Use Cases
Explore real-world applications of our solution. See how businesses across industries leverage our technology to solve complex challenges. From startups to enterprises, find out how we can transform your operations.
Certification of Neural Networks
In safety-critical systems, software certification and verification are essential processes to ensure that all components meet strict safety and performance standards. This is particularly crucial in AI and Neural Network Systems, where the methods used need to undergo formal evaluation to ensure their reliability and robustness. For these systems, a key aspect of certification involves extensive verification to confirm that the AI operates safely and effectively in these high-stakes environments.
AI Certification in Industrial Applications
Certification is a critical process in ensuring that AI and machine learning systems follow established standards and regulations. In industrial applications where AI controls physical systems and interacts with human operators, conducting proper assessment of AI systems is crucial to maintain the level of safety, performance, and reliability of these systems. Certification not only demonstrates compliance with industry norms but also builds trust among stakeholders; including customers, partners, and regulators.
Key advantages of certifying AI systems
- Increased Safety: Ensures that AI systems meet regulatory requirements, reduces the risk of errors in critical applications.
- Legal Compliance: Helps businesses comply with EU AI Act and international regulations, avoid legal issues.
- Enhanced Operational Efficiency: Improves the performance and reliability, leading to more efficient operations.
- Innovation and Competitiveness: Drives technological advancement, allows broader market access and attracting partners.
ISO/IEC 24029 is a key certification standard specifically focused on the assessment of AI systems. It provides guidelines for evaluating the robustness, accuracy, and transparency of AI technologies, which are increasingly being integrated into industrial applications. Obtaining ISO 24029 certification is essential for companies that want to ensure their AI systems are safe, reliable, and capable of performing as intended under various conditions. It also provides a competitive advantage by enabling broader market access, attracting more business partners, and increasing revenue opportunities.
AI Certification Simplified by Datenvorsprung
At Datenvorsprung, we understand the importance of achieving ISO/IEC 24029 certification for AI-driven industrial applications. In partnership with certification authorities, we provide comprehensive safety and robustness tests that are integral to the certification process. Our software tool provides formal & statistical verification of ML algorithms, ensuring they meet strict standards required by ISO/IEC 24029. Additionally, we offer ongoing assessment services to ensure that AI systems remain compliant as they evolve and as new threats or challenges arise. By integrating our tool into the certification process, companies can reduce time and costs, and maintain the trust of their stakeholders through continuous evaluation and adherence to industry standards. Our collaboration with industry experts ensures that AI technologies are safe, reliable, and ready for the market, by giving them competitive advantage and market access.
AI Controllers in Automation
Industrial automation is revolutionizing the manufacturing sector by efficiently managing complex and dynamic processes. With the rise of Industry 4.0 technologies, ensuring the safety and reliability of these systems has become a critical concern. Since AI often uses black-box models, where the system’s output is unpredictable, there is a growing need to formally evaluate and verify these systems. By designing safe AI controllers and integrating verification tools into the cyber-physical systems, companies can optimize processes and ensure the safety of their systems.
AI-Driven Industrial Transformation
AI controllers play a big role in industrial transformation, they are responsible for making real-time decisions that directly impact the operation of machinery or entire production lines. Safety is crucial in such environments where black-box machine learning models are used. Any malfunction or incorrect decision by an AI controller can lead to significant safety hazards, such as equipment failure, production downtime, or even harm to human operators.
For example an AI-driven robotic arm in a manufacturing plant that works alongside human operators needs to execute material handling tasks precisely. If the AI controller guiding the robot makes an incorrect decision, it could result in the robot causing injuries or production losses. Therefore the complexity of these systems requires comprehensive assessment to ensure that they operate safely and effectively under all conditions.
- Process Optimization: Reduces material waste and energy consumption through precise real-time control.
- Adaptability: Supports quick retraining and reconfiguration to handle different production sizes and requirements.
- Compatibility: Integrates various machine spare parts, suits different qualities and performance specifications.
