The Evolution of Embeddings in Machine Learning [Video]

In todays fast growing digital era,  Sudeep Meduri explores the progression of embeddings in AI, from foundational innovations to transformative applications. His insights emphasize embeddings’ growing potential and their critical role in advancing AI capabilities.

Tracing the Beginnings: From LSA to Word2Vec

Early Concepts in Data Representation

Embeddings were created to address a key machine learning challenge: converting discrete data into continuous vectors for better processing. Early methods like Latent Semantic Analysis (LSA) formed a “semantic space” for words but struggled with linear assumptions and polysemy.

The Word2Vec Revolution

Introduced in 2013, Word2Vec revolutionized language processing with CBOW and Skip-Gram architectures, capturing semantic and syntactic relationships and enabling analogical reasoning, like “king – man + woman = queen,” advancing embedding techniques.

Advancements in Natural Language Processing Embeddings

GloVe: Blending Local and Global Context

Building on Word2Vec, GloVe combined local context with global corpus statistics, creating richer embeddings that enhance NLP tasks by balancing local and global relationships, improving word similarity and analogy …