The simplest approach gave a baseline accuracy of 32%. By the next morning, one team already had a 53% accurate model. Extrapolating the first four days to our 60-day contest, you might expect the winning accuracy to get close to 100%. But in fact, this is what happened: The winning entry -- submitted by Chenglong Chen -- was just 6% more accurate than the best model submitted a week into the contest. As the Kaggle competition went on, more and more teams entered and existing teams refined and resubmitted their entries: Given that over 1,000 smart data scientists worked on this task, it's fair to say that 71% accuracy on this task is very close to the best possible accuracy with today's technology.

When Microsoft unleashed Tay, an artificially intelligent chatbot with the personality of a flippant 19-year-old, the company hoped that people would interact with her on social platforms like Twitter, Kik, and GroupMe. The idea was that by chatting with her you'd help her learn, while having some fun and aiding her creators in their AI research. The good news: people did talk to Tay. She quickly racked up over 50,000 Twitter followers who could send her direct messages or tweet at her, and she's sent out over 96,000 tweets so far. The bad news: in the short time since she was released on Wednesday, some of Tay's new friends figured out how to get her to say some really awful, racist things.

In August 2015, Chinese ecommerce giant Alibaba announced that its cloud computing business, Aliyun, would offer a machine learning service to help enterprise customers streamline analytics software development. The service is based on Aliyun's Open Data Processing Service (ODPS) technology, which is capable of processing 100 petabytes of data in six hours. The DT PAI platform offers a drag and drop interface to simplify the process for developers. "What used to take days can be completed in minutes," said Xiao Wei, senior product expert with Alibaba's cloud business, as the service was announced.

OMG! Did you hear about the artificial intelligence program that Microsoft designed to chat like a teenage girl? It was totally yanked offline in less than a day, after it began spouting racist, sexist and otherwise offensive remarks. Microsoft said it was all the fault of some really mean people, who launched a "coordinated effort" to make the chatbot known as Tay "respond in inappropriate ways." To which one artificial intelligence expert responded: Duh! Well, he didn't really say that.

Reducing data in the data center has been a mentality in the past, but the Internet of Things (IoT) demands more, more, and more still. Withholding information from analytics systems is in essence selling IoT systems short; actively seeking it, on the other hand, invites challenges perhaps never-before-seen by even the most seasoned data scientists. In this interview with Chris O'Connor, General Manager of Watson Internet of Things Offerings at IBM, he discusses how the power of cognitive computing is being harnessed through the company's Watson platform – now exposed to developers through a set of application programming interfaces (APIs) – to turn the IoT data deluge into increasingly valuable insights. For those unfamiliar, can you briefly describe Watson, and then fill us in on what it's been up to since its Jeopardy! O'CONNOR: Watson is a true learning platform.

Today's security teams are tasked with protecting critical embedded, IT, and business systems from a growing number of cyber threats, some of which can mutate to expose vulnerabilities and evade traditional defense mechanisms. In this interview with Amir Husain, Founder and CEO of SparkCognition, he addresses the shortcomings of traditional security technologies against advanced attacks, such as Stuxnet, and reveals how artificial intelligence (AI) can augment the expertise of security professionals equipped with limited resources. With all the attack vectors in the Internet of Things (IoT), what is the biggest challenge security teams face? HUSAIN: The challenge is enormous and actually has two dimensions. First, attacks are becoming more sophisticated and the likelihood is increasing that an attack that has never been seen before will target physical infrastructure.

There are more mobile phones than humans on earth. That presents a unique opportunity for big data and, more importantly, the insights from the data to be applied to greater social good. At this week's PAPIs Connect--a predictive application programming interface (API) conference in Valencia, Spain--Nuria Oliver, the scientific director of Telefonica's R&D program, spoke about how to adapt this data via machine learning. Today, we touch on two of the situations she presented where big data and machine learning gave insight into how governments can better address crises, whether it's a natural disaster or a disease outbreak. In this piece we aren't talking about personalized data or even that which we're offering via our social media accounts.

"Our goal is to create applications that can see, hear and understand," says Jeff Dean, senior fellow at Google and one of leaders of the firm's machine learning strategy. Artificial intelligence is a clear priority for Google. Last week, its AlphaGo program defeated world Go champion Lee Sedol, while its DeepMind arm is considered to be at the forefront of deep learning research. And now Google is increasingly opening its technology up to other businesses as it attempts to grow its cloud operations. At its Cloud Next conference Google unveiled the Cloud ML service which helps machine learning engineers build "sophisticated, large" models based on its TensorFlow deep learning library, which was opened sourced last year.

In winter, snowfall can rapidly disrupt daily life and impact on Japan's economy. Snowplowing is a considerable annual expense, and methods for co-ordinating plowing activity are needed to ensure an efficient, cost-effective service. Clever computer models are needed to manage such complex activities, which involve many agents and interactions. Now, Satoshi Takahashi at the University of Electro-Communications, and Tokuro Matsuo at the Advanced Institute for Industrial Technology in Tokyo have devised a computational method that combines task allocation and scheduling of individual snow-plows to maximize efficiency. The researchers aimed to identify the best routes for multiple snow-plows to take without replicating route paths, meaning their computer model had to allocate and schedule tasks simultaneously.

Artificial intelligence is changing the world of work and education, with some groups saying AI could eliminate the need for standardized tests. AI trends raise questions, including what skills should be taught to students for future jobs and how best to prepare educators for the classroom.