In this paper, a novel experienced deep reinforcement learning (deep-RL) framework is proposed to provide model-free resource allocation for ultra reliable low latency communication (URLLC) in the downlink of a wireless network. The proposed, experienced deep-RL framework can guarantee high end-to-end reliability and low end-to-end latency, under explicit data rate constraints, for each wireless user without any models of or assumptions on the users' traffic. In particular, in order to enable the deep-RL framework to account for extreme network conditions and operate in highly reliable systems, a new approach based on generative adversarial networks (GANs) is proposed. This GAN approach is used to pre-train the deep-RL framework using a mix of real and synthetic data, thus creating an experienced deep-RL framework that has been exposed to a broad range of network conditions. Formally, the URLLC resource allocation problem is posed as a power minimization problem under reliability, latency, and rate constraints. To solve this problem using experienced deep-RL, first, the rate of each user is determined. Then, these rates are mapped to the resource block and power allocation vectors of the studied wireless system. Finally, the end-to-end reliability and latency of each user are used as feedback to the deep-RL framework. It is then shown that at the fixed-point of the deep-RL algorithm, the reliability and latency of the users are near-optimal. Moreover, for the proposed GAN approach, a theoretical limit for the generator output is analytically derived. Simulation results show how the proposed approach can achieve near-optimal performance within the rate-reliability-latency region, depending on the network and service requirements. The results also show that the proposed experienced deep-RL framework is able to remove the transient training time that makes conventional deep-RL methods unsuitable for URLLC. A. Taleb Zadeh Kasgari and W . Saad are with Wireless@VT, Department of ECE, Virgina Tech, Blacksburg, V A, 24060, USA. M. Mozaffari is with Ericsson Research, Santa Clara, CA, 95054, USA, Email: mohammad.mozaffari@ericsson.com. Poor is with the Department of Electrical Engineering, Princeton University, Princeton, NJ, 08544, USA, Email: poor@princeton.edu. A preliminary version of this work appeared in IEEE ICC, [1]. I NTRODUCTION Ultra reliable low latency communication (URLLC) will be one of the most important features in next-generation 5G and beyond cellular networks as it will be necessary for mission critical applications such as Internet of Things (IoT) [2] sensing and control as well as remote control of autonomous vehicles and drones [3], [4]. Thus far, prior URLLC research has been mostly focused on applications that require low data rates such as uplink transmissions of IoT sensors [3], [5].

With the rapid adoption of machine learning systems in sensitive applications, there is an increasing need to make black-box models explainable. Often we want to identify an influential group of training samples in a particular test prediction. Existing influence functions tackle this problem by using first-order approximations of the effect of removing a sample from the training set on model parameters. To compute the influence of a group of training samples (rather than an individual point) in model predictions, the change in optimal model parameters after removing that group from the training set can be large. Thus, in such cases, the first-order approximation can be loose. In this paper, we address this issue and propose second-order influence functions for identifying influential groups in test-time predictions. For linear models and across different sizes of groups, we show that using the proposed second-order influence function improves the correlation between the computed influence values and the ground truth ones. For nonlinear models based on neural networks, we empirically show that none of the existing first-order and the proposed second-order influence functions provide proper estimates of the ground-truth influences over all training samples. We empirically study this phenomenon by decomposing the influence values over contributions from different eigenvectors of the Hessian of the trained model.

We present a new model-based algorithm for reinforcement learning (RL) which consists of explicit exploration and exploitation phases, and is applicable in large or infinite state spaces. The algorithm maintains a set of dynamics models consistent with current experience and explores by finding policies which induce high disagreement between their state predictions. It then exploits using the refined set of models or experience gathered during exploration. We show that under realizability and optimal planning assumptions, our algorithm provably finds a near-optimal policy with a number of samples that is polynomial in a structural complexity measure which we show to be low in several natural settings. We then give a practical approximation using neural networks and demonstrate its performance and sample efficiency in practice.

I. INTRODUCTION Active sensing (AS) is one of the most fundamental problems and challenges in mobile robotics which seeks to maximize the efficiency of an estimation task by actively controlling the sensing parameters [1]. AS can be divided into two sub-tasks: the identification of a point of interest (PoI) to achieve (e.g. In teams of heterogeneous robots that employ different sensory modalities, AS is of particular interest as it can be used to resolve observation ambiguities. Friston [2] states that minimizing free energy is equivalent to maximizing model evidence, which is equivalent to minimizing the complexity of accurate explanations for observed outcomes. Following this principle, if one could directly obtain an estimation of free energy through the current observation, a controller for sensing parameters can be learned that minimizes free energy.

