International Journal on Science and Technology

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How AI Learns Hidden Patterns: The Power of Embeddings in Predictions

Author(s) Huzaifa Fahad Syed
Country United States
Abstract Embedding techniques have revolutionized artificial intelligence by enabling machines to understand complex relationships in data through dense numerical representations. This article explores the technical foundations of embeddings, tracing their evolution from word-level models like Word2Vec and GloVe to
more sophisticated approaches like FastText and Sentence-BERT. The article examines how embeddings learn directly from data, capturing semantic and syntactic relationships in high-dimensional vector spaces. It demonstrates their practical applications across recommendation systems, healthcare, financial fraud detection, and natural language processing, where they consistently outperform traditional methods. The article also addresses computational challenges associated with large-scale embedding models and highlights advanced methods that balance performance with efficiency. The article emphasizes how embeddings have fundamentally transformed prediction systems by enabling machines to discover hidden patterns in data without explicit programming, establishing them as an essential component of modern AI
systems.
Keywords Vector representations, semantic similarity, neural language models, recommendation systems, fraud detection
Field Computer
Published In Volume 16, Issue 1, January-March 2025
Published On 2025-03-21
Cite This How AI Learns Hidden Patterns: The Power of Embeddings in Predictions - Huzaifa Fahad Syed - IJSAT Volume 16, Issue 1, January-March 2025. DOI 10.71097/IJSAT.v16.i1.2596
DOI https://doi.org/10.71097/IJSAT.v16.i1.2596
Short DOI https://doi.org/g892gf
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