The field of artificial intelligence is growing at an astounding pace, and Elsevier is keeping up. Our latest report, Artificial Intelligence: How knowledge is created, transferred, and used, breaks new ground by offering a comprehensive mapping and definition of artificial intelligence as a field of study along with insights into global research trends. "The new generation of technologies, commonly umbrellaed under AI, are so important, and yet there appears to be no shared understanding of its exact definition," said Dan Olley, Chief Technology Officer at Elsevier. "With this comprehensive study of research performance in AI, we aim to provide clarity on and insights into the field's dynamics, trends and parameters. The report is not a conclusion but the start of a discussion on how we best enter the era of AI and increasingly symbiotic technology."

With the growing demand and applicability of artificial intelligence, IIT Hyderabad is set to launch a full-fledged BTech program in AI starting from the academic year 2019-2020. Admissions to the course will be accepted based on the JEE Advanced score. With this, IIT Hyderabad becomes the first Indian educational institution to offer a full-fledged BTech programme in AI and reportedly the third institute globally after Carnegie Mellon University and Massachusetts Institute of Technology in the US. The course will reportedly only take in only 20 students. Students pursuing other degrees such as B.Tech. in chemical engineering or mechanical engineering can also pursue a minor in AI as well from the coming academic year onwards.

At most universities, meal plans allow college students to take advantage of on-campus cafeterias or chow down at local restaurants. Now, thousands of students at George Mason University will have another dining option at their disposal: on-demand food delivery via an autonomous robot on wheels. The school has received a fleet of 25 delivery robots that can haul up to 20 pounds each as they roll across campus at four miles per hour, according to Starship Technologies, the Estonia-based robotics company that created the delivery vehicles. The company -- which claims its robots can make deliveries in 15 minutes or less -- says the Fairfax, Va.-based school is the first campus in the country to incorporate robots into its student dining plan and has the largest fleet of delivery roots on any university campus. "Students and teachers have little free time as it is, so there is a convenience for them to have their food, groceries and packages delivered to them," said Ryan Tuohy, Starship Technology's senior vice president of business development.

After working as Electronic Engineer, I decided to change my career path to Data Scientist . To reach my Data Science career goal I have started to review Moocs about this field. All these courses are explain core machine learning algorithms. Also, in Coursera's Machine Learning course Andrew NG explained the mathematical background of these algorithms. If you want to learn what Machine Learning is and the way that you can use it, i strongly suggest you to take these entire three courses.

Vision-based deep reinforcement learning (RL), similar to deep learning, typically obtains a performance benefit by using high capacity and relatively large convolutional neural networks (CNN). However, a large network leads to higher inference costs (power, latency, silicon area, MAC count). Many inference optimization have been developed for CNNs. Some optimization techniques offer theoretical efficiency, but designing actual hardware to support them is difficult. On the other hand, "distillation" is a simple general-purpose optimization technique which is broadly applicable for transferring knowledge from a trained, high capacity, teacher network to an untrained, low capacity, student network. "DQN distillation" extended the original distillation idea to transfer information stored in a high performance, high capacity teacher Q-function trained via the Deep Q-Learning (DQN) algorithm. Our work adapts the DQN distillation work to the actor-critic Proximal Policy Optimization algorithm. PPO is simple to implement and has much higher performance than the seminal DQN algorithm. We show that a distilled PPO student can attain far higher performance compared to a DQN teacher. We also show that a low capacity distilled student is generally able to outperform a low capacity agent that directly trains in the environment. Finally, we show that distillation, followed by "fine-tuning" in the environment, enables the distilled PPO student to achieve parity with teacher performance. In general, the lessons learned in this work should transfer to other actor-critic RL algorithms.

We consider a two-agent MDP framework where agents repeatedly solve a task in a collaborative setting. We study the problem of designing a learning algorithm for the first agent (A1) that facilitates a successful collaboration even in cases when the second agent (A2) is adapting its policy in an unknown way. The key challenge in our setting is that the presence of the second agent leads to non-stationarity and non-obliviousness of rewards and transitions for the first agent. We design novel online learning algorithms for agent A1 whose regret decays as $O(T^{1-\frac{3}{7} \cdot \alpha})$ with $T$ learning episodes provided that the magnitude of agent A2's policy changes between any two consecutive episodes are upper bounded by $O(T^{-\alpha})$. Here, the parameter $\alpha$ is assumed to be strictly greater than $0$, and we show that this assumption is necessary provided that the {\em learning parity with noise} problem is computationally hard. We show that sub-linear regret of agent A1 further implies near-optimality of the agents' joint return for MDPs that manifest the properties of a {\em smooth} game.

Next-generation wireless networks (NGWN) have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of machine learning by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning, respectively. Furthermore, we investigate their employment in the compelling applications of NGWNs, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various machine learning algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.

We introduce and analyze a cooperative online learning setting in which a network of agents solve a common online convex optimization problem by sharing feedback with their network neighbors. Agents do not have to be synchronized. At each time step, only some of the agents are requested to make a prediction and pay the corresponding loss: we call these agents "active". As the feedback (i.e., the current loss function) received by the active agents is communicated to their neighbors, both active agents and their neighbors can use the feedback to update their local models. Asynchronous online learning settings with communication constraints naturally arise in many applications. Forexample, large-scale learning systems are often geographically distributed, and in domains such as finance or online advertising, typically each agent must serve high volumes of prediction requests. If agents keep updating their local models in an online fashion, then bandwidth and computational constraints may force them to limit communication by sharing feedbacks only with their neighbors.

Wireless Internet access has brought legions of heterogeneous applications all sharing the same resources. However, current wireless edge networks that cater to worst or average case performance lack the agility to best serve these diverse sessions. Simultaneously, software reconfigurable infrastructure has become increasingly mainstream to the point that dynamic per packet and per flow decisions are possible at multiple layers of the communications stack. Exploiting such reconfigurability requires the design of a system that can enable a configuration, measure the impact on the application performance (Quality of Experience), and adaptively select a new configuration. Effectively, this feedback loop is a Markov Decision Process whose parameters are unknown. The goal of this work is to design, develop and demonstrate QFlow that instantiates this feedback loop as an application of reinforcement learning (RL). Our context is that of reconfigurable (priority) queueing, and we use the popular application of video streaming as our use case. We develop both model-free and model-based RL approaches that are tailored to the problem of determining which clients should be assigned to which queue at each decision period. Through experimental validation, we show how the RL-based control policies on QFlow are able to schedule the right clients for prioritization in a high-load scenario to outperform the status quo, as well as the best known solutions with over 25% improvement in QoE, and a perfect QoE score of 5 over 85% of the time.

These transformations will open up new opportunities for individuals, the economy, and society, but they have the potential to disrupt the current livelihoods of millions of Americans. Whether AI leads to unemployment and increases in inequality over the long-run depends not only on the technology itself but also on the institutions and policies that are in place. This report examines the expected impact of AI-driven automation on the economy, and describes broad strategies that could increase the benefits of AI and mitigate its costs. Economics of AI-Driven Automation Technological progress is the main driver of growth of GDP per capita, allowing output to increase faster than labor and capital. One of the main ways that technology increases productivity is by decreasing the number of labor hours needed to create a unit of output.