AGI的元模型和框架
A Metamodel and Framework for AGI
摘要：
人工智能系统能否表现出超人的性能，但在关键方面，甚至缺乏单细胞生物的智能？对于狭义的AI系统，答案显然是“是”。动物、植物，甚至单细胞生物都学会了可靠地躲避危险，并向食物移动。这是通过一个物理知识保存元模型，自主生成有用的模型的世界。我们认为，保存知识的结构是至关重要的更高的智能管理越来越高的抽象层次，无论是人类还是人工。这是将AGI子系统应用于需要持续学习和适应的复杂现实问题的关键教训。在本文中，我们介绍了深度融合推理引擎（DFRE），它实现了一个知识保存的元模型和框架，用于构建应用AGI系统。的DFRE元模型表现出一些重要的基本知识保存属性，如对称和反对称关系之间的明确区分，并能够创建一个层次知识表示，清楚地划分抽象层次之间。DFRE元模型结合了这些功能，展示了这种方法如何以特定的方式使AGI受益，例如管理组合爆炸和实现累积，分布式和联邦学习。我们的实验表明，该框架实现了94%的准确率平均无监督目标检测和识别。这项工作的灵感来自AGI的最先进的方法，最近的AGI有志的工作，粒度计算社区，以及Alfred Korzybski的一般语义。
英文摘要：
Can artificial intelligence systems exhibit superhuman performance, but in critical ways, lack the intelligence of even a single-celled organism? The answer is clearly 'yes' for narrow AI systems. Animals, plants, and even single-celled organisms learn to reliably avoid danger and move towards food. This is accomplished via a physical knowledge preserving metamodel that autonomously generates useful models of the world. We posit that preserving the structure of knowledge is critical for higher intelligences that manage increasingly higher levels of abstraction, be they human or artificial. This is the key lesson learned from applying AGI subsystems to complex real-world problems that require continuous learning and adaptation. In this paper, we introduce the Deep Fusion Reasoning Engine (DFRE), which implements a knowledge-preserving metamodel and framework for constructing applied AGI systems. The DFRE metamodel exhibits some important fundamental knowledge preserving properties such as clear distinctions between symmetric and antisymmetric relations, and the ability to create a hierarchical knowledge representation that clearly delineates between levels of abstraction. The DFRE metamodel, which incorporates these capabilities, demonstrates how this approach benefits AGI in specific ways such as managing combinatorial explosion and enabling cumulative, distributed and federated learning. Our experiments show that the proposed framework achieves 94% accuracy on average on unsupervised object detection and recognition. This work is inspired by the state-of-the-art approaches to AGI, recent AGI-aspiring work, the granular computing community, as well as Alfred Korzybski's general semantics.
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。
