Hundreds of schoolchildren have been drafted in to make Amazon's Alexa devices in China as part of a controversial and often illegal attempt to meet production targets, documents seen by the Guardian reveal. Interviews with workers and leaked documents from Amazon's supplier Foxconn show that many of the children have been required to work nights and overtime to produce the smart-speaker devices, in breach of Chinese labour laws. According to the documents, the teenagers – drafted in from schools and technical colleges in and around the central southern city of Hengyang – are classified as "interns", and their teachers are paid by the factory to accompany them. Teachers are asked to encourage uncooperative pupils to accept overtime work on top of regular shifts. Some of the pupils making Amazon's Alexa-enabled Echo and Echo Dot devices along with Kindles have been required to work for more than two months to supplement staffing levels at the factory during peak production periods, researchers found.

From the research findings, it seems that most people still believe in the importance of the human touch in their interactions with brands. In our survey, 44% said that they do not think AI will impact them positively in any way and 52% said that it would make dealing with brands more impersonal. You can take a look at this recent blog by my colleague Dave Pattman, Innovation Director at Webhelp, for a deeper dive into the research results, but as I read the results it made me think about some of the complexities of using AI in the customer service environment. When designing any customer service solution the ultimate objective is to deliver a fantastic customer experience (CX), so AI should really just be one more tool or option – why has it recently been under so much scrutiny? It's clear to me that we have been barrelling along what the industry analyst firm Gartner calls'the hype cycle' for the past couple of years with AI.

"Hey Google, what are you?" If you want to witness the unfolding of an AI-fueled existential crisis, that's the question to ask your Google Home Mini. Google won't panic, it will just cheerfully tell you, "I'm your Google assistant." Then, if you're lucky, it may offer up some interesting facts for your enjoyment. But you'll still be no closer to answering your original question than you once were.

Voice-enabled interactions provide more human-like experiences in many popular IoT systems. Cloud-based speech analysis services extract useful information from voice input using speech recognition techniques. The voice signal is a rich resource that discloses several possible states of a speaker, such as emotional state, confidence and stress levels, physical condition, age, gender, and personal traits. Service providers can build a very accurate profile of a user's demographic category, personal preferences, and may compromise privacy. To address this problem, a privacy-preserving intermediate layer between users and cloud services is proposed to sanitize the voice input. It aims to maintain utility while preserving user privacy. It achieves this by collecting real time speech data and analyzes the signal to ensure privacy protection prior to sharing of this data with services providers. Precisely, the sensitive representations are extracted from the raw signal by using transformation functions and then wrapped it via voice conversion technology. Experimental evaluation based on emotion recognition to assess the efficacy of the proposed method shows that identification of sensitive emotional state of the speaker is reduced by ~96 %.

When it comes to pure, cutting the cord TVs, Amazon's Fire TV Edition paved new ground in 2018. It was low-priced and aimed at folks who were happy ditching cable, plugging in an antenna and using the set to watch Internet programming. Vizio's new V436-61, just out, goes even further. It does all of that, and more. Instead of just being able to use voice commands via the Amazon Alexa assistant, Vizio lets you use Apple's Siri and the Google Assistant as well.

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.