自然智能系统中的形态学计算和学习与AI
Morphological Computation and Learning to Learn In Natural Intelligent Systems And AI
摘要：
目前，以机器学习为形式的人工智能正在取得令人瞩目的进展，特别是深度学习（DL）领域[1]。深度学习算法从一开始就受到大自然的启发，特别是人类大脑，尽管我们对大脑功能的了解并不完整。从自然中学习是一个双向的过程，如[2][3][4]中所讨论的，计算是从神经科学中学习，而神经科学正在迅速采用信息处理模型。问题是，在这个发展阶段，来自计算本质的灵感可以为深度学习做出什么贡献，机器学习中的模型和实验可以在多大程度上激励、证明和引导神经科学和认知科学的研究，以及人工智能的实际应用。
英文摘要：
At present, artificial intelligence in the form of machine learning is making impressive progress, especially the field of deep learning (DL) [1]. Deep learning algorithms have been inspired from the beginning by nature, specifically by the human brain, in spite of our incomplete knowledge about its brain function. Learning from nature is a two-way process as discussed in [2][3][4], computing is learning from neuroscience, while neuroscience is quickly adopting information processing models. The question is, what can the inspiration from computational nature at this stage of the development contribute to deep learning and how much models and experiments in machine learning can motivate, justify and lead research in neuroscience and cognitive science and to practical applications of artificial intelligence.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
