Over the past year, discussions about Artificial intelligence (AI) have captured the attention of everyone from everyday citizens to the Pope. Despite this surge in awareness, the public largely misunderstands how AI functions and how closely its development mirrors traditional manufacturing processes. This fundamental misconception is driving a fierce, increasingly bipartisan backlash against the physical infrastructure necessary to sustain AI’s growth.
As it has evolved, AI is, at its core, best understood as an extremely sophisticated form of automated pattern recognition. What we call computer neural networks are first trained on enormous amounts of data gathered from the internet using algorithms — really just mathematical equations — to discover patterns or relationships; once trained, they apply those patterns to new questions. Because modern computers can detect patterns sometimes invisible to humans and then use those patterns to solve problems at an almost unbelievably fast speed, their output seems magical. Since AI can answer questions and perform tasks much like very smart humans, it is tempting to anthropomorphize it.
