Adversarial Attack
An adversarial attack is a technique that creates deliberately crafted inputs designed to fool a machine learning model into making incorrect predictions. These attacks reveal vulnerabilities in AI systems and are critical to AI safety research.
Understanding Adversarial Attack
Adversarial attacks expose critical vulnerabilities in machine learning models by crafting inputs with carefully calculated perturbations that cause confident but incorrect predictions. A classic example involves adding imperceptible pixel-level noise to an image of a panda, causing a convolutional neural network to classify it as a gibbon with high confidence. These attacks extend beyond computer vision to natural language processing, where subtle word substitutions can mislead sentiment analysis or text classification models. Adversarial attacks are categorized as white-box (attacker has full model access) or black-box (no model access), with techniques like FGSM and PGD being widely studied. Understanding these vulnerabilities is essential for AI safety, particularly in high-stakes applications like autonomous systems and medical diagnosis. The field has driven significant research into adversarial training and model robustness as countermeasures.
Category
AI Ethics & Safety
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Adversarial Training
Adversarial training is a defense strategy that improves model robustness by including adversarial examples in the training data. The model learns to correctly classify both normal and adversarially perturbed inputs.
AI Alignment
AI alignment is the research field focused on ensuring that AI systems pursue goals and behaviors consistent with human values and intentions. Alignment is considered one of the most important challenges in AI safety.
AI Ethics
AI ethics is the branch of ethics that examines the moral implications of developing and deploying artificial intelligence systems. It addresses fairness, transparency, privacy, accountability, and the societal impact of AI technology.
AI Safety
AI safety is the interdisciplinary field focused on ensuring AI systems operate reliably, beneficially, and without causing unintended harm. It encompasses alignment, robustness, interpretability, and governance research.
Bias in AI
Bias in AI refers to systematic errors or unfair outcomes in machine learning models that arise from biased training data, flawed assumptions, or problematic design choices. Addressing AI bias is essential for building fair and equitable systems.
Constitutional AI
Constitutional AI is an approach developed by Anthropic that trains AI systems to be helpful, harmless, and honest using a set of written principles. The model critiques and revises its own outputs based on these constitutional rules.
Deepfake
A deepfake is AI-generated synthetic media that convincingly replaces a person's likeness, voice, or actions in images, audio, or video. Deepfakes raise significant concerns about misinformation and identity fraud.
Explainable AI
Explainable AI (XAI) encompasses techniques that make AI system decisions understandable to humans. XAI is crucial for building trust, meeting regulatory requirements, and debugging model behavior.