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.
How is it different from a CRM?
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Google told TechCrunch this latest move isn’t AI-related, but it’s hard to see how the need to compete at a faster pace isn’t playing a role.
Что думаешь? Оцени!
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尽管目前大多数的 AI 生成内容，都被强制要求带上显示水印或者数字水印，但这套方案还是容易被绕过。