In the last year or so and going back many decades there has been extensive claims by major computational scientists, engineers, and others that AGI, artificial general intelligence, is five or ten years away, but without a scintilla of scientific evidence, for a broad body of these claims. Computers will become conscious, have a theory of mind, think and reason, will become more intelligent than humans, and so on. But the claims are science fiction, not science. This article reviews evidence for the following three propositions using extensive body of scientific research and related sources from the cognitive and neurosciences, evolutionary evidence, linguistics, data science, comparative psychology, self-driving cars, robotics. and the learning sciences. (1) Do computing machines think or reason? (2) Are computing machines sentient or conscious? (3) Do computing machines have a theory of mind?
Tera-MIND: Tera-scale mouse brain simulation via spatial mRNA-guided diffusion
Department of Biomedical Engineering, University of Basel, Switzerland（巴塞尔大学生物医学工程系）；Department of Pathology and Molecular Pathology, University Hospital, University of Zurich, Switzerland（苏黎世大学病理学与分子病理学系，苏黎世大学医院）；Computer Vision Lab, ETH Zurich, Switzerland（苏黎世联邦理工学院计算机视觉实验室）；Integrated System Laboratory, ETH Zurich, Switzerland（苏黎世联邦理工学院集成系统实验室）；Institute of Medical Genetics and Pathology, University Hospital Basel, Switzerland（巴塞尔大学医学遗传学与病理学研究所，巴塞尔大学医院）
State University of New York at New Paltz（纽约州立大学新帕尔兹分校）；Northeastern University（东北大学）
Turing inspired a computer metaphor of the mind and brain that has been handy and has spawned decades of empirical investigation, but he did much more and offered behavioral and cognitive sciences another metaphor--that of the cascade. The time has come to confront Turing's cascading instability, which suggests a geometrical framework driven by power laws and can be studied using multifractal formalism and multiscale probability density function analysis. Here, we review a rapidly growing body of scientific investigations revealing signatures of cascade instability and their consequences for a…
Beijing University of Posts and Telecommunications（北京邮电大学）；Carnegie Mellon University（卡内基梅隆大学）；Tsinghua University（清华大学）
Understanding how brain functions has been an intriguing topic for years. With the recent progress on collecting massive data and developing advanced technology, people have become interested in addressing the challenge of decoding brain wave data into meaningful mind states, with many machine learning models and algorithms being revisited and developed, especially the ones that handle time series data because of the nature of brain waves. However, many of these time series models, like HMM with hidden state in discrete space or State Space Model with hidden state in continuous space, only wor…
Mind-Omni: A Unified Multi-Task Framework for Brain-Vision-Language Modeling via Discrete Diffusion
NeuBCI Lab, State Key Laboratory of Brain Cognition；Brain-inspired Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China；School of Future Technology, University of Chinese Academy of Sciences, Beijing, China；School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China；Zhongguancun Academy, Beijing, China；Peking University, Beijing, China
The brain is a powerful tool used to achieve amazing feats. There have been several significant advances in neuroscience and artificial brain research in the past two decades. This article is a review of such advances, ranging from the concepts of connectionism, to neural network architectures and high-dimensional representations. There have also been advances in biologically inspired cognitive architectures of which we will cite a few. We will be positioning relatively specific models in a much broader perspective, while comparing and contrasting their advantages and weaknesses. The projects …
Humboldt-University of Berlin（柏林洪堡大学）；University of Tartu（塔尔图大学）
Can deep learning (DL) guide our understanding of computations happening in biological brain? We will first briefly consider how DL has contributed to the research on visual object recognition. In the main part we will assess whether DL could also help us to clarify the computations underlying higher cognitive functions such as Theory of Mind. In addition, we will compare the objectives and learning signals of brains and machines, leading us to conclude that simply scaling up the current DL algorithms will most likely not lead to human level Theory of Mind.
