Knowledge Distillation
A model compression technique where a smaller 'student' model is trained to replicate the behavior of a larger 'teacher' model, producing more efficient models that retain much of the original's capability.
Knowledge distillation, introduced by Hinton et al. in 2015, enables deploying powerful AI on resource-constrained devices like smartphones. The student model learns from the teacher's output probabilities (soft labels) rather than just the ground truth, capturing nuanced relationships between classes. Modern distillation has produced models like DistilBERT (40% smaller, 60% faster than BERT with 97% of its performance) and TinyLlama. The technique is central to making frontier AI capabilities accessible at lower cost.
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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 Compute
The computational resources — primarily GPU and TPU processing power — required to train and run AI models, typically measured in FLOP (floating-point operations) or GPU-hours.
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.
Data Center
A facility housing computer systems and infrastructure used to process, store, and distribute data — increasingly built specifically for AI training and inference workloads.
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.
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