NEW DELHI: A senior executive at a leading IT services company recently approached Sairee Chahal, founder of women career services portal SHEROES, to explore job options. The executive was worried there could be bad news for her at work. A patient of Samir Parikh at the department of mental health and behavioural sciences at Fortis Healthcare in New Delhi lost his job about two months ago. Along with the job, the patient lost complete self-confidence to face interviews. He used to get panic attacks and palpitations hours before an interview.

After enjoying years of steady growth, your business suddenly sees your customer satisfaction scores sinking. When you investigate, you find that your customer support team is simply not keeping up with the volume of requests that they're receiving. Customers have to wait two or more days for a first response, and they're voicing their discontent in growing numbers on social media. You don't have enough money in the budget to hire and train more support staff, so the only realistic solution is to use AI and automation. Which use cases should you try to automate, and which tools should you use?

Artificial intelligence (AI) -- defined by John McCarthy, one of the doyens of AI, as "the science and engineering of making intelligent machines" -- is slowly gaining relevance in the military domain. While commercial use of AI is widening, there are only three countries that are reported to be developing serious military AI technologies: the United States, China and Russia. AI promises a significant military advantage to a nation's offensive and defensive military capabilities. AI now has the capacity to be merged with sophisticated but untried, new weaponry, such as offensive cyber capabilities. This is an alarming development, as it has the potential to destabilize the balance of military power among the leading industrial nations.

The Trump administration might be building walls between America and some countries, but it is eager to forge alliances when it comes to shaping the course of artificial intelligence. The Organization for Economic Co-operation and Development (OECD), a coalition of countries dedicated to promoting democracy and economic development, has announced a set of five principles for the development and deployment of artificial intelligence. The announcement came at a meeting of the OECD Forum in Paris. The OECD does not include China, and the principles outlined by the group seem to contrast with the way AI is being deployed there, especially for face recognition and surveillance of ethnic groups associated with political dissent. Speaking at the event, America's recently appointed CTO, Michael Kratsios, said, "We are so pleased that the OECD AI recommendations address so many of the issues which are being tackled by the American AI Initiative."

Artificial Intelligence in Transportation Market reports provides a comprehensive overview of the global market size and share. Artificial Intelligence in Transportation market data reports also provide a 5 year pre-historic and forecast for the sector and include data on socio-economic data of global. The Artificial Intelligence in Transportation market size will grow from USD XX Million in 2018 to USD XX Million by 2024, at an estimated CAGR of XX%. The base year considered for the study is 2017, and the market size is projected from 2018 to 2023. Look insights of Global Artificial Intelligence in Transportation industry market research report at https://www.pioneerreports.com/report/361684

Binary determination of the presence of objects is one of the problems where humans perform extraordinarily better than computer vision systems, in terms of both speed and preciseness. One of the possible reasons is that humans can skip most of the clutter and attend only on salient regions. Recurrent attention models (RAM) are the first computational models to imitate the way humans process images via the REINFORCE algorithm. Despite that RAM is originally designed for image recognition, we extend it and present recurrent existence determination, an attention-based mechanism to solve the existence determination. Our algorithm employs a novel $k$-maximum aggregation layer and a new reward mechanism to address the issue of delayed rewards, which would have caused the instability of the training process. The experimental analysis demonstrates significant efficiency and accuracy improvement over existing approaches, on both synthetic and real-world datasets.

Satellite-based positioning system such as GPS often suffers from large amount of noise that degrades the positioning accuracy dramatically especially in real-time applications. In this work, we consider a data-mining approach to enhance the GPS signal. We build a large-scale high precision GPS receiver grid system to collect real-time GPS signals for training. The Gaussian Process (GP) regression is chosen to model the vertical Total Electron Content (vTEC) distribution of the ionosphere of the Earth. Our experiments show that the noise in the real-time GPS signals often exceeds the breakdown point of the conventional robust regression methods resulting in sub-optimal system performance. We propose a three-step approach to address this challenge. In the first step we perform a set of signal validity tests to separate the signals into clean and dirty groups. In the second step, we train an initial model on the clean signals and then reweigting the dirty signals based on the residual error. A final model is retrained on both the clean signals and the reweighted dirty signals. In the theoretical analysis, we prove that the proposed three-step approach is able to tolerate much higher noise level than the vanilla robust regression methods if two reweighting rules are followed. We validate the superiority of the proposed method in our real-time high precision positioning system against several popular state-of-the-art robust regression methods. Our method achieves centimeter positioning accuracy in the benchmark region with probability $78.4\%$ , outperforming the second best baseline method by a margin of $8.3\%$. The benchmark takes 6 hours on 20,000 CPU cores or 14 years on a single CPU.

Neural Networks (NNs) have been extensively used for a wide spectrum of real-world regression tasks, where the goal is to predict a numerical outcome such as revenue, effectiveness, or a quantitative result. In many such tasks, the point prediction is not enough, but also the uncertainty (i.e. risk, or confidence) of that prediction must be estimated. Standard NNs, which are most often used in such tasks, do not provide any such information. Existing approaches try to solve this issue by combining Bayesian models with NNs, but these models are hard to implement, more expensive to train, and usually do not perform as well as standard NNs. In this paper, a new framework called RIO is developed that makes it possible to estimate uncertainty in any pretrained standard NN. RIO models prediction residuals using Gaussian Process with a composite input/output kernel. The residual prediction and I/O kernel are theoretically motivated and the framework is evaluated in twelve real-world datasets. It is found to provide reliable estimates of the uncertainty, reduce the error of the point predictions, and scale well to large datasets. Given that RIO can be applied to any standard NN without modifications to model architecture or training pipeline, it provides an important ingredient in building real-world applications of NNs.

The problem of market clearing is to set a price for an item such that quantity demanded equals quantity supplied. In this work, we cast the problem of predicting clearing prices into a learning framework and use the resulting models to perform revenue optimization in auctions and markets with contextual information. The economic intuition behind market clearing allows us to obtain fine-grained control over the aggressiveness of the resulting pricing policy, grounded in theory. To evaluate our approach, we fit a model of clearing prices over a massive dataset of bids in display ad auctions from a major ad exchange. The learned prices outperform other modeling techniques in the literature in terms of revenue and efficiency trade-offs. Because of the convex nature of the clearing loss function, the convergence rate of our method is as fast as linear regression.

Dance is an art and when technology meets this kind of art, it's a novel attempt in itself. Several researchers have attempted to automate several aspects of dance, right from dance notation to choreography. Furthermore, we have encountered several applications of dance automation like e-learning, heritage preservation, etc. Despite several attempts by researchers for more than two decades in various styles of dance all round the world, we found a review paper that portrays the research status in this area dating to 1990 \cite{politis1990computers}. Hence, we decide to come up with a comprehensive review article that showcases several aspects of dance automation. This paper is an attempt to review research work reported in the literature, categorize and group all research work completed so far in the field of automating dance. We have explicitly identified six major categories corresponding to the use of computers in dance automation namely dance representation, dance capturing, dance semantics, dance generation, dance processing approaches and applications of dance automation systems. We classified several research papers under these categories according to their research approach and functionality. With the help of proposed categories and subcategories one can easily determine the state of research and the new avenues left for exploration in the field of dance automation.