Data discovery and augmentation: An insight into the art of data acquisition and the process of model fabrication

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Introduction

Artificial intelligence has grown at a tremendous pace in recent times. This has widened the processing capabilities and also the processing power of intelligent systems. Accordingly, the transmission costs have also increased. Different types of computationally expensive models have been conceived. These models indicate that the carbon footprint associated with our computational processes has also increased. This trend of increased computational and processing costs is especially true for natural language processing models that form the research basis of various artificial intelligence engineering courses. It has been observed that such models might be a natural projection of the technological advancement that we have been rightly tracing. However, the environmentalists argue that the high carbon costs that are incurred by the development of such models impede the type of research that we need under the present circumstances.

Red AI

One of the notable concerns that researchers have raised is the exponential increase of about 50000 times in the processing costs of computational elements. This has proved to be a major hurdle in the AI industry for various researchers, academicians as well as other organizations. Despite the large number of contributions to the data processing industry, one of the major concerns that has been raised is related to computational engineering. In more specific terms, the concern is related to computational efficiency. The natural question that arises here is whether the continuance with high processing systems benefits the research industry at the cost of the environment.

It is pertinent to take a note of various types of AI models that are associated with high computational costs. AlexNet, AlphaZero, and AlphaGo Zero are some of the most popular models that utilize deep learning techniques for data training purposes. However, the costs associated with these advanced models far exceed the costs used for nominal training of models using supervised machine learning techniques.

Green AI

In the last few years, scientists that have been researching AI have tried to develop a trade off between accuracy and efficiency. When it comes to a genre of AI which is both cost efficient and accurate, Green AI is the answer. As such, it is important to quantify a definition for Green AI that addresses the concerns regarding carbon footprint. The carbon footprint can be addressed if conditions around three main parameters are formulated.  The first parameters include the cost of processing various types of data sets. The second parameter includes the number of hyperparameters used and the third parameter is concerned about the accuracy of results. When it comes to expensive processing power, this concern can primarily be addressed by reducing the architectural complexity of the model.

The road ahead: Green AI versus Red AI

It is clear that Red AI is not the answer to the future of computational processes. This is because Red AI consumes more energy, increases the computational costs, and leads to decrease in efficiency. On the other hand, Green AI is perfectly suited for a sustainable, safe, and secure future as it demonstrates the hidden efficiency of our computational methodologies.