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Earlier this year, the BrXnd Dispatch reported on an approach to sampling called “silicon sampling.” The idea is that social scientists can use large language models—such as GPT-3—to simulate people’s opinions for polling and research purposes. This approach has the potential to make surveys much more accurate and cost effective—as well as faster.
The silicon sample is a fundamentally simple idea. Using software to manipulate text or images, the algorithm can generate artificially-selected data points that appear similar to real world samples—and provide researchers with key insights on how to proceed with human participants.
The idea has a lot of practical applications. A recent study from Stanford University’s Statistical Machine Learning Lab has used this approach to create fake political data from the words of people who volunteered for an online survey and were assigned specific demographic traits—such as age, gender, religion and race. The researchers were then able to see how well this data aligned with the actual data from a large, representative, national sample.
Similarly, silicon can be a good indicator of dirt contamination in an oil sample. The typical alarm point for silicon in oil is 20 ppm, and many commercial laboratories will recommend some type of lubricant change once that value is reached. However, trend analysis demonstrates that a proper evaluation of dirt contamination should take place before setting an alarm.
Silicon is a brittle, crystalline solid with a blue-grey metallic luster. It is a tetravalent metalloid and semiconductor, with the chemical symbol Si and atomic number 14. Silicon is one of the rarer elements in the earth’s crust and comes from meteorites, hot springs and volcanic eruptions. Silicon is a critical ingredient in glass, plastics and semiconductor materials. It enters the ocean as dissolved silicon dioxide and silicic acid, both of which are utilized by diatoms in their shell frustules.