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A clarification of misconceptions, myths and desired status of artificial intelligence

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arXiv 2020年

作者： Emmert-Streib, Frank Yli-Harja, Olli Dehmer, Matthias Predictive Society and Data Analytics Lab Faculty of Information Technology and Communication Sciences Tampere University Tampere Finland Institute of Biosciences and Medical Technology Tampere University of Technology Tampere Finland Department of Mechatronics and Biomedical Computer Science UMIT Hall in Tyrol Austria

The field artificial intelligence (AI) has been founded over 65 years ago. Starting with great hopes and ambitious goals the field progressed though various stages of popularity and received recently a revival in the form of deep neural networks. Some problems of AI are that so far neither’intelligence’ nor the goals of AI are formally defined causing confusion when comparing AI to other fields. In this paper, we present a perspective on the desired and current status of AI in relation to machine learning and statistics and clarify common misconceptions and myths. Our discussion is intended to uncurtain the veil of vagueness surrounding AI to see its true countenance. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

Neural Brain: A Neuroscience-inspired Framework for Embodied Agents

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arXiv 2025年

作者： Liu, Jian Shi, Xiongtao Nguyen, Thai Duy Zhang, Haitian Zhang, Tianxiang Sun, Wei Li, Yanjie Vasilakos, Athanasios V. Iacca, Giovanni Khan, Arshad Ali Kumar, Arvind Cho, Jae Won Mian, Ajmal Xie, Lihua Cambria, Erik Wang, Lin School of Electrical and Electronic Engineering Nanyang Technological University Singapore School of Robotics Hunan University China School of Intelligence Science and Engineering Harbin Institute of Technology Shenzhen China Department of Information and Communication Technology University of Agder Norway Department of Information Engineering and Computer Science University of Trento Italy Elm Company London United Kingdom Division of Computational Science and Technology KTH Royal Institute of Technology Sweden School of Artificial Intelligence and Data Science Sejong University Korea Republic of Department of Computer Science the University of Western Australia Australia College of Computing and Data Science Nanyang Technological University Singapore

The rapid evolution of artificial intelligence (AI) has shifted from static, data-driven models to dynamic systems capable of perceiving and interacting with real-world environments. Despite advancements in pattern recognition and symbolic reasoning, current AI systems, such as large language models, remain disembodied, unable to physically engage with the world. This limitation has driven the rise of embodied AI, where autonomous agents, such as humanoid robots, must navigate and manipulate unstructured environments with human-like adaptability. At the core of this challenge lies the concept of Neural Brain, a central intelligence system designed to drive embodied agents with human-like adaptability. A Neural Brain must seamlessly integrate multimodal sensing and perception with cognitive capabilities. Achieving this also requires an adaptive memory system and energy-efficient hardware-software co-design, enabling real-time action in dynamic environments. This paper introduces a unified framework for the Neural Brain of embodied agents, addressing two fundamental challenges: (1) defining the core components of Neural Brain and (2) bridging the gap between static AI models and the dynamic adaptability required for real-world deployment. To this end, we propose a biologically inspired architecture that integrates multimodal active sensing, perception-cognition-action function, neuroplasticity-based memory storage and updating, and neuromorphic hardware/software optimization. Furthermore, we also review the latest research on embodied agents across these four aspects and analyze the gap between current AI systems and human intelligence. By synthesizing insights from neuroscience, we outline a roadmap towards the development of generalizable, autonomous agents capable of human-level intelligence in real-world scenarios. Our project page is at Neural-Brain-for-Embodied-Agents. Copyright © 2025, The Authors. All rights reserved.

关键词： Anthropomorphic robots

Correction: AI content detection in the emerging information ecosystem: new obligations for media and tech companies

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Ethics and information technology 2024年第4期26卷 1-2页

作者： Knott, Alistair Pedreschi, Dino Jitsuzumi, Toshiya Leavy, Susan Eyers, David Chakraborti, Tapabrata Trotman, Andrew Sundareswaran, Sundar Baeza-Yates, Ricardo Biecek, Przemyslaw Weller, Adrian Teal, Paul D. Basu, Subhadip Haklidir, Mehmet Morini, Virginia Russell, Stuart Bengio, Yoshua Social Media Governance Project Global Partnership on AI Montreal Canada School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand University of Pisa Pisa Italy Chuo University Tokyo Japan Insight SFI Research Centre for Data Analytics School of Information and Communication University College Dublin Dublin Ireland School of Computing University of Otago Dunedin New Zealand Alan Turing Institute London United Kingdom University College London London United Kingdom Institute for Experiential AI Northeastern University Silicon Valley USA Warsaw University of Technology Warsaw Poland University of Cambridge Cambridge United Kingdom Computer Science and Engineering Department Jadavpur University Kolkata India Artificial Intelligence Institute Tubitak Bilgem Gebze Türkiye Center for Human-Compatible AI UC Berkeley Berkeley USA Mila - Quebec AI Institute Montreal Canada University of Montreal Montreal Canada

Comparison and Evaluation of Methods for a Predict+Optimize Problem in Renewable Energy

