Computational Processes for General Intelligence The SEOM Model

Geometry problem solving is a long-standing problem in artificial intelligence. The task requires generating explainable solving sequences based on text and diagram descriptions. Existing approaches have made great progress in geometry formal language extraction and interpretable solving. However, they neglect the graph structure information in formal language. This leads to poor prediction effect of the theorem, and too long reasoning time for problem solving and affects the accuracy of problem solving. In this paper, we construct the formal language graph and use a graph convolutional network to encode structure information of formal language. We propose an improved diagram parser for better diagram relation set extraction. The experimental results show that our method achieves better performance in interpretable geometry problem solving.

Although not always considered a domain of digital health, artificial intelligence (AI) and machine learning (ML) are inseparable, given that virtually all digital health tools utilize advance algorithm technologies. For example, a wearable device is not directly measuring sleep. It is measuring movement, electrical impulses, and other variables that are processed via algorithm and presented as a representation of sleep. Beyond digital devices, AI is being seen as a revolution in health care driven by the hopes that algorithms running on electronic medical record systems will predict when a patient is turning septic or that AI will free clinicians from tedious tasks. Much of this is coming true, but there is also a growing list of potential hazards such as trusting “black boxes,” racial bias, and poor technical execution. This chapter discusses ways to find the right balance between the progress and problems of AI in health care.

The paper provides a general overview of current discussions on the phenomenal character of abstract thinking, which throws a fresh light on the problem of agency of artificial intelligence. Narrow and wide interpretations of cognitive experience phenomenal character as presented in contemporary discussions have been analyzed. According to those supporting the strong version of narrow interpretation of the phenomenal nature of cognitive experience, phenomenal properties are only characteristic ofthe sensor-perceptive realm of cognition, while mental experience has purely intentional, but not phenomenal character.The moderate version of narrow interpretation admits that abstract thinking can also have phenomenal character — due to its links with sensual and emotional states. Moderate position of mental experience’s narrow interpretation problematizes the very point of agency of artificial intelligence. Wide interpretation of the phenomenal character of cognition insists that abstract thinking also has phenomenal character: abstract thinking is characterized by «experiencing» a certain cognitive state, «being within» this cognitive state. In the framework of wide interpretation,the intentional character of mental experience seems not be reducible to sensual. The paper analyzes the ontological argument against wide interpretation of phenomenal experience as well as arguments (logical and metaphysical ones) in favor of the possibility of specifically mental phenomenal states. Discussions on cognitive phenomenology throw a fresh light both on the problems of artificial intelligence’s agency and on fundamental problems of epistemology in general. These include the problems of perceptual intentionality and its influence on high-level cognition processes, the qualitative character of mental experience, and the relevance of introspection to abstract thinkingstudies. General conclusion, if abstract thinking has its own phenomenal character, not reducible to phenomenal character of sensual experience, this undermines the foundations of computational cognitive approach and raises research on the problems of artificial intelligence to a higher level.

The importance of the topic of the article is due to the fact that nowadays there is a need to understand that the creation of artificial intelligence is associated with the understanding and study of natural intelligence. The problem of defining artificial intelligence is largely reduced to defining intelligence in general: is intelligence monolithic or does this concept define a set of different abilities? To what extent intelligence can be created? Is it possible to create computers with intelligence? These and other questions have not yet been answered by the contemporary science, but these questions have greatly contributed to the formation of tasks and methodologies that form the basis of the theory and practice of contemporary artificial intelligence in philosophy. The purpose of the article is the need to study and substantiate the indicators of overall success in the development of artificial intelligence systems; proving that human psychoreality is an open system of nonlinear type; proving the effectiveness of logo and psycho-machines in the development of artificial intelligence in philosophy. The article gives a theoretical justification for the concepts of “artificial intelligence” and “logo and psycho machines”; the conditions for the effectiveness of the development of artificial intelligence in our time are highlighted, as the scientific direction of artificial intelligence is young, and its structure and range of issues are not so clearly defined. It is now possible to implement formal systems of reasoning in the machine and test their sufficiency for the manifestation of reasonableness in practice.

