Dubai Police on Wednesday announced plans for Future Societies 5.0, a summit of AI-led policing that aims to become the premier platform for inter-force technological advancement. Managed by Dubai World Trade Centre (DWTC) and taking place April 22-23, 2019, Future Societies 5.0 will be the region's first dedicated AI law enforcement event directly addressing how digital technology can allow international police forces to establish securer, safer societies through innovations such as AI surveillance, predictive policing and the deployment of robot officers. Artificial intelligence (AI) will be at its core, with discussion, analysis and technologies focusing on the central strategic pillars of AI policing: futuristic stations, crime prevention and investigation, forensics, road safety, crisis-disaster management, operations and customer service, a statement said. The announcement of the event follows this year's formation of the Dubai Police Strategic Plan (2018-2021) in which the force created a new General Department of Artificial Intelligence. By 2031, the objective is to have AI methods used across all areas of police, including security, forecasting of crime and traffic accidents, and developing the best techniques and AI tools to serve the needs of the people locally and internationally.

Containers are believed to be a start to something bigger, with tech heavyweights like Microsoft and Google selling and running enterprise software as pre-packaged Kubernetes applications. And this is backed by a bunch of reasons. Seema Kumar, Country Leader, Developer Ecosystem & Startups, IBM India/South Asia, spoke about the importance of containers in the information architecture and how a unified architecture can enable AI applications.

Important resources like minerals, oil and diamonds often go hand-in-hand with conflict and poor governance. But when it comes to one particular resource -- the most important resource of all -- many think a different theory will hold true. Often referred to as the water wars thesis, it suggests that growing water scarcity will drive violent conflict as access to water dries up for certain communities. Analysts worry that people, opportunistic politicians and powerful corporations will battle for dwindling water supply, inflaming tensions. In a new study, researchers tried to map out how water wars will emerge around the world and which countries are most likely to see water-related conflict in the coming decades.

Feature extraction from financial data is one of the most important problems in market prediction domain for which many approaches have been suggested. Among other modern tools, convolutional neural networks (CNN) have recently been applied for automatic feature selection and market prediction. However, in experiments reported so far, less attention has been paid to the correlation among different markets as a possible source of information for extracting features. In this paper, we suggest a CNN-based framework with specially designed CNNs, that can be applied on a collection of data from a variety of sources, including different markets, in order to extract features for predicting the future of those markets. The suggested framework has been applied for predicting the next day's direction of movement for the indices of S&P 500, NASDAQ, DJI, NYSE, and RUSSELL markets based on various sets of initial features. The evaluations show a significant improvement in prediction's performance compared to the state of the art baseline algorithms.

In this paper, the effectiveness and capability of convolutional neural networks have been studied in the classification of 8 skin diseases. Different pre-trained state-of-the-art architectures (DenseNet 201, ResNet 152, Inception v3, InceptionResNet v2) were used and applied on 10135 dermoscopy skin images in total (HAM10000: 10015, PH2: 120). The utilized dataset includes 8 diagnostic categories - melanoma, melanocytic nevi, basal cell carcinoma, benign keratosis, actinic keratosis and intraepithelial carcinoma, dermatofibroma, vascular lesions, and atypical nevi. The aim is to compare the ability of deep learning with the performance of highly trained dermatologists. Overall, the mean results show that all deep learning models outperformed dermatologists (at least 11%). The best ROC AUC values for melanoma and basal cell carcinoma are 94.40% (ResNet 152) and 99.30% (DenseNet 201) versus 82.26% and 88.82% of dermatologists, respectively. Also, DenseNet 201 had the highest macro and micro averaged AUC values for overall classification (98.16%, 98.79%, respectively).

Across various domains--such as health, education, and housing--improving societal welfare involves allocating resources, setting policies, targeting interventions, and regulating activities. These solutions have an immense impact on the day-to-day lives of individuals, whether in the form of access to quality healthcare, labor market outcomes, or how votes are accounted for in a democratic society. Problems that can have an out-sized impact on individuals whose opportunities have historically been limited often pose conceptual and technical challenges, requiring insights from many disciplines. Conversely, the lack of interdisciplinary approach can leave these urgent needs unaddressed and can even exacerbate underlying socioeconomic inequalities. To realize the opportunities in these domains, we need to correctly set objectives and reason about human behavior and actions. Doing so requires a deep grounding in the field of interest and collaboration with domain experts who understand the societal implications and feasibility of proposed solutions. These insights can play an instrumental role in proposing algorithmically-informed policies. In this article, we describe the Mechanism Design for Social Good (MD4SG) research agenda, which involves using insights from algorithms, optimization, and mechanism design to improve access to opportunity. The MD4SG research community takes an interdisciplinary, multi-stakeholder approach to improve societal welfare. We discuss three exciting research avenues within MD4SG related to improving access to opportunity in the developing world, labor markets and discrimination, and housing. For each of these, we showcase ongoing work, underline new directions, and discuss potential for implementing existing work in practice.

Conventional thinking in business has long been that strategic decisions are made by humans, while the focus of automation and machine learning should be on execution. With the speed of change and volume of market feedback today, as well as the advances in machine learning, Amazon, Alibaba, and others have proven the value of software-driven strategy decisions. For example, most e-commerce platforms today offer millions of products, with a changing mix daily and a changing market, such that it's virtually impossible to manually predict a strategy for mapping customer demographics to products displayed online. Only smart software can plow through the volume of live data, recognizing trends, customers, and match offerings to reality. Alibaba, today the counterpart in China to Amazon, eBay, and Google here, has demonstrated leadership in this area and provides guidance for all of us to learn from in a new book, "Smart Business," by Ming Zeng.

Advancements in robotics are continually taking place in the fields of space exploration, health care, public safety, entertainment, defense, and more. These machines--some fully autonomous, some requiring human input--extend our grasp, enhance our capabilities, and travel as our surrogates to places too dangerous or difficult for us to go. Gathered here are recent images of robotic technology, including a Japanese probe reaching a distant asteroid, bipedal-robot fighting matches in Japan, a cuddly cat-substitute robotic pillow, an automated milking machine, delivery bots, telepresence robots, technology on the fashion runway, robotic prosthetic limbs and exoskeletons, and much more.

When it comes to artificial intelligence, the United States and China are viewed as the industry leaders.

Data and tech expects share their takes on the current A.I. revolution A push for a global agreement on autonomous weapons is stalled, much to the chagrin of advocates who believe a treaty is urgently needed. Fully autonomous cars are years away, but it's the automobile where artificial intelligence could have a critical role for the greatest number of people. Artificial intelligence has its own insider jargon. Here are some crucial concepts and terms, defined and digested for the rest of us. From Singapore to Israel, countries besides the United States and China are striving to play a role in the field of artificial intelligence.