Dimensionality Reduction
A set of techniques that reduce the number of features or variables in a dataset while preserving important information, making data easier to visualize, process, and analyze.
Popular methods include Principal Component Analysis (PCA), t-SNE, and UMAP. Dimensionality reduction is critical for handling high-dimensional data like gene expression profiles (20,000+ features) or word embeddings (hundreds of dimensions). In modern AI, techniques like autoencoders learn compressed representations of data. These methods reduce computational costs, mitigate the curse of dimensionality, and help identify the most informative features for downstream tasks.
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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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