There's been a lot of press recently about Artificial Intelligence, or AI. Put simply, AI can be defined as the "capability of a machine to imitate intelligent human behavior" – in this case, the fairly unique human trait of intelligence. But intelligence can mean a lot of things – anything from finding the shortest distance from A to B to detecting a cancerous cell in a collection of thousands of slide photos. To understand why AI is such a popular news topic these days, we first have to understand what it is. The first thing to understand about AI is there are two distinct types: weak AI, which we can think of as specific intelligence, and strong AI, which we can think of as general intelligence.

Before U.K. voters went to the polls to decide whether to remain in the European Union, at least three prominent hedge funds sought ways to predict the outcome. Brevan Howard Asset Management, run by billionaire Alan Howard, used artificial intelligence technology to gauge sentiment on social media and conducted a poll prior to the June 23 referendum, a person with knowledge of the firm said. Robert Citrone's Discovery Capital Management developed a model using algorithms to track voting districts, while Crispin Odey's Odey Asset Management also surveyed citizens. The technologies and polling helped generate profits or minimized losses for the firms. Public surveys on the eve of the referendum suggested the vote was tight between the two camps, even as financial markets and betting odds indicated "Remain" was on course to win.

None of this rising popularity, however, would be possible without recent technological improvements. This goes beyond the improved machine learning tools that are now available to many businesses the world over, although that is certainly a significant development. When it comes to improving technologies that make it possible for machine learning to prosper, the first place to look is the spread of scalable computing power. Machine learning requires an immense amount of computing power to function correctly, a level that simply wasn't possible years ago. Now, in part due to the rise of the cloud, that computing power is more within reach than ever before.

When we think about big data, we usually think about the web: the billions of users of social media, the sensors on millions of mobile phones, the thousands of contributions to Wikipedia, and so forth. Due to recent innovations, web-scale data can now also come from a camera pointed at a small, but extremely complex object: the brain. New progress in distributed computing is changing how neuroscientists work with the resulting data -- and may, in the process, change how we think about computation. The brain consists of many neurons -- a hundred thousand in a fly or larval zebrafish, millions in a mouse, billions in a human. Its function depends on the neurons' activity, and how they communicate with one another.

Infor, a leading provider of business applications, has announced the acquisition of Predictix, a provider of machine-learning solutions for retailers. Predictix will become part of Infor CloudSuite Retail, a new suite of enterprise applications delivered in the cloud and designed for today's retailing landscape. The acquisition comes six months after Infor announced an investment in Predictix. "The synergies between Infor and Predictix were greater than we could have hoped, and we've come to appreciate a great cultural alignment where both teams have passionate people who work hard and want to make a difference in retail and beyond," said Charles Phillips, CEO of Infor. "Buying out the other Predictix investors makes sense to bring the teams together and provide the scale and resources needed to accelerate the retail revolution."

Machine learning is ubiquitous and used to make critical business and life decisions every day. Each machine learning problem is unique, so it can be challenging to manage raw data, identify key features that impact your model, train multiple models, and perform model assessments. This session explores the fundamentals of machine learning using MATLAB . Rory reviews typical workflows for both supervised (classification and regression) and unsupervised learning, through examples. This presentation demonstrates examples of new functionality in Statistics and Machine Learning Toolbox and Neural Network Toolbox .

A new class of smart machines is emerging that can help you automate your business and make life easier for professionals by eliminating many of the routine, manual aspects of their jobs, freeing them to work on more innovative and strategic areas. Products and technologies such as intelligent agents/digital assistants, artificial intelligence (AI), virtual reality (VR) systems, intelligent software agents, expert systems and robotic office devices are likely to become more common in work environments in the years to come. A report released in February 2016 by industry research firm Research and Markets, "Artificial Intelligence Market: Global Forecast to 2020," forecasts that the AI market will grow from 419.7 million in 2014 to 5.05 billion by 2020, at a compound annual growth rate of 54 percent from 2015 to 2020. The key factors driving this growth include diversified application areas of AI, improved productivity, and increased levels of customer satisfaction, the report says. The rising demand for intelligent systems is expected to propel the growth of the market in the next five years.

Microsoft CEO Satya Nadella is concerned about the power artificial intelligence will wield over our lives. In a post on Slate yesterday he advised the computing industry to start thinking now about how to design intelligent software to respect our humanity. "The tech industry should not dictate the values and virtues of this future," he wrote. Nadella called for "algorithmic accountability so that humans can undo unintended harm." He said that smart software must be designed in ways that let us inspect its workings and prevent it from discriminating against certain people or using private data in unsavory ways.

The European car insurance market generates 130.8bn in premiums with 1.3bn underwriting profit. At the same time, the major players have some of the lowest Net Promoter Score (NPS) ratings of any industry, meaning the companies do not inspire satisfaction or loyalty in their customers. Overall high acquisition cost, low engagement, no brand loyalty and high cost of retention among young people and new trends like car sharing, self driven cars are putting huge pressures to the car insurance industry. The current young generation is extremely price sensitive but at the same time brand conscious. To penetrate this market a company has to either give very good price or sell value with a strong brand association.

Drawing from artificial intelligence (AI) advances that often don't trickle down to consumers out of the commercial sector, the company behind the Overdrive robotic cars is preparing to ship an animated little bot. Anki's Cozmo, with its machine learning and dynamic personality, is redefining the term "bringing toys to life." Anki describes Cozmo as being a real-life version of the type of robot companions seen in films. When watching the little soda-can-sized robot take in the world around it, Pixar's Wall-E comes to mind. Cozmo can get around the real world using a set of caterpillar tracks, the continuous tracking technology that's often employed in tank designs.