What is relationship between Artificial Intelligence and Machine Learning?
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Relationship between artificial intelligence and machine learning
The link between Artificial Intelligence (AI) and Machine Learning (ML) is crucial, with AI frequently regarded as the broader notion that includes ML. Let's dig a little more into this relationship:
Definition and Application:
Artificial Intelligence (AI): Artificial intelligence (AI) is a vast subject of computer science that tries to construct machines or systems that can do tasks that normally require human intelligence. These skills include reasoning, problem solving, natural language comprehension, pattern recognition, and decision making.
Machine Learning (ML): ML is a subset of AI that focuses on the development of algorithms and statistical models that allow computers to learn from data and make predictions or judgments. ML is, in essence, a tool or approach used to build AI.
Dependency:
AI and machine learning are inextricably linked. ML is a fundamental component of many AI systems because it allows machines to learn and improve from data, which is required for intelligence.
Hierarchy:
AI is the umbrella term for several subfields, including machine learning. Natural language processing, computer vision, robotics, and expert systems are examples of AI subfields.
Adaptation and Learning:
ML is concerned with the creation of algorithms that can learn from data, adapt to new information, and improve over time. These techniques enable AI systems to get smarter as they encounter more data.
AI without machine learning:
While AI can exist in the absence of machine learning, it would frequently rely on pre-programmed rules and reasoning, making it less flexible and adaptive to new scenarios. ML enables AI systems to learn and adapt without the need for explicit programming.
Applications:
Chatbots and recommendation systems, as well as self-driving cars and healthcare diagnostics, are examples of AI applications. Many of these applications rely significantly on machine learning techniques to perform tasks like speech recognition, image categorization, and predictive analytics.
Loop of Feedback:
AI and machine learning are frequently used in a feedback loop. ML models require well-designed AI systems to deploy and operate well in real-world scenarios, whereas AI systems can use ML to improve their performance.
Data-Driven:
AI and ML are both heavily data-driven. To construct and refine their models, they require massive amounts of data for training, validation, and testing.
Evolution:
AI and machine learning are rapidly evolving fields. New approaches, algorithms, and hardware advances help to enhance these domains, resulting in more powerful and smarter AI systems.
Nature of Interdisciplinarity:
AI and machine learning relies on a wide range of fields, including computer science, mathematics, statistics, and cognitive psychology. Researchers and practitioners in various domains work together to push the limits of what AI can accomplish.