论文标题
形态学计算和学习在自然智能系统和AI中学习
Morphological Computation and Learning to Learn In Natural Intelligent Systems And AI
论文作者
论文摘要
目前，机器学习形式的人工智能正在取得令人印象深刻的进步，尤其是深度学习领域（DL）[1]。尽管我们对其大脑功能的了解不完全了解，但深度学习算法是从本质上，特别是人脑的启发。从自然界学习是一个双向过程，如[2] [3] [4]中所述，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，在开发的这一阶段，从计算性质的灵感可以有助于深度学习，以及机器学习中的许多模型和实验可以激励，证明和领导神经科学和认知科学的研究以及人工智能的实际应用。
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.
