New technologies such as Artificial Intelligence (AI), Cloud Machine Learning, Satellite Imagery and advanced analytics are empowering small-holder farmers in India to increase their income through higher crop yield and greater price control, Microsoft India said.

Companies in the Asia-Pacific region are ahead in the adoption of disruptive technologies such as Internet of Things, artificial intelligence, says a recent global survey of chief information officers (CIOs)by by Gartner, Inc. The survey covers CIOs worldwide, including 537 across 17 countries in Asia/Pacific (113 of those in Australia and New Zealand) and represents approximately $3.4 trillion in revenue/public sector budgets and $49 billion in IT spending. According to the survey, about 43 per cent of surveyed CIOs in Asia-Pacific region have said that either they have deployed or have plans for deployment of IoT technologies, compared to 37 per cent globally. Some 37 per cent have deployed AI compared to 25 per cent globally. In the region, 28 per cent CIOs have made investments in conversational interfaces, 20 per cent in virtual reality (VR) and augmented reality, while 13 per cent have adopted blockchain or distributed ledger technology.

IMAGE: New machine learning method developed by researchers at the University of Helsinki, Aalto University and Waseda University of Tokyo can use for example data on cell phones while guaranteeing data... view more Modern AI is based on machine learning which creates models by learning from data. Data used in many applications such as health and human behaviour is private and needs protection. New privacy-aware machine learning methods have been developed recently based on the concept of differential privacy. They guarantee that the published model or result can reveal only limited information on each data subject. "Previously you needed one party with unrestricted access to all the data. Our new method enables learning accurate models for example using data on user devices without the need to reveal private information to any outsider", Assistant Professor Antti Honkela of the University of Helsinki says.

We study the computational complexity of candidate control in elections with few voters, that is, we consider the parameterized complexity of candidate control in elections with respect to the number of voters as a parameter. We consider both the standard scenario of adding and deleting candidates, where one asks whether a given candidate can become a winner (or, in the destructive case, can be precluded from winning) by adding or deleting few candidates, as well as a combinatorial scenario where adding/deleting a candidate automatically means adding or deleting a whole group of candidates. Considering several fundamental voting rules, our results show that the parameterized complexity of candidate control, with the number of voters as the parameter, is much more varied than in the setting with many voters.

Customer retention campaigns increasingly rely on predictive models to detect potential churners in a vast customer base. From the perspective of machine learning, the task of predicting customer churn can be presented as a binary classification problem. Using data on historic behavior, classification algorithms are built with the purpose of accurately predicting the probability of a customer defecting. The predictive churn models are then commonly selected based on accuracy related performance measures such as the area under the ROC curve (AUC). However, these models are often not well aligned with the core business requirement of profit maximization, in the sense that, the models fail to take into account not only misclassification costs, but also the benefits originating from a correct classification. Therefore, the aim is to construct churn prediction models that are profitable and preferably interpretable too. The recently developed expected maximum profit measure for customer churn (EMPC) has been proposed in order to select the most profitable churn model. We present a new classifier that integrates the EMPC metric directly into the model construction. Our technique, called ProfTree, uses an evolutionary algorithm for learning profit driven decision trees. In a benchmark study with real-life data sets from various telecommunication service providers, we show that ProfTree achieves significant profit improvements compared to classic accuracy driven tree-based methods.

A vast majority of machine learning algorithms train their models and perform inference by solving optimization problems. In order to capture the learning and prediction problems accurately, structural constraints such as sparsity or low rank are frequently imposed or else the objective itself is designed to be a non-convex function. This is especially true of algorithms that operate in high-dimensional spaces or that train non-linear models such as tensor models and deep networks. The freedom to express the learning problem as a non-convex optimization problem gives immense modeling power to the algorithm designer, but often such problems are NP-hard to solve. A popular workaround to this has been to relax non-convex problems to convex ones and use traditional methods to solve the (convex) relaxed optimization problems. However this approach may be lossy and nevertheless presents significant challenges for large scale optimization. On the other hand, direct approaches to non-convex optimization have met with resounding success in several domains and remain the methods of choice for the practitioner, as they frequently outperform relaxation-based techniques - popular heuristics include projected gradient descent and alternating minimization. However, these are often poorly understood in terms of their convergence and other properties. This monograph presents a selection of recent advances that bridge a long-standing gap in our understanding of these heuristics. The monograph will lead the reader through several widely used non-convex optimization techniques, as well as applications thereof. The goal of this monograph is to both, introduce the rich literature in this area, as well as equip the reader with the tools and techniques needed to analyze these simple procedures for non-convex problems.

Google pulled some of its core businesses out of China seven years ago, after concluding that government controls and surveillance ran counter to its commitment to a free and open internet. Since then, as China's online scene has grown and prospered, the American search giant has been looking for ways to tiptoe back in. On Wednesday, it unveiled a small but symbolically significant move toward that end: a China-based center devoted to artificial intelligence. The move nods to the country's growing strength in A.I., thanks to substantial government funding prompted by Beijing's ambition of having a say in the technologies of the future. Google said the center would have a team of experts in Beijing, where the company has hundreds of employees in research and development, as well as other roles.

There is a war a-brewin', but this war will be fought with wits and not brute strength. Ever since Russian President Vladimir Putin's declaration that "the nation that leads in AI (Artificial Intelligence) will be the ruler of the world," the press and analysts have created hysteria regarding the ramifications of artificial intelligence on everything from public education to unemployment to healthcare to Skynet. Note: artificial intelligence (AI) endows applications with the ability to automatically learn and adapt from experience via interacting with the surroundings / environment. See the blog "Artificial Intelligence is not Fake Intelligence" for a more detailed explanation …read more

Want to watch this again later? Sign in to add this video to a playlist. Report Need to report the video? Sign in to report inappropriate content. Report Need to report the video?

The first element of building a successful IoT strategy is that it should be based on some business value such as percentage increase in revenue or reduction in cost or improvement in productivity/efficiency. The second element is that the commitment should be top-down: What is the leadership's commitment to the success of the IoT project? Third, it needs to have an ecosystem play, involving all the key ecosystem participants while the strategy is being built. The fourth one is the right set of skills and the capability to consume IoT. And the fifth element is having an end-to-end perspective that touches everyone in the organization for whom the project is relevant.