AI Glossary for Beginners: Key Terms with Clear Examples

1. Basic concepts

agent (noun): A system that can perceive its environment and take actions. The AI agent decided to move the robot forward.

algorithm (noun): A set of instructions for solving a problem or completing a task. The computer used an algorithm to sort the names alphabetically.

artificial intelligence (noun): The ability of a machine to perform tasks that usually require human intelligence. Speech recognition is a common example of artificial intelligence.

automation (noun): The use of machines or software to perform tasks without human help. Automation in factories has increased production speed.

ethics (noun): Moral rules or principles that guide behavior, especially when developing AI systems. Ethics are important when designing AI that affects people's lives.

robotics (noun): The field of designing and building robots. Robotics and AI often work together to create smart machines.

2. Data and datasets

annotation (noun): A note added to data to explain or label it. Image annotation helps AI recognize objects like cats and dogs.

big data (noun): Extremely large datasets that require special tools to manage and analyze. Big data helps companies predict customer behavior.

corpus (noun): A large collection of texts used for language research or AI training. The AI was trained on a corpus of news articles.

data (noun): Information collected for analysis or reference. The AI needs a lot of data to learn how to speak.

dataset (noun): A collection of related data used for training or testing AI models. The team created a new dataset of medical images for research.

labeling (noun): The process of tagging data with meaningful information. Labeling photos helps AI learn the difference between animals.

3. Training and learning

deep learning (noun): A type of machine learning that uses layered neural networks. Deep learning powers many voice assistants today.

fine-tuning (noun): Adjusting a pretrained model slightly to improve performance on a specific task. The AI model was fine-tuned to better understand legal documents.

machine learning (noun): A way for machines to learn from data without being programmed directly. Machine learning helps recommendation systems suggest movies.

pretraining (noun): Training a model on a large, general dataset before fine-tuning it for a specific task. Pretraining saves time when building new AI applications.

reinforcement learning (noun): A method where AI learns by receiving rewards or punishments. The robot learned to walk through reinforcement learning.

supervised learning (noun): A type of machine learning where data comes with correct answers. Supervised learning helped the AI recognize handwritten numbers.

training (noun): The process of teaching an AI model by showing it data and examples. Training a chatbot requires thousands of conversations.

transfer learning (noun): Using knowledge gained from one task to improve performance on a new task. Transfer learning helped the AI understand a new language quickly.

unsupervised learning (noun): A type of machine learning where the AI finds patterns without given answers. Unsupervised learning grouped customers by shopping habits.

4. Models and predictions

confidence score (noun): A number showing how sure an AI is about its prediction. The AI had a high confidence score when identifying the animal.

deployment (noun): Releasing a trained AI model for real-world use. Deployment of the AI app started last month.

feature (noun): An individual measurable property of data used by AI models. Height and weight are features for a health prediction model.

inference (noun): The process of an AI model making a prediction based on new data. The AI made an inference that the photo showed a beach.

model (noun): A mathematical system trained to make predictions or decisions based on data. The model could recognize different types of fruit.

model drift (noun): When a model's performance declines because real-world data changes. Model drift made the AI less accurate over time.

output (noun): The result produced by an AI system after processing input data. The chatbot's output was a helpful answer to my question.

scalability (noun): The ability of a system to handle more work without losing performance. The AI platform showed great scalability during peak traffic.

weight (noun): A value in a model that shows the importance of a feature. The AI adjusted its weights to improve its predictions.

5. Language and text

chatbot (noun): A computer program that simulates conversation with humans. The chatbot helped customers solve their problems quickly.

natural language processing (noun): A field of AI that helps computers understand human language. Natural language processing powers voice assistants like Siri.

prompt (noun): The text or question given to an AI to get a response. I gave the AI a short prompt to write a poem.

token (noun): A small piece of text, like a word or part of a word, used in AI models. The sentence was broken into tokens for the AI to understand.

6. Finding patterns

classification (noun): Sorting data into categories. Email classification helps separate spam from important messages.

clustering (noun): Grouping similar items together without labels. Clustering helped find groups of similar customers.

data mining (noun): Finding useful patterns and information in large amounts of data. Data mining revealed new buying trends.

latent space (noun): A hidden structure where AI organizes information in meaningful ways. The model found new ideas by exploring its latent space.

7. Errors and problems

bias (noun): A tendency of AI to make unfair or unbalanced decisions. Bias in AI hiring tools can cause discrimination.

ground truth (noun): The correct answers used to measure AI accuracy. The ground truth showed the AI's predictions were 95% correct.

hallucination (noun): When an AI generates false or misleading information. The chatbot's hallucination included made-up facts.

heuristic (noun): A simple rule used to solve problems quickly. The AI used a heuristic to choose the best move in the game.

overfitting (noun): When a model learns training data too well but fails on new data. Overfitting made the AI perform badly on real-world tasks.

validation (noun): Testing an AI model to make sure it works well. Validation showed that the model was ready to use.

8. Advanced and new areas

computational (adjective): Related to computers and calculations. Computational power is important for running AI models.

generative AI (noun): AI that creates new content like text, images, or music. Generative AI wrote a short story based on my idea.

multimodal (adjective): Involving more than one type of input, like text and images together. The multimodal AI understood both pictures and written questions.