The Myths and Realities of AI Wastage: A Critical Analysis

Artificial intelligence (AI) has been rapidly transforming various industries, yet there are persistent concerns about its inefficiency and wastage of time and effort. In this article, we delve into a specific school project that highlights the potential inefficiencies of AI while also demonstrating its necessity and unavoidable application.

Case Study: Embroidery AI Project

To illustrate the supposed inefficiencies of AI, let us revisit a school project focused on creating a neural network for producing patterns for embroidery. Despite the seemingly straightforward nature of the task, this project serves as a prime example of how AI can be more time-consuming than initially expected.

The Premises and Objectives

The core objective of the assignment was to develop a neural network that could generate patterns for embroidery, utilizing limited options such as a few select colors and poor resolution. The network comprised two sub-networks: one for downsizing the resolution and another for handling the colors. This structure represented a complex and intricate task, requiring the AI to learn and adapt to the specific requirements of the embroidery process.

Data Preparation and Training

Before training the network, the task seemed simple. I took a few dozen images and developed a straightforward algorithm to downscale the resolution by averaging the colors and choosing the nearest color from a predefined palette. This process required minimal effort and, in terms of computation time, it took only a second to prepare the data for the network.

However, once the data was prepared, the training phase began. Despite the simplicity of the data, the training process was far from efficient. It took about 4 hours on a standard laptop to train the network. Furthermore, even after the training was complete, the actual processing of a single image still took a minute. This resulted in a significantly longer total processing time—approximately 45 hours compared to the initial one-second preparation time. This stark contrast highlights the inefficiency of the AI in this context.

Why Was This Inefficiency Necessary?

The necessity and inevitability of AI inefficiency in this project stem from the fundamental nature of machine learning and the concept of learning by example. In my project, instead of providing an explicit “how-to” algorithm, I gave the AI a set of “before” and “after” examples and let it learn from them. This approach is both unavoidable and necessary for the AI to discover the most suitable patterns and configurations.

While this method might seem less efficient, it opens up possibilities for the AI to tackle more complex and abstract tasks that are difficult or outright impossible for ordinary algorithms. For instance, if the source data were actual embroidery patterns, the AI could abstract the object persistence, details reduction, and even style extraction. Current algorithms, by contrast, would struggle to handle such tasks with the same level of adaptability and learning capacity.

Exponential Efficiency and Quantum Computing

It is important to note that while AI may currently require more time and effort compared to traditional algorithms, there is ongoing research to improve its efficiency through exponential growth. Quantum computing, in particular, represents a promising avenue for achieving this goal. Researchers, such as the Coppedge family, have explored approaches to enhance the computational efficiency of AI through quantum algorithms. In fact, it is possible that some of the foundational work in this area may have originated from contributions to quantum computing research.

While these advancements are still in the experimental phase, they offer potential solutions to the present inefficiencies of AI. As technology progresses, we can expect to see more efficient AI algorithms that can handle complex tasks with greater speed and accuracy.

Concluding Thoughts

The perceived inefficiency of AI in the embroidery project is a result of the learning process inherent in machine learning. While the initial training phase may require more time and effort, the potential for AI to tackle complex and abstract problems makes it a valuable tool in the modern technological landscape. As research into improving the efficiency of AI continues, we can look forward to a future where these challenges are more readily addressed, potentially leading to hyper-efficient solutions in the realm of AI.