This paper presents Artificial Brain Operating System which imitates the behavior of human brain and its algorithm mimics thinking process. I describe goals of the proposed system, which are a) voluntary and self-directed action of speech and motion, b) visual and auditory understanding, c) skill and knowledge acquisition based on image processing, d) internal information processing standardization.

In recent years, the development of AI field is remarkable. In particular, it is remembrance that Alpha Go beat the world champion of Go. In 2019, the practical use of AI in the business area is advanced and what AI can solve or not will be clearly with people who are not specialists. Metastasis learning is one of the unresolved issues and this issue leads to the acquisition of versatility. Due to Metastasis learning, introduction of pleasant and unpleasant to AI is effective. In this paper, pleasant has nature of "dissolution of unpleasant", and therefore pleasant is based on unpleasant. It brings distinction to reward by unpleasant and ability to distinguish brings AI to unique purpose for each task.

This paper discusses the neural network applying the cell differentiation model. It is assumed that a largescale neural network will be required to deal with various applications flexibly in the future. This neural network aims to enable to inherit learning achievements, expand and improve its structure and parameters automatically.

We postulate that Artificial General Intelligence remains elusive because of numerous undisputed assumptions that are deeply rooted into the traditional understanding of intelligence. We claim that these assumptions shape an anthropocentric bias that prevents the development of a general theory of intelligence capable of explaining the behavior of not only human and machine intelligence, but also any other entity that exhibits intelligent behavior. The most important of these assumptions is the failure to recognize darwinian evolution as an intelligent entity despite the growing consensus about its superior capabilities to develop biological contrivances. In order to avoid underrating and neglecting evolution as intelligent, other assumptions must be dropped. Such is the case for the requirement of language, which is only relevant in social contexts. Moreover, the boundary of evolution as an agent distinguished from the environment is not well-defined, which suggests that agent boundaries are redundant in General Intelligence and results in an equal treatment of polymorphic robots and multi-agents, to name a few. By revealing these and other assumptions, we propose that human intelligence should be relieved from standing at the center of studies about General Intelligence.

We propose a framework of the study of computable induction, which is a computable version of Solomonoff's universal induction. As a concrete example, we consider a problem of confirmation, such as the probability that the sun will rise tomorrow. Although we can not tell the exact probabilities, we can deduce the rate of the convergence up to a multiplicative constant, which is slightly faster than Laplace's result.

On the symbol grounding problem, it is better to consider separating symbols for internal processing of thinking and symbols for communication. And it is essential that symbols for communication point to shared believes. Therefore, this paper reports of experiments to generate shared believes on the symbol grounding problem using the deep generation model.

In our paper we discuss the problem of tacit knowledge which probably is one of the biggest obstacles on the way to human-level language understanding. While the latest massive transformer-based NLP algorithms show the potential to generate natural text, translate and answer questions, these achievements are still insufficient to directly help machines to acquire common sense. We introduce the problem of tacit knowledge and various approaches to solving it by knowledge completion with automatic text generation which is meant to enrich existing texts and to improve machine learning process by filling the semantic gaps naturally omitted by human beings while generating a natural language. We present various types of knowledge additions we performed in the past and report on the latest achievements in Schankian scripts generation.