Researchers often call 2017 the year of the conversational computer in China. Leveraging recent advances in voice recognition and natural-language processing, e-commerce giant Alibaba and search giant Baidu have both been developing technology to crack voice-based communication. Now voice-operated products derived from Baidu and Alibaba's technology are coming to the Chinese market. The Tmall Genie, which has Alibaba's voice assistant, AliGenie, built in, is akin to the Amazon Echo. It can place online orders, check the weather, play your favorite music, and control other smart devices in your home through voice commands.

Are our machines turning into gods? Jie, the world's best player of the world's oldest board game, Go, had just met his match… in the form of a program called AlphaGo. In the space of a year, the program had become "almost like the god of Go," said Jie after losing to AlphaGo. Jie had been playing the game, viewed as too hard for machines to excel at, since he was 10. AlphaGo was only made by Google's parent, Alphabet, in 2014.

PSFK's Future of Health looks into all the ways artificial intelligence is transforming healthcare The state of your health shouldn't be a mystery, nor should patients or doctors have to wait long to find answers to pressing medical concerns. In PSFK's Future of Health Report, we dig deep into the latest in AI, big data algorithms and IoT tools that are enabling a new, more comprehensive overview of patient data collection and analysis. Machine support, patient information from medical records and conversations with doctors are combined with the latest medical literature to help form a diagnosis without detracting from doctor-patient relations. The impact of improved AI helps patients form a baseline for well-being and is making changes all across the healthcare industry. AI not only streamlines intake processes and reduces processing volume at clinics, it also controls input and diagnostic errors within a patient record, allowing doctors to focus on patient care and communication, rather than data entry.

The State Council, China's Cabinet, recently issued a national artificial intelligence development plan. This is a major step for its innovation-driven development strategy and making the country a global leader in science and technology. The three-step plan clarifies the strategic objectives for China's new generation of artificial intelligence. By 2020, the overall level of China's AI technology and its use should catch up with the world's leading level, by 2025 there should be major breakthroughs in theoretical research and progress in building an intelligent society, and by 2030 China should be one of the major artificial intelligence innovation centers globally. The plan advances six key tasks: establishing an open and synergetic artificial intelligence science and technology innovation system, cultivating a high-end and highly efficient intelligent economy, building a safe and convenient intelligent society, enhancing military and civilian integration in the artificial intelligence field, setting up safe and efficient intelligent infrastructure systems for the internet and big data, and deploying significant technology projects centered on a new generation of artificial intelligence.

The Israeli security forces have started to remove metal detectors installed at entry points to al-Aqsa Mosque compound in the occupied East Jerusalem. Sheikh Najeh Bakirat, the director of al-Aqsa Mosque, said overnight on Tueday that the move does not fullfil the demands of the Muslim worshippers as the security cameras are being kept. Israel installed metal detectors and security cameras after gunmen shot dead two Israeli guards near al-Aqsa compound - Islam's third holiest site - on July 14. Prime Minister Benjamin Netanyahu's cabinet voted to remove the metal detector gates after a meeting lasting several hours convening for a second time on Monday after they had broken off discussions a day earlier. Al Jazeera's Imran Khan, reporting from occupied East Jerusalem, said that hundreds of Palestinians protested against the security cameras with advanced face recognition software that won't be removed.

Conventionally, the questions on a test are assumed to be kept secret from test takers until the test. However, for tests that are taken on a large scale, particularly asynchronously, this is very hard to achieve. For example, TOEFL iBT and driver's license test questions are easily found online. This also appears likely to become an issue for Massive Open Online Courses (MOOCs, as offered for example by Coursera, Udacity, and edX). Specifically, the test result may not reflect the true ability of a test taker if questions are leaked beforehand. In this paper, we take the loss of confidentiality as a fact. Even so, not all hope is lost as the test taker can memorize only a limited set of questions' answers, and the tester can randomize which questions to let appear on the test. We model this as a Stackelberg game, where the tester commits to a mixed strategy and the follower responds. Informally, the goal of the tester is to best reveal the true ability of a test taker, while the test taker tries to maximize the test result (pass probability or score). We provide an exponential-size linear program formulation that computes the optimal test strategy, prove several NP-hardness results on computing optimal test strategies in general, and give efficient algorithms for special cases (scored tests and single-question tests). Experiments are also provided for those proposed algorithms to show their scalability and the increase of the tester's utility relative to that of the uniform-at-random strategy. The increase is quite significant when questions have some correlation---for example, when a test taker who can solve a harder question can always solve easier questions.

