This paper offers a multi-disciplinary review of knowledge acquisition methods in human activity systems. The review captures the degree of involvement of various types of agencies in the knowledge acquisition process, and proposes a classification with three categories of methods: the human agent, the human-inspired agent, and the autonomous machine agent methods. In the first two categories, the acquisition of knowledge is seen as a cognitive task analysis exercise, while in the third category knowledge acquisition is treated as an autonomous knowledge-discovery endeavour. The motivation for this classification stems from the continuous change over time of the structure, meaning and purpose of human activity systems, which are seen as the factor that fuelled researchers' and practitioners' efforts in knowledge acquisition for more than a century. We show through this review that the KA field is increasingly active due to the higher and higher pace of change in human activity, and conclude by discussing the emergence of a fourth category of knowledge acquisition methods, which are based on red-teaming and co-evolution.
To address modeling problems of brain-inspired intelligence, this thesis is focused on researching in the semantic-oriented framework design for image, audio, language and video. The Multimedia Neural Cognitive Computing (MNCC) model was designed based on the nervous mechanism and cognitive architecture. Furthermore, the semantic-oriented hierarchical Cross-media Neural Cognitive Computing (CNCC) framework was proposed based on MNCC, and formal description and analysis for CNCC was given. It would effectively improve the performance of semantic processing for multimedia information, and has far-reaching significance for exploration and realization brain-inspired computing.
Decades of research in artificial intelligence (AI) have produced formidable technologies that are providing immense benefit to industry, government, and society. AI systems can now translate across multiple languages, identify objects in images and video, streamline manufacturing processes, and control cars. The deployment of AI systems has not only created a trillion-dollar industry that is projected to quadruple in three years, but has also exposed the need to make AI systems fair, explainable, trustworthy, and secure. Future AI systems will rightfully be expected to reason effectively about the world in which they (and people) operate, handling complex tasks and responsibilities effectively and ethically, engaging in meaningful communication, and improving their awareness through experience. Achieving the full potential of AI technologies poses research challenges that require a radical transformation of the AI research enterprise, facilitated by significant and sustained investment. These are the major recommendations of a recent community effort coordinated by the Computing Community Consortium and the Association for the Advancement of Artificial Intelligence to formulate a Roadmap for AI research and development over the next two decades.
An empirically based cognitive model of real-world decision making was implemented in Shruti, a system capable of rapid, parallel relational reasoning. The system effectively simulates a two-tiered strategy associated with proficient decisions makers: Recognitional or reflexive activation of expectations and associated responses, accompanied by an optional, recursive process of critiquing and correcting, regulated by the stakes of the problem, the time available, and the remaining uncertainty. The model and implementation are inconsistent with the conventional claim that decision makers fall back on formal analytical methods when pattern recognition fails. Instead, they learn simple metacognitive strategies to leverage reflexive knowledge in novel situations. In addition, the model suggests that the development of executive attention functions (metacognitive strategies) may be necessary for, and integral to, the development of working memory, or dynamic access to long term memory, and that strategies developed for uncertainty handling may accelerate the reflexive learning of remotely connected concepts.