What is an embedding in AI?

An embedding in AI is a numerical representation (vector) that captures the characteristics and nuances of a concept in a way computers can process. It transforms words, images, or other data into mathematical vectors that preserve semantic relationships and allow machines to understand and manipulate complex concepts.

How does dimensionality affect AI embeddings?

Dimensionality determines the richness of an embedding's representation. Low-dimensional embeddings (like a child's understanding of chocolate) capture only simple associations, while high-dimensional embeddings (like a master chocolatier's knowledge) can represent many nuances and complex relationships. Modern AI models like GPT-4 use embeddings with thousands of dimensions to represent the complexity of human language.

Why is context important for embeddings?

Context is crucial for embeddings because the same word or concept can have different meanings in different situations. For example, the word 'tablet' has different meanings in a chocolate shop (chocolate bar), at a doctor's office (medicine), or in an electronics store (digital device). Good embedding models capture these contextual differences to provide more accurate representations of meaning.

How do you choose the right embedding for your AI application?

When choosing the right embedding for your AI application, consider: 1) Complexity needed - whether you need to capture fine nuances; 2) Domain specificity - whether you need general or specialized domain representation; 3) Resources available - your technical constraints and budget in terms of time and computation; 4) Training data appropriateness - whether the embedding's training data matches your application domain, temporal period, and contextual diversity.

Embeddings: A Matter of Chocolate

Artificial intelligence is at the heart of many innovations today, and among its fundamental concepts is embedding. But what exactly is an embedding? Let's discover it through a family story that will help us understand this complex concept intuitively.

What is an embedding?

Computers only understand 0s and 1s. When we present them with information like the word "chocolate", they cannot interpret its meaning directly. We must therefore transform this word into a numerical representation that a computer can process. This representation often takes the form of a mathematical vector.

An embedding is precisely this representation: a vector that captures the characteristics and nuances of the concept we want to represent. But what does it mean to "faithfully capture a concept"? This is where things get interesting.

A Story of Chocolate

Imagine you have a 2.5-year-old daughter. During the Christmas holidays, she discovered chocolate, associated with this magical period: Santa Claus, the Christmas tree, and presents. For her, all chocolate automatically becomes "Christmas chocolate". This simple association represents her "mental model" of chocolate, based on her limited experience.

This representation isn't wrong, but it's simplified and lacks nuances, as it focuses solely on the temporal aspect linked to Christmas. For example, it wouldn't allow for classification of different types of chocolate (dark, milk, white) due to its low number of dimensions.

In contrast, a master chocolatier would have a much richer and nuanced representation of "chocolate", including dimensions such as the origin of cocoa beans, cocoa content, or manufacturing methods.

Key Components of an Embedding

1. Dimensionality

Dimensionality determines the richness of the representation:

Low dimension (like the little girl): simple representation, limited to a few aspects
High dimension (like the master chocolatier): complex representation, capable of capturing many nuances

In modern AI models like GPT-4, embeddings can have thousands of dimensions to represent the complexity of human language.

2. Learning and Data

The quality of an embedding strongly depends on its learning:

Data volume

The little girl: experience limited to Christmas holidays
The master chocolatier: years of experience and learning
An AI model: millions or billions of training examples

Specialization

In certain domains (medicine, finance, law), it's preferable to have specialized embeddings, like a chocolatier who would focus solely on single-origin dark chocolate.

Data Quality

Relevant to the target domain
Diverse to cover different aspects
Representative of real-world conditions
Balanced to avoid biases

3. Context: A Crucial Element

Context plays a fundamental role in the relevance of embeddings. To understand its importance, let's return to our chocolate analogy:

Imagine our little girl hearing the word "tablet" in different situations:

In a chocolate shop: she'll think of a chocolate bar
At the doctor's: it will be a medicine tablet
In an electronics store: it will be a digital tablet

The Importance of the Training Dataset

Application Domain

Verify if the training domain corresponds to your use case
Example: An embedding trained on scientific articles might misinterpret casual language

Temporal Period

Ensure that training data is contemporary with your usage
Example: An embedding trained on texts from the 90s won't understand modern terms

Contextual Diversity

Examine if the data covers enough different contexts
Example: An embedding trained solely on American legal documents might poorly handle European law

How to Choose the Right Embedding?

Ask yourself these questions:

What complexity?

Do you need to capture fine nuances?
What richness of representation is necessary?

Which domain?

Is it a general or specialized domain?
Are there pre-trained embeddings available?

What resources?

What are your technical constraints?
What is your budget in terms of time and computation?

The Evolution of Embeddings

Let's return to our little girl. When Easter arrives, she discovers chocolate eggs. Her "mental model" must evolve to integrate this new information: chocolate is no longer solely linked to Christmas. This evolution recalls how embedding models can be updated and refined with new data. It's a continuous process of learning and adaptation, similar to our own learning.

Conclusion

Embeddings are at the heart of many modern AI applications. They allow machines to understand and manipulate complex concepts by transforming them into mathematical representations.

Like our little girl who progressively learns the complexity of the chocolate world, embeddings can evolve and enrich themselves. The choice of appropriate embedding depends on your specific needs, resources, and application domain.

The next time you taste chocolate, think about all the dimensions of its representation: from a child's simple joy to a master chocolatier's complex analysis. This is the richness of representation that embeddings try to capture in the digital world.