Earlier today Citrix held a media roundtable focused on its key trends outlook for 2020. The firm gathered information and insights from its local network of customers and partners to identify eight key trends that will likely shape many business decisions in the coming year. Unpacking said trends was Brendan McAvery, the regional director for Sub-Saharan Africa at Citrix. "2019 was a significant year for the technology sector. It brought rise to numerous technological innovations and new business models that have changed the face of global economies," he explained.

What are the key technology areas Ramco Systems will be focusing on in 2020? With this, Ramco focused on utilizing AI and ML-based algorithms to help organisations match the needs of changing business landscape. Our focus was on moving from Passive ERP (enterprise resource planning) to an Active ERP era, where systems could alert users on anomalies in data; reduce data entry by defaulting values or even pre-populating fields based on historical data through Smart Fill phrases, and so on. We have been leveraging behavioural analysis and prediction to reduce data entry and train the model to arrive at quick predictions. Also, with voice and chatbots becoming mainstream for customer/ employee engagement, UIs (user interfaces) have become passé, as we are witnessing users carry out transactions by chatting with the bot.

Entrepreneur and investor Peter Diamandis predicts that the future of shopping will be "always on", thanks to ubiquitous augmented reality. Artificial intelligence is in position to streamline and personalise the process, while virtual reality shopping can be successful if it creates a more social experience. Brands should prepare for far more data collection by asking the right questions and using AI to correlate more details. SAN FRANCISCO-- Here's the future of shopping, as Silicon Valley entrepreneur and investor Peter Diamandis sees it: augmented reality glasses will present an "always-on" shopping mode, artificially intelligent digital assistants will know your taste better than you and clothing will be made exactly to your measurements. And it could happen faster than one might think, he says.

Anna Sergeevna Bosman Department of Computer Science University of Pretoria Pretoria, South Africa anna.bosman@up.ac.za Abstract --Saliency maps are often used in computer vision to provide intuitive interpretations of what input regions a model has used to produce a specific prediction. A number of approaches to saliency map generation are available, but most require access to model parameters. This work proposes an approach for saliency map generation for black-box models, where no access to model parameters is available, using a Bayesian optimisation sampling method. The approach aims to find the global salient image region responsible for a particular (black-box) model's prediction. This is achieved by a sampling-based approach to model perturbations that seeks to localise salient regions of an image to the black-box model. Results show that the proposed approach to saliency map generation outperforms grid-based perturbation approaches, and performs similarly to gradient-based approaches which require access to model parameters. I NTRODUCTION Deep learning (DL) techniques have become a standard approach in computer vision. Specifically, the convolutional neural network (CNN) architecture has shown exceptional performance, achieving results comparable to human performance on image recognition tasks [1]-[3]. As a result, the CNN models are often deployed in real life as efficient black-box tools.

For example, it is of clinical interest to understand associations between: (a) alcoholism and the electrical activity of different brain regions over time collected from electroencephalography (EEG) (Li et al., 2010); (b) cognitive function and three-dimensional white-matter structure data collected from diffusion tensor imaging (DTI) (Goldsmith et al., 2014) for patients with multiple sclerosis (MS); and (c) cognitive impairment and brain's metabolic activity data collected from three-dimensional positron emission tomography (PET) imaging (Wang et al., 2014). Our work focuses on the problem of identifying brain network connections that are associated with neurocognitive measures for HIVinfected individuals. The outcome (response) is a continuous variable and the predictors are matrix representations of functional connectivity between the brain's cortical regions. Biophysical considerations motivate our interest in estimating a matrix of regression coefficients that has the following two properties: (i) it should be relatively sparse, since we aim to identify connections that most strongly predict the outcome; and more importantly, (ii) the response-related connections form clusters, since brain activity networks are known to consist of densely connected regions. These two properties translate to the coefficient matrix having relatively small clusters, or blocks of nonzero entries, which implies that it is low-rank. Hence, we aim to solve the matrix regression problem by estimating a coefficient matrix that is both sparse and low-rank. To further illustrate our approach, consider the three matrices in Figure 1. The one in the left panel is sparse, but full-rank, the one on the right panel is low-rank, but not sparse, while the one in the middle panel is both low-rank and sparse, which is the structure we are interested in. To find such a solution, we propose a regularization method called SParsity Inducing Nuclear Norm EstimatoR (SpINNEr).

