Better Than Bots: Welcome to the AI-Powered Era of Smart Collaboration

whitePaper | February 11, 2020

Deepwave Digital has leveraged the Artificial Intelligence Radio Transceiver (AIR-T) to create the first deep learning sensor for a 5G network. This network, the Citizens Broadband Radio Service (CBRS), will be the first spectrum sharing service provided by the telecommunications industry that leverages real time RF sensing. Critical to the operation of CBRS is the ability to determine if priority users are active on specific frequency channels. When no priority users are present, the spectrum may be reallocated for commercial networks to provide new enterprise services or additional bandwidth to existing services.

whitePaper | December 14, 2022

Data science is Vectra AI’s north star. We have always believed that data science and AI, if used properly, can transform our fight against cyberattacks and give an edge to defenders. However, not all AI is the same.

whitePaper | May 4, 2022

The pandemic has brought about significant economic disruption, thereby increasing the need for banks and financial institutions to be more agile. However, Covid-19 is not the only factor that drives automation and digitalization in the credit and lending process in credit institutions. Incumbent banks are increasingly facing pressure from neobanks, these having the strategic advantage of being “born digital”. In addition, customers demand fully digitalized processes with regard to the ease of use and fast turnaround times.

whitePaper | September 20, 2022

Artificial intelligence and machine learning (AI/ML) is on every organization’s radar. Businesses and IT professionals are finding some amazing use cases for AI/ML and already experiencing its benefits, but there’s still so much untapped and unrealized potential.

whitePaper | July 20, 2022

The growing diversity of actors and circumstances involved in AI/ML deployment makes establishing a set of ethical participatory practices especially difficult for this field. Even among practitioners working at the same organization, there can be substantial differences in both their knowledge about how AI/ML systems were created or will be used and their ability to incorporate inclusive practices. Additionally, the greater availability of AI development platforms, including “no-code” platforms, means that algorithmic models can be deployed without having deep expertise, expanding the number of circumstances automated systems are deployed in. This means many more instances where automated systems are being deployed without consideration of how the algorithms were developed, the provenance of the datasets and nature of the bias on which they were trained and tested, and the ethical implications of their development and deployment.

whitePaper | December 22, 2022

Machine learning, one of the key building blocks of AI, has been a part of the technological world since the 1950s, when the earliest programmers asked computers to make sense of large sets of data. Programmers have increasingly refined the ability of machines to study data in order to detect patterns that allow computers to then organise information, identify relationships, make predictions and detect anomalies. Today, modern applications of AI have already given us self-driving cars and virtual assistants and have helped us detect fraud and manage resources like electricity more efficiently. Sectors as diverse as retail, sports, banking, manufacturing and healthcare have all found applications for machine learning and AI.