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.

Many industry experts consider Google to have made the most progress on the road towards fully autonomous driving, but at this year's CES, Microsoft is revealing its own plans for putting drivers in the passenger seat. The company, best-known for the Windows PC operating system, is partnering with five firms including insurance company Swiss Re on what it's billing as a "collective vision of safe and secure end-to-end mobility." And it's one where artificial intelligence takes a front seat. Specifically, AI bots will make real-time connections between traffic situations and pedestrian density via the current suite of sensing tools used in vehicles, from car-to-car (V2V) and car-to-infrastructure (V2I) communication, to radar, camera and LIDAR systems to optimise driver safety and engagement. What's more, the same tech will use information gathered from a driver's personal preferences and daily routine.

More than six billion dollars: That's how much health care providers and consumers will be spending every year on artificial intelligence tools by 2021--a tenfold increase from today--according to a new report from research firm Frost & Sullivan. AI will be everywhere--from diagnosing cancer to providing weight-loss coaching, says Venkat Rajan, who has the great title of global director for the company's Visionary Healthcare Program. "Prior to 2015, most of what was happening was sort of academic: pilot programs, exploratory, proof of concept-type stuff," he says. AI's ability to sort through scads of information, and remember everything it has ever seen, could enable a digital (and congenial) version of Dr. House, the brilliant diagnostician from the eponymous TV show, says Rajan. "At first, it's a complete mystery, it could be one of ten different things," he says, about the process in the show, and real life, called differential diagnosis. "And then he's able to sort through various issues, you know, illuminate certain factors on why it's not one of these other conditions, and he's able to pull something from memory that figures out ultimately what it is, and they can provide the appropriate treatment." Robots won't steal doctors' jobs, says Rajan, but they will spare overworked docs some of the dangerous fatigue that can lead to mistakes.

We like John's predictions for the new year although interested to hear what you think is hot and what's not in Big Data for 2017!? WIRE)--The market has evolved from technologists looking to learn and understand new big data technologies to customers who want to learn about new projects, new companies and most importantly, how organizations are actually benefitting from the technology. According to John Schroeder, executive chairman and founder of MapR Technologies, Inc., the acceleration in big data deployments has shifted the focus to the value of the data. In the 1960s, Ray Solomonoff laid the foundations of a mathematical theory of AI, introducing universal Bayesian methods for inductive inference and prediction. In 1980 the First National Conference of the American Association for Artificial Intelligence (AAAI) was held at Stanford and marked the application of theories in software. AI is now back in mainstream discussions and the umbrella buzzword for machine intelligence, machine learning, neural networks, and cognitive computing.

Yesterday's big news in the world of chatbots was Google acquiring Api.ai, a company which allows developers to integrate natural language processing and understanding into their applications. Think of it like talking to someone, but instead, that someone is now your phone, and it actually understands what you're saying. Services like Api.ai and Wit.ai are capable of processing human speech patterns and filtering useful data like intent and context from it. Now that Api.ai is owned by Google, the battle between Api.ai and Wit.ai will probably intensify, as Wit.ai has been Facebook's property since January 5th of last year. In this article, I will describe the outcome of an independent test I ran last week, without any knowledge of the acquirement of Api.ai by Google.

Lynx, a small white humanoid, gave yoga instructions as it slid its chunky leg back for the pose. A bright blue light flashed across the side of its round head to indicate activity. After a few more leg movements, it came back into standing position when Alexa's voice boomed: "Your next exercise is waist stretching." Ubtech Robotics, the Chinese company that launched the Alpha robot series and JIMU coding bots for kids, has partnered with Amazon to bring Alexa's voice-recognition capabilities to their latest robot called Lynx. Starting in Spring this year, you will be able to interact with the robot as if it were your personal assistant.