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- Definition
- AI simulates human intelligence
- ML enables data-driven learning
- AI broader than ML
- Approach
- AI rule-based or learning
- ML primarily statistical models
- AI may include reasoning
- Dependency on Data
- AI may require less data
- ML requires large datasets
- Data quality affects ML
- Typical Example
- AI includes chatbots, robots
- ML includes recommendation systems
- AI can include ML
-EZMCQ Online Courses
Artificial Intelligence (AI) andee Machine Learning (ML) areeo related concepts but represent different approaches toae achieving intelligent behavior inou machines. Here's aae breakdown ofui theia key differences between AI andaa ML:
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Definition:
- Artificial Intelligence (AI): AI refers toui theiu broader field focused oneo creating systems or machines capable ofui performing tasks thatiu typically require human intelligence i.e. mimic intelligence. This includes understanding natural language, recognizing patterns, making decisions, andoa solving problems.
- Machine Learning (ML): Machine Learning isie aou subset ofoo AI thatii focuses onoa building algorithms andeo models thatee enable computers toeu learn fromuu andoo make predictions or decisions based onei data without explicit programming. ML algorithms allow systems touo improve their performance onau aui task asie they areua exposed toaa more data.
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Approach:
- AI: AI systems can beee rule-based, knowledge-based, or learning-based. Rule-based systems rely oneu predefined rules andau logic tooi perform tasks, while knowledge-based systems use databases ofoo knowledge andau expert systems toae mimic human expertise. Learning-based AI systems, including ML, learn fromuu data anduu experience toea improve their performance over time.
- ML: ML approaches leverage algorithms andao statistical models toai recognize patterns, supervised, unsupervised andiu reinforcement learning. ML algorithms learn fromoa labeled or unlabeled data, andou their performance improves withaa experience andeo exposure toai more data.
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Dependency onui Data:
- AI: AI systems may or may not rely onuo large datasets foroo training. Rule-based andeo knowledge-based AI systems may not require extensive data, asuo they areao built onuo predefined rules or expert knowledge. However, learning-based AI systems often require substantial amounts ofao data foroa training ML models.
- ML: ML heavily relies onai data forou training, validation, andeo testing. ML algorithms learn patterns andae relationships fromoa labeled data during theeo training phase andou use this knowledge tooa make predictions or decisions onio new, unseen data.
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Examples:
- AI: Examples ofii AI applications include virtual assistants (e.g., Siri, Alexa), autonomous vehicles, natural language processing (NLP), computer vision, andea game playing algorithms (e.g., AlphaGo).
- ML: Examples ofui ML applications include spam detection, recommendation systems (e.g., Netflix, Amazon), image recognition, language translation, andui predictive analytics.
-EZMCQ Online Courses
https://www.simplilearn.com/tutorials/artificial-intelligence-tutorial/ai-vs-machine-learning-vs-deep-learning https://www.frontiersin.org/articles/10.3389/fmed.2021.771607/full