What is difference between machine learning and artificial intelligence
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Understanding the Difference Between Machine Learning and Artificial Intelligence
Introduction to Artificial Intelligence (AI)
Artificial Intelligence (AI) is a broad field of computer science focused on creating systems capable of performing tasks that typically require human intelligence. These tasks include visual perception, speech recognition, decision-making, and language translation7. AI aims to emulate human abilities and behaviors, making machines act intelligently4 6.
Defining Machine Learning (ML)
Machine Learning (ML) is a subset of AI that enables computers to learn from data without being explicitly programmed. It involves using algorithms to parse data, learn from it, and make decisions or predictions based on that data2 3. ML is essentially about developing systems that can improve their performance on a task over time as they are exposed to more data4 6.
Key Differences Between AI and ML
Scope and Focus
- AI: The primary goal of AI is to create systems that can perform tasks requiring human intelligence. AI encompasses a wide range of techniques and approaches, including rule-based systems, expert systems, and machine learning6 8.
- ML: ML specifically focuses on the ability of systems to learn from data. It is concerned with developing algorithms that can identify patterns and make decisions with minimal human intervention2 3.
Methodology
- AI: AI systems can be built using various methods, including rule-based approaches where knowledge is encoded directly into the system. These systems may not necessarily learn from data but can still perform intelligent tasks6.
- ML: ML relies on data-driven approaches. Algorithms in ML build models based on training data, which allows them to make predictions or decisions without being explicitly programmed with rules1 4.
Learning and Adaptation
- AI: AI systems may or may not involve learning. For example, an expert system uses predefined rules to make decisions and does not adapt or learn from new data6.
- ML: Learning is a fundamental aspect of ML. ML systems continuously improve their performance as they are exposed to more data, adapting their models to better handle new information4 7.
Deep Learning: A Subset of Machine Learning
Deep Learning (DL) is a specialized subset of ML that uses artificial neural networks to model and solve complex problems. DL models are particularly effective for tasks such as image and speech recognition, often outperforming traditional ML models1 7. Deep learning represents a more advanced form of ML, leveraging large datasets and computational power to achieve high levels of accuracy and performance1.
Conclusion
In summary, while AI and ML are closely related, they are not the same. AI is the overarching field aimed at creating intelligent systems, whereas ML is a specific approach within AI focused on enabling systems to learn from data. Understanding the distinction between these terms is crucial for grasping the capabilities and applications of modern intelligent systems.
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Literature Review
Preprint2019·40citations·Niklas Kühl et al.·ArXiv
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2018·27citations·I. Nafea·Machine Learning - Advanced Techniques and Emerging Applications
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