IBM's Watson artificial intelligence technology has found plenty of white-collar work in places like hospitals and banks, but soon it will be off to get its hands dirty on the factory floor. Working with ABB, a maker of industrial plant, IBM has developed a new AI assistant to help factory workers spot manufacturing defects on the production line. Connected to an existing industrial monitoring system, ABB Ability, it will help manufacturers improve speed, yield, and uptime, according to ABB. The Cognitive Visual Inspection system, as IBM calls it, pipes images from a UHD (ultra-high-definition) camera to an instance of IBM's Watson software that has been trained to detect and classify production faults in real time. Watson can inspect parts up to five times faster than production line workers and even detect faults not visible to the human eye, according to IBM.

What happens when criminals figure out how to use robots to commit crimes? Christopher Markou, a Ph.D. candidate and Faculty of Law at the University of Cambridge, takes a look at the disturbing possibility in We could soon face a robot crimewave ... the law needs to be ready, a commentary he wrote for The Conversation. "How do we make sense of all this?" asks Markou. Should we shrug our shoulders as a society and get back to Netflix? Should we start making plans for how we deal with all of this?

Welcome to the Schindler Conference Call on key figures for the First Quarter 2017. I'm here together with Erich Ammann, our CFO we will go into all financial details later during the call. As an introduction to the remark it is fair to say that we continued our successful plan of the last year as we kept our direction towards top-line growth and also higher profitability. Let's have a closer look on our highlights of the first quarter 2017 on slide two. We made further operational and strategic progress. First, we were able to confirm our growth path. Orders received increased by 5.9% in local currencies and also operating revenue rose by 3.8% in local currencies. Operating revenue was therefore within our guidance of 3% to 5% growth in 2017. Our investments into our geographic diversification mainly into our strategic markets were paying off. Second, we also continued to improve our profitability. The EBIT margin increased to 11.5% and even 11.7% before restructuring costs. Net profit stayed flat at CHF179 million due to some temporary booking losses on the ALSO exchangeable bond. Third, we made further progress in our strategic initiatives. We are on track with our globally harmonized modular product platforms, but it is still a long way to go to finalize this, and we were also able to successfully launch our new Internet of Elevator and Escalator Solutions, Schindler Ahead. Yesterday, we launched officially our new Schindler Ahead initiative and I would like to stay a little bit with that topic. As you can see on slide number 3, we will create significant customer benefits in the future. We increased the uptime of our equipment with predictive maintenance, we offer comprehensive insights about all type of information of the equipment for a better building, maintenance and management, and we generate convenience with superior customer service by interactive and personalized passenger experience. On slide four, you find the solution concept of our enhanced service offerings. There are four elements to be mentioned. First the Cube, The Cube enables machine intelligence, all relevant machine data are collected, filtered and transmitted to the cloud platform. The Cube is an intelligent device, not only a transmitter or a gateway, as we can run apps and stream multimedia content and handle emergency calls. The second topic is the cloud platform. The cloud platform creates real time insights.

The creator of an app which changes your selfies using artificial intelligence has apologised because its "hot" filter automatically lightened people's skin. FaceApp is touted as an app which uses "neural networks" to change facial characteristics, adding smiles or making users look older or younger. But users noticed one of the options, initially labelled as "hot" made people look whiter. So I downloaded this app and decided to pick the "hot" filter not knowing that it would make me white. Yaroslav Goncharov, the creator and CEO of FaceApp, apologised for the feature, which he said was a side-effect of the "neural network".

IBM is set to pilot its Watson technology for tracking down rogue traders at financial firms. Watson gained fame by defeating a pair of'Jeopardy!' champions, including 74-time winner Jennings, in 2011. The new Watson Financial Services product will analyze emails, chats and trading data for suspicious activity. In 2012, when Watson appeared on Jeopardy, it had general knowledge - but IBM has since worked to develop Watson's specialized knowledge in other fields such as medical treatment and even cooking Bridget van Kralingen, the senior vice president of IBM Industry Platforms, said the tool is being piloted with fewer than 10 financial industry clients. But IBM is also developing other functions for the technology not just limited to finding rogue traders - one function will analyze regulatory text to identify legal obligations and help determine whether a company's compliance programs abide by set rules.

Using Computer Forensics to Investigate Employee Data Theft Dollars & Data: Law Firms Increasing Marketing, Business Development Tech Spend Clients Not Ready to'Talk to a Robot,' but Jackson Lewis Bets on Automating Compliance Tasks Machines Are Organizing Legal's Data, But Not Fast Enough Machines Are Organizing Legal's Data, But Not Fast Enough

Last year, Microsoft released an artificially intelligent Twitter chatbot named "Tay" aimed at engaging Millennials online. The idea was that Tay would spend some time interacting with users, absorb relevant topics and opinions, and then produce its own content. In less than 24 hours, Tay went from tweeting "humans are super cool" to racist, neo-Nazi one-liners, such as: "I f-- hate n--, I wish we could put them all in a concentration camp with kikes and be done with the lot." Needless to say, Microsoft shut down Tay and issued an apology. We need to hold the companies who make our AI-enabled devices accountable to a standard of ethics.

OpsVeda today announced that it will participate at SAPPHIRE NOW and ASUG Annual Conference being held May 16–18 in Orlando, Florida. The company will be showcasing its platform that leverages machine learning techniques for proactive detection and remediation of potential operational disruptions. OpsVeda presents the full stack real-time operational intelligence platform, to power automated operational decision making in the enterprise. It helps the operations team to re-capture an estimated 10-20% of the revenue and margin leakage due to out of stocks, chargebacks, changes in customer buying behavior, expedites, missed deliveries and inventory obsolescence. Many of these issues go undetected until it is too late for any corrective action.

Steve Cousins is founder and CEO of Savioke, which develops and deploys autonomous robots that work in human environments to improve people's lives. Steve was previously president and CEO of robotics incubator Willow Garage. When Bill Gates recently suggested robots should pay income tax like any other employee, I didn't immediately disagree. I applaud Gates' bold thinking to help solve one of society's biggest upcoming challenges: embracing automation in a way that "lifts all boats" instead of leaving large swaths of society behind. A robot tax would help offset the reduced revenues flowing into public coffers as machines take some jobs previously held by humans.

The unsuccessful experiment of Microsoft with its AI algorithm Tay (Tay), which within 24 hours after the beginning of interaction with people from Twitter turned into an inveterate racist, showed that the AI systems that are being created today can become victims of human prejudices and, in particular, stereotyped Thinking. Why this happens – tried to find out a small group of researchers from Princeton University. In addition, they developed an algorithm capable of predicting the manifestation of social stereotypes based on an intensive analysis of how people communicate with each other on the Internet. Many AI systems undergo training in understanding the human language using massive collections of text data. They are also called corps.