The AI Forum continues to publish the outputs of our research programme. Two new reports AI for Health in New Zealand / Haoura i te Atamai Iahiko and AI for Agriculture in New Zealand / Ahuwhenua i te Atamai Iahiko explore in depth the AI opportunities for New Zealand's crucial health and agriculture sectors. Continued thanks to the AI Forum's research programme partners for their foundational support to enable this work. AI Forum Executive Council members Christopher Laing (Xero) and Michael Witbrock (University of Auckland) were recently interviewed by Kathryn Ryan on RNZ's Nine To Noon show, listen to AI: two years for NZ to get it right. Meanwhile, I was interviewed at length by the Spinoff's Russell Brown in the latest episode of the Microsoft'Artificial Intelligence – Actually Interesting' podcast series: The cancer-fighting, wildlife-protecting, life-saving power of artificial intelligence.

Alan Finkel, speaking at an artificial intelligence summit at Monash University in Melbourne on Thursday, said there was a "golden opportunity" for Australia to be a world leader in scientific discovery while also holding to the ideals of a virtuous society. He said there was enormous potential for artificial intelligence to deliver substantial benefits to Australians in areas as varied as manufacturing and financial services. Chief Scientist Alan Finkel says the advent of artificial intelligence will need both technological as well as ethical considerations. But Dr Finkel pointed to how Google this month obtained a patent to use sensors and cameras to monitor home activity. It claims to be able to work out the title of a book someone is reading in their bed.

The nation's chief scientist has urged software developers on the cusp of artificial intelligence breakthroughs not to lose their "moral compass" amid fears humans will be treated as data points by our largest companies. Alan Finkel, speaking at an artificial intelligence summit at Monash University in Melbourne on Thursday, said there was a "golden opportunity" for Australia to be a world leader in scientific discovery while also holding to the ideals of a virtuous society. He said there was enormous potential for artificial intelligence to deliver substantial benefits to Australians in areas as varied as manufacturing and financial services. Chief Scientist Alan Finkel says the advent of artificial intelligence will need both technological as well as ethical considerations.Credit:Alex Ellinghausen But Dr Finkel pointed to how Google this month obtained a patent to use sensors and cameras to monitor home activity. It claims to be able to work out the title of a book someone is reading in their bed.

Fox News Flash top headlines for Oct. 31 are here. Check out what's clicking on Foxnews.com New Zealand has banned an "abhorrent" video game that the country's chief censor said glorifies the mass shooting at two mosques in Christchurch that killed 51 worshipers last March, according to a report. Chief Censor David Shanks said in a statement that the creators of the game set out to "produce and sell a game designed to place the player in the role of a white supremacist terrorist killer." He classified the game as objectionable, adding that in the game "anyone who isn't a white heterosexual male is a target for simply existing," Reuters reported.

Global Artificial Intelligence in BFSI Market size in 2017 is 2.50 million US$ and it is expected to reach 19.80 million US$ by the end of 2026 with a CAGR of 29.52% during 2017 -2026. Artificial intelligence (AI) in BFSI refers to the simulation of human intelligence into machines with the help of sophisticated machine learning, deep learning, chat-bots, cognitive computing, and natural language processing algorithms that help in customer relationship management, communication, and recruitment & wealth management. Artificial intelligence in BFSI is driven mainly by digital data .Artificial Intelligence (AI) is fast evolving as the go-to technology for banks across the world to personalize experience for individuals. Positive rise of AI-based application in BFSI such as customer support, fraud detection, improving employee efficiency, reduce fraud and security risks. Growing adoption of smart devices and growing penetration of internet services across the globe is fuelling amount of the data. A number of financial services institution are already generating value from artificial intelligence.

As video games give players more freedom to explore complex digital worlds, it becomes more challenging for a CG character to naturally move and interact with everything in it. So to prevent those awkward transitions between pre-programmed movements, researchers have turned to AI and deep learning to make video game characters move almost as realistically as real humans do. To help make video game characters walk, run, jump, and perform other movements as realistically as possible, video game developers will often rely on human performances that are captured and translated to digital characters. It produces results that are faster and better looking than animating video game characters by hand, but it's impossible to plan for every possible way a character will interact with a digital world, according to the researchers. Game developers try to plan for as many possibilities as they can, but they ultimately have to rely on software to transition between animations of a character walking up to a chair, and then sitting down on it, and more often than not, those segues feel stilted, unnatural, and can diminish a player's experience. Computer scientists from the University of Edinburgh and Adobe Research have come up with a novel solution they'll be presenting at the ACM Siggraph Asia conference being held in Brisbane, Australia, next month.