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Lux: useful Sankey Diagram on AI

Lux Research has used a Sankey Diagram—a type of flow diagram in which the width of a path is proportional to the flow quantity, and which is often used to visualize energy transfer (e.g., IEA)—to visualize the relationships of the different areas in Artificial Intelligence (AI).

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Lux breaks AI into three key areas: Applications, Domains, and Methods, and links these to the root disciplines.

  • AI applications are the complex tasks that computers must complete to successfully execute higher level functions.

  • Domains are essentially fields of study within AI.

  • Methods are the technical approaches that computer and data scientists apply to solve machine learning challenges.

In the diagram, AI applications are shown on the left. Their relationships with domains are illustrated via connections. For example, effective computing applications predominantly utilize computer vision (e.g., facial expression recognition), natural language processing (e.g., semantic analysis of text), and computer audition (e.g., inferring emotion from voice). The size of each connection is used to portray the relative importance/predominance of each domain to each application area.

The domains are then mapped to AI methods and techniques that they harness. For example, computer vision has been using deep learning successfully, especially in the past several years; previously, computer vision relied more heavily on older regression techniques, such as random forest decision tree models and support vector machines. Images and videos can, in total, comprise massive but somewhat redundant data sets; hence in some cases, dimensionality reduction serves to compress information in the process of making predictive inferences.

Lux then considers the connections between methods and technique and their root disciplines. For example, deep learning is, in essence, a class of methods that came from the machine learning community. More classic regression techniques are also claimed in machine learning but from a historical perspective are more heavily rooted in statistics.

The mapping shown in the figure is intended to be representative, but not exhaustive. In some cases, the relationships could actually be thought of like a Venn diagram. For example, while the figure taken literally would imply that statistics, data mining, and machine learning are independent, in reality there is a large amount of overlap among the three disciplines. Deep learning could also fairly be considered just another type of regression. However, Lux attempted to disentangle and make orthogonal as much as possible the important components of the landscape for clarity.

More importantly, conducting such an exercise enables understanding of AI methodologies and domains based on their core concepts and leads to insights on hype versus reality of some of the more common sight techniques. For example, deep learning approaches can be extremely useful, but will also have their limitations. Deep learning works well where there are massive data sets that are well labeled, and can bring huge advances in areas like speech, voice, and object recognition. This map also reinforces that there is no singular solution that will enable “intelligent machines,” but rather, AI will continue to grow as a combination of techniques and approaches used to solve discrete problems. Finally, it is clear that not all AI methods are suited to the same types of problems, as some techniques are best suited to huge data sets where the data is all of one type, whereas others are better when the input variables span a range of data types, such as images, text, and sound.

—Lux Research

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