TensorFlow enables developers to quickly and easily get started with deep learning in the cloud. The framework has broad support in the industry and has become a popular choice for deep learning research and application development, particularly in areas such as computer vision, natural language understanding and speech translation. You can get started using TensorFlow on AWS by launching the AWS Deep Learning AMI which comes bundled with TensorFlow, as well as other popular deep learning frameworks such as Apache MXNet and Gluon, Caffe, Caffe2, Theano, Torch, Keras, and the Microsoft Cognitive Toolkit.

On today's episode of "The Interview" with The Next Platform, we focus on how geographic information systems (GIS) is, as a field, being revolutionized by deep learning. This stands to reason given the large volumes of satellite image data and robust deep learning frameworks that excel at image classification and analysis–a volume issue that has been compounded by more satellites with ever-higher resolution output. Unlike other areas of large-scale scientific data analysis that have traditionally relied on massive supercomputers, our audio interview (player below) reveals that a great deal of GIS analysis can be done on smaller systems. However, with the addition of deep learning, the field could be investing in more GPU systems for training and still others for inference at scale. Using lower end TitanX GPUs from Nvidia, the team, which includes Sudeep Sarkar and Mauricio Pamplona Segunda that created a CNN approach to GIS land classification described here, it was shown that deep learning can be a successful tool in the box of GIS analysts.

Up to a dozen assignment expressions per TC definition sounds reasonable. In practice it depends on the layer types. There are basically two scaling limiters: compilation and especially autotuning time grows very fast as a function of a number of expressions; the amount of exploitable parallelism may decrease with the increasing number of inter-dependent operations. One of the goals of TC is to make it easy to define new layers and look at practically achieved performance. It should be as easy as moving the line between two TC defs and changing input/output tensor lists.

Can India's seemingly insurmountable socioeconomic problems be tackled using artificial intelligence (AI)? The answer may lie somewhere in the future--but one Indian-American billionaire duo has decided to make a start. Last month, US-based philanthropist brothers Romesh Wadhwani (70) and Sunil Wadhwani (64) established India's first AI research institute in Mumbai, looking to deliver scaleable, tech-led solutions to the country's ills like inadequate healthcare and educational facilities by honing the AI ecosystem. Their institute, Wadhwani AI, was launched on Feb. 18 by prime minister Narendra Modi and is located in Mumbai University's Vidyanagari campus in Kalina. With over four decades of experience in the US, running various tech enterprises and philanthropic organisations, the Wadhwanis expect the institute to mirror the likes of San Francisco-based non-profit OpenAI and MIT's Allen Institute for AI.

Existing power grids were designed to transmit electricity over relatively short distances, however, increasingly grids are required to supply major cities from remote offshore wind farms at the same time as integrating local generation. With generators feeding variable amounts of energy from renewable sources into the grid at all voltage levels, it is more difficult to balance supply and demand, and the risks of overloads and fluctuations increase. By 2020 it is estimated that there will be over 50 billion smart devices connected to the internet, creating vast quantities of data which can be harnessed to develop smart systems for managing electricity systems, both at a local and national level to reduce the costs of balancing the electricity system. Relying on traditional linear mathematical models to manage these processes is not feasible, since both the manpower required to encode the models and the computing power to process them would be extremely large. A more real-time approach is required.

If you've ever told Siri to call your friend Bob and she answers with, "Calling cops," you've seen the instability of artificial intelligence (AI) in action. Those mistakes are the limitation of the AI technology known as deep learning. They arise from the design of the deep neural network, as well as the network's "training," which applies mathematical optimization methods to massive amounts of data rather than hand-crafting rules to accomplish a specific task. Emory College mathematician Lars Ruthotto has dedicated his research to modeling and solving such 21st century problems with the innovative use of differential equations that date back to the late 1600s. The National Science Foundation has rewarded his efforts with a CAREER Award, its most prestigious honor for junior faculty.

If there is one thing about artificial intelligence (AI) most people agree on it's the fact that AI and the way in which its many'forms' are leveraged, whether it's in a context of cobots, sentiment analysis applications, autonomous decision-making in smart buildings, intelligence at the edge of IoT (Internet of Things) or any other solution, should serve human, business and societal goals one way or the other. That is of course easier said than done, it is an area of AI research for a reason. There are fears, there are different views on what societies need, there are several technologies which can be used by all industries and people (regardless of activities), there is the question about future applications enabled by rapidly evolving'forms' of AI such as deep learning, there is the aspect of regulation as lawmakers start looking at AI, there are discussions about what type of goals are ethical and there are ample pioneers, researchers and thinkers looking at machine ethics and computational ethics or even ethics towards robots. One of them is Nell Watson, a speaker at the AI for business event. Nell is, among others, an adjunct within the Artificial Intelligence and Robotics track of the Singularity University where she mainly lectures on machine intelligence, the relationship between people and robots and the future of society.

MIT and SenseTime today announced that SenseTime, a leading artificial intelligence (AI) company, is joining MIT's efforts to define the next frontier of human and machine intelligence. SenseTime was founded by MIT alumnus Xiao'ou Tang PhD '96 and specializes in computer vision and deep learning technologies. The MIT-SenseTime Alliance on Artificial Intelligence aims to open up new avenues of discovery across MIT in areas such as computer vision, human-intelligence-inspired algorithms, medical imaging, and robotics; drive technological breakthroughs in AI that have the potential to confront some of the world's greatest challenges; and empower MIT faculty and students to pursue interdisciplinary projects at the vanguard of intelligence research. SenseTime is the first company to join a new Institute-wide initiative, the MIT Intelligence Quest, since its launch earlier this month. The MIT Intelligence Quest seeks to leverage the Institute's strengths in brain and cognitive science and computer science to advance research into human and machine intelligence in service to all humanity.

We've heard plenty about AI – how AI is the future of work, how AI adoption raises new questions about ethics, how AI can transform your business – and it's no question that there is a lot of hype. It's time to reduce the confusion around how AI truly moves the needle for businesses and offer guidance on how to get started. Businesses need to understand their own capabilities, look to relevant use cases that match their situation, and look at any available data. Let's dive in more closely on each of these three steps: Do you have an in-house AI team? Like teaching a child to walk, businesses need to first evaluate their capabilities; would you characterize your company at the "crawl," "walk," or "run" stage in your Deep Learning development?

At the peak of the Japanese harvest, Makoto Koike's mother spends around eight hours a day sorting cucumbers from the family farm into different categories – a dull, time-consuming task that her son decided to automate. Although Makoto wasn't a machine learning expert, he started playing around with TensorFlow, Google's popular open-source machine learning framework, and developed a deep learning model that could sort cucumbers by size, shape and other attributes. The system isn't perfect (it has an accuracy rate of around 75%). But it's a sign of how AI could soon transform even the smallest family-run business. Giants like Google, Amazon, Microsoft, Apple and Facebook are, of course, well-aware of this transformative power.