AI Glossary
The definitive dictionary for AI, Machine Learning, and Governance terminology. From Flash Attention to RAG — look up any term.
H
Hallucination
When an AI model generates information that sounds plausible and confident but is factually incorrect, fabricated, or not grounded in its training data or provided context. The model essentially 'makes things up'.
Hallucination Detection
Methods and systems for automatically identifying when an AI model has generated false or unsupported information. Detection can compare outputs against source documents or use consistency checks.
Hallucination Rate
The frequency at which an AI model generates incorrect or fabricated information. It is typically measured as a percentage of responses containing hallucinations.
Hardware Acceleration
Using specialized hardware (GPUs, TPUs, FPGAs, ASICs) to speed up AI computation compared to general-purpose CPUs. Accelerators are optimized for the specific math operations used in neural networks.
Homomorphic Encryption
A form of encryption that allows computation on encrypted data without decrypting it first. The results, when decrypted, match what would have been computed on the plaintext.
Hugging Face
The leading open-source platform for sharing and discovering AI models, datasets, and applications. Hugging Face hosts the Transformers library and a community hub with thousands of pre-trained models.
Human Evaluation
Using human judges to assess AI model quality on subjective dimensions like helpfulness, coherence, creativity, and safety that automated metrics cannot fully capture.
Human-in-the-Loop
A system design where humans are integrated into the AI workflow to provide oversight, make decisions, correct errors, or handle edge cases that the AI cannot reliably manage alone.
Hybrid Search
A search approach that combines keyword-based (lexical) search with semantic (vector) search to get the benefits of both — exact matching for specific terms and meaning-based matching for conceptual queries.
Hyperparameter
Settings that are configured before training begins and control how the model learns, as opposed to parameters which are learned during training. Examples include learning rate, batch size, and number of layers.
Hyperparameter Tuning
The process of systematically searching for the best combination of hyperparameters for a model. Since hyperparameters are set before training, finding optimal values requires experimentation.