The paper presents definite review of mutual communication between analytical philosophy and phenomenology in the study of conscious experience’s phenomenal character. It is clearly demonstrated, that analytical philosophy tends to share the so-called “narrow” inter­pretation of conscious experience phenomenal character both in its strong and moderate ver­sions. Strong version denies that cognitive states may have phenomenal properties and in­sists that the latter adhere only to sensorial domain. Those who share moderate position, admits, that perceptual experience preserves its phenomenal character although laden with definite conceptual content. According to this position, conscious thoughts may possess their specific phenomenal character, but only due to perceptual states, with which they are obvi­ously tied. In their turn, those, who adhere to phenomenological tradition, support the so-called wide interpretation of conscious experience phenomenal character. Their point is, that our thoughts also have their specific phenomenal character, which differs from the former and cannot be reduced to sensory phenomenology. Those who accept this position acknowl­edge (logical and metaphysical) possibility of specific cognitive mental states; differ from any sensorial mental states. Vivid debates on cognitive phenomenology, dated back to the last years 80th of XX century and embrace wide range of international philosophical community, still continue. They throw a fresh light on the most fundamental problems of contemporary epistemology: perceptual intentionality and its influence on the high-level cognitive processes, qualia of mental experience (if any), the relevance of introspection in the study of abstract thinking, phenomenological description cognitive status. This analy­sis outlines directions of their further elaboration and not only paves the way to the new de­cision of the so-called “hard problem” of mind, but also raises to the new level of complex­ity the problems of artificial intelligence.

Purpose. The study presents the key aspects of the introduction and use of artificial intelligence (AI) in the construction industry. The challenges faced by builders when implementing AI, such as the high cost of equipment and the need for retraining, are discussed. The advantages of AI are also considered, including improving design efficiency, reducing costs and improving control over construction processes. Special attention is paid to the use of AI in monitoring construction sites, risk forecasting, automation of typical operations and the use of autonomous technology. The article emphasizes that despite the existing problems, it is a powerful tool for the transformation of the construction industry, which can significantly improve the quality and speed of construction.Methods. The research uses methods of statistical and comparative analysis, the «golden square» method in the context of digitalization of the IC, focused on managing complex situations.Results. A classifier of threats and risks for IC in difficult situations has been developed from the point of view of the sustainable development of artificial intelligence in the construction industry.Conclusion. Artificial intelligence in construction opens up new horizons for improving efficiency and optimizing processes. From BIM technologies to autonomous robots and drones, and is becoming an integral part of the modern construction industry. However, despite all the advantages, there are also challenges, such as the high cost of equipment and the need for retraining. Nevertheless, the potential in construction is huge, and its implementation promises significant improvements in the quality, speed and safety of construction work. The paper provides an overview of the problems of IC in modern conditions. The analysis of the application of the "golden square" method in the context of digitalization of the IC, focused on managing complex situations, is presented. A classifier of threats and risks for the UK in difficult situations from the point of view of sustainable development has been developed.

Contemporary interdisciplinary researches are now based upon modern information technologies. The core of the studies at the Russian Academy of Sciences is the program of artificial intelligence. The research program of artificial intelligence represents an attempt to unite a conglomerate of different disciplines connected by a common task – to create a theoretical model of “natural” intelligence and to develop, if possible, the way (or “technology”) to manufacture it in order to apply it in different areas of human activity where “natural” intelligence is now used. However, such a program, in a strict sense of the term, still hardly exists – rather it is a “vague” ideology of complex research, which would be able to solve the set of various tasks by means of modern engineering and various “cognitive sciences”. Nevertheless, the Learned Council on the problem of artificial intelligence, which is focused on elaborating the program of interdisciplinary research of human reason with a special reference to artificial intelligence, was established at the beginning of 2006 under the guidance of the Presidium of the Russian Academy of Sciences. Let me remark, by the way, that not only leading Russian scientists but also the students of the corresponding institutes have been involved in the research work. The Council organized the conference entitled “Artificial Intelligence As the Subject of Interdisciplinary Research” (January, 2005) to which not only philosophers but also computer scientists, mathematicians, logicians etc. greatly contributed. The conference gave rise to a permanent seminar. It works with enormous success under the guidance of the vicepresident of the Russian Academy of Sciences. Nevertheless, very few people pose the question whether such a subject exists at all – even in the sense in which mathematicians speak about the “existence” of the solution of this or that problem (even though it still has not been found). In general, the progress of scientific knowledge, not only in mathematics but also in “empirical” sciences, often originates from the effort to solve such a problem, which has no scientific solutions or does not

The architecture of knowledge of computer systems and some problems of artificial intelligence (AI) are considered such as knowledge acquisition, knowledge processing, knowledge formation, etc.

As the key nodes of the global comprehensive transportation network, ports play a pivotal role in global trade and shipping. With the progress of artificial intelligence technology and the promotion of intelligent port development concept, intelligent port will be the inevitable trend and choice of all countries in the world. This paper analyzes the application status and problems of artificial intelligence in promoting the construction of smart ports, discusses the typical application of artificial intelligence in the intelligent port upgrading, and details the practical application of artificial intelligence in the construction of intelligent port, including container electronic tags, unmanned driving technology, intelligent ship stowage, and intelligent port scheduling. Finally, the corresponding optimization countermeasures of intelligent port are put forward.

