The Role of 'Embeddings' in AI Products
A recent guide for product managers demystifies 'embeddings,' which are vector representations that encode the meaning of data like text or images. Understanding embeddings is considered foundational for PMs working on AI-powered features like semantic search and recommendations. This knowledge is increasingly becoming a core competency expected in AI PM interviews.
- The concept of representing words in a vector space dates back to the 1950s, with British linguist John Rupert Firth's proposal that "a word is characterized by the company it keeps." However, the term "word embeddings" was first coined in 2003 by researchers at Google. - Google's RankBrain, introduced in 2015, was one of the first large-scale uses of embeddings in search to better understand and handle ambiguous or novel queries. This was followed by the 2018 introduction of BERT (Bidirectional Encoder Representations from Transformers), which further improved contextual understanding by analyzing the relationship between all words in a sentence at once. - Pre-trained embedding models like Word2Vec (released by Google in 2013), GloVe (from Stanford researchers in 2014), and fastText are foundational tools that allow developers to build features without having to train their own models from scratch. - E-commerce and streaming platforms heavily rely on embeddings to power their recommendation engines. By representing users and items as vectors, these systems can identify and suggest products or content with similar underlying characteristics, even for new users or items with limited interaction data. - For customer support professionals transitioning to product management, embeddings offer a powerful way to analyze unstructured customer feedback. By converting text from surveys, reviews, and support tickets into vectors, PMs can identify recurring themes, sentiment, and emerging issues at scale. - The use of embeddings extends beyond text to other data types like images and audio. For example, visual search features on e-commerce sites use image embeddings to find visually similar products based on pixel data rather than just keyword tags. - The performance of embedding models is often evaluated using benchmarks like the Massively Multilingual Text Embedding Benchmark (MTEB), which tests models on a wide range of tasks and languages. Top-performing models from companies like Google and NVIDIA achieve high scores, indicating their ability to capture semantic meaning effectively. - Looking ahead, the development of multimodal embeddings, which can represent and connect information from different data types like text, images, and audio simultaneously, is a key area of research. This could enable more sophisticated AI applications, such as searching for a specific scene in a video using a descriptive text query.
