Artificial Intelligence is a broad field of computer science focused on creating systems capable of performing tasks that would typically require human intelligence. These tasks include problem solving, understanding natural language, recognizing patterns, making decisions, and more. AI encompasses various subfields such as robotics, natural language processing(NLP), computer vision and machine learning.
Machine Learning is the subset of AI that involves the development of algorithms and statistical models that enables computers to learn from and make predictions or decisions based on data. Unlike traditional programming where rules are explicitly programmed, machine learning models identify patterns and relationships within data to make informed decisions or predictions. ML can be further divided into several types such as supervised learning, unsupervised learning and reinforcement learning.
AI and Machine Learning are intrinsically linked. Machine learning is one of the primary techniques used to achieve artificial intelligence. While AI is the broader concepts of machines being able to carry out tasks in a “smart” way, machine learning is the specific approach to achieving this by allowing machines to learn from data.
AI systems often leverage machine learning techniques to improve their performance over time . Here’s how they work together:
In summary, while AI is the overarching field concerned with creating intelligent systems, machine learning is a crucial subset that enables these systems to learn from data and improve over time. The synergy between AI and machine learning has led to significant advancements in technology, enabling the development of sophisticated applications such as autonomous vehicles, recommendation systems and advanced diagnostic tools in healthcare. Understanding the interplay between AI and machine learning is essential for anyone interested in the field of computer science and its applications.
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