Artificial intelligence research started with computers trying to take the place of what humans have to do intelligently. Research started in the 1950s went through several ups and downs and reached the same period of the third boom as it is now. With the shocking situation where Alpha Go defeated Lee Se-dol, the strongest player in Go, in 2016, interest in artificial intelligence increased rapidly, and stories about the future situations that artificial intelligence will bring about, such as the possibility and singularity of artificial intelligence, are talked about even among the general public. Certainly, artificial intelligence has the potential to fundamentally change the way humans live, not just technological advances. The real value of artificial intelligence algorithms is prediction. At the center of all the algorithmic revolutions that have taken place in the various fields of modern society, there is one unchanging purpose: prediction. The algorithm for predicting the risk of recidivism used in US law enforcement fits precisely with the principle that individual humans behave in a predictable and consistent manner. While using the algorithm of predicting the risk of a second offender, an artificial intelligence algorithm, not a judge, actually takes the place of the defendant s decision on whether or not to be arrested or sentenced. Automated decision making continues to grow at an unprecedented rate and benefits because machine learning offers the possibility to extend the automated decision-making process, allowing wider, deeper decisions without human intervention. It is also very large and is expected to continue to expand its use. However, the risks are as great as the benefits of artificial intelligence algorithms. The risks of artificial intelligence algorithms outweigh the individual risks of any algorithmic technology specifically applied. Despite the widespread use of artificial intelligence in the social sphere, the reality is that the problems of artificial intelligence cannot be properly grasped. However, people who were treated differently due to automatic decision-making by artificial intelligence began to question the fairness or fairness of artificial intelligence, and in particular, while causing sensitive problems such as discriminatory results, controversy surrounding artificial intelligence algorithms gradually became more and more controversial. It is expanding. Even if a decision made by artificial intelligence is wrong or causes negative effects such as discrimination, there is a big problem that there are few means to object to this. In a situation where automated decision-making is more likely to threaten human autonomy and dignity, in order to secure an individual s autonomous personality, make sure that the entire process of the individual s automatic decision-making that affects him or her is fair and error-free. It is of utmost importance to know and allow humans to directly intervene if necessary. Although available data is increasing and algorithms are gradually improving, artificial intelligence algorithms do not completely eliminate the uncertainty posed by decision making. Eventually, there are cases in which humans, as finalists, have to make important decisions in situations where uncertainty has not been resolved. It is necessary to build a system in which artificial intelligence algorithms and human experts cooperate well to make the best decisions, and in addition to the direction of such a collaboration system establishment, the problem of legal responsibility due to errors in decision-making in artificial intelligence algorithms or collaboration systems is necessary. I want to deal with it in this paper.

The logical problem of artificial intelligence—the question of whether the notion sometimes referred to as ‘strong’ AI is self-contradictory—is, essentially, the question of whether an artificial form of life is possible. This question has an immediately paradoxical character, which can be made explicit if we recast it (in terms that would ordinarily seem to be implied by it) as the question of whether an unnatural form of nature is possible. The present paper seeks to explain this paradoxical kind of possibility by arguing that machines can share the human form of life and thus acquire human mindedness, which is to say they can be intelligent, conscious, sentient, etc. in precisely the way that a human being typically is.

Generic local computation

A statistical foundation for machine learning, with application to Go-Moku

Planning belongs to fundamental AI domains. Examples of planning applications are manufacturing, production planning, logistics and agentics. Over the decades planning techniques were improved and now they are able to capable real environment problems in the presence of uncertain and incomplete information. This article introduces the notion of so called classical planning, indicating connected with this computational complexity problems and possible ways of treating uncertainty.

Question answering (QA) is a very important, but not yet completely resolved problem in artificial intelligence. Solving the QA problem consists of two major steps: relevant fact selection and answering the question. Existing methods usually combine the two steps to solve the problem. A major technique is to add a memory component to infer answers from the chaining facts. It is not very clear how irrelevant facts affect the effectiveness of these methods. In this paper, we propose to separate the two steps and only consider the problem of relevant fact selection. We used a graphical probabilistic model Conditional Random Field (CRF) to model the interdependent relationship among the chaining facts in order to select the relevant ones. In our experiments on a benchmark dataset, we are able to select correctly all relevant facts from 13 tasks out of 19 tasks (F-scores of the rest of the 6 tasks range from 0.8 to 0.97). We also show that using our selector to pre-select relevant facts can substantially improve the accuracies of existing QA systems (e.g. MemN2N (from 88% to 94%) and LSTM (from 66% to 91%) in 13 tasks with complete information).