不难理解，布局AI既争眼下效率，也争未来行业话语权，速度至关重要，而收购和合作正是最省力、最快的办法。因此，我们能看到WPP早期收购 Satalia 作为集团AI技术中心；阳狮通过收购 Sapient 和 Epsilon 构筑数据底座，并在2025年继续收购 Lotame、Moov AI 等强化专项能力；宏盟对IPG的收购，更是直接放大了其在数据和AI领域的协同优势。
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There are a couple ways mitigate this drawback, both of which are outside the scope of this article. One is “garbage collection”: pruning tombstones from CRDTs, which prevents you from merging states with any changes made before the tombstones were removed. Another is creating an efficient format to encode the data. You can also combine these methods. Research suggests that this can result in as little as 50% overhead compared to the “plain” data CRDTs: The Hard Parts A talk on the latest research on CRDTs, originally given at the Hydra distributed computing conference on 6 July 2020.References: https://martin.kleppmann.co... youtu.be/x7drE24geUw?t=3587 . If you’d like to skip ahead and see some of this optimization in action, check out the final part in this series: Making CRDTs 98% More Efficient Making CRDTs 98% More Efficient | jakelazaroff.com State-based CRDTs grow monotonically, but that doesn't mean they can't be efficient. We'll learn how to compress the pixel editor state by 98%. jakelazaroff.com/words/making-crdts-98-percent-more-efficient/ . ↩
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