Electricity theft is a major problem around the world in both developed and developing countries and may range up to 40% of the total electricity distributed. More generally, electricity theft belongs to non-technical losses (NTL), which are losses that occur during the distribution of electricity in power grids. In this paper, we build features from the neighborhood of customers. We first split the area in which the customers are located into grids of different sizes. For each grid cell we then compute the proportion of inspected customers and the proportion of NTL found among the inspected customers. We then analyze the distributions of features generated and show why they are useful to predict NTL. In addition, we compute features from the consumption time series of customers. We also use master data features of customers, such as their customer class and voltage of their connection. We compute these features for a Big Data base of 31M meter readings, 700K customers and 400K inspection results. We then use these features to train four machine learning algorithms that are particularly suitable for Big Data sets because of their parallelizable structure: logistic regression, k-nearest neighbors, linear support vector machine and random forest. Using the neighborhood features instead of only analyzing the time series has resulted in appreciable results for Big Data sets for varying NTL proportions of 1%-90%. This work can therefore be deployed to a wide range of different regions around the world.

Detection of non-technical losses (NTL) which include electricity theft, faulty meters or billing errors has attracted increasing attention from researchers in electrical engineering and computer science. NTLs cause significant harm to the economy, as in some countries they may range up to 40% of the total electricity distributed. The predominant research direction is employing artificial intelligence to predict whether a customer causes NTL. This paper first provides an overview of how NTLs are defined and their impact on economies, which include loss of revenue and profit of electricity providers and decrease of the stability and reliability of electrical power grids. It then surveys the state-of-the-art research efforts in a up-to-date and comprehensive review of algorithms, features and data sets used. It finally identifies the key scientific and engineering challenges in NTL detection and suggests how they could be addressed in the future.

Non-technical losses (NTL) such as electricity theft cause significant harm to our economies, as in some countries they may range up to 40% of the total electricity distributed. Detecting NTLs requires costly on-site inspections. Accurate prediction of NTLs for customers using machine learning is therefore crucial. To date, related research largely ignore that the two classes of regular and non-regular customers are highly imbalanced, that NTL proportions may change and mostly consider small data sets, often not allowing to deploy the results in production. In this paper, we present a comprehensive approach to assess three NTL detection models for different NTL proportions in large real world data sets of 100Ks of customers: Boolean rules, fuzzy logic and Support Vector Machine. This work has resulted in appreciable results that are about to be deployed in a leading industry solution. We believe that the considerations and observations made in this contribution are necessary for future smart meter research in order to report their effectiveness on imbalanced and large real world data sets.

Inference and Estimation in Missing Information (MI) scenarios are important topics in Statistical Learning Theory and Machine Learning (ML). In ML literature, attempts have been made to enhance prediction through precise feature selection methods. In sparse linear models, LASSO is well-known in extracting the desired support of the signal and resisting against noisy systems. When sparse models are also suffering from MI, the sparse recovery and inference of the missing models are taken into account simultaneously. In this paper, we will introduce an approach which enjoys sparse regression and covariance matrix estimation to improve matrix completion accuracy, and as a result enhancing feature selection preciseness which leads to reduction in prediction Mean Squared Error (MSE). We will compare the effect of employing covariance matrix in enhancing estimation accuracy to the case it is not used in feature selection. Simulations show the improvement in the performance as compared to the case where the covariance matrix estimation is not used.