Tech companies like ChatGPT may run out of publicly available training data for AI language models by 2026-2032, potentially hindering progress in AI development. Researchers are exploring options like tapping into private data or using synthetic data generated by chatbots.
Key Points
Tech companies racing to secure high-quality data sources for AI language models
Researchers projecting depletion of public text data by 2026-2032
Exploration of alternatives like synthetic data and private data for AI training
Pros
Increased awareness of potential data shortage may prompt innovative solutions
Focus on alternative data sources like private data or synthetic data may lead to new advancements in AI development
Cons
Potential limitations in scaling up AI models efficiently due to data shortage
Risk of compromising privacy by tapping into sensitive private data for AI training