Africa has a unique opportunity to develop its competitiveness through artificial intelligence (AI). From agriculture and remote health to translating the 2,000-odd languages spoken across the continent, AI can help tackle the economic problems that Africa faces. Africa faces several known challenges in developing AI such as a dearth of investment, a paucity of specialised talent, and a lack of access to the latest global research. These hurdles are being whittled down, albeit slowly, thanks to African ingenuity and to investments by multinational companies such as IBM Research, Google, Microsoft, and Amazon, which have all opened AI labs in Africa. Innovative forms of trans-continental collaboration such as Deep Learning Indaba (a Zulu word for gathering), which is fostering a community of AI researchers in Africa, and Zindi, a platform that challenges African data scientists to solve the continent's toughest challenges, are gaining ground, buoyed by the recent "homecoming" of several globally-trained African experts in AI.

The Global Artificial Intelligence for Healthcare Applications market report enumerates highly classified information portfolios encompassing multi-faceted industrial developments with vivid references of market share, size, revenue predictions along with overall regional outlook. The report illustrates a highly dependable overview of the competition isle, with detailed assessment of business verticals. Post a systematic research initiative and subsequent evaluation overview, the global Artificial Intelligence for Healthcare Applications market mimicking its past growth performance is anticipated to strike a flourishing ROI and is therefore more likely to be on the favorable growth curve in the coming years. This versatile report describing the global Artificial Intelligence for Healthcare Applications market has entailed a range of information portfolios that have been segregated into indispensable and additional information streams that have been represented in the form of tables, pie-charts, graphs and the like to align with maximum reader understanding.

The healthcare industry is straining under the impact of COVID-19. The sudden influx of patients in hospitals is exposing vulnerabilities in the current healthcare system. Some hospitals became hotspots for infection, disrupting routine healthcare procedures, while others closed their Outpatient Departments (OPDs), fearing transmission. This dire situation ushered in a massive digital transformation of the healthcare industry to improve care quality, reduce operational costs, and save time for treatments. Although the pandemic accelerated the transformation and saw pioneering research in medical science, healthcare advancement is a phased evolution.

Amidst all the fast-paced technological innovations, contact centres continue to be at the frontline of delivering customer experience. "Even though businesses have identified different mechanisms to reach out to users such as mobile applications, notifications etc, users still reach out to the call center. Case in point, even when you are able to book a cab in under two minutes through the app, you will want to reach out to customer care if there is a problem," shares Sourabh Gupta, Co-Founder & CEO, Vernacular.ai, an AI-first SaaS business enhancing customer experience through intelligent voice conversations. However, Sourabh points out that innovation for contact centres has been overlooked and that's why today they are unable to offer the same convenience that the business provides digitally through other mediums. This gap has come to the fore amidst the pandemic.

To address this challenge, two recent works approach the problem from different perspectives. Tramer et al. (2020) Reliable evaluation of adversarial defenses is a outlines an approach for manually crafting adaptive attacks challenging task, currently limited to an expert that exploit the weak points of each defense. Here, a domain who manually crafts attacks that exploit the defense's expert starts with an existing attack, such as PGD (Madry inner workings, or to approaches based et al., 2018) (denoted as - in Figure 1), and adapts it based on on ensemble of fixed attacks, none of which may knowledge of the defense's inner workings. Common modifications be effective for the specific defense at hand. Our include: (i) tuning attack parameters (e.g., number key observation is that custom attacks are composed of steps), (ii) replacing network components to simplify the from a set of reusable building blocks, attack (e.g., removing randomization or non-differentiable such as fine-tuning relevant attack parameters, network components), and (iii) replacing the loss function optimized transformations, and custom loss functions.

We present a modelling framework for the investigation of supervised learning in non-stationary environments. Specifically, we model two example types of learning systems: prototype-based Learning Vector Quantization (LVQ) for classification and shallow, layered neural networks for regression tasks. We investigate so-called student teacher scenarios in which the systems are trained from a stream of high-dimensional, labeled data. Properties of the target task are considered to be non-stationary due to drift processes while the training is performed. Different types of concept drift are studied, which affect the density of example inputs only, the target rule itself, or both. By applying methods from statistical physics, we develop a modelling framework for the mathematical analysis of the training dynamics in non-stationary environments. Our results show that standard LVQ algorithms are already suitable for the training in non-stationary environments to a certain extent. However, the application of weight decay as an explicit mechanism of forgetting does not improve the performance under the considered drift processes. Furthermore, we investigate gradient-based training of layered neural networks with sigmoidal activation functions and compare with the use of rectified linear units (ReLU). Our findings show that the sensitivity to concept drift and the effectiveness of weight decay differs significantly between the two types of activation function.

