AI has progressed significantly, especially in the last five years, leading to a trend often likened to a “Moore’s Law for AI.” Researchers contribute to this growth, producing notable advancements like FlashAttention and speculative decoding, which improve model performance and efficiency. Despite rapid improvements, some argue progress is slowing, as recent models show only marginal enhancements over their predecessors. Major breakthroughs in AI—such as deep neural networks, transformers, and reinforcement learning—have historically emerged from tapping new data sources like ImageNet and the Internet. The theory suggests future breakthroughs may arise not from novel methods but from accessing untapped data, such as video content. This limitless potential, coupled with the challenge of processing vast sensor data from robots, signals that the next major leap in AI may hinge more on data utilization than new algorithms. As researchers focus on harnessing these new data sources, the trajectory of AI innovation may pivot dramatically.
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