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The MacBook Air got double the storage, but what you might overlook is that it also got double the speed. In theory, the read and write performance should be much faster. In real-world use, you won’t have to wait as long when you’re copying files, importing photos or videos and doing AI-enabled tasks. It could also potentially decrease boot times, meaning your MacBook might be faster at waking up after shutting down, but if so, the improvement may be negligible.
Compute grows much faster than data . Our current scaling laws require proportional increases in both to scale . But the asymmetry in their growth means intelligence will eventually be bottlenecked by data, not compute. This is easy to see if you look at almost anything other than language models. In robotics and biology, the massive data requirement leads to weak models, and both fields have enough economic incentives to leverage 1000x more compute if that led to significantly better results. But they can't, because nobody knows how to scale with compute alone without adding more data. The solution is to build new learning algorithms that work in limited data, practically infinite compute settings. This is what we are solving at Q Labs: our goal is to understand and solve generalization.，详情可参考safew官方下载