Imagine teaching a robot the difference between “bank” as a place to store money and “bank” as the side of a river. Not easy, right? Back in the day, computers treated words like isolated islands — no context, no connections, just cold, hard text. But then came embeddings, like a magical map that connected those islands, turning them into a rich, navigable world of meaning.
In this article, we’ll take a walk down memory lane to explore how embeddings have evolved from humble beginnings in the early days of information retrieval to the powerhouse models driving your favorite search engines, virtual assistants, and even the quirky recommendations Netflix gives you. Buckle up — it’s a journey full of breakthroughs, some wild numbers (175 billion parameters, anyone?), and a peek into how machines are learning to see, hear, and understand the world as we do… or at least trying. Ready to dive in? Let’s get started!
Mathematical Foundations
The concept of embeddings traces its origins to mathematics, where embedding a structure into another space while preserving properties is a common practice. For example:
- Manifold Embeddings: In differential geometry, complex manifolds (e.g., curved surfaces) are embedded into higher-dimensional Euclidean spaces for simplification.
- Graph Embeddings: Nodes in a graph are embedded into vector spaces to facilitate tasks like clustering or similarity measurement.
These principles laid the groundwork for computational embeddings, where data from diverse domains is mapped into meaningful vector spaces for machine analysis.
The Early Era: Embeddings in Information Retrieval
In the 1960s–1980s, embeddings emerged in information retrieval to represent text documents numerically. Early innovations included:
- Vector Space Model (VSM): Documents were represented as sparse vectors in high-dimensional spaces based on term frequency-inverse document frequency (TF-IDF). This method enabled basic document similarity calculations using metrics like cosine similarity.
Industrial Application: Early search engines like the first iterations of AltaVista and Lycos relied on these techniques to rank documents for user queries.
The NLP Revolution: Dense Word Embeddings
The development of dense word embeddings marked a major milestone in AI, replacing sparse, high-dimensional representations with compact, information-rich vectors. These embeddings captured semantic relationships between words, fundamentally transforming natural language processing (NLP).
Key Developments
Latent Semantic Analysis (LSA) (1990s):
- Used Singular Value Decomposition (SVD) to reduce high-dimensional term-document matrices into lower-dimensional spaces.
- Captured latent semantic structures but was computationally expensive and struggled with polysemy (words with multiple meanings).
Industrial Use: Early recommendation systems and document clustering tools.
Word2Vec (2013):
- Developed by Tomas Mikolov and team at Google, Word2Vec introduced neural network-based embeddings.
- Two models: Continuous Bag-of-Words (CBOW): Predicts a word from its context and Skip-Gram: Predicts the context given a word.
GloVe (Global Vectors for Word Representation) (2014):
- Developed at Stanford, GloVe leveraged co-occurrence matrices of words to build embeddings.
The Deep Learning Era: Contextual Embeddings
Traditional word embeddings like Word2Vec and GloVe assigned fixed vectors to words, regardless of their context. The advent of deep learning and transformers introduced contextual embeddings, enabling models to assign different vectors to words based on their usage in a sentence.
Key Milestones
ELMo (Embeddings from Language Models) (2018):
- Built on bi-directional LSTMs, ELMo generated word embeddings conditioned on the entire input sentence. Enabled dynamic, context-dependent embeddings. Model size: Trained with 93.6M parameters on the 1B Word Benchmark corpus.
Industrial Use: Advanced sentiment analysis in tools like Zendesk and personalized customer support.
BERT (Bidirectional Encoder Representations from Transformers) (2018):
- Developed by Google, BERT introduced deep, bidirectional transformer models. Embedding size: 768 dimensions for BERT-Base (110M parameters) and BERT-Large (340M parameters).
- Captures contextual nuances, such as polysemy, by using both left and right context. Trained on datasets like Wikipedia (2.5B words) and BooksCorpus (800M words).
GPT (Generative Pre-trained Transformer) (2018–2023):
- OpenAI’s GPT models advanced contextual embeddings further by pretraining on massive datasets and fine-tuning for specific tasks.
- GPT-3 (175 billion parameters) revolutionized embeddings for text generation and conversational AI.
- ChatGPT uses GPT models to embed and contextualize user queries dynamically.
To Sum up, The evolution of embeddings is tied to the increasing scale of models and datasets:
