Into the era of secure remote work

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 30, 2022

Traditional database management systems have become more essential than ever, as data volume, velocity, and variety all increase at breakneck speed. But the harsh truth is that legacy database management systems were never designed for a digital-first business climate shaped by such factors as cloud computing, pervasive mobility, microservices, new data sources, and cloud-native application development.

whitePaper | July 29, 2022

China is following a national strategy to lead the world in artificial intelligence by 2030, including by pursuing “general AI” that can act autonomously in novel circumstances. Open-source research identifies 30 Chinese institutions engaged in one or more of this project‘s aspects, including machine learning, brain-inspired AI, and brain-computer interfaces. This report previews a CSET pilot program that will track China’s progress and provide timely alerts.

whitePaper | September 22, 2021

The value of conversational AI is clear—meaning it’s time to take the next step and begin setting specific goals if you want to see real returns on your investment. Access this beginner’s guide to conversational AI to learn important industry terms and explore the questions you need to be asking as you explore different solutions.

whitePaper | January 28, 2022

Since more and more decisions that used to be made by humans are nowadays made either with the help of Artificial Intelligence (AI) or by AI alone, it is essential that the AI algorithms are “fair.” In this whitepaper, we discuss issues surrounding the fairness of AI applications, with a special focus on how it can be assessed independently and subsequently certified. We explain why an AI application cannot be classified – and subsequently certified – as “fair” or “unfair” in a general sense and propose an approach that makes it possible to classify it as “fair” or “unfair” under the (application)-specific definition of fairness.

whitePaper | September 28, 2022

Your customers need more than dashboards. They need instant, actionable insights and analytics. And you need to offer more than basic analytics to futureproof your application. Learn how embedding AI-powered analytics gives you a sustainable competitive advantage. Download the complete guide today.