AI Fundamentals: Datasets 101

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Jul 17, 2023

The discussion kicks off with the crucial role of datasets in AI training, debunking the myth that models like GPT-3 use the entire internet for data. It emphasizes the immense effort required for quality data selection and the evolution of training methods. Key examples like Common Crawl and debates around data quality versus quantity are highlighted. Ethical concerns regarding copyright and licensing for datasets are also explored, while the importance of deduplication and data curation is underscored to enhance model accuracy.

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INSIGHT

GPT-3's Training Data

The claim that GPT-3 was trained on the entire internet is false.
Its dataset is significantly smaller than the internet's estimated size.

INSIGHT

Datasets vs. Benchmarks

Benchmarks and datasets used to be intertwined in machine learning.
Now, models often train on large datasets and are evaluated on separate benchmarks.

ADVICE

Dataset Size and Scaling Laws

Consider dataset size and scaling laws when choosing a model size for training.
A 100 billion parameter model requires substantial data, so evaluate available datasets like Common Crawl and C4.

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