The article discusses a potential solution to the ‘peak data’ problem faced by artificial intelligence (AI) systems. As AI models become larger and more complex, they require an increasing amount of data and computational resources during training. However, there is a limit to the amount of data available, which could hinder the further development of AI. Researchers from Google’s DeepMind have proposed a technique called ’test-time compute’ that could help address this issue. Instead of relying solely on data during training, the technique allows AI models to perform additional computations during inference (when making predictions). This approach could enable AI systems to continue improving without requiring exponentially more data. The researchers estimate that by 2025, test-time compute could provide a 10x to 100x boost in AI capabilities compared to traditional methods. While the technique has limitations and challenges, it offers a promising solution to the peak data problem and could help sustain the rapid progress in AI.
AI has hit a peak data problem, but Google DeepMind researchers have a solution that could help it keep growing
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