We live in a world that's increasingly being shaped by complex algorithms and interactive artificial intelligence assistants who help us plot out our days and get from point A to point B. According to a new Princeton study, though, the engineers responsible for teaching these AI programs things about humans are also teaching them how to be racist, sexist assholes. The study, published in today's edition of Science magazine by Aylin Caliskan, Joanna J. Bryson, and Arvind Narayanan, focuses on machine learning, the process by which AI programs begin to think by making associations based on patterns observed in mass quantities of data. In a completely neutral vacuum, this would mean that AI would learn to provide responses based solely on objective, data-driven facts. But because the data sets fed to the AI are selected and influenced by humans, there's a degree to which certain biases become a part of the AI's diet. To demonstrate this, Caliskan and her team created a modified version of an Implicit Association Test, an exercise that tasks participants to quickly associate concrete ideas like people of color and women with abstract concepts like goodness and evil.

Accelerating trends in artificial intelligence (AI) and robotics point to significant economic disruption in the years ahead. Together, machine learning, natural-language recognition, biometrics, and decision management are converging toward what the World Economic Forum has described as the Fourth Industrial Revolution. To this point, technology has consistently generated more jobs than it destroys--but many now wonder if "this time is different." According to McKinsey & Company, half of all existing work activities could be automated by currently existing technologies, saving some $16 trillion in wages. Forecasts indicate that revenues from AI will expand from the current $8 billion to more than $47 billion by 2020.

ServiceChannel, the leading SaaS service automation platform for facilities managers and contractors, today introduced advanced machine learning capabilities to its flagship solution, enabling faster, more automated data-driven decision making capabilities for facilities managers. This Smart News Release features multimedia. "Machine learning is truly changing everything today, and the facilities management industry is no exception. With machine learning, our vision for transforming how facilities management professionals work is boundless," said Hugues Meyrath, chief product officer at ServiceChannel. "Decision Engine is revolutionizing decision making in facilities management by applying data and analytics to a previously arduous and manual process. This is the industry's first step toward adopting the incredible technologies commonplace in more traditional markets, enabling the automation of repetitive tasks and driving unprecedented efficiency."

At the International Astronautical Congress last September, in Guadalajara, Mexico, Elon Musk convinced many die-hard space engineers he could get a fleet of private rockets filled with thousands of people to Mars. Musk's speech was long on orbits, flight plans, and fuel costs. But it was short on how any of those colonists would survive. In fact, the Mars journey would likely be a dead end. Bathed in radiation and with nothing growing on it, the Red Planet is basically a graveyard.

In debates over the future of artificial intelligence, many experts think of the new systems as coldly logical and objectively rational. But in a new study, researchers have demonstrated how machines can be reflections of us, their creators, in potentially problematic ways. Common machine learning programs, when trained with ordinary human language available online, can acquire cultural biases embedded in the patterns of wording, the researchers found. These biases range from the morally neutral, like a preference for flowers over insects, to the objectionable views of race and gender. Identifying and addressing possible bias in machine learning will be critically important as we increasingly turn to computers for processing the natural language humans use to communicate, for instance in doing online text searches, image categorization and automated translations.

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Earlier this year, Bloomberg reached a milestone in open source development with the incorporation of the Learning-to-Rank plug-in into Apache Solr 6.4.0. The release of the plug-in was the culmination of a year's worth of close collaboration between two groups of Bloomberg software engineers in New York and London and the open source project's community to make it easier to re-rank search results using machine learning. In an exclusive Q&A with Computer Business Review's James Nunns, software engineers and project collaborators Diego Ceccarelli, Michael Nilsson and Christine Poerschke at Bloomberg (who also served as the Apache Lucene/Solr committer in this process) shared insights about their experience, challenges and learnings. Diego Ceccarelli: "Our project was intended to add Learning-to-Rank (LTR) functionality to open source enterprise search platform Apache Solr in order to improve both Federated Search and News Search on the Bloomberg Terminal. LTR is a technique for improving the relevance and performance of search that was proposed in academia more than 10 years ago. Today, several major commercial search engines use this technique but, although there is some software written to extend it on the web, we realized it didn't exist inside Solr, which we use to power search across a number of Terminal functions."

Let's admit that this looked like a bad election for video games at the outset, once it came down to Trump versus Clinton. As a senator in 2005, Clinton coauthored federal legislation to criminalize the sale of violent video games to minors. In a press conference that year she even claimed that the effects of violent games on kids' behavior was as bad as the result of lead poisoning on IQ, a claim that is…well…nuts. The worst video game news on the Trump front comes from one of Trump's tweets from 2012 following the awful Sandy Hook shooting in which a 20-year-old male killed numerous children and elementary school personnel as well as his mother and himself. Soon after, Trump tweeted "Video game violence & glorification must be stopped – it is creating monsters!" But in all fairness Trump was hardly the only person to blame video games.

It has been heavily criticised for its woeful response to remove jihadi videos and other shocking pages from the internet. But Google is finally claiming to have come up with a solution to crack down on vulgar content online – computer systems that can be'offended' like humans. Google hopes its systems will be able to see the difference between a jihadi with a gun and a scene from an action film – and take down the inappropriate material. Google is finally claiming to have come up with a solution to crack down on vulgar content online – computer systems that can be'offended' like humans. Google now wants the computers which monitor content being uploaded through YouTube and other channels to understand the nuances of what makes a video offensive.

As a computer science student many years ago, I learned a bit about artificial intelligence and its applications. At first, I was quite excited by the idea of computers thinking like humans. Then, I quickly realized: computers couldn't really "think"-- at least not the way that a human brain could. What if--I thought--we gave a computer large amounts of data and compute power. Could it do something that resembled thinking?