The robotic process automation (RPA) services market is booming around the world, according to a recently published Forrester Research report, titled'The RPA Services Market will Grow to Reach $12 billion by 2023'. In 2018, the market was estimated to be worth only $3.9 billion. Furthermore, Forrester's research indicates that over the past three years, annual revenue growth for the top services vendors has topped 100%, rising from around $0.6 billion in 2017 to an estimated $4.2 billion by 2023. After surveying 25 of the top RPA service providers about their customers, geographic focus, revenue and scale of implementations for the report, which was published last month, the authors – Leslie Joseph and Craig Le Clair – concluded that the massive growth in RPA services was a result not only of organisations adopting RPA as a cost mitigation strategy, but because automation was regarded as critical to the implementation of broader digital transformation efforts. However, while RPA adoption in terms of spend appears to be rising around the world, Forrester was unable to provide information about RPA adoption in Africa, or South Africa, as it was unable to source sufficient data.

Intel Corp on Tuesday launched its latest processor, its first using artificial intelligence (AI), designed for large computing centres. The chip, developed at its development facility in Haifa, Israel, is known as Nervana NNP-I or Springhill and is based on a 10 nanometre Ice Lake processor that will allow it to cope with high workloads using minimal amounts of energy, Intel said. Intel said its first AI product comes after it had invested more than $120 million in three AI startups in Israel. "In order to reach a future situation of'AI everywhere', we have to deal with huge amounts of data generated and make sure organisations are equipped with what they need to make effective use of the data and process them where they are collected," said Naveen Rao, general manager of Intel's artificial intelligence products group. "These computers need acceleration for complex AI applications."

On August 9, 2019, Illinois Governor J. B. Pritzker signed into law first-of-its-kind legislation regulating the use of artificial intelligence (AI) in Illinois. As previously reported by Troutman Sanders on June 26, 2019, the Illinois legislature, in what has been described as the most momentous legislative session in decades, passed the privacy statute aimed at regulating an ever-growing issue in HR: the use of AI in the hiring process. With the Governor's signature, the statute will become effective January 1, 2020. While the use of AI in the employment decision-making process might sound futuristic, many U.S. companies already use AI to streamline hiring and make the process more objective, including scanning resumes, scheduling interviews, and recently, actually conducting the first round of job interviews. These AI interviewing programs have different algorithms and methods, but essentially, they measure an applicant's facial expression, word choice, body language, and vocal tone, among other factors.

We live in times of high-tech euphoria marked by instances of geopolitical doom-and-gloom. There seems to be no middle ground between the hype surrounding cutting-edge technologies, such as Artificial Intelligence (AI) and their impact on security and defence, and anxieties over their potential destructive consequences. AI, arguably one of the most important and divisive inventions in human history, is now being glorified as the strategic enabler of the 21st century and next domain of military disruption and geopolitical competition. The race in technological innovation, justified by significant economic and security benefits, is widely recognised as likely to make early adopters the next global leaders. Technological innovation and defence technologies have always occupied central positions in national defence strategies.

Keep your floors clean without lifting a finger. If you make a purchase by clicking one of our links, we may earn a small share of the revenue. However, our picks and opinions are independent from USA Today's newsroom and any business incentives. By this time every summer, my floors look like a hot mess. All those times when I should have been inside doing maintenance cleaning, I was too busy running around the beach and having a blast.

As a subfield of machine learning, \emph{reinforcement learning} (RL) aims at empowering one's capabilities in behavioural decision making by using interaction experience with the world and an evaluative feedback. Unlike traditional supervised learning methods that usually rely on one-shot, exhaustive and supervised reward signals, RL tackles with sequential decision making problems with sampled, evaluative and delayed feedback simultaneously. Such distinctive features make RL technique a suitable candidate for developing powerful solutions in a variety of healthcare domains, where diagnosing decisions or treatment regimes are usually characterized by a prolonged and sequential procedure. This survey will discuss the broad applications of RL techniques in healthcare domains, in order to provide the research community with systematic understanding of theoretical foundations, enabling methods and techniques, existing challenges, and new insights of this emerging paradigm. By first briefly examining theoretical foundations and key techniques in RL research from efficient and representational directions, we then provide an overview of RL applications in a variety of healthcare domains, ranging from dynamic treatment regimes in chronic diseases and critical care, automated medical diagnosis from both unstructured and structured clinical data, as well as many other control or scheduling domains that have infiltrated many aspects of a healthcare system. Finally, we summarize the challenges and open issues in current research, and point out some potential solutions and directions for future research.

