Nov 23, 2023
AI encompasses diverse tech for machines to mimic human intelligence, while ML is a specific AI application for autonomous learning.
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ML enables machines to autonomously learn and improve from experience, a subset within the broader AI landscape.
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AI aims at solving complex tasks mimicking human intelligence, while ML focusses on teaching machines specific tasks for accurate outcomes.
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AI has wide applications, including ML; ML has a limited scope focused on structured and semi-structured data tasks.
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AI works with all data types; ML is limited to structured and semi-structured data, utilizing statistical models for learning.
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AI uses logic and decision trees; ML relies on self-learning algorithms, statistical models, and self-correction with new data.
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AI solves problems like humans; ML’s goal is teaching machines how to perform tasks, improving accuracy through pattern recognition.
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AI involves building systems that mimic human decision-making; ML produces models trained on algorithms for task-specific learning.
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AI’s broader goal is to develop intelligent systems; ML’s goal is to enhance accuracy through autonomous learning for specific tasks.
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