Into the era of secure remote work

whitePaper | December 30, 2022

The technological development of artificial intelligence (AI) is proceeding rapidly, with investment in AI-based solutions dramatically increasing across the globe. Europe is no exception.

whitePaper | December 23, 2022

The transformative potential of artificial intelligence (AI) is a kind of truism. Seeking to capture the benefits of the “Fourth Industrial Revolution” or “third wave of the digital revolution,” countries are prioritizing efforts to reorganize their public and private sectors, fund research and development (R&D), and establish structures and policies that unleash AI innovation.

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 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 | January 8, 2020

AI in manufacturing is a game-changer. It has the potential to transform performance across the breadth and depth of manufacturing operations. However, the massive potential of this new Industrial 4.0 era will only be realized if manufacturers really focus their efforts on where AI can add most value and then drive the solutions to scale.

whitePaper | May 15, 2023

The future of AI is hybrid. As generative AI adoption grows at record-setting speeds1 and drives higher demand for compute,2 AI processing must be distributed between the cloud and devices for AI to scale and reach its full potential – just like traditional computing evolved from mainframes and thin clients to today’s mix of cloud and edge devices. A hybrid AI architecture distributes and coordinates AI workloads among cloud and edge devices, rather than processing in the cloud alone. The cloud and edge devices, such as smartphones, vehicles, PCs, and IoT devices, work together to deliver more powerful, efficient, and highly optimized AI.