USING CONTENT MARKETING TO GENERATE DEMAND, CREATE NEW AUDIENCES

whitePaper | December 22, 2022

This study is promoted by Mobile World Capital Barcelona and NTT DATA in the framework of the Barcelona Digital Talent alliance. Mobile World Capital Barcelona is an initiative driving the digital advancement of society while helping improve people’s lives globally.

whitePaper | July 31, 2020

The current COVID pandemic has paralyzed large parts of our industry with significant movement limitations, occupancy restrictions, and in some cases complete shutdowns. Now, as the initial threat seems to be subsiding, governments and businesses must determine what is required to get back to some sense of normalcy. The biggest hurdles will be regulatory and/or safety-driven. Although the timelines and requirements for the easing of restrictions vary greatly, there are two common threads: limiting occupancy levels and contact tracing individuals who have tested positive for the virus.

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 | November 30, 2022

The first-generation low-code platforms required considerable adaptations and months of development in the late 1990s and early 2000s. Gartner expects that by 2024, low-code/no-code automation will account for 65% of all app development.

whitePaper | September 12, 2022

A seamless customer experience has quickly emerged as a critical differentiator for businesses across all industries.1 And the bar is set high – using artificial intelligence (AI) to deliver touches like personalization, gamification and around-the-clock customer support has become an all but everyday expectation for a large number of consumers.

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.