My newest Blog: AI transparency through open source!

AI is currently on everyone's lips, especially since the publication of the text AI tool ChatGPT by the US company OpenAI. However, it is often forgotten that AI has already been shaping our everyday lives for a long time; Alexa and Siri are just a few examples.

AI use is also picking up steam across industries in the business context due to its numerous benefits. With intelligent applications based on AI, companies can accelerate and optimize business-critical processes. For example, many companies are already using AI in production in the area of predictive maintenance. In general, the possible spectrum of applications can hardly be limited. It ranges from autonomous driving to improved risk analysis and fraud detection to early disease detection in healthcare.

While the use of AI offers many advantages in principle, it also brings considerable challenges. In addition to hardware investments, architectural, cultural and process-related aspects must also be considered when establishing an agile AI environment. At this point, inflexible proprietary solutions quickly reach their limits, as they make it difficult to integrate and operate complex AI workloads. The alternative is open source containers and Kubernetes DevOps practices. Only these technologies and methodologies provide data scientists with the much-needed agility, flexibility, portability, and scalability to train, test, and deploy models productively.

The technical foundation for the development, training and integration of AI models is provided by Red Hat with its open source-based hybrid cloud platform Red Hat OpenShift. It is already widely used as the basis for AI applications. Red Hat OpenShift is built on containers, Kubernetes, DevOps, and a broad ecosystem of partner technologies, providing a solid foundation for production-ready AI environments – coupled with AI cloud services and training for rapid adoption.

Many people are currently discussing not only the possibilities and limits, but also the dangers associated with AI, for example with regard to non-transparent dependency – not to mention any ethical issues. Here, too, open source in particular can score points with its fundamental values. Apart from technological aspects, these include above all openness and an open corporate culture with principles such as transparency, inclusivity and collaboration. This means transparent processes, decision-making and work results, consideration of different points of view with an active feedback culture, and close cooperation between different parties. These approaches lead to the emergence of trustworthy AI – with criteria such as traceability, explainability and monitorability. It can be assumed that the Artificial Intelligence Act being prepared by the EU will also provide a regulatory framework here in the future and set corresponding ethical guard rails. And it is precisely the guiding principles of open source that can serve as a model here.?