Training Data
The dataset used to train machine learning models, consisting of input-output pairs that define the model’s learning patterns. Quality, diversity, and scale directly determine model performance and bias.
- Key aspects:
- Supervised learning requires labeled examples
- Data bias can propagate to model outputs
- Data augmentation techniques expand effective dataset size
- Ethical AI considerations require careful data curation
- Foundational models like Bigram Language Models demonstrate how simple statistical patterns in text corpora (e.g., Shakespeare) form the basis for more complex GPT architectures, as detailed in Karpathy Bigram Language Model: GPT Foundation for Shakespeare Text Generation
Recent Reviews:
- Dave’s Garage - review of AI models (2026-04-14): Dave Plummer (retired Microsoft engineer) notes mid-2025 LLM landscape has evolved beyond ChatGPT-4 dominance, with Grok-3 and Gemini now competitive models trained on increasingl