RISC AI ACADEMY: Your path to AI expertise

From the symbolic beginnings of the 1950s to modern deep learning architectures: This module introduces key concepts like model training, hyperparameter tuning, and model evaluation. Practical examples illustrate why high-quality data is essential for reliable outcomes. A look at current generative AI methods builds a bridge to the advanced modules.

This module explores the technological foundations of modern language models (ChatGPT & Co.) and explains how AI models analyze, interpret, and generate text. It covers current developments such as integrating LLMs with custom data sources (RAG) and the emerging concept of agentic AI. The module concludes with a practical guide for successful implementation within your organization.

How do machines ‘see’ images and extract information? This module introduces the fundamentals of computer vision – from pixel values to deep learning approaches (Convolutional Neural Networks). It shows why dataset quality and iterative training cycles are critical. Challenges like data protection and adversarial inputs are also discussed.

This module demonstrates how AI-powered image processing is used in manufacturing to detect defects early, optimize processes, and enhance safety. Real-world examples (defect detection, inline inspection, anomaly detection) highlight the value for quality assurance and efficiency. The focus is on combining domain expertise with powerful vision models.

For AI projects to move from prototype to production, clean data management and reliable operations are essential. This module covers the basics of modern data engineering concepts (data pipelines, data lakes) and the principles of MLOps. Topics include versioning, monitoring, and handling data/concept drift.

Time series are the key data type for understanding dynamic processes (production systems, energy grids). This module teaches the fundamentals of time series analysis and focuses on industrial applications such as predictive maintenance, quality control, and demand forecasting. It pays special attention to challenges like noise, concept drift, and the need for robust models.

This module focuses on key questions around responsible AI: How can we ensure systems are fair, transparent, and accountable? Examples illustrate both opportunities and risks – from bias issues to human-machine collaboration. The goal is to raise awareness for ethical concerns and provide a framework for responsible use.

Artificial intelligence raises legal and ethical questions. This module outlines the key legal frameworks in Europe, focusing on the GDPR requirements and the provisions of the new EU AI Act. Practical examples show what obligations companies must consider when using AI – from transparency requirements to liability issues.

This module looks ahead: It outlines current innovation drivers (U.S. tech giants, European initiatives) and classifies technologies using a ‘Hype-to-Market Radar.’ Real-world examples from automotive, manufacturing, and healthcare show how AI is transforming value creation, workflows, and business models worldwide.

This module offers a compact overview of the most important current AI applications – from text and image generators to voice cloning tools. The focus is on practical value: participants gain tools and inspiration to tackle their own challenges with AI and see how AI is changing creative and productive workflows.

How can your company concretely benefit from AI? This module centers on real-world applications. Together with participants, we identify industry-specific challenges, translate them into use cases, and systematically assess their feasibility. The goal is a structured roadmap for potential AI projects.

Agentic AI describes the next evolution of AI systems: moving from pure answer machines to autonomous agents that use tools and make decisions. This module provides an overview of the core concepts (tool integration, new architectures) and lays the groundwork for discussing potential future applications.

AI-based coding assistants (GitHub Copilot, ChatGPT) are fundamentally transforming software development. This module shows how these systems support developers in their daily work (code completion, test generation). We discuss productivity gains as well as limitations, risks, and legal aspects (copyright, secure data handling).

This module demonstrates how functional software prototypes can be built using modern no-code and AI-assist tools – without traditional programming skills. Live demos show participants what ‘Vibe Coding’ means. At the same time, we clearly address the limitations: security, scalability, and maintainability remain core domains for professional developers.

This module shows how companies can systematically integrate AI into their value chains – from a ‘+AI’ mindset to a radical ‘AI+’ approach. It explains which criteria determine make-or-buy decisions and provides a decision matrix for investments (GPU, edge deployments). The result is a clear roadmap that aligns options, investment needs, and business value.