论文标题
智力的计算原理：神经网络的学习和推理
Computational principles of intelligence: learning and reasoning with neural networks
论文作者
论文摘要
尽管取得了重大成就和当前对机器学习和人工智能的兴趣，但对智力理论的追求，允许一般有效的问题解决，几乎没有取得进步。这项工作试图通过提出基于三个原则的新型智能框架来朝这个方向做出贡献。首先，学到的输入表示的生成性和反映性质。其次，一个扎根，内在动机和迭代的过程，用于解决问题和想象力。第三，使用抑制规则对因果组成表示的推理机制进行了临时调整。这些原则共同创建了一种系统方法，提供可解释性，持续学习，常识等等。该框架是从以下角度开发的：作为一种通用的问题解决方法，作为一种面向人类的工具，最后是大脑中信息处理的模型。
Despite significant achievements and current interest in machine learning and artificial intelligence, the quest for a theory of intelligence, allowing general and efficient problem solving, has done little progress. This work tries to contribute in this direction by proposing a novel framework of intelligence based on three principles. First, the generative and mirroring nature of learned representations of inputs. Second, a grounded, intrinsically motivated and iterative process for learning, problem solving and imagination. Third, an ad hoc tuning of the reasoning mechanism over causal compositional representations using inhibition rules. Together, those principles create a systems approach offering interpretability, continuous learning, common sense and more. This framework is being developed from the following perspectives: as a general problem solving method, as a human oriented tool and finally, as model of information processing in the brain.
