Machine Learning

Machine Learning (ML) is a subset of artificial intelligence that enables computers to learn patterns and improve their performance from experience without being explicitly programmed for each specific task. Instead of following hardcoded instructions, ML systems ingest training data, identify patterns, and develop models that can make predictions or decisions on new, unseen data. The field encompasses supervised learning (learning from labeled examples), unsupervised learning (finding patterns in unlabeled data), and reinforcement learning (learning through reward signals). ML powers many modern applications from fraud detection to recommendation systems. Example: Chainalysis uses machine learning models trained on blockchain transaction patterns to detect suspicious activities and identify illicit fund flows across Bitcoin and Ethereum networks. Why it matters for AI and data in Web3: Machine learning enables on-chain analytics, fraud prevention, portfolio optimization, and risk assessment for DeFi protocols, while decentralized ML training raises questions about data privacy and incentive alignment in trustless environments.

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