Information & Communication Technologies (ICT) in Education for Development

whitePaper | October 3, 2022

Contact center associates deal with complex incall tasks, across multiple systems. High staff turnover is common, and training up new staff can take weeks or even months. These challenges can quickly begin to affect engagement, performance and KPIs. With a cohesive digital strategy, your Digital Workforce has instant access to all customer information from one system, meeting the customer’s expectation of immediacy.

whitePaper | November 29, 2022

Artificial intelligence has long since graduated from science fiction and speculative use cases to widespread and diverse development across fintech sectors. CB Insights reported that financings for AI startups showed continued robust activity through Q2 2022, despite changes in market sentiment from the exuberance of 2021.

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 | December 26, 2022

IBM embraces five foundational pillars of trustworthy AI: Explainability, Fairness, Robustness, Transparency, and Privacy. These pillars underpin the development, deployment and use of AI systems. This document and IBM’s trustworthy AI pillars are meant to help you align on both process and outcomes.

whitePaper | August 25, 2022

The document focuses on the best practices to consider for API design by breaking it into its basic building blocks. Along with each recommendation the document also describes the reason and impact of a certain design decision through scenarios. The document provides recommendations on object representations, usage of headers and URL/URI templates to consider.

whitePaper | January 5, 2020

Machine learning (ML) is an application of artificial intelligence (AI) that allows systems to automatically learn and improve from exposure to more data without being explicitly programmed. ML focuses on the development of computer programs that can access data and use it to learn for themselves.1 While AI represents the broader concept of machines being able to carry out tasks in an intelligent way, machine learning is a current application of AI based on the idea that we can give machines access to data, and they can use that data to learn for themselves.