Here is a list of commonly used terms in AI along with a more casual and understandable explanation of each term:
- Artificial Intelligence (AI): It’s when machines or computers can do things that normally only humans can do, like think, learn and problem-solve.
- Machine Learning: It’s a way for computers to improve their performance by learning from data, without being explicitly programmed.
- Deep Learning: It’s a special kind of machine learning where computers can learn and improve by processing large amounts of data, like images or speech.
- Neural Network: It’s like the computer’s brain, made up of algorithms that can recognize patterns and make predictions based on the data it’s given.
- Natural Language Processing (NLP): It’s a way for computers to understand and interact with human languages, like speech recognition and language translation.
- Computer Vision: It’s a way for computers to understand and interpret visual information like images and videos.
- Robotics: It’s when machines are used to do tasks that would typically require human intelligence, like assembling cars or exploring other planets.
- Expert Systems: It’s a type of AI that’s designed to do tasks that typically require human expertise, like giving medical diagnoses or legal advice.
- Reinforcement Learning: It’s a way for computers to learn by trial and error, where it gets rewards or penalties for their actions.
- Generative Adversarial Networks (GANs): It’s a type of neural network that can create new data, like images or speech, by learning from real examples.
- Supervised Learning: It’s a way for computers to learn by being given labelled data, where the correct answer is already known.
- Unsupervised Learning: It’s a way for computers to learn by finding patterns or relationships in data, without being given the correct answer.