Artificial Intelligence (AI) and Machine Learning (ML) are often used interchangeably, but they represent different concepts within the realm of computer science. AI is a broad field that encompasses various techniques and technologies for creating intelligent systems, while ML is a subset of AI focused on algorithms and statistical models that enable systems to learn from data.
AI refers to the development of computer systems that can perform tasks that typically require human intelligence. These tasks include problem-solving, decision-making, language understanding, perception, and more. AI aims to create machines that can mimic cognitive functions such as learning and problem-solving.
1: Narrow AI (Weak AI):AI systems designed to perform a specific task. Examples include virtual assistants like Siri, Alexa, and recommendation systems.
2: General AI (Strong AI): AI systems with generalized cognitive abilities, meaning they can perform any intellectual task that a human can. This type of AI is still theoretical and does not yet exist.
3: Artificial Superintelligence:AI that surpasses human intelligence across all fields. This concept is largely theoretical and a topic of debate among experts.
ML is a subset of AI that focuses on the development of algorithms that allow computers to learn from and make predictions based on data. Rather than being explicitly programmed to perform a task, ML models learn patterns from data and improve over time.
1: Supervised Learning: The model is trained on labeled data, meaning each training example has an associated output label. The model learns to predict the label from the input data.
2: Unsupervised Learning: The model is trained on unlabeled data and must find patterns or structures in the data.
3:Semi-Supervised Learning: Combines both labeled and unlabeled data to improve learning accuracy.
4: Reinforcement Learning: The model learns by interacting with an environment, receiving rewards or penalties based on its actions.