AI Winter
A period of reduced funding, interest, and progress in AI research, typically following a cycle of over-hyped expectations and subsequent disillusionment with the technology's capabilities.
Two major AI winters occurred: the first from 1974-1980 after early perceptron limitations were exposed, and the second from 1987-1993 after expert systems failed to meet expectations. Each winter saw dramatic funding cuts — DARPA reduced AI research budgets by over 80% during the first winter. The current AI boom, driven by deep learning breakthroughs since 2012, has lasted over a decade — the longest period of sustained AI progress. Some researchers warn that unmet expectations around AGI or generative AI profitability could trigger another downturn.
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Related Terms
Artificial General Intelligence (AGI)
A hypothetical form of AI that can understand, learn, and apply knowledge across any intellectual task at or above human level, rather than being specialized for specific tasks.
AI Alignment
The research field focused on ensuring AI systems behave in accordance with human values and intentions, particularly as systems become more capable.
Capex (Capital Expenditure)
Long-term investment spending by companies on physical assets like data centers, GPU clusters, and networking infrastructure — the backbone of AI deployment at scale.
Enterprise AI Adoption
The rate at which businesses integrate AI technologies into their operations, measured across functions like customer service, software development, marketing, and supply chain management.
Foundation Model
A large AI model trained on broad data that can be adapted to a wide range of downstream tasks — examples include GPT-4, Claude, Gemini, and Llama.
Machine Learning
A subset of AI where systems learn patterns from data rather than being explicitly programmed, improving their performance on tasks through experience without human-written rules.
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