Industrially Scalable AI

The Industrial Scalable AI area works at the intersection of AI, scalability and industrial challenges, where we address the following problems:

Robustness: How can we ensure that industrially scalable AI systems operate reliably under different working conditions? It's crucial to explore methods that ensure consistent AI performance across different environments.

Flexibility: How can industrially scalable AI adapt to varying tasks and changing products? It is important to determine how AI can be versatile in handling different tasks and adapting to product changes.

Cost-effectiveness: What methods can make large-scale implementations of industrially scalable AI economically feasible? Identifying cost-effective strategies is critical to the widespread acceptance of AI solutions.

Trustworthiness: How can we build and maintain trust in industrially scalable AI systems among users? Developing methods to ensure that AI systems are transparent and trustworthy is crucial for user trust.

Our solutions

Open Source Datasets: Providing publicly available datasets are essential to industrial applications. These datasets provide valuable training and evaluation resources for developing cost-effective and robust AI models.

Synthetic Data Generation: Creating synthetic data allows us to augment our training sets. We’ll explore methods to generate realistic data that mimics real-world scenarios, aiding in robustness and scalability.

Foundational Models: Creating foundational machine learning models is essential. We’ll explore pretrained, validated  models and architectures that serve as building blocks for Industrially Scalable AI systems.

Few-Shot and Zero-Shot Learning: Investigating techniques for learning from limited examples (few-shot learning) and even without any labeled data (zero-shot learning) will be crucial. These approaches enhance adaptability.

Distributed AI: Exploring distributed AI, especially Federated learning, enabling secure collaboration between different organizations and to make computation scalable by running closer to the edge.

Safe & Secure AI: Creating methods for ensuring that AI functions as intended and that it follows policies, standards and regulations.  Especially, we are looking at methods for testing AI, and for making AI transparent regarding its reasoning.