Towards Strong AI
Towards Strong AI
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DOI：
10.1007/s13218-021-00705-x
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发表时间：
2021-02-26
影响因子：
2.9
通讯作者：
Butz, Martin V.
中科院分区：
文献类型：
--
作者：
Butz, Martin V.
强大的人工智能--在所有方面至少与人类一样智能的人工智能--仍然遥不可及。目前的人工智能缺乏常识，即无法推断、理解或解释数据背后隐藏的过程、力量和原因。深度人工神经网络(ANN)的主流机器学习研究甚至可以被描述为行为主义。相比之下，认知科学的各种证据来源表明，人脑参与了从自身产生的感觉运动经验中积极发展成分生成性预测模型(CGPM)。在进化形成的归纳学习和信息处理偏差的引导下，他们表现出将收集的经验组织成事件预测编码的倾向。同时，他们通过认知驱动和动态平衡驱动来推断和优化行为和注意力。我认为，人工智能研究应该把更多的重点放在学习导致注册观测的隐藏原因的CGPM上。被赋予适当的信息处理偏见，人工智能可能会发展成能够解释它所面临的现实，对它进行推理，并找到适应的解决方案，使其成为强大的人工智能。看到如此强大的人工智能可以配备超过人类的智力和计算资源，由此产生的系统可能有潜力将我们的知识、技术和政策引导到可持续的方向。但显然，强大的人工智能也可能被用来更多地操纵我们。因此，我们将把良好的、深远的、长期的、以动态平衡为导向的目的放在这些机器上。
Strong AI-artificial intelligence that is in all respects at least as intelligent as humans-is still out of reach. Current AI lacks common sense, that is, it is not able to infer, understand, or explain the hidden processes, forces, and causes behind data. Main stream machine learning research on deep artificial neural networks (ANNs) may even be characterized as being behavioristic. In contrast, various sources of evidence from cognitive science suggest that human brains engage in the active development of compositional generative predictive models (CGPMs) from their self-generated sensorimotor experiences. Guided by evolutionarily-shaped inductive learning and information processing biases, they exhibit the tendency to organize the gathered experiences into event-predictive encodings. Meanwhile, they infer and optimize behavior and attention by means of both epistemic- and homeostasis-oriented drives. I argue that AI research should set a stronger focus on learning CGPMs of the hidden causes that lead to the registered observations. Endowed with suitable information-processing biases, AI may develop that will be able to explain the reality it is confronted with, reason about it, and find adaptive solutions, making it Strong AI. Seeing that such Strong AI can be equipped with a mental capacity and computational resources that exceed those of humans, the resulting system may have the potential to guide our knowledge, technology, and policies into sustainable directions. Clearly, though, Strong AI may also be used to manipulate us even more. Thus, it will be on us to put good, far-reaching and long-term, homeostasis-oriented purpose into these machines.