Games of Thought
Artificial Intelligence (AI) has rapidly become an integral part of our lives. It is revolutionizing industries such as healthcare, aviation, transportation, smart homes and more. From personalized recommendations to communication or autonomous vehicles, AI’s transformative capabilities are reshaping the way we live and work.
Importance of AI Robustness in Ensuring Reliable and Safe Systems
As AI continues to expand its reach, ensuring the reliability and safety of AI systems becomes paramount.
Robustness in AI training plays a critical role in guaranteeing that these systems perform consistently, accurately, and predictably under different circumstances. A robust AI model can withstand various challenges, including noisy data, changes in the environment, and even deliberate attempts to deceive the system.
In training, robustness is the process of equipping AI models to handle different scenarios effectively and maintain their accuracy and reliability in real-world applications. This can be achieved with two approaches: training for global or for local robustness, which are discussed in our next article.
Regulatory attempts and guidelines will shape AI development
Many nations, supranational bodies and institutions are currently working on regulations for AI development and deployment. Their main goal is to enhance trust and ensure the accountability, transparency and reliability of AI, which is of particular importance in high stake safey critical use cases. Independent auditing companies like TÜV SÜD and EASA are providing guidelines on this matter, too (see for example European Union Aviation Safety Agency 2021, EASA Concept Paper First usable guidance for Level 1 machine learning applications, Issue 01, Link).
Uncertainties are inevitable in real-world data, arising from noisy sensors, varying environmental conditions, and incomplete information. Robust AI models should be able to account for and cope with such uncertainties to provide reliable results. Additionally, adversarial inputs are crafted with the intent of deceiving AI systems, making robustness against these attacks crucial for maintaining security and trust in AI applications.
Find out how we approach this problem and how global and local robustness come into play in our next articles!
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