This story was contributed to TechCabal by Conrad Onyango/bird. African governments are ramping up national strategies on the adoption of Artificial Intelligence (AI) in a fresh hunt for crucial data that would help improve public service delivery and governance. AI is no longer a preserve of the private sector as Africa's public sector hops on a global trend where governments join the hunt for robust data to transform how they deliver services to an increasingly tech-savvy population. Oxford Insights in its'Government AI Readiness Index 2021,' shows governments across the continent are turning to AI to improve their public services and gain strategic economic advantages. More governments, the report says, are building up AI ecosystems-backed by national strategies to capitalize on a 10-year global boom that has seen private sector firms commercialize AI research and development.

Human pose estimation in two-dimensional images videos has been a hot topic in the computer vision problem recently due to its vast benefits and potential applications for improving human life, such as behaviors recognition, motion capture and augmented reality, training robots, and movement tracking. Many state-of-the-art methods implemented with Deep Learning have addressed several challenges and brought tremendous remarkable results in the field of human pose estimation. Approaches are classified into two kinds: the two-step framework (top-down approach) and the part-based framework (bottom-up approach). While the two-step framework first incorporates a person detector and then estimates the pose within each box independently, detecting all body parts in the image and associating parts belonging to distinct persons is conducted in the part-based framework. This paper aims to provide newcomers with an extensive review of deep learning methods-based 2D images for recognizing the pose of people, which only focuses on top-down approaches since 2016. The discussion through this paper presents significant detectors and estimators depending on mathematical background, the challenges and limitations, benchmark datasets, evaluation metrics, and comparison between methods.

At a moment in time when the value of business agility is becoming clearer than ever, a growing range of diverse brands and businesses are prioritizing agility and engaging in digital transformations. Business agility unlocks greater operational flexibility, more nimble and responsive pivots to new processes or priorities, and smarter and more strategic decision-making -- all of which is enormously valuable in an increasingly tech-savvy and competitive professional environment, as discussed in my previous article. One of the most powerful tools that can be deployed to make that transformation successful is artificial intelligence (AI). AI-powered tools and tech form the backbone of systems and solutions that help companies become more agile. What follows is a brief overview of how agile methodologies emerged and why AI delivers such valuable synergies with agile business.

Time series forecasting is fundamental for various use cases in different domains such as energy systems and economics. Creating a forecasting model for a specific use case requires an iterative and complex design process. The typical design process includes the five sections (1) data pre-processing, (2) feature engineering, (3) hyperparameter optimization, (4) forecasting method selection, and (5) forecast ensembling, which are commonly organized in a pipeline structure. One promising approach to handle the ever-growing demand for time series forecasts is automating this design process. The present paper, thus, analyzes the existing literature on automated time series forecasting pipelines to investigate how to automate the design process of forecasting models. Thereby, we consider both Automated Machine Learning (AutoML) and automated statistical forecasting methods in a single forecasting pipeline. For this purpose, we firstly present and compare the proposed automation methods for each pipeline section. Secondly, we analyze the automation methods regarding their interaction, combination, and coverage of the five pipeline sections. For both, we discuss the literature, identify problems, give recommendations, and suggest future research. This review reveals that the majority of papers only cover two or three of the five pipeline sections. We conclude that future research has to holistically consider the automation of the forecasting pipeline to enable the large-scale application of time series forecasting.

Algorithm configuration (AC) is concerned with the automated search of the most suitable parameter configuration of a parametrized algorithm. There is currently a wide variety of AC problem variants and methods proposed in the literature. Existing reviews do not take into account all derivatives of the AC problem, nor do they offer a complete classification scheme. To this end, we introduce taxonomies to describe the AC problem and features of configuration methods, respectively. We review existing AC literature within the lens of our taxonomies, outline relevant design choices of configuration approaches, contrast methods and problem variants against each other, and describe the state of AC in industry. Finally, our review provides researchers and practitioners with a look at future research directions in the field of AC.

Mobile edge computing (MEC) is considered a novel paradigm for computation-intensive and delay-sensitive tasks in fifth generation (5G) networks and beyond. However, its uncertainty, referred to as dynamic and randomness, from the mobile device, wireless channel, and edge network sides, results in high-dimensional, nonconvex, nonlinear, and NP-hard optimization problems. Thanks to the evolved reinforcement learning (RL), upon iteratively interacting with the dynamic and random environment, its trained agent can intelligently obtain the optimal policy in MEC. Furthermore, its evolved versions, such as deep RL (DRL), can achieve higher convergence speed efficiency and learning accuracy based on the parametric approximation for the large-scale state-action space. This paper provides a comprehensive research review on RL-enabled MEC and offers insight for development in this area. More importantly, associated with free mobility, dynamic channels, and distributed services, the MEC challenges that can be solved by different kinds of RL algorithms are identified, followed by how they can be solved by RL solutions in diverse mobile applications. Finally, the open challenges are discussed to provide helpful guidance for future research in RL training and learning MEC.

Image segmentation tasks have seen lots of developments in recent years, and have become one of the most researched topics in Computer Vision⁶. One of the standards for segmentation is represented by the Deep Labelling for Image Segmentation architecture, also known as DeepLab. The approach was developed by Chen et al.¹ ² ³ ⁴ and different versions employing different mechanisms were proposed over time. In this article, a brief overview of the different DeepLab algorithms and their basic functioning will be given. The first appearance of the DeepLab architecture is found in [1].

We are looking for an exceptional and ambitious Senior Data Scientist to help our team of world class engineers, data scientists and commercial executives develop our Causal AI platform. You'll wear many hats, serving as the domain and data science expert for our solutions in Investment Management. We offer an intellectually stimulating environment, working in an interdisciplinary team and an inclusive culture. We are a high-calibre, mission-driven team building technology that improves our world. This is an exciting role for a smart, creative person with a strong technical background and desire to apply his/her skills in a stimulating scientific environment.

Human Activity Recognition (HAR) plays a significant role in the everyday life of people because of its ability to learn extensive high-level information about human activity from wearable or stationary devices. A substantial amount of research has been conducted on HAR and numerous approaches based on deep learning and machine learning have been exploited by the research community to classify human activities. The main goal of this review is to summarize recent works based on a wide range of deep neural networks architecture, namely convolutional neural networks (CNNs) for human activity recognition. The reviewed systems are clustered into four categories depending on the use of input devices like multimodal sensing devices, smartphones, radar, and vision devices. This review describes the performances, strengths, weaknesses, and the used hyperparameters of CNN architectures for each reviewed system with an overview of available public data sources. In addition, a discussion with the current challenges to CNN-based HAR systems is presented. Finally, this review is concluded with some potential future directions that would be of great assistance for the researchers who would like to contribute to this field.

Teaching artificial intelligence (AI) is challenging. It is a fast moving field and therefore difficult to keep people updated with the state-of-the-art. Educational offerings for students are ever increasing, beyond university degree programs where AI education traditionally lay. In this paper, we present an experience report of teaching an AI course to business executives in the United Arab Emirates (UAE). Rather than focusing only on theoretical and technical aspects, we developed a course that teaches AI with a view to enabling students to understand how to incorporate it into existing business processes. We present an overview of our course, curriculum and teaching methods, and we discuss our reflections on teaching adult learners, and to students in the UAE.