AI systems are rapidly advancing in two categories: Predictive AI and Generative AI, demonstrated by Large Language Models. The NIST AI Risk Management Framework emphasizes the need for secure and reliable AI operations. A study by NIST Trustworthy and Responsible AI outlines a comprehensive taxonomy and strategies for controlling Adversarial Machine Learning (AML) attacks. Read more at MarkTechPost.
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Artificial Intelligence (AI) Systems: Practical Solutions and Value
Categories of AI Systems
AI systems are divided into Predictive AI and Generative AI. Large Language Models (LLMs) are examples of generative AI, creating original content, while predictive AI focuses on making predictions using data.
Operational Characteristics
Safe, reliable, and resilient operations are crucial for trustworthy AI. The NIST AI Risk Management Framework and AI Trustworthiness taxonomy highlight the necessary operational characteristics for trustworthy AI.
Adversarial Machine Learning (AML)
A team of researchers from NIST has advanced the field of AML by creating a thorough taxonomy of terms and providing definitions. The taxonomy covers Machine Learning techniques, attack lifecycle phases, attacker objectives, and strategies for controlling AML attacks.
Research Contributions
The research offers a common vocabulary for discussing AML, a comprehensive taxonomy of AML attacks, and strategies for mitigating AML attacks. It also provides a critical analysis of current mitigation strategies.
Practical AI Solutions
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