Artificial Intelligence (AI) is a broad field of computer science that aims to create machines and systems that can perform tasks that would typically require human intelligence, such as understanding natural language, recognizing images and speech, and making decisions.
Machine learning (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 decisions without being explicitly programmed.
The key difference between AI and ML is that AI refers to the overall concept of creating intelligent machines and systems, while ML specifically refers to the use of algorithms and statistical models to enable computers to learn from data.
There are several types of machine learning, including:
- Supervised learning: the computer is provided with labeled training data and uses this data to learn to make predictions or decisions.
- Unsupervised learning: the computer is provided with unlabeled data and uses this data to find patterns or structure in the data.
- Reinforcement learning: the computer is provided with a goal or objective and learns to make decisions by receiving feedback in the form of rewards or penalties.
- Deep learning: a subset of ML that uses artificial neural networks with multiple layers to learn from data, particularly useful for image and speech recognition.
AI and ML technologies are using in a wide range of applications, such as self-driving cars, speech recognition, image recognition, natural language processing, and financial forecasting. With the increasing availability of big data and powerful computing resources, the field is rapidly advancing and is expecting to lead to many new and innovative applications in the future.