Strategic Partnership for AI-Driven Industrial Automation
At Datenvorsprung, we have developed a specialized tool that enhances safety and operational excellence in industrial automation. Our tool is designed to statistically verify the behavior of AI controllers, adhering to previously defined safe operation zones for ML-driven robots. This verification process can be integrated directly into the model development and testing pipeline, allowing to monitor the system behavior as new data is introduced. By using our tool, companies can confidently deploy AI-driven automation solutions and significantly reduce the risk of unintended behavior in AI-driven systems, such as industrial controllers. Ultimately, our technology provides companies with the robustness, reliability, and efficiency needed to maintain competitive skills.
Datenvorsprung offers services for industrial automation.
- Safe AI Controller Development: We design customized AI controllers for industrial applications to perform flexible tasks, ensuring they are robust, adaptable, and capable of performing safely in a wide range of operational scenarios.
- AI Verification System: Using our in-house verification tool (patent pending), we provide thorough statistical analysis and validation of AI controllers, to provide guarantees that machinery performs as intended.
Partnership With Industry Leaders
In partnership with Beckhoff Automation GmbH, Datenvorsprung developed a use case to leverage the expertise of both companies in creating a robust, AI-driven control system. This collaboration brought together Beckhoff’s advanced hardware capabilities and Datenvorsprung’s cutting-edge AI software solutions to manage and verify machine learning-driven robots in industrial environments.
In this collaboration, Datenvorsprung developed an AI controller for the challenging pendulum swing-up task used in the Beckhoff hardware. After designing the AI, we used our in-house verification tool to verify the system’s safety and robustness, maintaining stability. This integrated setup allowed for the simulation and verification of a classic control problem, such as the cart-pole system, and also set an example for applicability of our tool in industrial automation.
Component Robustness
In modern industrial systems, sensors play a vital role in data collection and decision-making. However, relying on multiple sensors can become costly and problematic, especially in dynamic environments. For AI-driven applications, the data input to machine learning models must remain consistent and dependable. Ensuring system robustness to work effectively with fewer, more resilient sensors reduces equipment redundancy and minimizes costs. The ability to use alternative data sources such as cameras when necessary gives flexibility and operational efficiency to companies, lowering the risk of system failure.
System Robustness Against Fluctuating Component Properties
In industrial automation, sensors are exposed to a wide range of challenges, including temperature fluctuations, electromagnetic interference, mechanical vibrations, and potential physical obstructions. The ability of a sensor to function optimally in these conditions without degradation in performance is a testament to its robustness. Additionally, the accurate transmission of sensor data is imperative for the effective training and real-time deployment of machine learning (ML) models. Any deviation or noise in the sensor data can lead to suboptimal decision-making by the AI system, which in turn could compromise the operational integrity of the entire system.
Our Solution
At Datenvorsprung, we focus on system robustness by providing advanced tools for analyzing how AI systems perform with different sensor characteristics, such as varying quality, price, or manufacturer. Our technology ensures that your AI-driven system remains consistent and reliable, regardless of the sensors used, without the need for individual testing under all conditions. Additionally, our tool allows smooth integration of alternative data sources, such as camera inputs, to take over when sensors fail or perform poorly. This adaptive capability minimizes equipment redundancy, reduces operational costs, and enhances overall efficiency. By embedding these solutions into system design, companies can achieve greater operational flexibility and long-term cost savings, ensuring their AI systems perform reliably in real-world environments.
Key Benefits of Robustness Analysis Solutions by Datenvorsprung
- Performance Consistency Across Component Variants: Anticipating system behavior to maintain optimal performance across different components, regardless of manufacturer, type, or price range.
- Error Handling and Failover: Allowing smooth integration of alternative data sources, such as camera inputs, to take over when sensors fail or perform poorly in AI systems, reducing operational costs and increasing efficiency.
- Ensuring reliability of AI systems: Ensuring desired system behavior under varying environment conditions to sustain long-term robustness and safety.
By implementing our tools and AI methodologies, our partners can guarantee system robustness, which is essential for the reliable operation of AI-driven automation processes. They can be more flexible in operations and minimize equipment redundancy, reduce operational costs, and increase overall efficiency.
