The new computational paradigm of conceptual computation has been introduced in the research program of Artificial Mathematical Intelligence. We provide the explicit artificial generation (or conceptual computation) for the fundamental mathematical notion of topological groups. Specifically, we start with two basic notions belonging to topology and abstract algebra, and we describe recursively formal specifications in the Common Algebraic Specification Language (CASL). The notion of conceptual blending between such conceptual spaces can be materialized computationally in the Heterogeneous Tool Set (HETS). The fundamental notion of topological groups is explicitly generated through three different artificial specifications based on conceptual blending and conceptual identification, starting with the concepts of continuous functions and mathematical groups (described with minimal set-theoretical conditions). This constitutes in additional heuristic evidence for the third pillar of Artificial Mathematical Intelligence.
A Survey of Quantum-Cognitively Inspired Sentiment Analysis Models
Beijing Institute of Technology（北京理工大学）；University of Copenhagen（哥本哈根大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Zhengzhou University of Light Industry（郑州轻工业大学）；The Open University（开放大学）
Automatic service composition in mobile and pervasive computing faces many challenges due to the complex and highly dynamic nature of the environment. Common approaches consider service composition as a decision problem whose solution is usually addressed from optimization perspectives which are not feasible in practice due to the intractability of the problem, limited computational resources of smart devices, service host's mobility, and time constraints to tailor composition plans. Thus, our main contribution is the development of a cognitively-inspired agent-based service composition model …
NumerosityVLM: A Cognitively Inspired Benchmark for Interpreting Numerosity Representations in Vision-Language Models
The University of Manchester（曼彻斯特大学）；University of Michigan（密歇根大学）；Sheffield Hallam University（谢菲尔德哈勒姆大学）；Tufts University（塔夫茨大学）；ENSTA, Institut Polytechnique de Paris（巴黎综合理工学院国立高等先进技术学校）
Incremental learning attempts to develop a classifier which learns continuously from a stream of data segregated into different classes. Deep learning approaches suffer from catastrophic forgetting when learning classes incrementally, while most incremental learning approaches require a large amount of training data per class. We examine the problem of incremental learning using only a few training examples, referred to as Few-Shot Incremental Learning (FSIL). To solve this problem, we propose a novel approach inspired by the concept learning model of the hippocampus and the neocortex that rep…
Symphony: A Cognitively-Inspired Multi-Agent System for Long-Video Understanding
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Kuaishou Technology（快手科技）；School of Future Technology, University of Chinese Academy of Sciences（中国科学院大学未来技术学院）
Patients with Frontotemporal Dementia (FTD) have impaired cognitive abilities, executive and behavioral traits, loss of language ability, and decreased memory capabilities. Based on the distinct patterns of cortical atrophy and symptoms, the FTD spectrum primarily includes three variants: behavioral variant FTD (bvFTD), non-fluent variant primary progressive aphasia (nfvPPA), and semantic variant primary progressive aphasia (svPPA). The purpose of this study is to classify MRI images of every single subject into one of the spectrums of the FTD in a hierarchical order by applying data-driven te…
Vector Symbolic Algebras for the Abstraction and Reasoning Corpus
Centre for Theoretical Neuroscience（理论神经科学中心）；David R. Cheriton School of Computer Science（大卫·R·切里顿计算机科学学院）；University of Waterloo（滑铁库大学）；Department of Philosophy（哲学系）；Department of Systems Design Engineering（系统设计工程系）
University of Chicago（芝加哥大学）；Santa Fe Institute（圣塔菲研究所）
Neither artificial intelligence designed to play Turing's imitation game, nor augmented intelligence built to maximize the human manipulation of information are tuned to accelerate innovation and improve humanity's collective advance against its greatest challenges. We reconceptualize and pilot beneficial AI to radically augment human understanding by complementing rather than competing with human cognitive capacity. Our approach to complementary intelligence builds on insights underlying the wisdom of crowds, which hinges on the independence and diversity of crowd members' information and app…
Artificial Neural Networks (ANNs) were devised as a tool for Artificial Intelligence design implementations. However, it was soon became obvious that they are unable to fulfill their duties. The fully autonomous way of ANNs working, precluded from any human intervention or supervision, deprived of any theoretical underpinning, leads to a strange state of affairs, when ANN designers cannot explain why and how they achieve their amazing and remarkable results. Therefore, contemporary Artificial Intelligence R&D looks more like a Modern Alchemy enterprise rather than a respected scientific or tec…
This article surveys engineering and neuroscientific models of planning as a cognitive function, which is regarded as a typical function of fluid intelligence in the discussion of general intelligence. It aims to present existing planning models as references for realizing the planning function in brain-inspired AI or artificial general intelligence (AGI). It also proposes themes for the research and development of brain-inspired AI from the viewpoint of tasks and architecture.
