25 artificial intelligence terms you need to know

As artificial intelligence (AI) becomes more integrated into our daily lives, it's important to understand the terminology that comes with it. Here are 25 AI terms that you need to know:

1. Artificial Intelligence: A field of computer science that aims to create machines that can perform tasks that usually require human intelligence.


2. Machine Learning: A subset of AI that involves training algorithms to learn patterns in data and make predictions.


3. Deep Learning: A type of machine learning that involves training artificial neural networks to recognize patterns in data.


4. Neural Networks: A series of algorithms that mimic the human brain's structure and function to recognize patterns in data.


5. Natural Language Processing (NLP): A branch of AI that enables computers to understand, interpret, and generate human language.


6. Computer Vision: A field of AI that involves enabling machines to interpret and understand visual data from the world around them.


7. Robotics: The study and development of robots and their behaviours.


8. Chatbot: A computer program designed to simulate conversation with human users.


9. Big Data: Large and complex data sets that are difficult to process using traditional data processing methods.


10. Algorithm: A set of instructions given to a computer to perform a specific task.


11. Predictive Analytics: The use of data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data.


12. Artificial General Intelligence (AGI): AI that has the ability to perform any intellectual task that a human can.


13. Artificial Narrow Intelligence (ANI): AI that is designed to perform a specific task or set of tasks.


14. Supervised Learning: A type of machine learning where the algorithm is trained on labelled data to make predictions on new, unlabelled data.


15. Unsupervised Learning: A type of machine learning where the algorithm is trained on unlabelled data to discover patterns and relationships.


16. Reinforcement Learning: A type of machine learning where an algorithm learns through trial and error by receiving rewards or punishments.


17. Overfitting: When a machine learning model is trained too well on a specific set of data, it may perform poorly on new data.


18. Underfitting: When a machine learning model is too simple, it may not be able to capture the complexity of the data and may perform poorly on new data.


19. Bias: When a machine learning model is systematically inaccurate in its predictions due to inherent biases in the data or algorithms.


20. Variance: The amount of variation or flexibility in a machine learning model.


21. Ensemble Learning: Combining multiple machine learning models to improve performance and reduce errors.


22. Convolutional Neural Networks (CNN): A type of neural network designed for image processing and recognition.


23. Recurrent Neural Networks (RNN): A type of neural network designed for sequence processing, such as natural language.


24. Generative Adversarial Networks (GAN): A type of neural network designed for generating new, synthetic data.


25. Explainable AI (XAI): AI models and techniques that can be easily understood and interpreted by humans.

As AI continues to evolve and become more ubiquitous, these terms will become even more important to understand. Whether you're working in AI or simply using it in your daily life, knowing these terms will help you better navigate the world of artificial intelligence.