Simulation to Reality (Sim2Real Gap)
In many industrial applications, simulators play a vital role in developing and testing machine learning (ML) models. However, a significant challenge arises when these models, trained and validated in simulated environments, fail to perform reliably in real-world scenarios. This challenge, often referred to as the “reality gap,” is one of the most critical obstacles in deploying ML models for industrial use. The reality gap stems from several factors, including issues with system identification, incomplete or simplified modeling, and inherent hardware limitations. Bridging this gap is critical for ensuring that ML models operate safely and effectively in real-world industrial scenarios.
Our Solution For The Sim2Real Gap
By performing interval analysis on the system’s operational parameters, we can determine the boundaries in which the system will operate reliably. If the system stays within broad boundaries during simulation, we can have a more confident idea about the performance in the real world. Our technology provides two key insights: a clear yes-or-no answer on whether the simulated system will work in real-world conditions, also an understanding of the parameter ranges within which the system can operate safely and effectively.
An example of the Sim2Real gap can be seen in a robot arm controlled by a deep learning model and tested in simulation. While the robot might perform well in a controlled simulation, its performance could decrease in a real-world manufacturing environment with variable conditions, potentially causing errors or safety risks. Bridging this gap is crucial for ensuring that ML models can operate safely and effectively when deployed outside of the simulation, where the environment is far less predictable. Addressing simulation gap can be challenging and might be encountered in various industries such as automotive, aerospace and healthcare.
Benefits Of Our Technology
- Enhanced System Identification: Improved accuracy in identifying system dynamics within simulations, leading to more realistic models.
- Improved Modeling Completeness: Advanced techniques that account for a broader range of variables so that proper functioning is assured in reality. This minimizes discrepancies between simulated and real environments.
- Controller Assessment: Robustness testing for controllers in simulations to predict and optimize real-world performance.
Datenvorsprung helps to narrow the Sim2Real gap, enabling safer and more effective deployment of AI-driven solutions in industrial environments.
Autonomous Driving
In autonomous driving systems, ensuring both safety and robust performance is critical due to the complex and unpredictable nature of real-world environments. Autonomous vehicles must navigate a variety of conditions, from different traffic patterns to various weather and lighting scenarios, while adhering to regulatory standards and providing a reliable and comfortable user experience. Achieving these objectives requires verification of AI Systems controlling these vehicles to maintain consistent safety and performance.
Challenges in Autonomous Driving Systems
Our In-House Technology
Datenvorsprung addresses the challenges in autonomous driving systems by providing formal verification and analysis tools for the AI algorithms that control these vehicles. Our tool ensures that the AI operates within predefined safe bounds across various scenarios, such as different road and weather conditions. By proactively identifying potential safety risks and ensuring compliance with industry standards, our tool helps prevent accidents and builds trust among users and regulators. By integrating our tool into the development and testing phases, automotive companies can accelerate the certification and deployment of their autonomous vehicles, ensuring they are ready for real-world challenges and meet the highest safety standards.
- Verification of Prediction and Control Systems: Our tool evaluates the behavior of prediction algorithms and control systems within autonomous vehicles. It ensures that the vehicle’s decision-making processes are verified in real-world scenarios.
- Safe Operational Boundaries: By establishing confidence intervals and safety margins, our tool assesses if ML models operate in the pre-defined operational bounds, helping to deal with the uncertainties in the black-box ML models, ensuring safe vehicle operation.
- Stability in Different Conditions: Our solution can be used over as new data is obtained or as the ML model is updated. This repetitive evaluation helps maintain the reliability of the models.
- Adaptation to Varied Conditions: The tool helps tracking the vehicle’s ability to adapt to a range of environment conditions such as road and weather. This adaptability is crucial for maintaining effective performance.
Through these advanced methodologies, Datenvorsprung’s solutions improve the overall user experience by ensuring that autonomous vehicles operate smoothly, safely, and predictably. By integrating formal verification and robustness mechanisms in AI systems, our tool plays a vital role in advancing autonomous driving technology, contributing to safer and more reliable transportation solutions.