Graph Neural Network (GNN)
A type of neural network designed to operate on graph-structured data, capturing relationships between connected entities such as social networks, molecules, or knowledge graphs.
GNNs process data represented as nodes and edges, making them ideal for social network analysis, drug discovery, fraud detection, and recommendation systems. DeepMind's AlphaFold 2 used graph-based attention mechanisms to predict protein structures with atomic accuracy. Pinterest uses GNNs to power recommendations for its 400+ million users. The molecular graph representation enables GNNs to predict drug properties, accelerating pharmaceutical research pipelines from years to months.
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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.
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.
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.
Model Training
The computationally intensive process of teaching an AI model by feeding it data and adjusting its parameters to minimize errors, often requiring thousands of GPUs running for weeks or months.
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