Local causal structure learning aims to discover and distinguish direct causes (parents) and direct effects (children) of a variable of interest from data. While emerging successes have been made, existing methods need to search a large space to distinguish direct causes from direct effects of a target variable T. To tackle this issue, we propose a novel Efficient Local Causal Structure learning algorithm, named ELCS. Specifically, we first propose the concept of N-structures, then design an efficient Markov Blanket (MB) discovery subroutine to integrate MB learning with N-structures to learn the MB of T and simultaneously distinguish direct causes from direct effects of T. With the proposed MB subroutine, ELCS starts from the target variable, sequentially finds MBs of variables connected to the target variable and simultaneously constructs local causal structures over MBs until the direct causes and direct effects of the target variable have been distinguished. Using eight Bayesian networks the extensive experiments have validated that ELCS achieves better accuracy and efficiency than the state-of-the-art algorithms.

Markov Decision Process (MDP) is the underlying model for optimal planning for decision-theoretic agents in stochastic environments. Although much research focuses on solving MDP problems both in tabular form or using factored representations, none focused on tensor decomposition methods. Solving MDPs using tensor algebra offers the prospect of leveraging advances in tensor-based computations to further increase solver efficiency. In this paper, we develop an MDP solver for a multidimensional problem using a tensor decomposition method to compress the transition models and optimize the value iteration and policy iteration algorithms. We empirically evaluate our approach against tabular methods and show our approach can compute much larger problems using substantially less memory, opening up new possibilities for tensor-based approaches in stochastic planning

By adhering to the dictum, "No causation without manipulation (treatment, intervention)", cause and effect data analysis represents changes in observed data in terms of changes in the causal factors. When causal factors are not amenable for active manipulation in the real world due to current technological limitations or ethical considerations, a counterfactual approach performs an intervention on the model of data formation. In the case of object representation or activity (temporal object) representation, varying object parts is generally unfeasible whether they be spatial and/or temporal. Multilinear algebra, the algebra of higher-order tensors, is a suitable and transparent framework for disentangling the causal factors of data formation. Learning a part-based intrinsic causal factor representations in a multilinear framework requires applying a set of interventions on a part-based multilinear model. We propose a unified multilinear model of wholes and parts. We derive a hierarchical block multilinear factorization, the M-mode Block SVD, that computes a disentangled representation of the causal factors by optimizing simultaneously across the entire object hierarchy. Given computational efficiency considerations, we introduce an incremental bottom-up computational alternative, the Incremental M-mode Block SVD, that employs the lower-level abstractions, the part representations, to represent the higher level of abstractions, the parent wholes. This incremental computational approach may also be employed to update the causal model parameters when data becomes available incrementally. The resulting object representation is an interpretable combinatorial choice of intrinsic causal factor representations related to an object's recursive hierarchy of wholes and parts that renders object recognition robust to occlusion and reduces training data requirements.

The majority of organisations globally lack the internal resources to support critical artificial intelligence and machine learning initiatives, according to a new study from Rackspace Technology. The survey, Are Organisations Succeeding at AI and Machine Learning? "This study shines a light on the struggle to balance the potential benefits of AI and ML against the ongoing challenges of getting AI/ML initiatives off the ground," Rackspace says. "While some early adopters are already seeing the benefits of these technologies, others are still trying to navigate common pain points such as lack of internal knowledge, outdated technology stacks, poor data quality or the inability to measure ROI." Participants of the survey in the APJ region rated themselves slightly higher at 18% compared to global statistics a 17% for advanced maturity in AI/ML). APJ participants were more likely to be using AI/ML in more applications and use cases, and are spending significantly more on average than global participants ($1.3 million vs $1.06 million).