AI’s “New Fuel”: Where Does It Come From? The answer lies within the realm of “data engineering.”
As we accelerate the pace of artificial intelligence to mimic human-like speed, one might wonder where this “fuel” originates. The answer is found in the new paradigm of “data engineering.” Unlike traditional engineering, which produces tangible goods such as steel or cars, data engineering focuses on the high-quality and specialized production of data, serving as the endless “grain” for large AI models.
Many companies are currently experimenting with data engineering, particularly in the “cloud” sector, which has specific characteristics. Here, there are no flying machines; instead, it involves a tightly-knit network of small-scale nodes. Every day, approximately 500,000 high-quality data points flow from this “pipeline.” This comes from a company specializing in advanced data engineering. This company, led by a visionary, has integrated over 4,000 sensors into its operations and has developed the world’s smallest angular measurement devices, enabling them to monitor angular changes in real time.
Data pipelines continually log sensor data, featuring quality sensor packages and various data points, including visual, touch, auditory, and trajectory information. According to the founder, this type of data allows machines to learn without “seeing” actions, yet they can still “sense” context.
So, what exactly is “data engineering”? It represents a crucial segment of data value extraction. Currently, AI companies face a significant challenge: a severe shortage of high-quality data. A new wave dubbed “data engineering” is emerging to address this shortfall. It is not like traditional manufacturing, which produces cars or machinery, but rather a specialized production and “refinement” of data aimed at converting raw data into high-quality data assets suitable for artificial intelligence.
According to experts, the essence of data engineering lies in its ability to create a foundational structure for AI. It aims to provide high-quality data resources that can directly feed into AI systems, enhancing efficiency and effectiveness. The University of Transport and Information Management has emphasized the importance of foundational data infrastructure for AI development, as there is currently a lack of basic facilities.
The efficiency of data engineering significantly depends on the establishment of foundational facilities, enabling a smooth flow of data. As we approach the AI era, data has become the core production element, similarly to how water and electricity serve as vital resources. The infrastructure necessary for data engineering is essential for providing data to AI companies.
Looking ahead, the potential of data engineering is poised to grow, continually supplying AI with the necessary “grain.” It will become a cornerstone of national data infrastructure. With advancements in data collection technologies, the integration of data engineering will accelerate, supporting industries in realizing their true potential.
As we progress, the “data engineering” field is expected to evolve, adapting to new challenges and continuing to provide the essential data required for AI development.
Original article by NenPower, If reposted, please credit the source: https://nenpower.com/blog/the-rise-of-ai-understanding-the-origins-of-the-data-economy/
