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.
Understanding AI Alignment
AI alignment is the critical research challenge of ensuring that increasingly powerful AI systems pursue objectives that are truly consistent with human values, preferences, and intentions. The problem is deceptively complex: even specifying what humans actually want is difficult, and small misalignments in objectives can lead to dramatically unintended behaviors as systems become more capable. Current alignment research spans techniques like reinforcement learning from human feedback (RLHF), constitutional AI, and interpretability methods that help researchers understand model decision-making. Real-world alignment failures can range from recommendation algorithms that optimize engagement at the cost of user wellbeing to more hypothetical scenarios involving artificial general intelligence. Leading organizations like Anthropic, OpenAI, and DeepMind dedicate significant resources to alignment research, viewing it as essential for AI safety. The field sits at the intersection of machine learning, philosophy, and cognitive science.
Category
AI Ethics & Safety
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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.
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 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.