AI Winter refers to periods of reduced funding and interest in artificial intelligence research and development. These phases are often characterized by unmet expectations and overhyped promises that lead to disillusionment among investors and researchers. During an AI Winter, many projects are abandoned, and the pace of innovation slows significantly. This phenomenon has occurred several times in the history of AI, notably in the late 1970s and late 1980s. Understanding AI Winter is crucial for recognizing the cyclical nature of technological advancements and the importance of realistic goal-setting in AI development.
A/B testing compares two versions of a product to optimize performance and improve user engagement.
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AI Fundamentals