Experts and politicians in China are worried that a rush to integrate artificial intelligence into weapons and military equipment could accidentally lead to war between nations. According to a new report published by US national security think tank Center for a New American Security (CNAS), Chinese officials increasingly see an "arms race" dynamic in AI as a threat to global peace. As countries scramble to reap the benefits of artificial intelligence in various domains, including the military, the fear is that international norms shaping how countries communicate will become outdated, leading to confusion and potential conflict. "The specific scenario described to me [by one anonymous Chinese official] is unintentional escalation related to the use of a drone," Gregory C. Allen, an adjunct senior fellow at CNAS and author of the new report, tells The Verge. As Allen explains, the operation of drones both large and small has become increasingly automated in recent years.

New Delhi: Driven by a surge in online food orders especially among millennials, leading food delivery platforms are embracing Artificial Intelligence (Ai) in a big way to better read fast-changing consumer behaviour, minimise errors and enhance customer experiences. According to Bengaluru-based research firm RedSeer, the Indian online food delivery market is expected to hit $4 billion by 2020 and to handle and leverage terabytes of data for delivery efficiency has led food aggregators Swiggy and Zomato bet big on AI and Machine Learning (ML). "Swiggy's mission is to bring unparalleled convenience into the lives of urban consumers. We do this by operating a three-way, hyper-local marketplace where we match consumer demand with supply from restaurants and delivery partners," Dale Vaz, Head of Engineering and Data Science, Swiggy, told IANS. "We use AI/ML across this three-way marketplace to deliver a wow customer experience, unlock business growth and drive operational efficiency," added Dale, who joined Swiggy in July last year from Amazon India.

Martin F.R. works as a Technology Journalist at Analytics India Magazine. He usually likes to write detail-oriented articles which are well-researched in articulated formats. Other than covering updates on analytics, artificial intelligence & data science, his interests also include covering politics, economics, finance, consumer electronics, global affairs and issues regarding public policy matters. When not writing any articles, he usually delves into reading biographies of successful entrepreneurs or experiments with his new culinary ideas.

Organizing Data Unchained Malaysia was one of the most rewarding things I have ever done in my career. In November 2018, we invited 100 brilliant data enthusiasts, out of 300 candidates, to a resort in Kuala Lumpur to solve a data problem and to create a suitable business model for it. We gave participants anonymized sample internet and phone call data, connected car records and some points of interest. We asked them to predict the destinations towards which cars are moving, to create a safe driving index and to build a business plan that uses these two models commercially -- all this in just 24 hours. It was crucial for participants to possess a good blend of technical and business skills to make it through the competition.

A Japanese probe sent to examine an asteroid in order to shed light on the origins of the solar system is expected to land on the rock later this month, officials said Wednesday. The Japan Aerospace Exploration Agency said the Hayabusa2 probe is expected to touch down on the Ryugu asteroid at 8 a.m. on Feb. 22. "The landing point is decided and how we're going to land is confirmed, so we want to do our best to achieve this without making mistakes," JAXA project manager Yuichi Tsuda told reporters. The announcement comes after the agency delayed the touchdown for several months in October, saying they needed more time to prepare the landing as the latest data showed the asteroid's surface was more rugged than expected. Scientists are already receiving data from other probes deployed on the surface of the asteroid. In October, JAXA successfully landed a new 10-kilogram observation robot known as Mascot (Mobile Asteroid Surface Scout).

When America's biggest bank, JPMorgan Chase, hired Apoorv Saxena in August 2018 as its global head of AI and machine-learning services based in San Mateo, Calif., finance industry watchers saw that as a sign that the bank was making a big bet on artificial intelligence to shape its future strategies. Saxena previously headed product management for cloud-based artificial intelligence at Google. At JPMorgan Chase, he also oversees asset and wealth management artificial intelligence technology. According to Saxena, AI will help financial services companies expand banking penetration worldwide, launch new products and deepen customer engagements. AI has helped technology companies and others outside of traditional banking enter financial services, such as with mobile banking and digital money offerings.

We introduce a neural architecture for navigation in novel environments. Our proposed architecture learns to map from first-person views and plans a sequence of actions towards goals in the environment. The Cognitive Mapper and Planner (CMP) is based on two key ideas: a) a unified joint architecture for mapping and planning, such that the mapping is driven by the needs of the task, and b) a spatial memory with the ability to plan given an incomplete set of observations about the world. CMP constructs a top-down belief map of the world and applies a differentiable neural net planner to produce the next action at each time step. The accumulated belief of the world enables the agent to track visited regions of the environment. We train and test CMP on navigation problems in simulation environments derived from scans of real world buildings. Our experiments demonstrate that CMP outperforms alternate learning-based architectures, as well as, classical mapping and path planning approaches in many cases. Furthermore, it naturally extends to semantically specified goals, such as 'going to a chair'. We also deploy CMP on physical robots in indoor environments, where it achieves reasonable performance, even though it is trained entirely in simulation.

We achieve four objectives: (1) each agent is allowed to make a report that expresses her preference about violating her budget constraint, a feature not achieved by mechanisms that only elicit quasi-linear reports; (2) these reports are finite dimensional; (3) computation is feasible in polynomial time; and (4) incentive properties of envy-free mechanisms that elicit quasi-linear reports are preserved.

We consider the reinforcement learning problem of training multiple agents in order to maximize a shared reward. In this multi-agent system, each agent seeks to maximize the reward while interacting with other agents, and they may or may not be able to communicate. Typically the agents do not have access to other agent policies and thus each agent observes a non-stationary and partially-observable environment. In order to resolve this issue, we demonstrate a novel multi-agent training framework that first turns a multi-agent problem into a single-agent problem to obtain a centralized expert that is then used to guide supervised learning for multiple independent agents with the goal of decentralizing the policy. We additionally demonstrate a way to turn the exponential growth in the joint action space into a linear growth for the centralized policy. Overall, the problem is twofold: the problem of obtaining a centralized expert, and then the problem of supervised learning to train the multi-agents. We demonstrate our solutions to both of these tasks, and show that supervised learning can be used to decentralize a multi-agent policy.

Abstract-- In this paper we propose the use of multiple local binary patterns(LBPs) to effectively classify land use images. We use the UC Merced 21 class land use image dataset. Task is challenging for classification as the dataset contains intra class variability and inter class similarities. Our proposed method of using multi-neighborhood LBPs combined with nearest neighbor classifier is able to achieve an accuracy of 77.76%. Further class wise analysis is conducted and suitable suggestion are made for further improvements to classification accuracy. INTRODUCTION The world is changing rapidly, new technology and infrastructure is resulting in faster growth. To meet the demands of the growing populations, cities are expanding and land use pattern are changing to accommodate the needs.