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Reinforcement learning is a type of machine learning where an agent learns to make decisions by interacting with an environment and receiving rewards or penalties for its actions. Over time, it develops a strategy that maximises cumulative reward.
Reinforcement learning powers some of AI's most impressive achievements — from game-playing agents to robotic control — and is key to training AI systems that must make sequential decisions.
DeepMind's AlphaGo used reinforcement learning to master the board game Go, defeating the world champion in a landmark 2016 match.
Machine Learning (ML)
Machine learning is a subset of AI where systems learn from data and improve their performance over time without being explicitly programmed for every scenario. Instead of following hard-coded rules, ML models identify patterns in data and use them to make predictions or decisions.
Supervised Learning
Supervised learning is a type of machine learning where the model is trained on labelled data — input-output pairs where the correct answer is provided. The model learns to map inputs to outputs and can then predict the correct output for new, unseen inputs.
AI Safety
AI safety is a research field focused on ensuring that AI systems behave as intended and do not cause unintended harm. It encompasses technical challenges like robustness and reliability, as well as broader concerns about long-term risks from increasingly capable systems.
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