Artificial Intelligence (AI) and Machine Learning (ML) are closely related fields, but they are not the same thing. AI is a broad field that encompasses many different subfields and technologies, while ML is a specific subfield of AI.
At a high level, AI is the simulation of human intelligence in machines that are programmed to think and learn like humans. AI systems can be designed to perform a wide variety of tasks, such as recognizing speech, understanding natural language, making decisions, and playing games. AI systems can be rule-based, where the system follows a set of predefined rules to accomplish a task, or they can be based on machine learning algorithms, which allow the system to learn from data and make decisions on its own.
ML, on the other hand, is a specific subfield of AI that is focused on the development of algorithms and statistical models that allow computers to improve their performance on a task with experience. It is a system that can automatically learn and improve from experience without being explicitly programmed. ML algorithms are used to build models that can be used to make predictions or take actions based on input data.
Some subfields of AI that do not fall under the category of ML are:
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Rule-based Systems: This subfield is focused on developing AI systems that follow a set of predefined rules to accomplish a task, rather than learning from data.
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Expert Systems: This subfield is focused on developing AI systems that can mimic the decision-making abilities of human experts in specific domains, like medical diagnosis or financial analysis.
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Planning and Scheduling: This subfield is focused on developing algorithms and models that allow machines to plan and schedule actions, such as in autonomous robots or self-driving cars.
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Natural Language Processing (NLP): This subfield focuses on developing algorithms and models that allow machines to understand, interpret, and generate human language, using techniques like parsing, semantic analysis and generation.
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Computer Vision: This subfield is focused on developing algorithms and models that allow machines to understand and interpret images and videos, using techniques like object detection and image segmentation.
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Robotics: This subfield combines AI and engineering to develop robots that can perform a wide variety of tasks, such as manufacturing, transportation, and search and rescue.
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Evolutionary algorithms: This subfield deals with the application of evolutionary biology concepts such as natural selection, genetic and crossover to optimise the solution for a problem.
One key difference between the two is that AI is the overall concept and goal, a simulation of human intelligence, while ML is a method to achieve AI. It is a way of training the computer by providing data and allowing it to learn from that data and improve its performance over time.
Another difference is the way AI is trained. AI systems can be trained using a variety of methods, including rule-based programming and machine learning, but ML specifically focused on learning from data and using that learning to improve performance.
In summary, AI is a broad field that encompasses many different technologies, including machine learning, which is a specific subfield of AI that is focused on the development of algorithms and statistical models that allow computers to improve their performance on a task with experience.
