Future of Web-Scale Training Sets: Unpacking Data Poisoning Concerns

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SUMMARY

The discussion centers on the implications of data poisoning in web-scale training sets, referencing an article from The Economist and an arXiv paper (https://arxiv.org/abs/2302.10149). Data poisoning, which has historical roots in practices like keyword stuffing, poses challenges for AI companies that rely on public web data. The consensus suggests that while data poisoning is a concern, it may be perceived as both a long-term issue and an overreaction, similar to past challenges faced by search engines.

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Frabjous
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I read an article in the April 6 edition of The Economist (regretfully behind a paywall) about poisoned datasets. Here’s an arxiv article it referenced.
https://arxiv.org/abs/2302.10149

What is the future of web-scale training sets? Is data poisoning a start-up pang, a long-term issue or an overreaction.
 
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Data poisoning started way back when with keyword stuffing to trick search engines. Nothing new here. If the public web is the source, AI companies will have to program their AI to avoid certain patterns like search engines already do today.

So I guess my opinion is a mix of long-term issue and overreaction.
 
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