# On Vector Embeddings. Q&A with Neil Kanungo

Q1. Despite their impressive capabilities, computers cannot easily understand text, images, audio, or other human-readable data formats. Why?

Computers inherently operate on binary data, and their processing capabilities are rooted in numerical and logical operations. Human-readable data formats like text, images, and audio are rich in qualitative nuances, context, and cultural subtleties that are not directly translatable to the binary language of computers. For instance, the meaning of a word can change based on context, and the emotional content of an image or piece of audio is subjective and complex. This discrepancy between qualitative input and binary processing necessitates a form of translation that can bridge the gap, which is where vector embeddings come into play.

Q2. When it comes to the analysis of these types of data, we can instead represent them as numerical “vectors” which can be better processed computationally. Why?

Numerical vectors are a universal language that computers can understand and manipulate. By translating different types of data into vectors, we can leverage the computer’s ability to perform quantitative analyses. This is because vectors in mathematical space have properties like magnitude and direction, which can be used to perform operations such as addition, scaling, and rotation. These operations can correspond to real-world concepts like similarity, difference, and change, allowing us to use computational methods to analyze and interpret complex data.

Q3. What Is A Vector Embedding?

A vector embedding is essentially a translation of non-numerical data into a vector space. This process involves capturing the essential qualities of the data in a form that preserves relationships and properties in a lower-dimensional space. For example, words with similar meanings may be positioned closely in a vector space, allowing us to quantify semantic similarity. The process of creating embeddings often involves machine learning models, such as neural networks, which are trained to identify and encode these relationships.

Q4. In Machine Learning applications, vectors can have thousands of dimensions – too many to even attempt to visualize. How can vector embedding help in practice then?

Raw unstructured data is challenging to work with directly due to its complexity and the computational resources required. Vector embeddings simplify this by providing a distilled representation that captures the most relevant aspects of the data for a given task. This allows for more efficient storage and faster computation, as the embeddings retain the essential information in a more compact form. In practice, this means we can perform operations like finding similar items or classifying data without having to grapple with the full complexity of the original dataset.

Q5. Let´s consider an example. What is a vector embedding for an image and what is it useful for?

For an image, a vector embedding might represent various features such as edges, textures, colors, and object relationships within the image. This is useful for many applications, such as image recognition, where the goal is to identify objects within images. Instead of working with raw pixel data, which is high-dimensional and noisy, embeddings allow us to work with a cleaner, more abstract representation of the image’s content.

Q6. An embedding can be learned and reused across models. What does it mean?

Learning an embedding means training a model to convert data into vectors in such a way that the vectors capture useful properties of the data. Once an embedding has been learned, it can be applied to new, similar data without retraining from scratch. This is beneficial because it saves time and computational resources, and it allows the knowledge captured by the embedding to be transferred to new tasks, which is a core concept in transfer learning.

Qx. Anything else you wish to add?

The field of vector embeddings is a rich area of study because it sits at the intersection of human understanding and computational efficiency. By converting complex, qualitative data into a quantitative form, embeddings allow us to apply the full suite of computational tools to problems that were previously inaccessible to quantitative methods. This has implications for a wide range of applications, from search engines that can understand the meaning behind queries to recommendation systems that can predict user preferences.

To get the full lowdown on this topic, head over to

https://kdb.ai/learning-hub/fundamentals/vector-embeddings/

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Neil KanungoVP of Product Led Growth, KX