# Latent space transformations

## Their hidden power in AI and machine learning

Getting machines to understand the information we want to give it is quite the task. Especially, given the level of complexity of the information we give it. For example, when trying to process an image for classification algorithms, how does the algorithm recognise the paws of a dog or the curvature of a boat?

We need to simplify the information for simpler processing and manipulation. Similar to how you would take summarised notes in a lecture instead of copying everything. While information is lost, the key features are kept. That is where the term “latent space” comes in.

__What are latent spaces?__

In the realm of mathematics, various types of spaces play crucial roles. One such space is the linear space, which encompasses the number line—a fundamental construct. Then there's Euclidean space, a broader category that encompasses 2D, 3D, and higher-dimensional spaces. However, as the number of dimensions increases, the mathematical intricacies become exceedingly complex, often pushing the limits of computational feasibility.

In a latent space transformation, we essentially reduce the dimensions of the space in which the data exists and create an abstract representation of the key features in a lower dimension space. This is has a host of benefits with main one being a reduction in the compute power needed to process the data.

It’s an example of data compression and a direct instance of dimension reduction with neither being new concepts.

__Example: auto-encoders__

Auto-encoders are a type of neural network. They consist of an encoder-to-decoder architecture (see image with caption).

The transformation allows us to process and store the input data more efficiently. In addition, once trained, autoencoders can sample data from the latent space to generate new data points also called data generation of a synthetic nature.

__Other applications of latent space__

Now that we can store our information more effectively for computers to understand, there are a host of applications for the technique you might want to be aware of:

- Natural Language Processing: Latent space models have been used in natural language processing for tasks such as text classification, sentiment analysis, and machine translation.

- Audio Processing: Latent space models have been used for music analysis, speech recognition, and audio processing.

- Computer Vision: This we have partially discussed already.

- Anomaly Detection: Latent space models can be used to recognise security failures in Cybersecurity, or potentially fraud in the financial system.

The applications of data reduction would be endless but those are just few applications in technology right now.

*Written by Temi Abbass*