Trying to be effective (no matter who exactly and in what field) a person face the problem which inevitably destroys all our attempts to easily get to a desired goal. The problem is the existence of some insuperable barriers for our mind, anotherwords barriers for principles of thinking. They are our clue and main reason for research. Here we investigate these barriers and their features exposing the nature of mental process. We start from special structures which reflect the ways to define relations between objects. Then we came to realizing about what is the material our mind uses to build thoughts, to make conclusions, to understand, to form reasoning, etc. This can be called a mental dynamics. After this the nature of mental barriers on the required level of abstraction as well as the ways to pass through them became clear. We begin to understand why thinking flows in such a way, with such specifics and with such limitations we can observe in reality. This can help us to be more optimal. At the final step we start to understand, what ma-thematical models can be applied to such a picture. We start to express our thoughts in a language of mathematics, developing an apparatus for our Spatial Theory of Mind, suitable to represent processes and infrastructure of thinking. We use abstract algebra and stay invariant in relation to the nature of objects.
This paper summarises how the "SP theory of intelligence" and its realisation in the "SP computer model" simplifies and integrates concepts across artificial intelligence and related areas, and thus provides a promising foundation for the development of a general, human-level thinking machine, in accordance with the main goal of research in artificial general intelligence. The key to this simplification and integration is the powerful concept of "multiple alignment", borrowed and adapted from bioinformatics. This concept has the potential to be the "double helix" of intelligence, with as much …
In this paper, I put forward that in many instances, thinking mechanisms are equivalent to artificial intelligence modules programmed into the human mind.
Graduate Technological Educational Institute of Patras（帕特雷技术教育学院）；University of South Africa（南非大学）
Computational thinking is a new problem soling method named for its extensive use of computer science techniques. It synthesizes critical thinking and existing knowledge and applies them in solving complex technological problems. The term was coined by J. Wing, but the relationship between computational and critical thinking, the two modes of thiking in solving problems, has not been yet learly established. This paper aims at shedding some light into this relationship. We also present two classroom experiments performed recently at the Graduate Technological Educational Institute of Patras in …
Understanding Critical Thinking in Generative Artificial Intelligence Use: Development, Validation, and Correlates of the Critical Thinking in AI Use Scale
School of Social Sciences, Nanyang Technological University（南洋理工大学社会科学学院）；Interdisciplinary Graduate Programme, Nanyang Technological University（南洋理工大学跨学科研究生项目）；College of Health and Human Sciences, Purdue University（普渡大学健康与人类科学学院）；School of Social Sciences, Singapore Management University（新加坡管理学院社会科学学院）；Faculty of Information Technology, Monash University（墨尔本大学信息技术学院）
What is the nature of curiosity? Is there any scientific way to understand the origin of this mysterious force that drives the behavior of even the stupidest naturally intelligent systems and is completely absent in their smartest artificial analogs? Can we build AI systems that could be curious about something, systems that would have an intrinsic motivation to learn? Is such a motivation quantifiable? Is it implementable? I will discuss this problem from the standpoint of physics. The relationship between physics and intelligence is a consequence of the fact that correctly predicted informat…
Thinking Beyond Tokens: From Brain-Inspired Intelligence to Cognitive Foundations for Artificial General Intelligence and its Societal Impact
Center for research in Computer Vision, University of Central Florida（计算机视觉研究中心，中央佛罗里达大学）；Cornell University（康奈尔大学）；Department of Electronics Engineering, Mehran University of Engineering & Technology（电子工程系，工程与技术大学）；Department of Imaging Physics, The University of Texas MD Anderson Cancer Center（成像物理系，德克萨斯大学MD安德森癌症中心）；Intelligent Transportation Systems, University of Tennessee（智能交通运输系统，田纳西大学）；Department of Electrical Engineering, City University of Hong Kong（电气工程系，香港城市大学）；Department of Mechanical Engineering, Bartin University（机械工程系，巴廷大学）；Vector Institute, Toronto Canada（多伦多加拿大向量研究所）；Manchester Metropolitan University（曼彻斯特 Metropolitan 大学）；Center for Data Science, New York University（数据科学中心，纽约大学）；Meta Research（Meta 研究）；Amazon Research（亚马逊研究）；Centre for Artificial Intelligence Research and Optimization, Torrens University Australia（人工智能研究与优化中心，塔伦斯大学澳大利亚）；University Research and Innovation Center, Obuda University（研究与创新中心，奥布达大学）
