Learning-AI
Will we run out of data? An analysis of the limits of scaling datasets in Machine Learning
February 2023
tl;dr: We will run out of high quality language data by 2026, and low-quality language data will run out by 2030-2050. Image data will run out much slower, perhaps by 2030-2060. We need to boost data efficiency in the long run to keep ML algorithm going.
Overall impression
The paper is full of interesting statistics for language datasets (web, papers, books, etc), and inspiring statistical modeling of growth trends.
The current trend of ever-growing ML model may slow down significantly if data efficiency is not drastically improved or new data sources become available.
The biggest lesson that can be read from 70 years of AI research is that general methods that leverage computation are ultimately the most effective, and by a large margin. – Richard Sutton, The Bitter Lesson
Key ideas
- This report mainly focuses on unlabeled data, as unsupervised/self-supervised models has successfully created foundation models that can be fine-tuned for several downstream tasks using small amount of labeled data and large amount of unlabeled data.
- Possible future directions for scaling ML models
- data efficiency. Now much less efficient than human being.
- data sources.
- Simulation data and bridging sim2real gap
- Major economic shift may impact the production of data, such as autonomous driving for images.
- About half of the progress made by LLM over the past 4 years can be contributed to data. –> Coming up with efficient data consuming architecture without significant bottleneck is the correct way to scale up ML models.
Technical details
- High quality language data sources has a common property that they pass usefulness/quality filters (QA).
- user generated contents by dedicated internet contributors, such as wikipedia.
- produced and peer reviewed by subject matter experts
- Optimal dataset size is proportional to the square root of the compute budget. This is for language model though.
- Language training dataset doubles every 16 months, while vision is slower at 42 months (3.5 years). The largest language dataset has 3 orders of magnitude more data points than vision.
