Multidimensional AI

We can also use multi dimensional data in ML very effectively.  This is an advanced technique that we have used in some situations that were superficially simplistic, but layered in with the situation - became quite complex.  

In machine learning, we typically reduce the dimensions (or features) to zoom in on the most important ones.  There are various techniques like Principal Component Analysis (PCA) or Singular Value Decomposition (SVD) that is common.  

But in Multi Dimensional AI, we make use of the high degree of complexity in a realistic sense. 

Multi-dimensional machine learning (ML) refers to the use of machine learning techniques to analyze and make predictions from data that has multiple dimensions or variables. This can be contrasted with traditional ML approaches, which often only consider data with a single dimension or variable.

Multi-dimensional ML can be useful in a variety of applications, including data analysis, predictive modeling, and pattern recognition. By considering multiple dimensions of data, multi-dimensional ML can provide a more nuanced and accurate understanding of the underlying patterns and relationships within the data. This can be particularly useful for tasks that require a high degree of complexity or that involve large and complex datasets.

Some common techniques used in multi-dimensional ML include principal component analysis (PCA), singular value decomposition (SVD), and multi-dimensional scaling (MDS). These techniques can be used to reduce the dimensionality of the data, making it easier to analyze and interpret, while still retaining the most important information and relationships within the data.

Overall, multi-dimensional ML can be a valuable tool for businesses that need to make sense of complex and multi-dimensional data. By considering multiple dimensions of data, it can provide a more accurate and detailed understanding of the patterns and relationships within the data, enabling businesses to make more informed decisions and predictions.