Despite recent breakthroughs in the field of artificial intelligence (AI) - or more specifically machine learning (ML) algorithms for object recognition and natural language processing - it seems to be the majority view that current AI approaches are still no real match for natural intelligence (NI). More importantly, philosophers have collected a long catalogue of features which imply that NI works differently from current AI not only in a gradual sense, but in a more substantial way: NI is closely related to consciousness, intentionality and experiential features like qualia (the subjective …
Thinking Ahead: Foresight Intelligence in MLLMs and World Model
College of Software, Nankai University, China（南开大学软件学院）；The University of Hong Kong, China（香港大学）；NKIARI, Shenzhen Futian, China（NKIARI，深圳福田，中国）；AAIS & VCIP, Nankai University, China（AAIS与VCIP，南开大学，中国）；A*STAR Institute of Advanced Intelligence and Computing, Singapore（新加坡A*STAR先进智能与计算研究所）
A Systematic Assessment of OpenAI o1-Preview for Higher Order Thinking in Education
AI4STEM Education Center（AI4STEM教育中心）；University of Georgia（佐治亚大学）；National GENIUS Center（国家GENIUS中心）；School of Computing, University of Georgia（佐治亚大学计算学院）；School of Teacher Education, Nanjing Normal University（南京师范大学教师教育学院）；National Institute of Education, Singapore（新加坡国家教育学院）
From Data-Driven to Purpose-Driven Artificial Intelligence: Systems Thinking for Data-Analytic Automation of Patient Care
Utrecht University（乌特勒支大学）；Delft University of Technology（代尔夫特理工大学）；University Medical Center Utrecht（乌特勒支大学医学中心）；Organisation for Economic Co-operation and Development（经济合作与发展组织）
Narrative intelligence is the ability to craft, tell, understand, and respond affectively to stories. We argue that instilling artificial intelligences with computational narrative intelligence affords a number of applications beneficial to humans. We lay out some of the machine learning challenges necessary to solve to achieve computational narrative intelligence. Finally, we argue that computational narrative is a practical step towards machine enculturation, the teaching of sociocultural values to machines.
In this paper, we will expound upon the concepts proffered in [1], where we proposed an information theoretic approach to intelligence in the computational sense. We will examine data and meme aggregation, and study the effect of limited resources on the resulting meme amplitudes.
Paradigms of Intelligence Team, Google（谷歌智能范式团队）；University of Chicago（芝加哥大学）；Santa Fe Institute（圣菲研究所）；Antikythera, Berggruen Institute（安提基特拉与伯格格林研究所）；University of California, San Diego（加州大学圣地亚哥分校）
Rubric-based Learner Modelling via Noisy Gates Bayesian Networks for Computational Thinking Skills Assessment
Istituto Dalle Molle di Studi sull’Intelligenza Artificiale (IDSIA)（达勒·莫勒人工智能研究所）；SUPSI（瑞士南部应用科学大学）；USI（瑞士意大利语区大学）；Department of Education and Learning (DFA)（教育与学习系）
Relentless progress in artificial intelligence (AI) is increasingly raising concerns that machines will replace humans on the job market, and perhaps altogether. Eliezer Yudkowski and others have explored the possibility that a promising future for humankind could be guaranteed by a superintelligent "Friendly AI", designed to safeguard humanity and its values. I argue that, from a physics perspective where everything is simply an arrangement of elementary particles, this might be even harder than it appears. Indeed, it may require thinking rigorously about the meaning of life: What is "meaning…
Computational Intelligence algorithms have gained a lot of attention of researchers in the recent years due to their ability to deliver near optimal solutions.
This paper presents an information theoretic approach to the concept of intelligence in the computational sense. We introduce a probabilistic framework from which computational intelligence is shown to be an entropy minimizing process at the local level. Using this new scheme, we develop a simple data driven clustering example and discuss its applications.
Report from Workshop on Dialogue alongside Artificial Intelligence
Boston University（波士顿大学）；University of Oslo（奥斯陆大学）；The Hebrew University of Jerusalem（耶路撒冷希伯来大学）；The University of Hong Kong（香港大学）；University of Virginia（弗吉尼亚大学）；University of Cambridge（剑桥大学）；Harvard University（哈佛大学）；Freie Universität Berlin（柏林自由大学）；University of Pittsburgh（匹兹堡大学）
The goal of transfer learning (TL) is providing a framework for exploiting acquired knowledge from source to target data. Transfer learning approaches compared to traditional machine learning approaches are capable of modeling better data patterns from the current domain. However, vanilla TL needs performance improvements by using computational intelligence-based TL. This paper studies computational intelligence-based transfer learning techniques and categorizes them into neural network-based, evolutionary algorithm-based, swarm intelligence-based and fuzzy logic-based transfer learning.
On the Provable Performance Guarantee of Efficient Reasoning Models
Nanyang Technological University, Singapore（新加坡南洋理工大学）；Southern University of Science（南方科技大学）；The Chinese University of Hong Kong, Shenzhen, China（香港中文大学（深圳））
