Twitter has made no secret of its interest in machine learning in recent years, and on Monday the company put its money where its mouth is once again by purchasing London startup Magic Pony Technology, which has focused on visual processing. "Magic Pony's technology -- based on research by the team to create algorithms that can understand the features of imagery -- will be used to enhance our strength in live [streaming] and video and opens up a whole lot of exciting creative possibilities for Twitter," Twitter cofounder and CEO Jack Dorsey wrote in a blog post announcing the news. The startup's team includes 11 Ph.Ds with expertise across computer vision, machine learning, high-performance computing and computational neuroscience, Dorsey said. They'll join Twitter's Cortex group, made up of engineers, data scientists and machine-learning researchers. The acquisition follows several related purchases by the social media giant, including Madbits in 2014 and Whetlab last year.

Gartner's latest Magic Quadrant for Advanced Analytics predicts that by 2018, more than half of all large organizations worldwide will use advanced analytics and respective algorithms to compete in their markets. Advanced, predictive analytics are about calculating trends and future possibilities, simulating options, predicting potential outcomes, and making recommendations. It reports to more sophisticated methods based on statistics, like descriptive and predictive data mining, simulations and optimizations that find trends and patterns in data, and finally machine learning.

Drowning in data is a real hazard with the Internet of Things (IoT). How should decisions be made with this flood of sensor data? A hybrid approach combining human intelligence and computing power works well. People are good at making decisions that require nuance and judgement, such as identifying hate speech in online postings. Computerized analytics is better at quickly processing large volumes of data.

A few years before he passed away, Roger Ebert stirred up a lot of game fan anger when he claimed that "video games can never be art". In response, livid gamers submitted numerous examples of games that can (rightly) be used to refute that argument. But when it comes to the vast majority of games that people are playing on their mobile devices, Ebert was right. Over the last decade, we've seen the rise of mega-hits like Farmville, Candy Crush Saga, Clash of Clans, Kim Kardashian: Hollywood, Hearthstone, and many more of the "freemium" games that litter the mobile marketplace. They're free to play as long as you're willing to grind your way through a minimal experience.

Learning needs to be less like memorization, and more like…Angry Birds. Half of school dropouts name boredom as the number one reason they left. The post is about why the future of education will be about flipping our current model on its head and about how key exponential technologies like AI, VR and gamification are going to drive a revolution in education. In the traditional education system, you start at an "A," and every time you get something wrong, your score gets lower and lower. You start with zero, and every time you come up with something right, your score gets higher and higher. It completely flips the way we currently learn, and it's addictively fun.

What Is the Business Imperative for Cognitive Computing? Cognitive Computing is becoming the foundation for a new generation of solutions that have the potential to transform business. Unlike traditional approaches to building solutions, a cognitive computing approach allows the data to help determine the way applications are designed. This contrasts with conventional software development that begins with defining logic based on the current way a business operates. In her session at 18th Cloud Expo, Judith S. Hurwitz, President and CEO of Hurwitz & Associates, Inc., put cognitive computing into perspective with its value to the business.

Last month, Google (NASDAQ:GOOG) (NASDAQ:GOOGL) announced, among other things, the Tensor Processing Unit (TPU). While a number of Seeking Alpha commenters have hailed this innovation (here and here), none of them have really touched on the technicals reasons behind this, other than general comments on'better machine learning'. This article is a brief summary of my thoughts on Google's move into custom machine learning hardware. Let's begin by clarifying what exactly the TPU is. It's an application specific integrated circuit (ASIC).

Computers could soon be helping to diagnose cancer in patients with the help of artificial intelligence that has been trained to spots the early signs of the disease. An AI machine capable of accurately diagnosing breast cancer 92 per cent of the time has been developed by researchers. While it is still not quite as good as human specialists – who are correct 96 per cent of the time – it suggests that AI could soon be used to speed up and improve cancer screening. Scientists have used machine learning to create an artificial intelligence system capable of diagnosing breast cancer from lymph node biopsies with 92 per cent accuracy (cancer cells in a lymph node pictured). When combined with a human pathologist this accuracy increased to 99.5 per cent The system was developed by computer scientists at Harvard Medical School gave a machine learning algorithm slides of lymph nodes from breast cancer patients.

In the future, will artificial intelligence be so sophisticated that it will be able to tell when someone is trying to deceive it? A Carnegie Mellon University professor and his team is working on technology that could move this idea from the realm of science fiction to reality. Their work -- rooted in game theory and machine learning -- is part of a larger push for more advanced AI. As AI becomes more commonplace in the technology we use every day, detractors and supporters are becoming more vocal about its potential risks and benefits. For some, smarter AI sets up a dangerous precedent for a future too reliant on machines to make decisions about everything from medical diagnoses to the operation of self-driving cars.

As in other industries, business leaders in the automotive and financial-services industries have an urgent need for trusted and actionable real-world insights that can help them know and serve their customers better while enabling rapid innovation. Too often, however, executives have had to operate with uncertain, incomplete, and inconsistent information. Now advances in artificial intelligence (AI) have made the construction of data-based real-world models and simulations a reality. A 2015 Tech Pro Research survey indicated that 24 percent of businesses across industries are currently using AI or had plans to do so within the year. While the health-care sector has been among the leading adopters of AI, financial-services and automotive companies are also increasingly turning to assisted, augmented, and autonomous intelligence.