WHY MACHINE LEARNING IS THE FUTURE OFPREDICTIVE AND INDUSTRIAL MAINTENANCE

whitePaper | October 20, 2022

Telecoms need to be able to incorporate new technologies and next-generation connectivity such as 5G to customers and end users.

whitePaper | August 10, 2020

Data is the most important asset businesses have. As the world has become increasingly digital, data has grown in volume and value. In fact, this most valuable resource of modern society doubles in volume every year and that growth rate is likely to accelerate even further. Of course, this creates a challenge: how to store data securely and how to be sure that every operation with data is secured? Today loss of data means loss of everything: business, identity, future, and even life in some cases.

whitePaper | September 15, 2022

The concept of Artificial Intelligence (AI) and Machine Learning (ML) dates back to the 1950s with researchers trying to use machines to simulate human intelligence in machines. However, their use in the financial sector has been limited. With enhanced customer service and mounting cost pressures, financial institutions (FIs) have now started to look at AI as a possible solution for improving cost and operational efficiencies.

whitePaper | June 13, 2022

As businesses move beyond rigid rules and strictly linear processes in IT service management, there’s a world of opportunity for artificial intelligence-powered automation in both proactive and reactive service delivery. We discuss what that smart tech-driven world can look like and how you can get there.

whitePaper | January 20, 2022

Machine learning (ML) algorithms are moving to the IoT edge due to various considerations such as latency, power consumption, cost, network bandwidth, reliability, privacy and security. Hence, there is an increasing interest in developing neural network (NN) solutions to deploy them on low-power edge devices such as the Arm Cortex-M microcontroller systems. To enable that, we present CMSIS-NN, an open-source library of optimized software kernels that maximize the NN performance on Cortex-M cores with minimal memory footprint overhead. We further present methods for NN architecture exploration, using image classification on CIFAR-10 dataset as an example, to develop models that fit on such constrained devices.

whitePaper | December 23, 2019

We are witnessing a new genre of companies shaping the business landscape by leveraging emerging technologies and applying them with such potency to create competitive advantage. These organisations have a quest to scale; they are big on customer experience; they are open to partnerships and are able to deploy their products and services at scale through APIs; they are big adopters of the cloud and quite often are born in the cloud; they embrace design thinking and are happy to return to first principles to find innovative solutions to business problems; and they are relentless at leveraging the power of artificial intelligence (AI) for insights and decision making.