The problem of specifying high-level knowledge bases for planning becomes a hard task in realistic environments. This knowledge is usually handcrafted and is hard to keep updated, even for system experts. Recent approaches have shown the success of classical planning at synthesizing action models even when all intermediate states are missing. These approaches can synthesize action schemas in Planning Domain Definition Language (PDDL) from a set of execution traces each consisting, at least, of an initial and final state. In this paper, we propose a new algorithm to unsupervisedly synthesize STRIPS action models with a classical planner when action signatures are unknown. In addition, we contribute with a compilation to classical planning that mitigates the problem of learning static predicates in the action model preconditions, exploits the capabilities of SAT planners with parallel encodings to compute action schemas and validate all instances. Our system is flexible in that it supports the inclusion of partial input information that may speed up the search. We show through several experiments how learned action models generalize over unseen planning instances.

The health of people and communities around the world has been improving over time. For example, the steep decline in child and maternal mortality is a key indicator of positive momentum. However, progress has not been equal across the globe, and there is a great need to focus on societal issues such as reducing health inequity and improving access to care for underserved populations. While researchers work to unlock life-saving discoveries and develop new approaches to pressing health issues, advancements in technology can help accelerate and scale new solutions. That is why we are launching AI for Health, a new $40 million, five-year program to empower researchers and organizations with AI to improve the health of people and communities around the world.

When a mysterious illness first pops up, it can be difficult for governments and public health officials to gather information quickly and coordinate a response. But new artificial intelligence technology can automatically mine through news reports and online content from around the world, helping experts recognize anomalies that could lead to a potential epidemic or, worse, a pandemic. In other words, our new AI overlords might actually help us survive the next plague. These new AI capabilities are on full display with the recent coronavirus outbreak, which was identified early by a Canadian firm called BlueDot, which is one of a number of companies that use data to evaluate public health risks. The company, which says it conducts "automated infectious disease surveillance," notified its customers about the new form of coronavirus at the end of December, days before both the US Centers for Disease Control and Prevention (CDC) and the World Health Organization (WHO) sent out official notices, as reported by Wired.

Navigating a new indoor space without any prior knowledge or even a map is a challenging task for a human, let alone a robot. To help develop intelligent machines that interact more effectively with complex 3D environments, Facebook researchers developed a GPU-accelerated deep reinforcement learning model that achieves near 100 percent success in navigating a variety of virtual environments without a pre-provided map. To achieve this breakthrough, the team focused their work on developing an efficient approach to scaling RL models, which require a significant number of training samples, using multi-node distribution. "A single parameter server and thousands of (typically CPU) workers may be fundamentally incompatible with the needs of modern computer vision and robotics communities," the researchers explained in their post, Near-perfect point-goal navigation from 2.5 billion frames of experience. "Unlike Gym or Atari, 3D simulators require GPU acceleration…. The desired agents operate from high-dimensional inputs (pixels) and use deep networks, such as ResNet50, which strain the parameter server. Thus, existing distributed RL architectures do not scale and there is a need to develop a new distributed architecture."

It was almost ten years ago when Sherry Turkle warned that the world was headed for a place where humans would be interacting socially with machines, like robots. Turkle is a MIT professor and social scientist who has been working on human-technology interaction and what it will mean for the human race. She is the author of several books including Alone Together and Reclaiming Conversation which explore the impact of technology on some of the aspects that actually make humans humans. Over the years, through her books and numerous talks, Sherry Turkle has explained the dangers of people trying to replace each other with machines including the smartphone and robots, but the world seems to have taken little heed as today we see companies inventing robots for all sorts of tasks and even for human relationships. Remember the Chinese inventor of a female robot whom he married in 2017?