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.
Machine learning encompasses supervised learning, unsupervised learning, and reinforcement learning. Over 250,000 ML-related papers are published annually on arXiv. AI systems now exceed median human performance on most standard academic benchmarks. The field has seen dramatic capability improvements, with new benchmarks being saturated within 15 months of release — down from 3+ years a decade ago.
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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.
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.
Frontier Model
The most capable and advanced AI models at any given time, typically trained with the largest compute budgets and achieving state-of-the-art performance on benchmarks.
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