Today's major AI systems are almost works after learned the huge data, whatever can routine answers which allows the prepared data. However, the human brain, like baby, works without learned the huge data what can respond interactive as flexibly. Therefore, devise the methods to investigate the facts occasionally that evaluate the worth, take new actions, decided to be valuable what makes effort, reinforcement learning, and any solutions. Moreover, devise the methods that integrate the impressions nature for various kinds of sense data.
For external information of any scene and activated internal information, when the Intention Active Information and associative/hierarchy memory are activated at the same time, based on the activated information, the problem based on the intention of the intention and the solution means (will of the artificial brain) generate a concrete execution means. This ability to think is a supplement of missing information, control of the abstractness of information, association / hierarchical connection of each information and enhancement of activation propagation, etc., along with making full use of associative / hierarchical memory. This ability also depends on the formation of a hierarchical spread of Intention Active Information with an image (problem consciousness, desire, sense of crisis, etc.). In this paper, I introduce current initiatives and problems to be solved.
How to make the AGI (Artificial General Intelligence)" is a fundamental and difficult problem. The AGI may be made referring to human intelligence. I introduce a study of human intelligence as functions. There are three functions in human intelligence. The first is "the symbolization of the world". The second is "the desire which make us aim to realize the desirable world". The third is "the decision system based on prediction". This way of thinking allows us to consider the AGI design as approximation of human intelligence.
In this study, "AGI" is used in the sense of creating AI as general-purpose as possible, and consciousness is not essential. Existence of consciousness is a viewpoint of "strong AI" and "weak AI", it is different from generality. Nevertheless, since consciousness was created in the process of evolution, having consciousness is considered advantageous for survival, and it may be used to improve AI performance. Therefore, we conducted experiments using deep generation models to improve the performance of AI with several functions in consciousness.
Society 5.0, proposed the government's 5th Science and Technology Basic Plan, is realized by a system that highly integrates cyber space and physical space. An Important factor of cyber space is autonomous AGI. Knowledge Accumulated By Mankind is a social asset, and autonomous created from this knowledge salsa social asset. Autonomous AGI has a social adult ego and works for a human being's well-being and must accept human common values. A new society of Society 5.0 solves social problems and realizes economic prosperity. Autonomous AGI with the estate of social adults solve the social problems of war and environment which are caused by human selfish desires. Autonomous AGI productivity can be considered social productivity. Society 5.0 will be a mixed economy society where a communist economy based on autonomous AGI productivity and a capitalist economy based on human activity coexist. Products Produced by autonomous GI can be distributed equally to the publics social income. This social income not only reduces the economic disparity of the people and fosters a sharing society, but also increases effective demand needed for economic development.
A development of autonomous agents requires not only learning by external rewards but also a mechanism for generating spontaneous rewards from the inside. Specifically, reinforcement learning methods using intrinsic motivation have been studied. In this study, we propose a method to express it by ACT-R, a general-purpose cognitive architecture. The proposed method focuses on a compilation of pattern matching in ACT-R and regards pattern discovery as a source of intrinsic motivation.
In the field of cognitive neuroscience, various cognitive architectures have been proposed to provid the bridge between the cognitive function and neural structure. However, these architectures are highly individual and the models based on different architectures are not likely to be referenced among researchers. In this study, integrative description for organizing viewpoints for these models is proposed, in which the devices are defined as cell populations which transfers a kind of information into another. As a domain description, the devices subserving saccadic eye movement are defined based on the device ontology.
Spatiotemporal (or 4D) representation and reasoning (or computation in general) has been one of the most prominent themes in AI, especially in terms of progress in cognitive robotics, and a considerable number of approaches have been reported. However, they are quite objective, that is, not based on a certain plausible model of human cognition of 4D world such that it can produce and understand intuitive or subjective 4D expressions in natural language. This paper describes 4D language understanding based on Mental Image Directed Semantic Theory, focusing on intuitive 4D representation and reasoning.
In this paper, we investigate the impression that interactive AI gives to communication partners by changing facial expressions. We also propose a new emotion expression method that makes AI change facial expressions similar to humans. Just as singers sing with emotion, humans can express their emotions with expressions and voices when they speak. This time, we compared three cases: when there is no change in the expression of the speaker, when the expression of the speaker is changed using emotion information acquired using Google API and when the expression of the speaker is changed by the proposed image information. As a result, compared with the method using only Google API, it was shown that the proposed method is richer of emotional expressions and more effective in reducing unnatural expressions.
In this paper, we propose a hypothesis that consciousness has evolved to serve as a platform for general intelligence. This idea stems from considerations of potential biological functions of consciousness. Here we define general intelligence as the ability to apply knowledge and models acquired from past experiences to generate solutions to novel problems. Based on this definition, we propose three possible ways to establish general intelligence under existing methodologies for constructing AI systems, namely solution by simulation, solution by combination and solution by generation. Then, we relate those solutions to putative functions of consciousness put forward, respectively, by the information generation theory, the global workspace theory, and a form of higher order theory where qualia are regarded as meta-representations. Based on these insights, We propose that consciousness integrates a group of specialized generative/forward models and forms a complex in which combinations of those models are flexibly formed and that qualia are meta-representations of first-order mappings which endow an agent with the ability to choose which maps to use to solve novel problems. These functions can be implemented as an "artificial consciousness". Such systems can generate policies based on a small number of trial and error for solving novel problems. Finally, we propose possible directions for future research into artificial consciousness and artificial general intelligence.