From reactive to cognitive: brain-inspired spatial intelligence for embodied agents
Department of Computer Science and Technology, Institute for AI, BNRist Center, Tsinghua-Bosch Joint ML Center, THBI Lab（计算机科学与技术系、人工智能研究院、BNRist中心、清华-博世联合机器学习中心、THBI实验室）；Institute of Artificial Intelligence（人工智能研究院）；Department of Psychological and Cognitive Sciences（心理学与认知科学系）
Reflect After Failure: Brain-Inspired Self-Evolving Agents for Embodied Spatial Intelligence
City University of Hong Kong（香港城市大学）；East China Normal University（华东师范大学）；University of Electronic Science and Technology of China（电子科技大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；The University of Hong Kong（香港大学）
The pictorial jigsaw (PJ) puzzle is a well-known leisure game for humans. Usually, a PJ puzzle game is played by one or several human players face-to-face in the physical space. In this paper, we focus on how to solve PJ puzzles in the cyberspace by a group of physically distributed human players. We propose an approach to solving PJ puzzle by stigmergy-inspired Internet-based human collective intelligence. The core of the approach is a continuously executing loop, named the EIF loop, which consists of three activities: exploration, integration, and feedback. In exploration, each player tries …
The University of Georgia（佐治亚大学）；The University of Texas at Arlington（德克萨斯大学阿灵顿分校）；University of Electronic Science and Technology of China（电子科技大学）；Northwestern Polytechnical University（西北工业大学）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；ShanghaiTech University（上海科技大学）；Shanghai United Imaging Intelligence Co., Ltd.（上海联影智能科技有限公司）
Artificial General Intelligence (AGI) has been a long-standing goal of humanity, with the aim of creating machines capable of performing any intellectual task that humans can do. To achieve this, AGI researchers draw inspiration from the human brain and seek to replicate its principles in intelligent machines. Brain-inspired artificial intelligence is a field that has emerged from this endeavor, combining insights from neuroscience, psychology, and computer science to develop more efficient and powerful AI systems. In this article, we provide a comprehensive overview of brain-inspired AI from …
The claims that big data holds the key to enterprise successes and that Artificial Intelligence is going to replace humanity have become increasingly more popular over the past few years, both in academia and in the industry. However, while these claims may indeed capture some truth, they have also been massively oversold, or so we contend here. The goal of this paper is two-fold. First, we provide a qualified defence of the value of less data within the context of AI. This is done by carefully reviewing two distinct problems for big data driven AI, namely a) the limited track record of Deep L…
In this paper, the idea of a new artificial intelligence based optimization algorithm, which is inspired from the nature of vortex, has been provided briefly. As also a bio-inspired computation algorithm, the idea is generally focused on a typical vortex flow / behavior in nature and inspires from some dynamics that are occurred in the sense of vortex nature. Briefly, the algorithm is also a swarm-oriented evolutional problem solution approach; because it includes many methods related to elimination of weak swarm members and trying to improve the solution process by supporting the solution spa…
Oslo Metropolitan University（奥斯陆城市大学）；Norwegian University of Science and Technology（挪威科技大学）；Simula Metropolitan（西穆拉城市研究院）
In this work, a conceptual bio-inspired parallel and distributed learning framework for the emergence of general intelligence is proposed, where agents evolve through environmental rewards and learn throughout their lifetime without supervision, i.e., self-learning through embodiment. The chosen control mechanism for agents is a biologically plausible neuron model based on spiking neural networks. Network topologies become more complex through evolution, i.e., the topology is not fixed, while the synaptic weights of the networks cannot be inherited, i.e., newborn brains are not trained and hav…
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）
The advent of artificial intelligence has changed many disciplines such as engineering, social science and economics. Artificial intelligence is a computational technique which is inspired by natural intelligence such as the swarming of birds, the working of the brain and the pathfinding of the ants. These techniques have impact on economic theories. This book studies the impact of artificial intelligence on economic theories, a subject that has not been extensively studied. The theories that are considered are: demand and supply, asymmetrical information, pricing, rational choice, rational ex…
