Ever since the advent of computerized methods for solving mathematical problems, the concept of surveyability has played a central role in the debate surrounding what constitutes a mathematical proof. Ordinarily, it is by surveying the argument presented that the mathematician ascertains the truth of the conclusion, but with the advent of computer assisted technologies, there are mathematical conclusions known to be true without anyone ever having been able to survey the argument in its entirety. What this seems to suggest is that what is called "mathematical knowledge" encompasses two different types of knowledge: one gained through the act of surveying a proof, and the other through computerized empirical experiments. The goal of this thesis is to investigate the connection between surveyability and the acquisition of mathematical knowledge, thereby elucidating the difference between the two epistemological categories. The claim is that this can be accomplished by applying Heidegger's account of unconcealment to the notion of mathematical truth, supported by a Wittgensteinian analysis of the act of surveying as a type of reproduction of the proof. While much has been written on how his early mathematical training influenced Heidegger's philosophy, attempts at applying elements from his thinking to problems belonging to the philosophy of mathematics are rare. This investigation has the ambition of making a convincing case for the potential in this kind of approach.

Проблематику алгоритмизации мышления и исследования в области создания систем искусственного интеллекта объединяет вопрос «Может ли машина мыслить?» Несмотря на то, что две данные области по-разному отвечают на вопрос о возможности мышления машины, результаты достигнутые в одной области могут повлиять на другую. Объектом исследования являются проблематика алгоритмизации мышления и интеллект в концепции Дж. Хокинса. Предметом исследования являются ограничения на алгоритмизацию в связи с моделью «память-предсказание». Цель исследования - рассмотреть проблематику алгоритмизации мышления в связи с концепцией Дж. Хокинса. Методы, применяемые в исследовании: концептуальный и логический анализ. Новизна данной диссертационной работы заключается в сопоставлении проблематики алгоритмизации мышления с современным исследование в области создания ИИ, концепцией Дж. Хокинса. В результате исследования установлено, что в основе интеллекта лежит модель «память-предсказание». Используя данную модель, становится возможным решить практически все проблемы, связанные с ограничениями на алгоритмизацию мышления. Выяснено, что концепт обозримости доказательства можно применить для оптимизации работы интеллектуальных систем. / The problem of algorithmizing thinking and research in the field of creating artificial intelligence systems unites the question "Can the machine think?" Although these two areas of knowledge respond differently to the question of the machine's thinking capabilities, the results achieved in one area can affect the other. The object of research work are problems of algorithmization of thinking and intellect in the theory of J. Hawkins. The subject of the research work are constraints on algorithmization in connection with the memory-prediction model. The purpose of the research work is to consider the problems of algorithmizing thinking in connection with the theory of J. Hawkins. Methods used in the research work: conceptual and logical analysis. The novelty of this research work is to compare the problems of algorithmizing thinking with modern research in the field of creating AI, the concept of J. Hawkins. As a result of the research it was established that the intellect is based on the memory-prediction model. Using this model, it becomes possible to solve almost all the problems associated with limitations on the algorithmization of thinking. It is clarified that the concept of surveyability of proof can be applied to optimize the operation of intelligent systems.