Artificial intelligence systems could face a shortage of training data, like the vast amount of text available online, hindering their development and progress. A study predicts this data depletion between 2026-2032, likening it to a 'gold rush' exhausting finite resources.
Tech giants are racing to acquire quality data sources for training AI language models. However, the long-term sustainability of AI development is uncertain due to the potential scarcity of new online content, pushing companies towards using sensitive private data or synthetic data.
The limitation in data availability poses a significant constraint on scaling up AI models efficiently, impacting their abilities and outputs. Scaling up models has been crucial for enhancing capabilities and output quality, indicating a potential bottleneck in future AI advancements.
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