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.

From computer vision to game playing, artificial intelligence (AI) has made a lot of progress in the past few years. Companies such as Google and Facebook have already placed huge bets on this technology, and over the next decade, AI features will steadily creep into one product after another. In this O'Reilly report, you'll examine the state of AI today and where we might be headed in coming years. To explain today's AI capabilities, authors Ben Lorica and Mike Loukides look at prominent examples such as Google's AlphaGo, self-driving cars, and face recognition--AIs that consist of narrow solutions to specific problems. Can researchers develop general intelligence flexible enough for an AI to learn without supervision, or choose what it wants to learn?

Natural Language Processing seems to have become somewhat of a commodity in recent years. More than a few companies have sprung up that offer basic NLP capabilities through a cloud API. If you'd like to know whether a text carries a positive or negative message, or what people or companies it mentions, you can just send it to one of these black boxes, and receive the answer in less than a second. Superficially, all these NLP APIs look more or less the same. Textrazor, AlchemyAPI, Aylien, MeaningCloud and Lexalytics all offer similar services (named entity recognition, sentiment analysis, keyword extraction, topic identification, etc.), and do so through similar interfaces.

In a TEDx talk entitled "How Computers are Learning to Be Creative", Blaise Agüera y Arcas, Google principal scientist, demonstrated how neural networks recognizing images can run them in reverse-- thus generating them. Of this, he noted that perception and creativity are highly linked together. With Google's neural network models, machine perception and machine creativity are no longer that far-fetched. Arcas identified perception as the process by which simple objects are transformed by the mind into overwhelmingly different concepts. With today's technology, even computers are capable of perception. Creativity, on the other hand, is actually-- as far as Arcas is concerned in his speech-- the "flip side" of the former.