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What are Embeddings in Machine Learning?

Understanding ML Embeddings

Explore the concept of embeddings in machine learning, their role, and how they enhance data representation and analysis in AI applications.

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Welcome to our Learning Center, where we explain « what are embeddings » in machine learning. Exploring multi-dimensional vectors, neural networks, and to better understand how embeddings are shaping the future of artificial intelligence.

Machine learning models are continuously pushing the boundaries of what’s possible. One of the foundational elements propelling these advancements is embeddings. These representations of values or objects, whether they be text, images, or audio, serve as the basis for machine learning models and semantic search algorithms. In this comprehensive exploration, we will delve deep into the world of embeddings, deciphering their significance, how they work, and their pivotal role in shaping the future of AI including their integration into Edgio’s edge platform.

The Essence of Embeddings

At its core, embeddings can be thought of as the translators of the digital world. They take entities, be it words, documents, or even cities, and convert them into a mathematical form that machines can understand. This transformation is rooted in capturing the essential traits and characteristics of these objects, encapsulating them into vectors that represent their unique qualities and relationships.

In essence, embeddings empower machine learning models to discern similarity among objects. Whether it’s finding similar documents, images, or cities, embeddings pave the way for computers to grasp the intricate web of relationships that exists within our digital world. As such, they are the building blocks of artificial intelligence, enabling machines to understand and reason about data. To explore more about AI and its capabilities, consider reading about What is Artificial Intelligence.

The Core Concept: Vectors in Machine Learning

To truly grasp the concept of embeddings, we must first understand the fundamental concept upon which they are built: vectors. In mathematics, a vector is an array of numbers that defines a point in a multi-dimensional space. In the realm of machine learning, vectors serve as the compass that guides us through this multi-dimensional space, allowing us to navigate and make sense of complex data.

In more practical terms, a vector is a list of numerical values, such as {1989, 22, 9, 180}. Each number in this list represents the position of an object along a specified dimension. This concept becomes invaluable when we seek to find similarities or relationships between objects, similar to how we utilize predictive AI to anticipate future trends and behaviors.

Example of How to Use a Vector in ML

Consider the example of latitude and longitude coordinates. These two dimensions, north-south (latitude) and east-west (longitude), succinctly pinpoint any location on Earth. For instance, the city of Vancouver, British Columbia, Canada can be represented as the latitude and longitude coordinates {49°15’40″N, 123°06’50″W}. This concise list of two values forms a simple vector, defining Vancouver’s geographical location.

Now, imagine the task of finding a city near Vancouver. A human observer might consult a map, whereas a machine learning model could examine the latitude and longitude vectors. By identifying a location with similar coordinates, such as Burnaby at {49°16’N, 122°58’W}, the model deduces that Burnaby is in close proximity to Vancouver. This example illustrates how vectors enable machines to understand spatial relationships.

Adding Dimensions to Vectors

To further illustrate the power of vectors, let’s expand our geographical example. Imagine we wish to identify a city similar to Vancouver, not just in terms of proximity but also in population size. Here, we introduce a third dimension to our vector, representing population size.

The vector for Vancouver now becomes {49°15’40″N, 123°06’50″W, 662,248*}. In this three-dimensional space, Burnaby’s vector {49°16’N, 122°58’W, 249,125*} no longer appears particularly close to Vancouver. Instead, the machine learning model might identify Seattle, Washington, US, as a more suitable match with a vector of {47°36’35″N, 122°19’59″W, 749,256**}.

In this scenario, we’ve added a third dimension to our vectors, analogous to introducing a Z-axis to our latitude and longitude coordinates. This allows us to consider both geographical proximity and population size when seeking similar locations.

*Population data as of 2021.
**Population data as of 2022.

Beyond Three Dimensions: Complex Multi-dimensional Vectors

In the world of machine learning, vectors are not limited to just three dimensions. They can encompass a multitude of dimensions, each capturing a unique aspect of an object. Consider the challenge of determining the similarity between two TV shows. Numerous factors come into play, such as episode length, genre, viewer demographics, and more.

In this intricate multi-dimensional space, each TV show can be represented as a point along numerous dimensions, akin to latitude, longitude, and population size. Here’s an example illustrating how this works:

  1. Seinfeld (Sitcom) – Vector: {[Sitcom], 1989, 22-24 minutes per episode, 9 seasons, 180 episodes}
  2. Wednesday (Horror) – Vector: {[Horror], 2022, 46-57 minutes per episode, 1 season, 8 episodes}

In this representation, Seinfeld and Wednesday occupy vastly different points in the multi-dimensional space of TV shows. They differ not only in genre and debut year but also in episode length and the number of seasons and episodes. This multidimensional approach allows machine learning models to discern the intricate nuances that define similarity or dissimilarity between objects.

While our examples above incorporate only a handful of dimensions, in practice, machine learning models often operate in much higher-dimensional spaces, sometimes spanning hundreds or even thousands of dimensions. These complex multi-dimensional vectors serve as the foundation for advanced AI applications.

Unveiling the Magic: How Embeddings Work

Embeddings are the product of deep learning models, particularly neural networks. They are created through a process that involves converting objects into vectors using deep learning techniques. An « embedding » essentially represents the output of this process—a vector meticulously crafted by a deep learning model with the express purpose of facilitating similarity searches.

Here’s how it works:

Neural Networks and Hidden Layers

Neural networks, inspired by the structure of the human brain, form the basis of embedding creation. These networks comprise multiple layers, including an input layer, an output layer, and, most crucially, hidden layers. It is within these hidden layers that embeddings are brought to life, similar to how serverless computing leverages cloud resources for efficient application deployment.

Hidden layers in neural networks possess the remarkable ability to transform input data in various ways, based on the architecture and design of the model. The creation of embeddings typically takes place within these hidden layers, before subsequent layers process the input further.

Imagine you wanted to create embeddings for TV shows. Instead of manually specifying each TV show’s position along a multitude of dimensions, a neural network’s hidden layer takes on this task. It automatically generates embeddings that capture the essential attributes of each TV show, leveraging the rich data fed into the network.

Initially, a programmer may guide the network by providing examples of how to create embeddings, which dimensions to consider, and other relevant information. However, as the neural network trains, the embedding layer gradually becomes proficient at operating independently, refining its ability to generate embeddings that capture the essence of each object.

Embeddings in Large Language Models (LLMs)

The concept of embeddings takes a fascinating turn when applied to large language models (LLMs), such as those powering AI tools like ChatGPT. In these advanced models, embedding extends beyond individual objects to encompass entire contexts.

In essence, Each word or token in a sentence, paragraph, or article becomes an embedding, capturing not only the meaning of individual words but also the intricate relationships and semantic nuances within the text. This level of analysis is akin to the sophisticated data processing found in DevOps best practices, where multiple variables are considered for optimal outcomes.

Conclusion

In conclusion, embeddings serve as the cornerstone of machine learning’s ability to decipher the intricacies of our digital world. These mathematical representations enable machines to understand, reason about, and uncover relationships between various objects, from text to images and more. As we continue our exploration of artificial intelligence, it’s essential to recognize the profound significance of embeddings, not only in shaping the present but also in driving the innovations of the future.

For more insights and best practices on leveraging Artificial Intelligence, explore our learning center.

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