A – Algorithm: A set of rules a machine follows to perform tasks and make decisions.

B – Bias: A systematic error in AI caused by flawed data or assumptions, leading to unfair outcomes.

C – Chatbot: An AI tool that simulates human conversation via text or voice.

D – Deep Learning: A subset of machine learning that uses neural networks with many layers.

E – Ethics: The moral principles guiding how AI should be developed and used responsibly.

F – Fine-Tuning: Customizing a pre-trained model to perform better on specific tasks.

G – Generative AI: AI that can create new content, like text, images, music, or code.

H – Hallucination: When an AI generates incorrect or fabricated information.

I – Inference: The process of using a trained AI model to make predictions or decisions.

J – Just-in-Time Learning: Adaptive AI learning in real time based on user input or environment.

K – Knowledge Graph: A network of entities and relationships used to help AI understand context.

L – Large Language Model (LLM): A powerful AI trained on vast text data to understand and generate human-like language.

M – Machine Learning: A method where machines learn from data to improve performance on tasks.

N – Neural Network: A model inspired by the human brain, forming the backbone of deep learning.

O – Overfitting: When an AI model performs well on training data but poorly on new, unseen data.

P – Prompt Engineering: Crafting effective inputs to get accurate and relevant outputs from AI systems.

Q – Quantization: Reducing the precision of numbers used in AI models to make them faster and more efficient.

R – Reinforcement Learning: Training AI by rewarding it for making the right decisions over time.

S – Supervised Learning: A type of machine learning using labeled data to train models.

T – Transformer: The architecture behind most modern language models, like GPT and BERT.

U – Unsupervised Learning: Training AI on unlabeled data to discover hidden patterns.

V – Vision AI: AI systems that interpret and understand visual information like images and video.

W – Weak AI: AI designed for specific tasks, unlike AGI (Artificial General Intelligence).

X – Explainability: The ability to understand and trust how an AI made a decision.

Y – Yield Prediction: A common use case of AI in agriculture and finance, forecasting outcomes from data.

Z – Zero-shot Learning: The ability of an AI to perform tasks it wasn’t explicitly trained on.