👉 Losing computing, often referred to as the "AI winter," is a period in the history of artificial intelligence research marked by reduced funding, interest, and progress. This decline began in the 1970s when early AI systems, despite promising theoretical potential, struggled to deliver practical results. These systems often faced limitations in computational power, data availability, and algorithmic sophistication, leading to high expectations being unmet. As a result, the field experienced a significant setback, with many researchers losing interest and support. However, this period also spurred a reevaluation of AI approaches, leading to more focused and effective research in the 1980s and beyond. Despite the initial setback, the resilience of the AI community eventually led to renewed advancements, particularly with the resurgence of machine learning and neural networks in recent years.