IMT Lille Douai（IMT里尔杜埃）；CRIStAL UMR CNRS 9189（法国国家科学研究中心信息、自动化与信号处理实验室）；University of Mons（蒙斯大学）
Visual attention estimation is an active field of research at the crossroads of different disciplines: computer vision, artificial intelligence and medicine. One of the most common approaches to estimate a saliency map representing attention is based on the observed images. In this paper, we show that visual attention can be retrieved from EEG acquisition. The results are comparable to traditional predictions from observed images, which is of great interest. For this purpose, a set of signals has been recorded and different models have been developed to study the relationship between visual at…
Mind the Gap: Promoting Missing Modality Brain Tumor Segmentation with Alignment
School of Robotics Xi’an Jiaotong-Liverpool University Suzhou, China；School of Advanced Technology Xi'an Jiaotong-Liverpool University Suzhou, China；School of Advanced Technology Xi’an Jiaotong-Liverpool University Suzhou, China；Data Science Research Center Duke Kunshan University Suzhou, China
Reconstructing the Mind's Eye: fMRI-to-Image with Contrastive Learning and Diffusion Priors
Princeton Neuroscience Institute（普林斯顿神经科学研究所）；Medical AI Research Center (MedARC)（医学人工智能研究中心（MedARC））；Ecole Normale Supérieure, PSL University（巴黎高等师范学院，PSL大学）；University of Toronto（多伦多大学）；Hebrew University of Jerusalem（希伯来大学）；EleutherAI；Stability AI
There are many new forms of interfacing human users to machines. We persevere here electric mechanical form of interaction between human and machine. The emergence of brain-computer interface allows mind-to-movement systems. The story of the Pied Piper inspired us to devise some new heuristics for interfacing human motor system using brain waves by combining head helmet and LumbarMotionMonitor For the simulation we use java GridGain Brain responses of classified subjects during training indicates that Probe can be the best stimulus to rely on in distinguishing between knowledgeable and not kno…
Bringing analytic rigor to agentic AI for science: The Brain Researcher platform for neuroimaging data analysis
Stanford University（斯坦福大学）；Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)（三机构神经成像与数据科学转化研究中心（TReNDS））；Georgia State University（佐治亚州立大学）；Georgia Institute of Technology（佐治亚理工学院）；Emory University（埃默里大学）；Inria（法国国家信息与自动化研究所）；CEA（法国原子能和替代能源委员会）；Université（大学）
Universidad Nacional Autónoma de México (UNAM)（墨西哥国立自治大学）
The extended mind hypothesis has stimulated much interest in cognitive science. However, its core claim, i.e. that the process of cognition can extend beyond the brain via the body and into the environment, has been heavily criticized. A prominent critique of this claim holds that when some part of the world is coupled to a cognitive system this does not necessarily entail that the part is also constitutive of that cognitive system. This critique is known as the "coupling-constitution fallacy". In this paper we respond to this reductionist challenge by using an evolutionary robotics approach t…
BrainPilot: Automating Brain Discovery with Agentic Research
College of AI, Tsinghua University（清华大学人工智能学院）；Shanghai Qizhi Institute（上海期智研究院）；Business School, Renmin University of China（中国人民大学商学院）；School of Physics, Beihang University（北京航空航天大学物理学院）；School of Information and Software Engineering, University of Electronic Science and Technology of China（电子科技大学信息与软件工程学院）；Behavioral and Cognitive Neuroscience Center, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University（复旦大学脑科学与智能技术研究院行为与认知神经科学中心）；College of Engineering, Georgia Institute of Technology（佐治亚理工学院工程学院）；School of Life Sciences & IDG/McGovern Institute for Brain Research, Tsinghua University（清华大学生命科学学院&清华-IDG/麦戈文脑科学研究院）；School of Computing and Artificial Intelligence, Southwest Jiaotong University（西南交通大学计算机与人工智能学院）；Weixian College, Tsinghua University（清华大学未央学院）；Department of Psychological and Cognitive Sciences, Tsinghua University（清华大学心理学与认知科学系）
The brain is often called a computer and likened to a Turing machine, in part because the mind can manipulate discrete symbols such as numbers. But the brain is a dynamical system, more like a Watt governor than a Turing machine. Can a dynamical system be said to operate using "representations"? This paper argues that it can, although not in the way a digital computer does. Instead, it uses phenomena best described using mathematic concepts such as chaotic attractors to stand in for aspects of the world.
Department of Electrical Engineering and Computer Science（电气工程与计算机科学系）；York University（约克大学）
There is no denying the tremendous leap in the performance of machine learning methods in the past half-decade. Some might even say that specific sub-fields in pattern recognition, such as machine-vision, are as good as solved, reaching human and super-human levels. Arguably, lack of training data and computation power are all that stand between us and solving the remaining ones. In this position paper we underline cases in vision which are challenging to machines and even to human observers. This is to show limitations of contemporary models that are hard to ameliorate by following the curren…
Brain science and artificial intelligence have made great progress toward the understanding and engineering of the human mind. The progress has accelerated significantly since the turn of the century thanks to new methods for probing the brain (both structure and function), and rapid development in deep learning research. However, despite these new developments, there are still many open questions, such as how to understand the brain at the system level, and various robustness issues and limitations of deep learning. In this informal essay, I will talk about some of the concepts that are centr…
