Thrill-K Architecture: Towards a Solution to the Problem of Knowledge Based Understanding
This addresses the problem of inflexibility and inefficiency in AI systems for researchers and developers, though it appears incremental as it builds on existing hybrid approaches.
The paper tackles the limitations of end-to-end learning systems, such as high computational demands and lack of explainability, by proposing the Thrill-K architecture, which integrates neural learning with various knowledge sources to enable inference, learning, and intelligent control.
While end-to-end learning systems are rapidly gaining capabilities and popularity, the increasing computational demands for deploying such systems, along with a lack of flexibility, adaptability, explainability, reasoning and verification capabilities, require new types of architectures. Here we introduce a classification of hybrid systems which, based on an analysis of human knowledge and intelligence, combines neural learning with various types of knowledge and knowledge sources. We present the Thrill-K architecture as a prototypical solution for integrating instantaneous knowledge, standby knowledge and external knowledge sources in a framework capable of inference, learning and intelligent control.