines prefer cats over dogs, and some are far more descriptive with their color knowledge. But which is the best overall? Perficient Digital's image recognition accuracy study looked at image recognition -- one of the hottest areas of machine learning. It looked at Amazon AWS Rekognition, Google Vision, IBM Watson, and Microsoft Azure Computer Vision to compare images.

The list of companies that enlist human workers to listen in on your personal conversations just won't quit growing. According to an investigation by Motherboard, Microsoft-owned Skype has been listening to audio of users speaking to one another via its translation service, which uses AI to convert language in nearly real-time. While Skype states that it may analyze audio of phone calls conducted through the service to improve its abilities, it has never explicitly stated that humans would be doing that work. Skype conversations are being recorded and analyzed by Microsoft contractors. The company says it is meant to improve its translation service.

Users increasingly rely on social media feeds for consuming daily information. The items in a feed, such as news, questions, songs, etc., usually result from the complex interplay of a user's social contacts, her interests and her actions on the platform. The relationship of the user's own behavior and the received feed is often puzzling, and many users would like to have a clear explanation on why certain items were shown to them. Transparency and explainability are key concerns in the modern world of cognitive overload, filter bubbles, user tracking, and privacy risks. This paper presents FAIRY, a framework that systematically discovers, ranks, and explains relationships between users' actions and items in their social media feeds. We model the user's local neighborhood on the platform as an interaction graph, a form of heterogeneous information network constructed solely from information that is easily accessible to the concerned user. We posit that paths in this interaction graph connecting the user and her feed items can act as pertinent explanations for the user. These paths are scored with a learning-to-rank model that captures relevance and surprisal. User studies on two social platforms demonstrate the practical viability and user benefits of the FAIRY method.

Men who send other people unsolicited images of their genitals are likely to be more narcissistic and sexist that those who do not, psychologists have found. Researchers surveyed over a thousand men to compare the personalities and motivations of those who sent intimate images and those who did not. Rather than for personal gratification, men who share images of their genitals typically do so hoping to arouse the recipient and get images back in return. A small minority of participants reported sending the private photos in order to intentionally elicit a negative response from women. The researchers conclude that the practice can neither be construed as solely sexist or as a positive sexual outlet.

As confusion reigned among Shanghai citizens over the correct classification of waste since the city's trash sorting regime kicked in on July 1, China's tech giants have been quick to jump in with solutions. Shanghai was the first Chinese city to enforce new regulations for waste management, as the superpower attempts to curb its trash production that has already overtaken the United States'. Chinese e-commerce giant JD.com announced last week that it had launched artificial intelligence (AI) solutions to make trash classifications smarter. The Snapshop trash sorting function can be found in the JD Finance app. Users may search for "垃圾分类(trash sorting)" to open it.

Wikifriend offers a platform which uses artificial intelligence-driven conversational technology to facilitate access to digital content that would otherwise require articulated steps for enjoyment. The platform allows businesses to offer a continuous support service guaranteeing a personalized conversational level improving the customer experience. Wikifriend is a human-to-machine conversational interface that facilitates the use of complex computer applications for any person, simply by using voice commands. From brand reputation management in digital media and fake news recognition to Health Care, Financial Services, Home Automation, and Computer Vision, Wikifriend brings disruptive innovation to the industry. Merge German semantic engine and high-security level sounds good to customers?

There could be a time in the not too distant future whereby AI has replaced many of our jobs, but rather than mean we are all out of work, such a move would allow us to focus on doing more meaningful jobs that don't need to have an impact on the economy for the world to still operate. "You always see jobs opening up in previous industrial revolutions, but I think at some point AI is going to be capable of doing pretty much anything," explains Dr Alex Allan, the CTO and founder of AI London startup Kortical to Pocket-lint in an interview for the Pocket-lint podcast. "The potential advantage of that is that the cost of living could go to virtually nothing for everyone. Imagine if you have an AI doctor in your phone that could be just as good as a GP? It means anyone in the world who has got a smartphone, which is more than 50 per cent of people, would have access to healthcare. "There will be challenges of how we transition from a society where everyone has to have a job, to one where potentially you might not need that many jobs that directly influence the economy.

The technology we use at home is slowly converging with the enterprise environment. The innovation taking place within the virtual assistant (VA) market -- and its potential to create an entirely new workplace reality -- is particularly exciting to see. Artificial intelligence-powered, voice-controlled assistants like Alexa, Google Assistant, Siri, and Cortana are already commonplace in many homes. This popularity is building pressure to integrate VA functionality into enterprise technology, as well, which could significantly reconfigure and enhance the employee experience. By 2021, Gartner, Inc. predicts that "25 percent of digital workers will use a virtual employee assistant (VEA) on a daily basis. This will be up from less than 2 percent in 2019."*

Not all voice assistants can handle the same requests. We put Siri, Alexa and Google to the test. The number of voice assistants used worldwide could rise from 2.5 billion in 2018 to 8 billion in 2023, according to Juniper Research. A recent survey by Clutch found that 48% of respondents in the U.S. owned a digital assistant, and 33% planned to buy one within the next three years. The firm also found that 69% of voice assistant owners used their devices every day.

Decades of research in artificial intelligence (AI) have produced formidable technologies that are providing immense benefit to industry, government, and society. AI systems can now translate across multiple languages, identify objects in images and video, streamline manufacturing processes, and control cars. The deployment of AI systems has not only created a trillion-dollar industry that is projected to quadruple in three years, but has also exposed the need to make AI systems fair, explainable, trustworthy, and secure. Future AI systems will rightfully be expected to reason effectively about the world in which they (and people) operate, handling complex tasks and responsibilities effectively and ethically, engaging in meaningful communication, and improving their awareness through experience. Achieving the full potential of AI technologies poses research challenges that require a radical transformation of the AI research enterprise, facilitated by significant and sustained investment. These are the major recommendations of a recent community effort coordinated by the Computing Community Consortium and the Association for the Advancement of Artificial Intelligence to formulate a Roadmap for AI research and development over the next two decades.