The impact of technology like artificial intelligence (AI) in the country can be measured from the influence of digital technologies on the economic elements and GDP which is 8 percent. The percentage is expected to increase to 60 percent in the next two years. Providing the relevant data, Anant Maheshwari, President, Microsoft India said that India has the highest involvement of AI in the workplace. Tech, Media and Entertainment companies hoped for a zoomed focus on disruptive technologies in their pre-budget expectations. During the Budget 2019 presentation speech which was delivered on February 1, 2019, Acting Finance Minister Piyush Goyal said "In order to take the benefits of artificial intelligence and related technologies to the people, a national programme on AI has been envisaged".

The traditional Chinese calendar, which incorporates 12 zodiac animals, leverages moon cycles to mark the new year. In 2019, this date falls on February 5th and begins the Year of the Pig. To celebrate, Tuesday's Google Doodle incorporates an AI Experiment that novelly uses your device's front-facing camera to teach shadow puppetry. Chinese shadow puppetry is an ancient art form for storytelling that uses silhouette figures and music to pass down cultural history. Cut-out paper or hand figures are placed in front of a light source and projected on a translucent screen.

Dr. Graham Allison -- a specialist in national security at Harvard, where he has taught for five decades -- tells me: "The story beneath the story is the Great Rivalry between a meteorically rising China and a ruling U.S." The lead story of last Sunday's New York Times ("In 5G Race With China, U.S. Pushes Allies to Fight Huawei") trumpets the contest over 5G cellular networks, which exponentially accelerate online speed and ubiquity. Be smart, from Axios chief tech correspondent Ina Fried: Often forgotten is how much China and the U.S. still need one another.

Medical Imaging 2019 is an addition to the successful series of Medical Imaging and Clinical Research conferences; it is with immense pleasure and pride that we announce our upcoming "5th World Congress on Medical Imaging and Clinical Research" during June 17-18th, 2019 at Rome, Italy. Medical imaging is a technical process which creates Visual representation of interior body for clinical analysis and medical intervention, as well as visual representation of the function of some organs. Medical imaging seeks to reveal internal structures hidden by the skin and bones. Medical imaging is often perceived to designate the set of techniques that noninvasively produce images of the internal aspect of the body. Medical imaging also diagnoses and treats disease.

The industry ministry has begun a project to support the development of artificial intelligence tools that help diabetics improve their diet and daily habits, to prevent their condition from worsening, sources have said. The ministry hopes to collect data from some 2,000 diabetics whose condition is very bad by year-end through a clinical trial in cooperation with the Japan Diabetes Society. The data, which will include weight and the numbers of steps taken, will serve as high-quality training data for AI development. Training data is the initial set of information used by AI computer systems to learn how to draw accurate conclusions on a topic. A project linking clinical research with data collected from so many diabetics is unprecedented anywhere in the world, according to sources, which include a ministry executive.

Aakrit Vaish is the CEO and Co-Founder of Haptik, a Conversational AI company that helps enterprises engage their users through chatbots. Haptik has recently been listed in the AI Time Journal TOP 25 Artificial Intelligence Companies 2018. Haptik was started as a consumer app that quickly became one of the most popular personal assistant apps in India. The company now serves many large enterprises and is working to become a Voice-first conversational AI company and expand globally. Q: How was Haptik started?

With the rapid development of digital information, the data volume generated by humans and machines is growing exponentially. Along with this trend, machine learning algorithms have been formed and evolved continuously to discover new information and knowledge from different data sources. Learning algorithms using hyperboxes as fundamental representational and building blocks are a branch of machine learning methods. These algorithms have enormous potential for high scalability and online adaptation of predictors built using hyperbox data representations to the dynamically changing environments and streaming data. This paper aims to give a comprehensive survey of literature on hyperbox-based machine learning models. In general, according to the architecture and characteristic features of the resulting models, the existing hyperbox-based learning algorithms may be grouped into three major categories: fuzzy min-max neural networks, hyperbox-based hybrid models, and other algorithms based on hyperbox representation. Within each of these groups, this paper shows a brief description of the structure of models, associated learning algorithms, and an analysis of their advantages and drawbacks. Main applications of these hyperbox-based models to the real-world problems are also described in this paper. Finally, we discuss some open problems and identify potential future research directions in this field.

We introduce Act2Vec, a general framework for learning context-based action representation for Reinforcement Learning. Representing actions in a vector space help reinforcement learning algorithms achieve better performance by grouping similar actions and utilizing relations between different actions. We show how prior knowledge of an environment can be extracted from demonstrations and injected into action vector representations that encode natural compatible behavior. We then use these for augmenting state representations as well as improving function approximation of Q-values. We visualize and test action embeddings in three domains including a drawing task, a high dimensional navigation task, and the large action space domain of StarCraft II.

Previous VoIP steganalysis methods face great challenges in detecting speech signals at low embedding rates, and they are also generally difficult to perform real-time detection, making them hard to truly maintain cyberspace security. To solve these two challenges, in this paper, combined with the sliding window detection algorithm and Convolution Neural Network we propose a real-time VoIP steganalysis method which based on multi-channel convolution sliding windows. In order to analyze the correlations between frames and different neighborhood frames in a VoIP signal, we define multi channel sliding detection windows. Within each sliding window, we design two feature extraction channels which contain multiple convolution layers with multiple convolution kernels each layer to extract correlation features of the input signal. Then based on these extracted features, we use a forward fully connected network for feature fusion. Finally, by analyzing the statistical distribution of these features, the discriminator will determine whether the input speech signal contains covert information or not.We designed several experiments to test the proposed model's detection ability under various conditions, including different embedding rates, different speech length, etc. Experimental results showed that the proposed model outperforms all the previous methods, especially in the case of low embedding rate, which showed state-of-the-art performance. In addition, we also tested the detection efficiency of the proposed model, and the results showed that it can achieve almost real-time detection of VoIP speech signals.

While marketing budget allocation has been studied for decades in traditional business, nowadays online business brings much more challenges due to the dynamic environment and complex decision-making process. In this paper, we present a novel unified framework for marketing budget allocation. By leveraging abundant data, the proposed data-driven approach can help us to overcome the challenges and make more informed decisions. In our approach, a semi-black-box model is built to forecast the dynamic market response and an efficient optimization method is proposed to solve the complex allocation task. First, the response in each market-segment is forecasted by exploring historical data through a semi-black-box model, where the capability of logit demand curve is enhanced by neural networks. The response model reveals relationship between sales and marketing cost. Based on the learned model, budget allocation is then formulated as an optimization problem, and we design efficient algorithms to solve it in both continuous and discrete settings. Several kinds of business constraints are supported in one unified optimization paradigm, including cost upper bound, profit lower bound, or ROI lower bound. The proposed framework is easy to implement and readily to handle large-scale problems. It has been successfully applied to many scenarios in Alibaba Group. The results of both offline experiments and online A/B testing demonstrate its effectiveness.