Mercer Bell is a customer experience agency. Technically, we were the first in this market as far as being a trademark CX agency. What does that mean nowadays? Nowadays it's a really big complicated broad church of things we do for our clients, including working with aspects of AI. That is everything from deploying it for our clients on an ongoing basis, helping clients message features of artificial intelligence to their clients, and then actually building bespoke things, particularly in the machine learning space for our clients on an ongoing basis.

Newswire) VSBLTY Groupe Technologies Corp. (CSE: VSBY) (5VS.F) (VSBGF), a leading retail software and technology company, announced today that-in partnership with Onyx-Cognivas Pty.-it is launching two privately-led security deployments in South Africa to support community safety initiatives. The state-of-the-art security technology will protect two prominent high-rise residential apartment buildings in the upmarket Sandton area, a high income residential, financial and business suburb of Johannesburg with a population of 225,000. The rollout plan is to deploy this technology across several apartment blocks, a hotel and commercial properties in the precinct-with the objective of deploying a "private Smart City". In addition, advanced custom sensory applications are planned to be installed in a well-known petroleum group with convenience stores/service stations throughout South Africa. The announcement was made by Jay Hutton, VSBLTY co-founder and CEO, who said, "We are excited to provide complete Smart City-like security solutions in Sandton. This state-of-the-art technology uses the power of machine learning and computer vision."

NIWA and Microsoft Corp. are teaming up to make artificial intelligence handwriting recognition more accurate and efficient in a project that will support climate research. The project aims to develop better training sets for handwriting recognition technology that will "read" old weather logs. The first step is to use weather information recorded during a week in July 1939 when it snowed all over New Zealand, including at Cape Reinga. NIWA climate scientist Dr. Andrew Lorrey says the project has the potential to revolutionise how historic data can be used. Microsoft has awarded NIWA an AI for Earth grant for the artificial intelligence project, which will support advances in automating handwriting recognition.

AI-enabled networks are going to employ advanced data analytics which will create methods wise, adaptive, self-aware, prescriptive and proactive. Ultimately, increasing usage of AI in controlling networks will play a major role in decreasing associated operating expenses and in addressing a lot of the obstacles that service providers have suggested are actually preventing insights from information being acted upon. With unprecedented growth in smartphone penetration, the democratization of web, Hd video use and use of advanced technologies as VR and AR, massive quantities of information are constantly reaching a mobile community today. The Ericsson Mobility Report predicts that the complete movable details visitors each month inside the region is actually likely to increase by more than 7 times between 2018 to 2024. As outlined by an Ericsson, suppliers from Southeast Asia, Oceania and India want more AI in the network of theirs as they think it is going to serve as an important component for dealing with the improved traffic and other complexities.

On the first episode of the final season of HBO comedy series Silicon Valley, tech startup engineer Bertram Gilfoyle lets an AI version of himself take over his instant messaging duties. "Do you need the real me for this conversation?" he asks his colleague. It may sound extreme, but the existence of spellcheckers predates the personal computer by a decade. Since 1992, grammar checking has also come as standard in word processors. For the better part of a generation, we've been OK with robots watching and correcting our language, occasional run-ins with Clippy aside.

Hardware, specifically designed for machine learning accelerate training and performance of neural networks and reduce the power consumption. Country: UK Funding: $310M Graphcore is a semiconductor company that develops accelerators for AI and machine learning. It aims to make a massively parallel Intelligent Processing Unit that holds the complete machine learning model inside the processor. Wave Computing is developing the Wave Dataflow Processing Unit (DPU), employing a disruptive, massively parallel dataflow architecture. When introduced, Wave's DPU-based solution will be the world's fastest and most energy efficient deep learning computer family.

The high frequency of road accidents makes driver safety one of the biggest challenges facing Fleet Management each day. In the US alone, 6 million car accidents every year happen every year, with more than 40,000 motor vehicle accident-related deaths in 2017. Several factors come into play when looking at the cause of traffic accidents. It could be the weather, changing road conditions, or the fault of other road users such as another driver or pedestrian. Apart from the risks posed by accidents to drivers, companies face significant losses when such accidents and traffic violations occur.

Macdeo was commenting on recent research which was conducted by Dell Technologies and the Institute for the Future, an independent research group based in California, which found that the work and learning environments of 2030 are already being shaped by the technology trends of today. Human and machine partnerships will create more equitable workplaces by evaluating candidates based on their capabilities, rather than gender, age or class. Employees will collaborate in entirely different, immersive ways using technologies such as XR, empowering workers more than ever before. AI will complement and augment human capabilities rather than replace them, and a deep understanding of AI and human and machine systems will unlock human potential and set workers apart. The research explored how technologies such as collaborative AI, multimodal interfaces, extended reality (XR), and secure distributed ledgers could change the congruence between humans and machines, while simultaneously enhancing collaboration within organisations.

In this paper, mm-Pose, a novel approach to detect and track human skeletons in real-time using an mmWave radar, is proposed. To the best of the authors' knowledge, this is the first method to detect >15 distinct skeletal joints using mmWave radar reflection signals. The proposed method would find several applications in traffic monitoring systems, autonomous vehicles, patient monitoring systems and defense forces to detect and track human skeleton for effective and preventive decision making in real-time. The use of radar makes the system operationally robust to scene lighting and adverse weather conditions. The reflected radar point cloud in range, azimuth and elevation are first resolved and projected in Range-Azimuth and Range-Elevation planes. A novel low-size high-resolution radar-to-image representation is also presented, that overcomes the sparsity in traditional point cloud data and offers significant reduction in the subsequent machine learning architecture. The RGB channels were assigned with the normalized values of range, elevation/azimuth and the power level of the reflection signals for each of the points. A forked CNN architecture was used to predict the real-world position of the skeletal joints in 3-D space, using the radar-to-image representation. The proposed method was tested for a single human scenario for four primary motions, (i) Walking, (ii) Swinging left arm, (iii) Swinging right arm, and (iv) Swinging both arms to validate accurate predictions for motion in range, azimuth and elevation. The detailed methodology, implementation, challenges, and validation results are presented.

Generalisation of a deep neural network (DNN) is one major concern when employing the deep learning approach for solving practical problems. In this paper we propose a new technique, named approximated orthonormal normalisation (AON), to improve the generalisation capacity of a DNN model. Considering a weight matrix W from a particular neural layer in the model, our objective is to design a function h(W) such that its row vectors are approximately orthogonal to each other while allowing the DNN model to fit the training data sufficiently accurate. By doing so, it would avoid co-adaptation among neurons of the same layer to be able to improve network-generalisation capacity. Specifically, at each iteration, we first approximate (WW^T)^(-1/2) using its Taylor expansion before multiplying the matrix W. After that, the matrix product is then normalised by applying the spectral normalisation (SN) technique to obtain h(W). Conceptually speaking, AON is designed to turn orthonormal regularisation into orthonormal normalisation to avoid manual balancing the original and penalty functions. Experimental results show that AON yields promising validation performance compared to orthonormal regularisation.