AI Winter
An AI winter is a period of reduced funding, interest, and research progress in artificial intelligence. Historical AI winters occurred in the 1970s and late 1980s, often following inflated expectations and undelivered promises.
Understanding AI Winter
AI winters are historical periods marked by dramatic declines in funding, public enthusiasm, and research output in artificial intelligence, typically triggered by a gap between inflated expectations and actual technological capabilities. The first major AI winter occurred in the 1970s after the Lighthill report criticized the field's slow progress, leading to significant cuts in government research funding. A second winter hit in the late 1980s when expert systems failed to deliver on commercial promises and specialized AI hardware companies collapsed. These downturns offer important lessons for today's AI landscape, where massive investments in deep learning and large language models like ChatGPT have created enormous hype. Understanding AI winters helps researchers, investors, and policymakers maintain realistic expectations about what current AI systems can achieve. The field's cyclical nature underscores the importance of sustained basic research even when commercial applications dominate the conversation.
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Fundamentals
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AGI
Artificial General Intelligence (AGI) refers to a hypothetical AI system with human-level cognitive abilities across all intellectual tasks. Unlike narrow AI, AGI would be able to learn, reason, and solve problems in any domain without task-specific training.
Algorithm
An algorithm is a step-by-step procedure or set of rules for solving a computational problem. In AI, algorithms define how models learn from data, make predictions, and optimize their performance.
Artificial General Intelligence
Artificial General Intelligence is a theoretical form of AI that would match or exceed human cognitive abilities across all domains. AGI remains an aspirational goal rather than a current reality in AI research.
Artificial Intelligence
Artificial Intelligence is the broad field of computer science focused on creating systems that can perform tasks requiring human-like intelligence. AI encompasses machine learning, natural language processing, computer vision, and robotics.
Artificial Narrow Intelligence
Artificial Narrow Intelligence (ANI) refers to AI systems designed to perform specific tasks, such as image recognition or language translation. All current AI systems, including large language models, are forms of narrow intelligence.
Artificial Superintelligence
Artificial Superintelligence (ASI) is a hypothetical AI that would surpass human intelligence in every cognitive dimension. The prospect of ASI raises profound questions about control, alignment, and the future of humanity.
Dynamic Programming
Dynamic programming is an algorithmic technique that solves complex problems by breaking them into simpler overlapping subproblems. It is used in reinforcement learning, sequence alignment, and optimal control.
Emergent Behavior
Emergent behavior refers to capabilities that appear in large AI models that were not explicitly trained for or predicted. Examples include in-context learning and chain-of-thought reasoning in large language models.