论文标题
Qualia的目的：如果人类思维不是（仅）信息处理该怎么办？
The purpose of qualia: What if human thinking is not (only) information processing?
论文作者
论文摘要
尽管最近在人工智能（AI）领域取得了突破 - 或更具体地说是机器学习（ML）用于对象识别和自然语言处理的算法 - 这似乎是多数观点，即当前的AI方法仍然没有自然智力（NI）的真正匹配。 More importantly, philosophers have collected a long catalogue of features which imply that NI works differently from current AI not only in a gradual sense, but in a more substantial way: NI is closely related to consciousness, intentionality and experiential features like qualia (the subjective contents of mental states) and allows for understanding (e.g., taking insight into causal relationships instead of 'blindly' relying on correlations), as well as aesthetical and ethical judgement除了我们可以使用（明确或数据引起的隐式）规则将其放入编程机器的规则之外。此外，心理学家发现NI的范围从无意识的心理过程到集中信息处理，从体现和隐含的认知到“真实”的代理和创造力。因此，NI似乎通过在“含义的位置”而不是信息来超越任何神经生物学功能主义，这与“过去的良好时尚”，象征性的AI以及当前基于深层神经网络的潮流，“基于深度神经网络”的浪潮都不同，这两个浪潮都基于深层神经网络，“亚符号” AI，这两个都共享了思维思想的信息（仅）信息处理。在下文中，我提出了NI作为信息处理以及“捆绑推动”的另一种观点，讨论一个示例，说明捆绑包推力如何缩短信息处理，并为神经生物学和信息理论的科学实验提出了第一个思想，作为进一步的研究。
Despite recent breakthroughs in the field of artificial intelligence (AI) - or more specifically machine learning (ML) algorithms for object recognition and natural language processing - it seems to be the majority view that current AI approaches are still no real match for natural intelligence (NI). More importantly, philosophers have collected a long catalogue of features which imply that NI works differently from current AI not only in a gradual sense, but in a more substantial way: NI is closely related to consciousness, intentionality and experiential features like qualia (the subjective contents of mental states) and allows for understanding (e.g., taking insight into causal relationships instead of 'blindly' relying on correlations), as well as aesthetical and ethical judgement beyond what we can put into (explicit or data-induced implicit) rules to program machines with. Additionally, Psychologists find NI to range from unconscious psychological processes to focused information processing, and from embodied and implicit cognition to 'true' agency and creativity. NI thus seems to transcend any neurobiological functionalism by operating on 'bits of meaning' instead of information in the sense of data, quite unlike both the 'good old fashioned', symbolic AI of the past, as well as the current wave of deep neural network based, 'sub-symbolic' AI, which both share the idea of thinking as (only) information processing. In the following I propose an alternative view of NI as information processing plus 'bundle pushing', discuss an example which illustrates how bundle pushing can cut information processing short, and suggest first ideas for scientific experiments in neuro-biology and information theory as further investigations.
