AI Glossary & Terms

The three main types are: Supervised Learning (learns from labeled training data), Unsupervised Learning (finds patterns in unlabeled data), and Reinforcement Learning (learns through interaction with environment using rewards and penalties). Each type serves different problem-solving purposes.

Generative AI creates new content (text, images, code, audio) based on training data and prompts. Conversational AI specifically focuses on natural language dialogue and interaction with humans. Generative AI can be used within conversational systems, but they serve different primary purposes.

Training data is the dataset used to teach AI models to make predictions or decisions. Quality and quantity of training data directly impact model performance. Poor or biased training data leads to inaccurate or unfair AI systems, making data preparation crucial for successful AI implementation.

Neural networks are computing systems inspired by biological neural networks. They consist of interconnected nodes (neurons) that process information in layers. Each connection has weights that adjust during training, enabling the network to learn patterns and make predictions on new data.

NLP enables computers to understand, interpret, and generate human language. Applications include chatbots, language translation, sentiment analysis, text summarization, voice assistants, content generation, and document analysis. It's fundamental to most conversational AI systems.

Model accuracy measures how often an AI model makes correct predictions. It's calculated as the percentage of correct predictions out of total predictions made. However, accuracy alone isn't sufficient - precision, recall, F1-score, and other metrics provide a more complete performance picture.

AI bias occurs when algorithms produce systematically unfair results due to biased training data, flawed algorithms, or human prejudices. Prevention includes using diverse training data, testing across different groups, implementing fairness metrics, regular auditing, and diverse development teams.

On-premise AI runs on company-owned infrastructure, providing more control and security but requiring higher upfront costs and maintenance. Cloud AI uses third-party cloud services, offering scalability and lower initial costs but with ongoing usage fees and data privacy considerations.

Explainable AI refers to methods that make AI decision-making transparent and understandable to humans. It's crucial for building trust, ensuring compliance with regulations, identifying potential biases, debugging models, and enabling informed decision-making in critical applications like healthcare and finance.