Glossary of Common Terms in Artificial Intelligence

Artificial intelligence has introduced numerous specialized terms. This glossary defines some of the most common words and phrases encountered in discussions about AI. The definitions are based on standard usage in the field.

Large language models, or LLMs, refer to AI systems trained on vast amounts of text data to generate human-like responses. These models power applications such as chatbots and text generators. They process and predict language patterns to produce coherent outputs.

Hallucinations describe instances where AI models generate incorrect or fabricated information presented as factual. This occurs due to limitations in training data or model architecture. Developers work to mitigate hallucinations through improved validation techniques.

Machine learning is a subset of AI that enables systems to learn from data without explicit programming. Algorithms identify patterns and make predictions based on input. It forms the foundation for many AI applications, including image recognition and recommendation systems.

Neural networks are computational models inspired by the human brain's structure. They consist of interconnected nodes that process information in layers. These networks are essential for tasks like natural language processing and computer vision.

refers to models that create new content, such as text, images, or audio, based on learned patterns.

Tools using this technology assist in creative tasks and automation. Examples include writing assistants and art generators. Bias in AI occurs when models reflect unfair prejudices from training data, leading to skewed outputs.

Addressing bias involves diverse datasets and ethical guidelines. It affects fairness in applications like hiring tools and facial recognition. Training data comprises the datasets used to teach AI models.

Quality and diversity of this data influence model performance. Ongoing refinements ensure accuracy and reliability in real-world use. Ethics in AI encompasses principles for responsible development and deployment.

It includes considerations of privacy, transparency, and societal impact. Frameworks guide decisions to align technology with human values.