How to Get Your Enterprise Digitally Ready and Agile

whitePaper | January 11, 2023

Improving the trustworthiness of artificial intelligence (AI) systems is a shared priority for the private and public sectors, as indicated by prolific research and guidelines in recent years.

whitePaper | February 16, 2023

Artificial Intelligence (AI) is answer to challenges that telecoms must face in a time of rapidly changing market requirements, but it is not easy to adapt immediately. Many requirements can be solved by experienced experts. Unfortunately, access to their knowledge is increasingly limited, their education is usually expensive, there are not enough of them, and they are not always able to respond with sufficient speed.

whitePaper | August 14, 2020

As consumers and customers, we are accustomed to easy, intuitive and superfast transactions driven by data. Therefore, businesses have the various challenges of managing and mining the data they produce and use, but also ensuring that their digital experience is 100% reliable for their customers — so the chain of trust is never broken.

whitePaper | December 29, 2022

In our modern world, taking advantage of Artificial Intelligence (AI) to gain insights from data is becoming more prevalent day by day. Graphical Processing Unit (GPU) systems use multiple cores to perform parallel processing, running select workloads to decrease processing times. Compared to GPUs, Central Processing Units (CPUs) have fewer cores; previously, this resulted in less capacity for parallelized processing. To move beyond this limitation, Intel has released new hardware that runs typical AI mathematical computations more efficiently on the CPU, and has also released libraries with hardware optimizations that enable an additional increase in performance.

whitePaper | November 10, 2019

Large enterprises need to implement AI solutions quickly and efficiently, but many don’t have the right people, process and technology. Firms can accelerate progress to AI maturity through use of a robust workbench, an operating model built on a data science-led pyramid of skills, and pre-built solutions when data complexity and talent scarcity are too big a challenge.

whitePaper | December 15, 2019

Artificial Intelligence (AI), and specifically Machine Learning (ML), are being tested in an increasing number of fields, including data-centric environments. Image or text analysis, speech recognition, chatbot interactions, custom machine learning models… all these are elements that could enable the AI journey of a public safety and security organisation. This document dives into different aspects of integrating AI & ML in Public Safety activities, at different levels and in different domains of activity. It presents ethical and regulatory considerations, real examples from Public Safety Answering Points (PSAPs) and Emergency Response Organisations (EROs), and also initiatives that can benefit the public sector greatly, with a series of recommendations at the end.