General AI Concepts

• Artificial Intelligence (AI): A branch of computer science focused on creating systems capable of performing tasks that would typically require human intelligence.

• Algorithm: A set of rules or procedures for solving a problem, often implemented in computer code.

• Data Set: A collection of data used to train or test AI models.

• Inference: The process of using a trained AI model to make predictions or decisions based on new data.

Machine Learning Concepts

• Machine Learning (ML): A subset of AI that allows computers to learn from data and make decisions without explicit programming.

• Supervised Learning: A type of machine learning where an algorithm is trained on labeled data.

• Unsupervised Learning: A type of machine learning where an algorithm learns patterns from unlabeled data.

• Reinforcement Learning: A type of machine learning where an agent learns to make decisions by interacting with an environment to achieve a goal.

• Overfitting: When an AI model learns the training data too well, including its noise and outliers, and performs poorly on new, unseen data.

• Underfitting: When an AI model is too simple to capture the underlying patterns in the data, leading to poor performance.

• Feature Extraction: The process of selecting or transforming variables (features) from the data to improve an AI model's performance.

Deep Learning Concepts

• Deep Learning: A specialized form of machine learning inspired by the architecture and function of the brain, using neural networks with many layers.

• Neural Network: A computational model inspired by biological neural networks, consisting of interconnected nodes (neurons).

• Convolutional Neural Network (CNN): A type of neural network particularly effective for tasks like image recognition.

• Recurrent Neural Network (RNN): A type of neural network designed to handle sequential data like time series or natural language.

• Backpropagation: The primary algorithm for performing gradient descent on neural networks, used to minimize the error in the model's predictions.

• Activation Function: A mathematical function applied to a node in a neural network, determining the node's output.

Generative AI Concepts

• Generative AI: A branch of AI focused on creating new data that resembles a given dataset.

• Generative Adversarial Network (GAN): A type of neural network used for generative tasks, consisting of a Generator and a Discriminator that are trained together.

• Autoencoder: A type of neural network used for unsupervised learning, often for dimensionality reduction or feature learning.

• Transformer Architecture: A type of neural network architecture particularly effective for tasks involving sequences, like natural language processing.

Ethical and Social Concepts

• Algorithmic Bias: Systematic errors in the functioning of an AI model that produce unfair or discriminatory outcomes.

• Explainability: The ability to understand and interpret the decisions made by an AI model.

• Data Privacy: The ethical handling, protection, and usage of data, particularly sensitive or personal information.