If you feel overwhelmed by an ever-growing social circle, fear not. Your brain can keep up with all those new faces, thanks to one region that continues to grow even in adulthood. The discovery is surprising, because most changes to the brain as it matures involve the altering of existing connections between neurons. But brain scans have revealed that one area of the cortex, the fusiform gyrus, gets much larger as we age. The fusiform gyrus is thought to play a role in recognising faces, something that adults are better at doing than children.

BMW Group, Intel, and computer-vision maker Mobileye today announced they will collaborate to create a fleet of about 40 autonomous BMW vehicles that will hit U.S. and European roads by the second half of 2017. The pilot project is aimed at demonstrating advances the three companies have made in technology that enables fully self-driving vehicles. The BMW 7 Series will be used as the initial platform for Intel processor and Mobileye computer vision technology during the global trials. Mobileye, an Israeli technology company, is a leader in developing both software and proprietary computer chip technology (EyeQ) to support vision-based advanced driver assistance systems (ADAS) that provide warnings for collision prevention and mitigation. Mobileye has also partnered with other automakers and multi-billion-dollar tier 1 auto parts maker Delphi to supply its technology to the industry.

Inc. surprised some with one of its next-gen commerce announcements. Amazon Go, which promises to eliminate the checkout altogether, generated headlines in mainstream media and prompted some to contemplate the potential demise of retail as we know it. Lost in all this hype is the fact that the brick-and-mortar apocalypse is no more likely today than it was before Amazon's recent endeavor. Of course, Amazon, which is the world's largest internet retailer, has set the standard for commerce reinvention with fast delivery, near-invisible payments and other perks tied to its Amazon Prime membership platform . Amazon has outpaced the rapid growth of digital commerce in the retail industry globally, increasing its own market share from 12% in 2011 to 19% in 2016, according to the latest data from Euromonitor International.

With the increased attention on fraud and cybersecurity, companies are turning to artificial intelligence (AI) and machine learning to strengthen fraud mitigation efforts to predict the trustworthiness or riskiness of an online transaction. In an effort to achieve the same, iovation, a provider of device-based solutions for authentication and fraud prevention, launched the iovationScore machine learning fraud prevention solution. Michael Thelander, Product Marketing Manager, iovation in an interview with Techseen discussed this new solution and the advent of machine learning in fraud detection. Thelander: iovationScore is less about "fraud detection" than it is a tool for "real-time predictive analysis." One of its uses is to provide immediate insight into how risky a device is, from the very first moment that device appears on your digital doorstep.

With Qubit's machine learning engine, you get actionable insights - helping you prioritize customer segments and initiatives for the biggest impact Customer data is one of the biggest and most complex data sets companies ever have to handle. This is because of the volume of customer interactions companies gather, across hundreds of different data points - behavioral, quantitative and qualitative - all streaming in at speed. This scale and complexity can also make it one of the most difficult data sets to put to work.

Deep Learning is a hot buzzword of today. The recent results and applications are incredibly promising, spanning areas such as speech recognition, language understanding and computer vision. Indeed, Deep Learning is now changing the very customer experience around many of Microsoft's products, including HoloLens, Skype, Cortana, Office 365, Bing and more. Deep Learning is also a core part of Microsoft's development platform offerings with an extensive toolset that includes: the Microsoft Cognitive Toolkit, the Cortana Intelligence Suite, Microsoft Cognitive Services APIs, Azure Machine Learning, the Bot Framework, and the Azure Bot Service. Our Deep Learning based language translation in Skype was recently named one of the 7 greatest software innovations of the year by Popular Science, and this technology has now helped machines achieve human-level parity in conversational speech recognition.

The vehicle is Toyota's Concept-I car, and it claims to represent a friendlier, people-focused approach to future mobility. While the car is only a concept and is not on sale, it gives a glimpse into the firm's vision for the future of automobiles. The vehicle is Toyota's Concept-i car, that represents a friendlier, people-focused appraoch to future mobility The Concept-I was unveiled at the CES technology show in Las Vegas, and was produced by the firm's CALTY design centre in California. The basic philosophy for the design is'kinetic warmth' - the belief that mobility technology should be warm, welcoming and fun. Bob Carter, Senior Vice President of Automotive Operations at Toyota, said: 'At Toyota we recognise that the important question isn't whether future vehicles will be equipped with automated or connected technologies, it is the experience of the people who engage with those vehicles. 'Thanks to Concept-i and the power of artificial intelligence, we think the future is a vehicle that can engage with people in return.'

One sector which appears safe for now is academia; at the end of 2016 a team of researchers gave up making a robot which could pass the entrance exam for Tokyo University. Noriko Arai, a professor at the National Institute of Informatics, told Kyodo news agency: "AI is not good at answering the type of questions that require an ability to grasp meanings across a broad spectrum". The spread of AI isn't limited to Japan; our NHS is trialing artificial intelligence as an alternative to the 111 helpline, and bosses have said AI is the next frontier for online retail. Professor Steven Hawking warned in October last year of the "disruption" AI could bring to our economy. He said that the technology promised to bring great benefits, such as eradicating disease and poverty, but "will also bring dangers, like powerful autonomous weapons or new ways for the few to oppress the many".

There are certain practices in Deep Learning that are highly recommended, in order to efficiently train Deep Neural Networks. In this post, I will be covering a few of these most commonly used practices, ranging from importance of quality training data, choice of hyperparameters to more general tips for faster prototyping of DNNs. Most of these practices, are validated by the research in academia and industry and are presented with mathematical and experimental proofs in research papers like Efficient BackProp(Yann LeCun et al.) and Practical Recommendations for Deep Architectures(Yoshua Bengio). A lot of ML practitioners are habitual of throwing raw training data in any Deep Neural Net(DNN). And why not, any DNN would(presumably) still give good results, right?

Deep Learning has been the core topic in the Machine Learning community the last couple of years and 2016 was not the exception. In this article, we will go through the advancements we think have contributed the most (or have the potential) to move the field forward and how organizations and the community are making sure that these powerful technologies are going to be used in a way that is beneficial for all. One of the main challenges researchers have historically struggled with has been unsupervised learning. We think 2016 has been a great year for this area, mainly because of the vast amount of work on Generative Models. Moreover, the ability to naturally communicate with machines has been also one of the dream goals and several approaches have been presented by giants like Google and Facebook.