AI Landscape
Understand the broad AI landscape by exploring its key subfields such as machine learning, deep learning, natural language processing, speech recognition, computer vision, robotics, and generative AI. Learn how these components differ and work together to build intelligent systems that mimic human abilities, improve decision-making, and enable creative outputs.
AI is not ML
AI and ML are often used interchangeably. However, this is not exactly true because these two fields are unequal. ML is a subfield of AI that focuses on developing algorithms and statistical models that enable computers to learn and make data-based decisions. In contrast, AI focuses on creating intelligent systems through reasoning, learning, problem-solving, perception, and language understanding. ML systems improve their performance on a given task with experience, i.e., as they are exposed to more data, they learn and refine their models. The goals of both fields are different, too: AI focuses on mimicking human intelligence, while ML develops models that can make accurate predictions, often with the help of data.
We can make AI equivalent to ML by employing ML models to solve our tasks. Is this accurate?
AI landscape
Let’s explore the AI landscape and how it is connected to the other subfields of computer science.
Machine learning
Machine learning is a subset of AI that ...