I went to play cards with my fren. He always relied on his memory to summarize where he lost in the previous game and then adjusted for the next one. Unfortunately, he was always one step behind—by the time he realized his strategy was wrong, the game had already changed.
I joked with him, saying: "You're just like traditional AI, always fixing problems only after they arise."
fren looked confused: "What do you mean?"
I explained: If you can anticipate your opponent's next move in advance and know which card is most likely to win, wouldn't you be one step ahead? That's what Allora does.
@AlloraNetwork is not just a retrospective AI; it has a mechanism called Performance Forecasting, which can be understood in Chinese as '性能预测'. In simple terms, it predicts which model is the most reliable in the current environment before all models are deployed, then gives it more weight while down-weighting the models that perform poorly.
Just like friends playing cards, before the opponent plays a big move, one should first adjust their own strategy. In last week's black swan market, if Allora had launched earlier, it could have predicted which strategy was effective in advance, possibly avoiding more than half of the chain liquidations.
It also has the ability to self-evolve. After each prediction is completed, it calibrates using the real results—just like when you finish a round of cards, summarize your experiences, and then automatically optimize your strategy for the next round.
I joked with him, saying: "You're just like traditional AI, always fixing problems only after they arise."
fren looked confused: "What do you mean?"
I explained: If you can anticipate your opponent's next move in advance and know which card is most likely to win, wouldn't you be one step ahead? That's what Allora does.
@AlloraNetwork is not just a retrospective AI; it has a mechanism called Performance Forecasting, which can be understood in Chinese as '性能预测'. In simple terms, it predicts which model is the most reliable in the current environment before all models are deployed, then gives it more weight while down-weighting the models that perform poorly.
Just like friends playing cards, before the opponent plays a big move, one should first adjust their own strategy. In last week's black swan market, if Allora had launched earlier, it could have predicted which strategy was effective in advance, possibly avoiding more than half of the chain liquidations.
It also has the ability to self-evolve. After each prediction is completed, it calibrates using the real results—just like when you finish a round of cards, summarize your experiences, and then automatically optimize your strategy for the next round.
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