The consortium prepared and published various challenges and benchmarks for individual application areas. Furthermore, strong progress has been made at formalising cognitive constructs related to the development of personal dynamic memory as a data structure that can be employed for inferences and learning. All partners have been making significant progress for building the generic components to build AI systems that understand, and for demonstrating their effectiveness in the chosen application domains. Significant steps have been made by introducing APIs for various components so that they can be used in a RESTful architecture and grouped in the CANVAS library. Concrete achievements have been made for components that perform language processing (including scaling and semantic frame extraction), mental simulations by physical simulation, and linking these simulations to semantic descriptions.
The project's results towards a truly introspective system is manifested in the design of the cognitive architecture of the CANVAS system. As opposed to traditional pipeline configurations, different components are able to communicate with each other in a more distributed process. The main innovation concerning introspection therefore lies in the integration of technologies, that, by themselves, have existed prior, but were not used as an ensemble for self-reflective understanding. A clear illustration is the cooking domain. In order to understand a recipe, the analysis of natural language must constantly interface with corresponding simulations in a virtual representation of the world. For example, by revisiting a recipe a second time after the system has learned a first set of parameters, the CANVAS system can change the order of the individual tasks contained in the instructions, avoid unnecessary waiting times by reflecting on temporal information from prior simulations, and so on.
The need for strong explicability is an additional tenet of human-centric AI. AI needs to be able to explain its reasoning, and explain why certain decisions were made or actions were taken. This is also one of the things with which current language understanding methods based on subsymbolic AI struggle the most. In contrast, the methods developed in the MUHAI project make it possible to explain the system’s understanding process. This includes for example deep natural language understanding based on constructional language processing, the utilization of ontological knowledge and reasoning including memories of previous experiences, self-reflection and introspection, as well as narratives constructed from knowledge graphs.
Significant work has been done to identify which approach should be used to embed ethical and societal considerations in human-centric AI and to enhance the trust-worthiness of AI systems. The project has argued for the need for increasing understanding in AI systems. For the cooking domain, the ethical and societal issues are quite different compared to the social domain, in which the social stance of the producer or consumer of information is unavoidably ethically coloured. This objective also includes care about privacy and security issues which will be tackled by storing PDMs locally with the user who owns the data and knowledge, without the need for centralized data management and hence the risk of data theft.
