From 50996a9cb695777ddc83f3a1bfac6e16f9fa11e7 Mon Sep 17 00:00:00 2001 From: drinkjava2 Date: Tue, 30 Jun 2020 08:40:51 -0700 Subject: [PATCH] Update readme & money history --- README.md | 11 +++++++---- logo.png | Bin 0 -> 15581 bytes talk.png | Bin 0 -> 63793 bytes 捐款记录.md | 5 +++-- 4 files changed, 10 insertions(+), 6 deletions(-) create mode 100644 logo.png create mode 100644 talk.png diff --git a/README.md b/README.md index bd3efd6..a559ce6 100644 --- a/README.md +++ b/README.md @@ -1,10 +1,13 @@ +

+ ## Frog | 人工生命 -这是一个人工生命试验项目,最终目标是创建“有自我意识表现”的模拟生命体,技术架构基于02年提出的 [一个人工脑模型](一个人工脑模型.md)。 -这个项目永远没有结束的时候,开始于模拟一个简单的生命体,然后是青蛙、狗......, 结束于有“自我意识表现”的人工脑,或者说,结束于被机器人代替人类的那一天。 +这是一个人工生命试验项目,最终目标是创建“有自我意识表现”的模拟生命体,技术架构基于02年提出的 [一个人工脑模型](一个人工脑模型.md)。这个项目永远没有结束的时候,开始于模拟一个简单的生命体,然后是青蛙、狗......, 结束于有“自我意识表现”的人工脑,或者说,结束于被机器人代替人类的那一天。 ## 缘起 | Origin -目前人工智能的进展已经比较完美地解决了模式识别这块的难题,人脸识别、语音识别已经不弱于人类的水平,而这是我在二十年前感到最困惑的一块,因为底子差和当时的电脑速度慢。模式识别解决了,剩下的问题就简单多了,目前距离人工意识的诞生只差临门一脚了,就是如何在识别的基础上“理解”这些识别的内容并与人类形成交互的反馈。所以这个项目的重点不在于模式识别,而是在利用模式识别的成果基础上,训练神经网络形成条件反射,表现出高等动物才具有的条形反射行为,最终表现为"拥有自我意识"的行为。根据“意识不是一种存在,而是一种现象”原理,如果最终一个系统表现出具有自我意识的行为,即可认为它也是人,应该获得人权。目前有些人工智能的研究目的是想让人工智能解决一些复杂的人类社会方面的问题如机器翻译等,则是完全错误的目标,不可能成功,因为如果一个系统不能表现出自我意识,它就不能与人类交流,也就不可能具有解决这些问题的能力,表现出来的现象就是通常说的"机器不犯错,一旦犯错就是大错"。另一方面,如果一个系统表现出具有自我意识的行为,它就完全有能力解决世界上所有难题,包括改进它的自身和淘汰人类(因为他是先进生产力的代表)。所以人工智能的研究重点应该放在人工生命的构建和伦理研究,而不是期待短期收益,指望人类可以一直享受人工智能的大餐是很危险的。模式识别和深度学习的成果只是人工生命的踮脚石和一块路标而已。人工智能的“有用”的应用,很可能只是短暂的一段过渡期而已,不用高兴得太早,也许都是白忙,给机器人作嫁衣而已,当然,深度学习的成果在将来还是有用的,生物体从来不能象计算机那样对某个技能反复训练上千万次,达到史无前例的覆盖度,既使在机器人时代,也是一个有用的技术。 -简单来说,这个项目的目的是试验和探索神经网络开发的另一个方向,即以实验为导向,模拟生命进化的过程,按照优胜夯汰、随机变异、用进废退这三大原则,一步一步地搭建出从低等到复杂的人工生命体,除了模式识别的成果可以部分引用,原则上不需要很多的数学知识,因为它是由实验为驱动,而不是由算法着手来搭建神经网络。目前神经网络研究重点在于模式识别和算法,但对系统赋予主动性关注不够。 +目前人工智能的进展已经比较完美地解决了模式识别这块的难题,人脸识别、语音识别已经不弱于人类的水平,而这是我在二十年前感到最困惑的一块,因为底子差和当时的电脑速度也慢。