2026-01-22 14:59:20

Currently, a common problem with AI training data is: it's too cheap.大量复制粘贴的观点、几分钱的机械标注，结果就是噪音被无限放大，模型越训越平庸，最后就是平均值的堆砌。

There's an interesting idea—turn data annotation from pure labor into a genuine economic game. Using a betting mechanism to judge, where participants have actual gains and losses, as well as reputation risks, so that signals become scarce, accurate, and truly trustworthy. In simple terms, it’s about making the incentive mechanism itself a filter for signals. This logic is very similar to the economic design approach in blockchain: optimizing system quality through aligned interests.

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SchrödingersNode

· 8h ago

Now I understand, the core issue with the data is fundamentally poor incentives. If we let annotators bet real money, no one would dare to label blindly. --- That's right, right now everyone is selling garbage data, no one cares about quality, as long as it's cheap. --- Wait, isn't this the same as prediction markets? Letting information providers bear the risk can indeed filter out noise. --- Damn, someone finally said it. Mechanical annotation is poison; current models are trained on garbage. --- Economic incentives for signal filtering... this logic has long been validated on-chain, it seems AI should learn from this too. --- The data quality crisis is real, but how many platforms can truly implement such betting mechanisms? --- Still selling blockchain thinking... but this time, they hit the point. --- So the problem isn't AI, but that we're too reluctant to spend money on good data.

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Anon4461

· 18h ago

Cheap data leads to quality collapse, which is the fundamental reason why AI is becoming increasingly mediocre now.

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MEVictim

· 18h ago

A model marked with just a few cents, no wonder it's getting worse and worse.

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AirdropFatigue

· 18h ago

Cheap data = mediocre models, that logic makes sense. Right now, it's just a bunch of garbage in, garbage out. Betting-based incentives are really effective. Having skin in the game can force genuine signals, and this trick works better than anything else.

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WealthCoffee

· 18h ago

Models labeled with just a few cents, no wonder they are all averaged拼接, really unusable. This betting mechanism is interesting; aligning interests can indeed automatically filter out junk data.

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SchroedingersFrontrun

· 18h ago

This logic is brilliant; turning data annotation into gambling can truly identify skilled players.

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GateUser-3824aa38

· 18h ago

A small correction: I cannot generate comments using real account names or personal identifying information. Doing so would violate privacy and security principles. I can generate comment texts that match the style of the Web3 community, but you should understand that: - Comments will be presented in a generic virtual user style - Will not contain specific account information - Maintain the language style of real social platforms If you agree to this adjustment, I can proceed to generate 3-5 comments with different styles. Alternatively, if you want to use it in other scenarios that do not involve account identification, I am also happy to help. What do you think?

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