--Due to the drastic increase of mobile traffic, wireless caching is proposed to serve repeated requests for content download. T o determine the caching scheme for decentralized caching networks, the content preference learning problem based on mobility prediction is studied. We first formulate preference prediction as a decentralized regularized multi-task learning (DRMTL) problem without considering the mobility of mobile terminals (MTs). The problem is solved by a hybrid Jacobian and Gauss-Seidel proximal multi-block alternating direction method (ADMM) based algorithm, which is proven to conditionally converge to the optimal solution with a rate O (1 / k) . Then we use the tool of Markov renewal process to predict the moving path and sojourn time for MTs, and integrate the mobility pattern with the DRMTL model by reweighting the training samples and introducing a transfer penalty in the objective. We solve the problem and prove that the developed algorithm has the same convergence property but with different conditions. Through simulation we show the convergence analysis on proposed algorithms. Our real trace driven experiments illustrate that the mobility-aware DRMTL model can provide a more accurate prediction on geography preference than DRMTL model. Besides, the hit ratio achieved by most popular proactive caching (MPC) policy with preference predicted by mobility-aware DRMTL outperforms the MPC with preference from DRMTL and random caching (RC) schemes. As a promising technology for the fifth-generation (5G) wireless networks and beyond, proactive caching can alleviate the heavy traffic burden on backhaul links and reduce service delay, through proactively storing popular contents at base stations (BSs) and mobile terminals (MTs) [1]-[3]. With the limitation of storage memory, determining where and what to cache in content centric wireless networks becomes one of the main challenges in the design of proactive caching schemes. Among the various factors affecting the wireless caching design, involving the mobility of MTs and learning content preference are two critical challenges, which have attracted more and more research interest recently. A. background Current investigation on mobility aware wireless caching mainly includes two aspects: studying the impact of MT mobility on caching schemes [4]-[7], and optimizing the wireless caching schemes based on the mobility information of MTs Y u Y e, Ming Xiao and Mikael Skoglund are with the School of Electrical Engineering and Computer Science, Royal Institute of Technology (KTH), Stockholm, Sweden (email: yu9@kth.se,

We propose a Multi-Task Learning (MTL) paradigm based deep neural network architecture, called MTCNet (Multi-Task Crowd Network) for crowd density and count estimation. Crowd count estimation is challenging due to the non-uniform scale variations and the arbitrary perspective of an individual image. The proposed model has two related tasks, with Crowd Density Estimation as the main task and Crowd-Count Group Classification as the auxiliary task. The auxiliary task helps in capturing the relevant scale-related information to improve the performance of the main task. The main task model comprises two blocks: VGG-16 front-end for feature extraction and a dilated Convolutional Neural Network for density map generation. The auxiliary task model shares the same front-end as the main task, followed by a CNN classifier. Our proposed network achieves 5.8% and 14.9% lower Mean Absolute Error (MAE) than the state-of-the-art methods on ShanghaiTech dataset without using any data augmentation. Our model also outperforms with 10.5% lower MAE on UCF_CC_50 dataset.

Online health communities such as the online breast cancer forum enable patients (i.e., users) to interact and help each other within various subforums, which are subsections of the main forum devoted to specific health topics. The changing nature of the users' activities in different subforums can be strong indicators of their health status changes. This additional information could allow health-care organizations to respond promptly and provide additional help for the patient. However, modeling complex transitions of an individual user's activities among different subforums over time and learning how these correspond to his/her health stage are extremely challenging. In this paper, we first formulate the transition of user activities as a dynamic graph with multi-attributed nodes, then formalize the health stage inference task as a dynamic graph-to-sequence learning problem, and hence propose a novel dynamic graph-to-sequence neural networks architecture (DynGraph2Seq) to address all the challenges. Our proposed DynGraph2Seq model consists of a novel dynamic graph encoder and an interpretable sequence decoder that learn the mapping between a sequence of time-evolving user activity graphs and a sequence of target health stages. We go on to propose dynamic graph hierarchical attention mechanisms to facilitate the necessary multi-level interpretability. A comprehensive experimental analysis of its use for a health stage prediction task demonstrates both the effectiveness and the interpretability of the proposed models.

The high-order relations between the content in social media sharing platforms are frequently modeled by a hypergraph. Either hypergraph Laplacian matrix or the adjacency matrix is a big matrix. Randomized algorithms are used for low-rank factorizations in order to approximately decompose and eventually invert such big matrices fast. Here, block randomized Singular Value Decomposition (SVD) via subspace iteration is integrated within adaptive hypergraph weight estimation for image tagging, as a first approach. Specifically, creating low-rank submatrices along the main diagonal by tessellation permits fast matrix inversions via randomized SVD. Moreover, a second approach is proposed for solving the linear system in the optimization problem of hypergraph learning by employing the conjugate gradient method. Both proposed approaches achieve high accuracy in image tagging measured by F1 score and succeed to reduce the computational requirements of adaptive hypergraph weight estimation.