Within Google Cloud conversations about intelligent technologies, what correctly distinguishes machine learning (ML) from the broader field of artificial intelligence (AI)?
AI is a specialized branch within ML that applies statistical models only to structured datasets collected in the cloud.
ML describes any automated data processing in the cloud, and AI is just another name for the same concept.
ML is a subset of AI that uses data-driven algorithms to learn and predict, while AI is the wider field of enabling machines to perform tasks requiring human intelligence.
ML refers to systems that mimic human reasoning through rules coded by developers, whereas AI is limited to creating data analytics dashboards.
Artificial intelligence is an umbrella term for any technique that enables computers to perform tasks that normally require human cognition, such as understanding language, recognizing images, or making decisions. Machine learning sits inside that umbrella: it uses statistical algorithms that automatically learn patterns from data and improve over time without being explicitly programmed for every rule. Therefore, ML is a subset of AI. The incorrect options either reverse the relationship, describe ML as only rule-based coding, or claim the two terms are interchangeable-none of which reflects the accepted definitions.
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What are examples of tasks AI can perform that require human cognition?
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