Comments (4)
chatgpt挺好的,在大模型+现有各种AI成果的融合上,还大有可为,但chatgpt似乎自称并非agi系统什么的,所以虽挺好,但与我并非同行,不好公开评价;但听一个朋友说:
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接上: gpt是大模型+语义等成果+人工基础上的改进,它并不具备agi中大家普遍要求的那些能力,站在我自己的熵减机的角度,语言的熵比较明确 (输入时,一个概念可能较为明确的被几个字所指代,譬如,你看到一个苹果的样子形状色泽,然后气味,口味等),它们共同定义了苹果,而什么定义了形状色泽气味口味呢?又是更加微观的信息,)
而从文字出发,这整个从微观到宏观浮现定义的过程,就会简略的多,也更容易达成;这种短爽快的方式洽洽错过了agi中最重要的东西;
综上: 无论是对agi系统要求的整体度,还是内部对信息处理的细微过程,我认为gpt都是远远不够的,尽管它把认为容易体现出效果的那部分做出来,已经算最容易博得大众所认可,但越是有自己想法的同学,越难以被这一点刺激到g点
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近期又遇到了一点疑问:是否gpt系列模型的效果可以验证在人工智能方面也可以“力大砖飞”呢,采用超大模型进行训练,根据神经网络基本原理可以知道网络层数和每层节点高到一定程度,总是可以拟合上任意给定函数。由此也应该能做到AGI的效果吧。退一步讲,着重模型深度和广度而非具体设计,在可预期未来是否会出现以力破巧的效果,即大模型先于特定设计小模型逼近广义AGI
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大模型只是个统计学加涌现的系统,它与智能有交集,但交集很小,别把它当agi看。不用回复我了,我对gpt没兴趣,尽管它很火。
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Related Issues (7)
- 关于优化策略的问题 HOT 2
- 日常Issues交流 HOT 10
- 希望可以多一些簡單點的範例 =w= HOT 2
- 这个具体能做什么呢
- 基于什么语言实现的? HOT 1
- 目前有确实的demo吗?有训练效果吗? HOT 2
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