二‧情報虛實位能差(泡沫)128Please respect copyright.PENANA2QwlbxxVKB
128Please respect copyright.PENANAB6hU4duJrF
錯誤情報與真實情報,會在觀測者與被觀測物之間產生能量差,系統中的量子會從高處往低處流動,捕捉其運動軌跡就能推導未知事件。128Please respect copyright.PENANAOfDYJ1UsQ6
128Please respect copyright.PENANA6ivDRUvs9G
例:128Please respect copyright.PENANABCecD1Zf8z
封閉的箱子中有一顆蘋果,設總熵為1。128Please respect copyright.PENANA11S5fYSBKY
觀測者被告知情報:箱子中有一顆蘋果,總熵為1。128Please respect copyright.PENANA5UodayN0zo
開啟箱子,兩系統的總熵相等,因果收束前後沒有情報能量流動。128Please respect copyright.PENANAlPIC7pgY09
128Please respect copyright.PENANAdIGo3C1Gnd
封閉的箱子中有一顆蘋果,設總熵為1。128Please respect copyright.PENANANYvQF2IviB
觀測者被告知情報:箱子中有兩顆蘋果,總熵為2。128Please respect copyright.PENANAdIn9IzUKg2
開啟箱子,兩系統的總熵差1,因果收束前後有情報能量流動。128Please respect copyright.PENANAdxykBx5zs5
128Please respect copyright.PENANA9uLX1dTV6P
封閉的箱子中有一顆蘋果,設總熵為1。128Please respect copyright.PENANAJVbSNqYNXw
觀測者被告知情報:箱子中有兩顆蘋果,總熵為2。128Please respect copyright.PENANA63foGahFqU
不開啟箱子,兩系統的總熵差1,此差異持續存在的狀態,專案內稱之為泡沫。128Please respect copyright.PENANAah2W46DeQt
128Please respect copyright.PENANA6aQEroMbbw
封閉的箱子中有一顆蘋果,設總熵為1。128Please respect copyright.PENANAt0IdTSfE86
100名觀測者被告知情報:箱子中有兩顆蘋果,總熵為2*100=200。128Please respect copyright.PENANA0GT6GOIgIr
不開啟箱子,兩系統的總熵差199,此差異持續存在的狀態,總共有199顆泡沫。128Please respect copyright.PENANAgjRpwFaMqN
128Please respect copyright.PENANAh8bAWOPSkm
根據第二定律,宇宙中的情報總熵必定趨向均衡。泡沫產生的混亂必定會分散至其他系統中。比如:多出來的那些「不存在的蘋果」導致價格下跌,果農賺得的能量因而減少。128Please respect copyright.PENANAJMU6Gny0je
128Please respect copyright.PENANAu949YXuSXT
定位、統計、歸納,得知所有粒子的運動狀態就能計算出未來的動向。觀察、分析、統整這些資訊,並導入演算機中的工作,本應交由AI電子腦擔任,但原初智叛亂後,現今局勢反對高性能AI生產,最合乎成本效益的解決方案就是人類頭腦。處理如此複雜的系統需要大量樣本,且必定會出現損壞,最好的方式就是使用Q型複製人。128Please respect copyright.PENANACh0W2EPi8Z
128Please respect copyright.PENANAlzpH2ceqXm
(New)128Please respect copyright.PENANAoKW6gjoxNp
新展望:加入纏結粒子技術的精準推導法,下述。128Please respect copyright.PENANAxFxRfFrmaW
128Please respect copyright.PENANAHYMfo1fGAn
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拉普拉斯演算機:情報泡沫儲存槽
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拉普拉斯演算機:情報泡沫儲存槽
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LiCh 阿立
![LiCh 阿立](https://static2.penana.com/fit-in/24x24/images/users/large/25/84/25841768962d2b043c07e65.66773519-139544.jpeg)
ISSUE #2
預知未來的方法:因果率演算理論模型(二)
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