ANN
Abbreviation with 2 meanings
| Stands for | Approximate nearest neighbor (search) |
|---|---|
| Meanings | 2 |
| Categories | Technology & Computing |
ANN has two meanings. It stands for Approximate nearest neighbor (search) (artificial intelligence) or Artificial neural network (artificial intelligence).
Meanings of ANN at a glance
| # | ANN stands for | Context |
|---|---|---|
| 1 | Approximate nearest neighbor (search) | Technology & Computing › Artificial Intelligence |
| 2 | Artificial neural network | Technology & Computing › Artificial Intelligence |
1. Approximate nearest neighbor (search)
Technology & Computing · Artificial Intelligence Abbreviations
Algorithms that quickly find the items most similar to a query vector among millions or billions of vectors by accepting slightly imperfect results. ANN search powers vector databases, semantic search, recommendations and retrieval-augmented generation.
Example: The vector index uses ANN search, returning the top 10 matches from 50 million embeddings in under 20 ms.
Popular techniques include HNSW graphs, inverted file indexes and product quantization, implemented in libraries such as Meta's FAISS and Google's ScaNN. Recall, the share of true nearest neighbors found, is traded against speed and memory. In older AI literature ANN means artificial neural network; mentions of vectors, indexes and similarity point to the search meaning.
2. Artificial neural network
Technology & Computing · Artificial Intelligence Abbreviations
The full name for a neural network in machine learning, used to distinguish the computational model from the biological neural networks of the brain. An ANN learns by adjusting connection weights between layers of simple units.
Example: The textbook's chapter on ANN training covers gradient descent and backpropagation.
Older literature and introductory courses favor ANN, while practitioners mostly say "neural network" or name a specific architecture. In vector search ANN means approximate nearest neighbor, a different concept; mentions of layers, weights and training point to the neural-network meaning.
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