Scientific Computing: Scientific Research as a Precision Tool

The creation of a complex system that utilizes real-world data to solve problems often requires a high degree of precision. This level of precision is often not possible without the use of scientific research. As someone with experience working with artificial intelligence and assembly-line type automation, I can confirm that prior scientific research is important for analytical systems in a factory setting (especially those working with organic objects).

For example, if a food company wants to design a system for checking the quality of potato chips, data would need to be collected on the chips themselves before a solution could be developed. The first step would be to define what quality of product is acceptable for shipment. Next, what are the properties of such a chip? What is its shape, size, color, pH level, trans fat content, etc.? After the acceptable quality is determined, it is necessary to find the properties of what is not an acceptable quality. These quality levels can be determined by scientific tests and data analysis to determine what properties present in the product make it tasty, unhealthy, visually appetizing, etc. Then, using this data, thresholds can be created that define exactly what makes a "good" chip and what makes a "bad" one. From there, it is possible to create algorithms that analyze the product using a variety of sensors and dispose of defective chips.

Green potato chips are sometimes considered undesirable by consumers

During my internship, I successfully assisted my mentor in the application of using scientific research and analysis to flower bulbs (see previous post on A.I.). In organic materials, properties of objects vary greatly depending on the genetic makeup of the object. It is very important to properly prepare for this diversity when utilizing computers to solve problems!