AI Scaling Paradigm

An AI Scaling Paradigm is a scaling approach that drives AI capability improvement through systematic resource scaling (of compute resources, training data, and model parameters).

• AKA: AI Scaling Strategy, Compute Scaling Paradigm, Scale-Driven AI Approach.
• Context:
  • It can typically follow AI Scaling Power Laws relating ai scaling resource inputs to ai scaling performance outputs.
  • It can typically enable AI Scaling Emergent Capability at ai scaling threshold scales.
  • It can typically drive AI Scaling Performance Improvement through ai scaling compute increases.
  • It can typically support AI Scaling Model Enhancement via ai scaling parameter growth.
  • It can typically facilitate AI Scaling Breakthrough Achievement at ai scaling extreme scales.
  • ...
  • It can often combine with AI Scaling Algorithmic Innovation for ai scaling compound improvements.
  • It can often reveal AI Scaling Saturation Limits at ai scaling plateau points.
  • It can often require AI Scaling Infrastructure Investment for ai scaling compute resources.
  • It can often demonstrate AI Scaling Discontinuous Jumps in ai scaling capability emergence.
  • ...
  • It can range from being a Pre-Training AI Scaling Paradigm to being an Inference-Time AI Scaling Paradigm, depending on its ai scaling application phase.
  • It can range from being a Compute-Focused AI Scaling Paradigm to being a Data-Focused AI Scaling Paradigm, depending on its ai scaling resource emphasis.
  • It can range from being a Linear AI Scaling Paradigm to being an Exponential AI Scaling Paradigm, depending on its ai scaling growth pattern.
  • It can range from being an Efficient AI Scaling Paradigm to being a Brute-Force AI Scaling Paradigm, depending on its ai scaling optimization approach.
  • ...
  • It can integrate with Language Model Scaling Law for ai scaling prediction models.
  • It can connect to AI Benchmark Saturation Phenomenon for ai scaling limit identification.
  • It can interface with AI Reasoning Model for ai scaling capability demonstration.
  • It can communicate with International Math Olympiad Benchmark for ai scaling milestone tracking.
  • It can synchronize with AI Scientific Discovery Automation System for ai scaling research applications.
  • ...
• Example(s):
  • Kaplan Scaling Laws predicting ai scaling loss reduction with ai scaling compute budgets.
  • Chinchilla Scaling Laws optimizing ai scaling token-parameter ratios.
  • GPT Scaling Progression from GPT-2 to GPT-4 showing ai scaling capability growth.
  • PaLM Scaling Achievement reaching 540B parameters with ai scaling breakthrough performance.
  • ...
• Counter-Example(s):
  • Algorithmic Efficiency Approach, which improves through better algorithms without ai scaling size increase.
  • Data Efficiency Method, which prioritizes data quality over ai scaling data quantity.
  • Architecture Innovation, which enhances through structural improvements rather than ai scaling parameter increase.
• See: Scaling Approach, Language Model Scaling Law, Deep Learning Scaling Laws Relationship, LLM Inference-Time Scaling Law, AI Benchmark Saturation Phenomenon, Moore's Law, 2020 ScalingLawsforNeuralLanguageMod.

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