The Role of AI & Machine Learning in Securing Healthcare Organizations

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 | April 16, 2020

To your credit, you’ve done a little research and picked up a drawing tool here, an inspections tool, a bidding tool, and a financials tool. You’ve embraced the future or The Present, as it’s now called and are keeping abreast of ever-changing best practices.

whitePaper | August 17, 2020

For years, employees drove to an office, opened their computer, logged onto a corporate network with their username and password and got on with their workday. But working standard hours in a fixed office location is becoming more and more rare. In fact, a report by Gallup found that 43 percent of American employees work remotely.1 This growing mobile workforce requires the same, secure access to resources as their onsite counterparts — without connecting to the corporate network. And both onsite and remote employees need secure ways to access the expanding number of applications and resources that are hosted in the cloud. To accommodate, enterprise technology and IT practices must adapt.

whitePaper | August 22, 2022

Now in its ninth year, the Microsoft Vulnerabilities Report provides a unique analysis of the vulnerability landscape in Microsoft’s ecosystem. Historically, the report has delivered a holistic annual view of the vulnerabilities within Microsoft’s platforms and products, and has established an undeniable business case for the importance of removing admin rights to reduce risk.

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 | January 20, 2022

Machine learning (ML) algorithms are moving to the IoT edge due to various considerations such as latency, power consumption, cost, network bandwidth, reliability, privacy and security. Hence, there is an increasing interest in developing neural network (NN) solutions to deploy them on low-power edge devices such as the Arm Cortex-M microcontroller systems. To enable that, we present CMSIS-NN, an open-source library of optimized software kernels that maximize the NN performance on Cortex-M cores with minimal memory footprint overhead. We further present methods for NN architecture exploration, using image classification on CIFAR-10 dataset as an example, to develop models that fit on such constrained devices.