Artificial Intelligence research advances are transforming technology as we know it. The AI research community is solving some of the most technology problems related to software and hardware infrastructure, theory and algorithms. Interestingly, the field of AI AI research has drawn acolytes from non-tech field as well. Case in point -- prolific Hollywood actor Kristen Stewart's highly publicized paper on Artificial Intelligence, originally published at Cornell University library's open access site. Stewart co-authored the paper, titled "Bringing Impressionism to Life with Neural Style Transfer in Come Swim" with American poet and literary critic David Shapiro and Adobe Research Engineer Bhautik Joshi.

Just decades ago, artificial intelligence (AI) was limited to the realm of science fiction, awaiting the potent mix of huge pools of data and cheap, readily accessible supercomputing power to unleash its true potential. More than just technology buzzwords, AI and machine learning today are augmenting human abilities, enabling us to better harness our innate creativity to solve complex problems and discover new ways of doing things. In this e-guide, find out how financial institutions are using AI-powered chatbots to serve customers, what AI means for the future of work and how one Singapore-based startup is using declassified military methodologies to produce data-driven insights.

Data technologies have been around for some time now. But, increase in data generation and availability of servers on the cloud has enabled an entire generation of startups working on ideas which were unthinkable a few years back. At Analytics Vidhya, we love start ups and we love data! So, mixing the two provides us with the heady mix which we thrive on. That is where this article was born. Today, we will look at 10 exciting startups in the Analytics / Data Science / Machine Learning / Artificial Intelligence based in India, which are looking to disrupt the world in coming years.

The Google-owned computer algorithm AlphaGo is retiring from playing humans in the ancient Chinese game of Go after defeating the world's top player this week. AlphaGo defeated 19-year-old world number one Ke Jie of China on Saturday to sweep a three-game series that was closely watched as a measure of how far artificial intelligence (AI) has come. Ke Jie anointed the program as the new'Go god' after his defeat. AlphaGo last year became the first computer programme to beat an elite player in a full Go match, and its successes have been hailed as groundbreaking due to the game's complexity. Go has an incomputable number of moves, putting a premium on human-like'intuition' and strategy.

Just when you thought there was an app for practically everything, prepare to have your mind blown. Popular dating apps like Bumble and Tinder have dominated and morphed the modern dating world to what we know it to be today. But for individuals with a niche interest in all things Disney: Mouse Mingle hopes to help them find the one. Likewise, the website prides itself on being a "place to connect people who love Disney and who want that same magic in their relationship." No longer will obsessive Disney fans need to venture to one of the famed Disney theme parks to pick up a potential mouse-loving soulmate.

The first major event at Computex Taipei 2017 was the Powering The AI Revolution keynote by NVIDIA CEO Jensen Huang. Although the keynote was heavy on artificial intelligence technologies like NVIDIA Isaac and NVIDIA Volta, Jensen also announced other technologies like GeForce GTX with Max-Q Design. In this 90 minute long keynote, Jensen reveals the future of artificial intelligence and robotics. In that vision, the GPU is taking over from the CPU in delivering the petaflops of computing power required to deliver artificial intelligence. He reveals the new NVIDIA technologies that will power the next-generation AI applications – the new NVIDIA Tesla V100, which is the largest GPU ever made, and the NVIDIA HGX server that hosts eight of these GPUs to deliver almost 1 petaflops of compute performance in a single chassis!

Sometimes a shift in power happens so subtly that you don't even notice it is occurring. Take the increasingly important field of artificial intelligence. For all the talk of autonomous vehicles and smart home appliances in Silicon Valley, some of the most innovative work in artificial intelligence is being done far away, in China. Beijing is supporting A.I. research with vast sums of money and is helping to move those innovations into China's private sector, Paul Mozur and John Markoff write. And China is spending more just as the United States appears to be ready to pull back on such investing.

The continuing development of Semantic Web technologies and the increasing user adoption in the recent years have accelerated the progress incorporating explicit semantics with data on the Web. With the rapidly growing RDF (Resource Description Framework) data on the Semantic Web, processing large semantic graph data have become more challenging. Constructing a summary graph structure from the raw RDF can help obtain semantic type relations and reduce the computational complexity for graph processing purposes. In this paper, we addressed the problem of graph summarization in RDF graphs, and we proposed an approach for building summary graph structures automatically from RDF graph data. Moreover, we introduced a measure to help discover optimum class dissimilarity thresholds and an effective method to discover the type classes automatically. In future work, we plan to investigate further improvement options on the scalability of the proposed method.

We investigate the generalizability of deep learning based on the sensitivity to input perturbation. We hypothesize that the high sensitivity to the perturbation of data degrades the performance on it. To reduce the sensitivity to perturbation, we propose a simple and effective regularization method, referred to as spectral norm regularization, which penalizes the high spectral norm of weight matrices in neural networks. We provide supportive evidence for the abovementioned hypothesis by experimentally confirming that the models trained using spectral norm regularization exhibit better generalizability than other baseline methods.

In this paper, we propose gcForest, a decision tree ensemble approach with performance highly competitive to deep neural networks. In contrast to deep neural networks which require great effort in hyper-parameter tuning, gcForest is much easier to train. Actually, even when gcForest is applied to different data from different domains, excellent performance can be achieved by almost same settings of hyper-parameters. The training process of gcForest is efficient and scalable. In our experiments its training time running on a PC is comparable to that of deep neural networks running with GPU facilities, and the efficiency advantage may be more apparent because gcForest is naturally apt to parallel implementation. Furthermore, in contrast to deep neural networks which require large-scale training data, gcForest can work well even when there are only small-scale training data. Moreover, as a tree-based approach, gcForest should be easier for theoretical analysis than deep neural networks.