Artificial Intelligence refers to computer systems designed to mimic human intelligence. These systems perform tasks such as learning, reasoning, and problem-solving.

Self-driving cars use AI algorithms to process sensor data and make driving decisions. They are a clear example of how AI is applied in real-world scenarios.

Machine learning is a subset of AI where systems learn from data. It enables computers to adapt their behavior based on patterns and past experiences.

A chatbot is an AI application that mimics human conversation through text or voice interactions. They are widely used in customer service and informational tasks.

Artificial Intelligence is most closely associated with Computer Science and Engineering. This area provides the theoretical and practical foundations necessary to develop AI systems.

Machine learning models acquire rules and patterns directly from data without being explicitly programmed. In contrast, classical programming requires manual coding of each rule by the developer.

Supervised learning involves training AI models with labeled data where the desired output is known. This technique is commonly used for tasks such as classification and regression.

Unsupervised learning allows models to uncover hidden patterns or groupings in data without pre-assigned labels. This approach is essential for clustering and anomaly detection.

Hidden layers in neural networks perform non-linear transformations that help capture complex relationships in the data. This enables the network to learn and generalize from the input features.

Overfitting occurs when a model captures noise along with the underlying pattern in the training data, compromising its performance on unseen data. It is a common problem that limits a model's generalization capabilities.

Dropout regularization randomly disables a fraction of neurons during training, which helps prevent the network from becoming too reliant on any given feature. This technique promotes a more robust model that generalizes better to new data.

Reinforcement learning trains agents to make sequential decisions by maximizing cumulative rewards. It is distinct from supervised and unsupervised learning in that it relies on feedback from actions taken within an environment.

Cross-validation involves partitioning data into multiple subsets and using each subset for validation in turn. This method helps ensure that the model's performance is robust and less susceptible to overfitting on a single train-test split.

Computer Vision is dedicated to enabling machines to understand and interpret visual information. This field is widely used in applications such as image recognition and autonomous navigation.

Principal Component Analysis (PCA) is a statistical method used to reduce the number of variables in a dataset while preserving as much variance as possible. This technique simplifies complex datasets, making them easier to visualize and analyze.

Backpropagation is a key algorithm in neural network training, enabling the computation of gradients for each weight. This process facilitates the adjustment of weights in order to minimize errors during training.

The bias-variance trade-off highlights the need to balance a model's simplicity (bias) with its flexibility (variance). Achieving this balance is essential to create models that are both accurate and generalizable.

Generative Adversarial Networks (GANs) leverage two competing networks: one generates data while the other discriminates between real and generated data. This adversarial process drives both networks to improve, resulting in high-quality generated outputs.

Algorithmic biases occur when the data or design of an AI system inadvertently reinforces stereotypes or prejudices. This can lead to unfair, unethical outcomes, making it critical to address bias in AI development.

Explainable AI seeks to make the decision-making process of complex models transparent, which is particularly important in high-stakes applications. The challenge lies in achieving both strong performance and clear explainability without compromising either.