Large language models have resulted from three decades of accessible online text, but their data may deplete soon. Studies indicate that high-quality English web text could run out before the end of the decade due to current consumption rates. The next phase in AI will rely on self-generated data, requiring the right experiences for effective learning. Scaling AI involves multiple resources, evaluated as flops, which symbolize the effort consumed in training systems. Effective experience collection and exploration will drive future advances in AI technology.
Large language models have emerged from three decades of freely available human text online, resembling fossil fuels—abundant but finite, at risk of depletion soon.
The next wave of AI progress will focus on improving experience collection, emphasizing the acquisition of quality experiences rather than just increasing parameters.
Scaling AI systems involves addressing various resources, such as compute cycles and synthetic data generation, all considered in a unified measure called flops.
Experience collection costs are vital to advancing AI; exploration is key, requiring a thoughtful strategy for acquiring new, informative experiences.
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