This paper describes the application of artificial intelligence to the creation of digital art. AI is a computational paradigm that codifies intelligence into machines. There are generally three types of artificial intelligence and these are machine learning, evolutionary programming and soft computing. Machine learning is the statistical approach to building intelligent systems. Evolutionary programming is the use of natural evolutionary systems to design intelligent machines. Some of the evolutionary programming systems include genetic algorithm which is inspired by the principles of evoluti…
Bio-inspired AI: Integrating Biological Complexity into Artificial Intelligence
McGovern Institute for Brain Research, MIT（麻省理工学院麦戈文脑科学研究所）；Allen Discovery Center at Tufts University（塔夫茨大学艾伦发现中心）；Center for Brains, Minds, and Machines, MIT（麻省理工学院脑、心智与机器中心）；Wyss Institute for Biologically Inspired Engineering, Harvard University（哈佛大学威斯生物启发工程研究所）
Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems
MetaGPT；Université de Montréal（蒙特利尔大学）；Mila - Quebec AI Institute（米拉-魁北克人工智能研究所）；Nanyang Technological University（南洋理工大学）；Argonne National Laboratory（阿贡国家实验室）；University of Sydney（悉尼大学）；Penn State University（宾夕法尼亚州立大学）；Microsoft Research Asia（微软亚洲研究院）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；The Hong Kong University of Science and Technology（香港科技大学）；University of Southern California（南加州大学）；Yale University（耶鲁大学）；Stanford University（斯坦福大学）；University of Georgia（佐治亚大学）；The Ohio State University（俄亥俄州立大学）；King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）；Duke University（杜克大学）；The Hong Kong Polytechnic University（香港理工大学）；Google DeepMind（谷歌DeepMind）
Integration of cognitive tasks into artificial general intelligence test for large models
Southern University of Science and Technology（南方科技大学）；Shanghai AI Laboratory（上海人工智能实验室）；Tencent AI lab（腾讯AI实验室）；iFLYTEK AI Research（科大讯飞人工智能研究院）；Beijing Institute of Technology（北京理工大学）；Peking University（北京大学）；University of Macau（澳门大学）；Tsinghua University（清华大学）
In this article, we provide and overview of what we consider to be some of the most pressing research questions facing the fields of artificial intelligence (AI) and computational intelligence (CI); with the latter focusing on algorithms that are inspired by various natural phenomena. We demarcate these questions using five unique Rs - namely, (i) rationalizability, (ii) resilience, (iii) reproducibility, (iv) realism, and (v) responsibility. Notably, just as air serves as the basic element of biological life, the term AIR5 - cumulatively referring to the five aforementioned Rs - is introduced…
Kirchhoff Institute for Physics, Heidelberg University（海德堡大学基尔霍夫物理研究所）；Department of Physiology, University of Bern（伯尔尼大学生理学系）；University of Bern（伯尔尼大学）；Heidelberg University（海德堡大学）；Kirchhoff Institute for Physics（基尔霍夫物理研究所）
Future developments in artificial intelligence will profit from the existence of novel, non-traditional substrates for brain-inspired computing. Neuromorphic computers aim to provide such a substrate that reproduces the brain's capabilities in terms of adaptive, low-power information processing. We present results from a prototype chip of the BrainScaleS-2 mixed-signal neuromorphic system that adopts a physical-model approach with a 1000-fold acceleration of spiking neural network dynamics relative to biological real time. Using the embedded plasticity processor, we both simulate the Pong arca…
Brain-inspired and Self-based Artificial Intelligence
Brain-inspired Cognitive AI Lab, Institute of Automation, Chinese Academy of Sciences, China.；Beijing Key Laboratory of Safe AI；Beijing Institute of AI Safety；Key Laboratory of Brain Cognition；Brain-inspired Intelligence Technology, Chinese Academy of Sciences, China.；University of Chinese Academy of Sciences, China.；Department of Philosophy, School of Humanities, University of Chinese Academy of Sciences, China.
This paper is about the meaning of understanding in scientific and in artificial intelligent systems. We give a mathematical definition of the understanding, where, contrary to the common wisdom, we define the probability space on the input set, and we treat the transformation made by an intelligent actor not as a loss of information, but instead a reorganization of the information in the framework of a new coordinate system. We introduce, following the ideas of physical renormalization group, the notions of relevant and irrelevant parameters, and discuss, how the different AI tasks can be int…
Knowledge Lab, University of Chicago（芝加哥大学知识实验室）；Santa Fe Institute（圣塔菲研究所）
Data-driven artificial intelligence models fed with published scientific findings have been used to create powerful prediction engines for scientific and technological advance, such as the discovery of novel materials with desired properties and the targeted invention of new therapies and vaccines. These AI approaches typically ignore the distribution of human prediction engines -- scientists and inventor -- who continuously alter the landscape of discovery and invention. As a result, AI hypotheses are designed to substitute for human experts, failing to complement them for punctuated collecti…
