A warm welcome to the new tutorial on “Difference between Machine Learning and Artificial Intelligence.” Machine Learning and Artificial intelligence are two hot topics and often seem to be used interchangeably. But these terms aren’t the same thing.
“The ability of machines to work and think, like the human brain, is called Artificial Intelligence.”
AI refers to the imitation of human intelligence processes using machines, particularly computer systems. It revolves around developing systems capable of performing tasks that typically demand human intelligence. AI can be seen as enhancing machines, particularly computers, with human-like intelligence. It’s important to note that AI isn’t a single system but a field of study focused on equipping machines with the ability to tackle problems similarly to human brains.
Machine Learning is a specific branch of Artificial Intelligence (AI). To put it simply, all instances of Machine Learning fall under the umbrella of AI, but not all AI can be categorized as Machine Learning. Machine Learning involves creating systems with the ability to learn independently. In essence, it’s about enabling computers to learn by themselves. This field delves into computer algorithms and statistical models, empowering computer programs to enhance their performance through experience. Machine Learning is a subset of AI where computers grasp insights from data without requiring explicit programming instructions. Instead, they learn from their experiences, discern patterns, refine their performance over time, and make predictions.
John McCarthy, widely recognized as one of the godfathers of AI, defined it as “the science and engineering of making intelligent machines.”
Machine Learning is the science of getting computers to act by feeding them data and letting them learn a few tricks without being explicitly programmed.
Difference between Machine learning and Artificial Intelligence
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1. Objective Emphasis:
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– Artificial Intelligence emphasizes achieving successful outcomes.
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– Machine Learning emphasizes achieving accurate results.
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2. Nature and Implementation:
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– AI is conceptual, not a standalone system, but can be integrated into systems to make them intelligent.
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– ML is a system itself, capable of extracting insights from datasets.
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3. Application Focus:
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– AI is applied in decision-making processes.
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– ML is employed to learn from experiences and data.
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4. Modeling Approach:
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– AI mimics human cognitive processes.
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– ML develops algorithms that learn autonomously.
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5. Outcome Result:
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– AI’s outcome is often increased wisdom or intelligence.
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– ML’s outcome is typically increased knowledge or experience.
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6. Relationship:
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– Machine Learning is a means of achieving Artificial Intelligence. ML is a subset of AI used to make systems smarter.
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“Machine learning is the tendency of machines to learn from data analysis and achieve Artificial Intelligence.”
Summary:
AI goes beyond Machine Learning, incorporating methods like rule-based systems and expert systems. In essence, AI covers the broader notion of creating intelligent machines. Conversely, Machine Learning is a specific technique within AI that concentrates on teaching machines to learn from data, steadily refining their performance as they go.
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