An automated army of pro-Donald J. Trump chatbots overwhelmed similar programs supporting Hillary Clinton five to one in the days leading up to the presidential election, according to a report published Thursday by researchers at Oxford University. The chatbots -- basic software programs with a bit of artificial intelligence and rudimentary communication skills -- would send messages on Twitter based on a topic, usually defined on the social network by a word preceded by a hashtag symbol, like #Clinton. Their purpose: to rant, confuse people on facts, or simply muddy discussions, said Philip N. Howard, a sociologist at the Oxford Internet Institute and one of the authors of the report. If you were looking for a real debate of the issues, you weren't going to find it with a chatbot. "And a lot of what they pass around is false news."

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Over the last decade, there has been many talks about big data and machine learning in HR. Oh, we are just looking at the tip of the iceberg. In earlier days, HR's primary duties were record maintenance and payroll. Over time other duties such as employee training, uniformity and well-being was added to their tasks. Later on, recruitment and skilled workforce selection was added to their duties.

Michael Kagan: The ever-growing demand for higher performance drives technology innovations for HPC, which then spreads to other markets. We have witnessed several technology transitions over the years, such as the transition from SMP to clusters, or from single core to multi-core. We are now going through another technology transition, which some call Co-Design. There are many technology efforts to re-architect the data center from a CPU-centric architecture to a data-centric architecture in order to overcome the new performance bottlenecks. The new data centers will need to allow data operations and analysis everywhere in order to get insights in real time.

"In 2015 Chartis's continuous market scan of end-user organizations identified Ayasdi as one of the most innovative AI players in the risk and compliance space," said Peyman Mestchian, Managing Partner at Chartis. "We have been particularly impressed by the practical use-cases and proof points provided by Ayasdi and how its large-scale machine intelligence-based applications and overall platform strategy help some of the world's largest financial services firms to reduce the cost and risk of compliance." Ayasdi is well positioned globally to help financial services firms leverage AI to address pressing challenging including CCAR/stress testing, risk management, internal surveillance, client intelligence, market intelligence, fraud detection and anti-money laundering. Ayasdi's enterprise machine intelligence platform ingests and processes large volumes of internal data, market data, and/or third party data and then applies multiple machine learning, statistical and geometric algorithms to gain insight and predict the future. For some applications, such as anti-money laundering, Ayasdi operates autonomously behind the scenes to help reduce false positives in existing rule-base AML systems.

Facebook has snapped up a facial recognition startup to help it win the war it waged against Snapchat. The social network has acquired FacioMetrics, a Carnegie Mellon University spinoff that developed a few face detection apps, including one that can recognize seven different emotions in human faces. Those applications are no longer available in any app store. A Facebook spokesperson told TechCrunch that the company plans to use the startup's technology to enhance its Snapchat-like AR filters for Facebook videos and Live broadcasts. It could lead to new AR masks, new special effects and even new ways to trigger their animations.

Oren Etzioni, a well-known AI researcher, complains about news coverage of potential long-term risks arising from future success in AI research (see "No, Experts Don't Think Superintelligent AI is a Threat to Humanity"). After pointing the finger squarely at Oxford philosopher Nick Bostrom and his recent book, Superintelligence, Etzioni complains that Bostrom's "main source of data on the advent of human-level intelligence" consists of surveys on the opinions of AI researchers. He then surveys the opinions of AI researchers, arguing that his results refute Bostrom's. It's important to understand that Etzioni is not even addressing the reason Superintelligence has had the impact he decries: its clear explanation of why superintelligent AI may have arbitrarily negative consequences and why it's important to begin addressing the issue well in advance. Bostrom does not base his case on predictions that superhuman AI systems are imminent.

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Apps and sites that can be used to hire people for individual tasks like picking up groceries or designing a new logo have taken off in recent years, promising a more efficient and fairer marketplace for employment. However, a new study out of Northeastern University in Boston suggests that racial and sexual discrimination may be common on two popular "gig economy" platforms. Researchers led by Christo Wilson, an assistant professor at Northeastern, and Ancsa Hannák, a PhD student, examined TaskRabbit, a platform for hiring people to run errands, and Fiverr, a marketplace for creative services. On both, they found evidence of bias along racial and gender lines. And it's troubling because the gig economy promised to be not only more efficient and flexible, but also less biased--since algorithms do the work of connecting people.

Researchers from the US have developed an artificial intelligence (AI) system that surfs the internet, extracts information from the available plain text and organizes it for quantitative analysis in very less time. Recently at the Association for Computational Linguistics' Conference on Empirical Methods on Natural Language Processing, researchers from the Massachusetts Institute of Technology (MIT) Computer Science and Artificial Intelligence Laboratory won a best-paper award for a new approach to information extraction that turns conventional machine learning on its head. Most machine-learning systems work by combing through training examples and looking for patterns that correspond to classifications provided by human annotators. In their new paper, the MIT researchers trained their system on scanty data -- because in the scenario they're investigating, that's usually all that's available. But then they find the limited information an easy problem to solve.