GPT-3
OpenAI's 175-billion-parameter language model released in 2020, which demonstrated that scaling up model size produced emergent capabilities like few-shot learning and code generation.
GPT-3 was a watershed moment in AI, showing that a single model could perform translation, coding, summarization, and creative writing with minimal task-specific training. Its 175 billion parameters were 10x larger than any previous model. GPT-3's few-shot learning ability — performing tasks from just a few examples in the prompt — was unexpected and launched the prompt engineering field. The model's API generated over $1 billion in annualized revenue for OpenAI and attracted Microsoft's $13 billion investment.
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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.
ChatGPT
OpenAI's conversational AI assistant, launched in November 2022, which catalyzed the current generative AI boom by demonstrating the capabilities of large language models to a mainstream audience.
Fine-Tuning
The process of further training a pre-trained AI model on a specific, smaller dataset to specialize it for a particular task or domain, requiring far less compute than training from scratch.
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.
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