FAST – Feedback-guided Automation of Sub-tasks

Are you ready to revolutionize your decision-making processes? Our FAST Framework empowers clients to tackle decision-making tasks driven by visual information — such as quality inspections and medical diagnoses — even when data is scarce. At Fraunhofer IKS, we specialize in reliably automating these decisions by pinpointing samples ready for automation while seamlessly delegating the remainder to an expert.

Challenge: Application of Machine Learning with Limited Data

One of the biggest hurdles in applying Machine Learning (ML) to real-world scenarios is the necessity for extensive training data. This challenge is particularly daunting for small enterprises or specialized tasks (like medical applications), where the costs and risks associated with data collection and labeling can be prohibitive. Our framework is designed for professionals facing automation tasks reliant on visual information, particularly when expert insight is available but data is limited.

Solution: Focused Sub-Problems and Expert Feedback

Instead of attempting to solve the entire problem at once, we identify a manageable sub-problem using a small initial data set. We deploy a targeted system that excels in this specific area, minimizing errors and maximizing reliability. Samples outside this sub-problem are expertly handled, ensuring that your team can focus on what they do best. Furthermore, expert feedback continuously enhances the system, allowing for increased automation over time.

Key Benefits:

• Immediate Impact: Deploy a partial solution quickly, skipping lengthy data collection phases, and start benefiting right away.
• Lower Barriers & Risks: Enjoy easy setup with minimal initial data requirements.
• Enhanced Efficiency: Free up experts to concentrate on complex issues that truly require their expertise.
• Build Trust: Foster acceptance and confidence in the system by collaborating with it from the outset.

Insight Background

At the forefront of innovation, we aim to deepen the understanding of complex relationships within safety-critical systems. The FAST Framework addresses the critical question: "How can limited data facilitate early and reliable automation through AI?" By providing a robust framework for the development of dependable AI systems, we empower organizations to achieve reliable automation even with minimal data.

Our research focuses on trustworthy artificial intelligence, safety assurance, and resilient software systems. One of the groundbreaking outcomes of this research is the FAST FrameworkJoin us in revolutionizing the way AI is integrated into safety-critical environments, making reliable automation accessible to all.

In-depth information

• Visual inspection – FAST: How less data leads to early and reliable automation through AI
• Reliable AI Enables Automation of Quality Inspection in Industry
• Insufficient Data? No Problem!
• Subtasks enable solving complex inspection problems
• The process is directed by DEEP