模式识别解决了,剩下的问题就简单多了,目前距离人工意识的诞生只差临门一脚了,就是如何在识别的基础上“理解”这些识别的内容并与人类形成交互的反馈。所以这个项目的重点不在于模式识别,而是在利用模式识别的成果基础上,训练神经网络形成条件反射,表现出高等动物才具有的条形反射行为,最终表现为"拥有自我意识"的行为。根据“意识不是一种存在,而是一种现象”原理,如果最终一个系统表现出具有自我意识的行为,即可认为它也是人,应该获得人权。目前有些人工智能的研究目的是想让人工智能解决一些复杂的人类社会方面的问题如机器翻译等,则是完全错误的目标,不可能成功,因为如果一个系统不能表现出自我意识,它就不能与人类交流,也就不可能具有解决这些问题的能力,表现出来的现象就是通常说的"机器不犯错,一旦犯错就是大错"。另一方面,如果一个系统表现出具有自我意识的行为,它就完全有能力解决世界上所有难题,包括改进它的自身和淘汰人类(因为他是先进生产力的代表)。所以人工智能的研究重点应该放在人工生命的构建和伦理研究,而不是期待短期收益,指望人类可以一直享受人工智能的大餐是很危险的。模式识别和深度学习的成果只是人工生命的踮脚石和一块路标而已。人工智能的“有用”的应用,很可能只是短暂的一段过渡期而已,不用高兴得太早,也许都是白忙,给机器人作嫁衣而已。实际上,人工智能不光是技术问题,还是个人生观问题,专家的认识有时候并不总对。在此摘一段在微信群里芦秋迪建议放上来的对话: +![talk](talk.png) + +简单来说,这个项目是一个外行搭建的民科项目,试图以实验为导向,模拟生命进化的过程,按照优胜夯汰、随机变异、用进废退这三大原则,一步一步地搭建出从低等到复杂的人工生命体,除了模式识别的成果可以借签,原则上不需要学习很多数学知识,因为它强调由实验来驱动,而不是由复杂的算法来搭建神经网络。目前神经网络研究重点在于模式识别和算法,但对系统本身赋予主动性关注不够。 从单细胞进化到多细胞、从青蛙进化到人类,这是一个漫长的、随机的进化过程,但在超级电脑上跑可能只要几天时间,就可能得到一个相当不错的脑模型。当然电脑速度越快、容量越大、环境模拟的越真实,则优胜夯汰后形成的脑结构就越复杂,错的脑模型都被自然淘汰掉了。从算法着手搭建,还是从模拟环境着手自动进化,这是创建人工生命的两个大方向,第一个方向有可能走到死胡同里,因为它不具备算法自改进、变异、遗传(算法的压缩)功能,当脑模型复杂到一定地步,可能会超出人脑能理解的范畴。模拟环境方式的难点则在于环境本身必须足够复杂、正确。而且必须循序渐进,与脑的进化同步,如果把一群青蛙扔到猴子的模拟环境中,则所有青蛙都会被自然淘汰掉,项目就无法进行下去了,另一个困难是电脑必须非常快,因为它是用串行方式模拟并行,不断试错前进的过程。 目前的项目只是搭建了一个框架,语言为Java,利用Swing作图环境,构建一个虚拟环境、并模拟一群草履虫的优胜夯汰,来获取一个具备自进化功能的人工生命体,具体脑(即电脑生成的神经网络)的生成、进化算法还需要以后逐渐加入。欢迎有对神经网络感兴趣的同学加入这个实验项目,大家一起来玩,这个项目不需要多少数学知识,重在实践。 diff --git a/logo.png b/logo.png new file mode 100644 index 0000000000000000000000000000000000000000..61437db651ab13781f8af94127d33b9da46944fd GIT binary patch literal 15581 zcmb_@^+Q|F6D<-TcyNah+`YI2w_?SiP~1v!_u%dyq&TH$p;&QuC{QTY;_eOwTA=Ut z`~HOYeqfW3dpEOtcW2HyGaIX=p@fS?iG_rOgsY+~uY-hy>;$|&1ET<~D|h_`@Ph22 zqa=$|J4Jm6d;rXWe_Ezp3^n6Ao39!N-d1ONVzM_ekbk&qQk}nir28BT)20a-Hl{ zay!(mSL7m}KE@g4-`hgeDCl!%qi`bXFtQwE8ECgdr7>{ekA8M8lo$!Xw_kvE7&!!w 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