AI development involves building intelligent machines that perform tasks typically performed by humans, including problem-solving and decision-making. These machines use complex algorithms and large data sets to learn and improve over time. Achieving a high level of AI requires meticulously collecting and preprocessing data to reduce biases and errors in models, which can lead to incorrect predictions or decisions. Ultimately, AI needs to be able to explain its results so that developers can troubleshoot and correct any problems.
The concept of inanimate objects endowed with intelligence has captivated human imagination for millennia. In ancient Greece, the god Hephaestus is credited with forging robot-like servants out of gold and engineers in Egypt built statues that could move by hidden mechanisms operated by priests. The 13th-century Spanish theologian Ramon Llull and the 17th-century philosopher Rene Descartes helped lay the foundations of modern AI, writing about symbolic logic that described human thought processes and reasoning.
Throughout the 20th century, advancements in computing fueled the evolution of AI. British mathematician Alan Turing conceived of the idea of a machine that could theoretically emulate any other machine, paving the way for digital computers and eventually AI. Princeton mathematician John von Neumann formulated the architecture of the stored-program computer, allowing the computer to keep its programs and data in memory, a precursor to AI.
Today, a wide range of businesses and organizations deploy AI for productivity and efficiency improvements, customer service and other business processes. In retail, AI helps create personalized shopping experiences, manage inventory and forecast demand. In manufacturing, it identifies machine faults, optimizes production lines and improves quality control. AI is also used to power chatbots and virtual assistants that provide round-the-clock customer support, resolving routine inquiries and routing more complex issues to human agents.