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arXiv 2022年

作者： Bergmeir, Christoph de Nijs, Frits Sriramulu, Abishek Abolghasemi, Mahdi Bean, Richard Betts, John Bui, Quang Dinh, Nam Trong Einecke, Nils Esmaeilbeigi, Rasul Ferraro, Scott Galketiya, Priya Genov, Evgenii Glasgow, Robert Godahewa, Rakshitha Kang, Yanfei Limmer, Steffen Magdalena, Luis Montero-Manso, Pablo Peralta, Daniel Kumar, Yogesh Pipada Sunil Rosales-Pérez, Alejandro Ruddick, Julian Stratigakos, Akylas Stuckey, Peter Tack, Guido Triguero, Isaac Yuan, Rui Department of Data Science and Artificial Intelligence Monash University Melbourne Australia School of Mathematics and Physics University of Queensland Brisbane Australia Centre for Energy Data Innovation School of Information Technology and Electrical Engineering University of Queensland Brisbane Australia School of Electrical and Electronics Engineering University of Adelaide Adelaide Australia Honda Research Institute Europe GmbH Offenbach am Main63073 Germany School of Information Technology Deakin University Melbourne Australia Building and Property Division Monash University Melbourne Australia EVERGi MOBI Vrije Universiteit Brussel Brussels Belgium School of Economics and Management Beihang University Beijing China E.T.S. Ingenieros Informáticos Universidad Politécnica de Madrid Madrid28660 Spain Disciple of Business Analytics University of Sydney Australia IDLab Department of Information Technology Ghent University - imec Belgium Department of Computer Science Centro de Investigación en Matem áticas Monterrey66629 Mexico Mines Paris PSL University Sophia Antipolis06904 France DaSCI Andalusian Institute in Data Science and Computational Intelligence Granada Spain Department of Computer Science and Artificial Intelligence University of Granada Granada Spain

Algorithms that involve both forecasting and optimization are at the core of solutions to many difficult real-world problems, such as in supply chains (inventory optimization), traffic, and in the transition towards carbon-free energy generation in battery/load/production scheduling in sustainable energy systems. Typically, in these scenarios we want to solve an optimization problem that depends on unknown future values, which therefore need to be forecast. As both forecasting and optimization are difficult problems in their own right, relatively few research has been done in this area. This paper presents the findings of the "IEEE-CIS Technical Challenge on Predict+Optimize for Renewable Energy Scheduling," held in 2021. We present a comparison and evaluation of the seven highest-ranked solutions in the competition, to provide researchers with a benchmark problem and to establish the state of the art for this benchmark, with the aim to foster and facilitate research in this area. The competition used data from the Monash Microgrid, as well as weather data and energy market data. It then focused on two main challenges: forecasting renewable energy production and demand, and obtaining an optimal schedule for the activities (lectures) and on-site batteries that lead to the lowest cost of energy. The most accurate forecasts were obtained by gradient-boosted tree and random forest models, and optimization was mostly performed using mixed integer linear and quadratic programming. The winning method predicted different scenarios and optimized over all scenarios jointly using a sample average approximation method. Copyright © 2022, The Authors. All rights reserved.

关键词： Forecasting

Explainable Artificial Intelligence and Machine Learning: A reality rooted perspective

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arXiv 2020年

作者： Emmert-Streib, Frank Yli-Harja, Olli Dehmer, Matthias Predictive Society and Data Analytics Lab Faculty of Information Technology and Communication Sciences Tampere University Tampere Finland Institute of Biosciences and Medical Technology Tampere University of Technology Tampere Finland Institute for Intelligent Production Faculty for Management University of Applied Sciences Upper Austria Steyr Campus Steyr4040 Austria

We are used to the availability of big data generated in nearly all fields of science as a consequence of technological progress. However, the analysis of such data possess vast challenges. One of these relates to the explainability of artificial intelligence (AI) or machine learning methods. Currently, many of such methods are non-transparent with respect to their working mechanism and for this reason are called black box models, most notably deep learning methods. However, it has been realized that this constitutes severe problems for a number of fields including the health sciences and criminal justice and arguments have been brought forward in favor of an explainable AI. In this paper, we do not assume the usual perspective presenting explainable AI as it should be, but rather we provide a discussion what explainable AI can be. The difference is that we do not present wishful thinking but reality grounded properties in relation to a scientific theory beyond physics. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

Generating Approximate Ground States of Molecules Using Quantum Machine Learning

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arXiv 2022年

作者： Ceroni, Jack Stetina, Torin F. Kieferová, Mária Marrero, Carlos Ortiz Arrazola, Juan Miguel Wiebe, Nathan Xanadu TorontoONM5G 2C8 Canada Department of Mathematics University of Toronto TorontoONM5S 3E1 Canada Simons Institute for the Theory of Computing BerkeleyCA94704 United States Berkeley Quantum Information and Computation Center University of California BerkeleyCA94720 United States Centre for Quantum Computation and Communication Technology Centre for Quantum Software and Information University of Technology SydneyNSW2007 Australia AI & Data Analytics Division Pacific Northwest National Laboratory RichlandWA99354 United States Department of Electrical & Computer Engineering North Carolina State University RaleighNC27607 United States Department of Computer Science University of Toronto ONM5S 1A1 Canada High Performance Computing Group Pacific Northwest National Laboratory RichlandWA99354 United States

The potential energy surface (PES) of molecules with respect to their nuclear positions is a primary tool in understanding chemical reactions from first principles. However, obtaining this information is complicated by the fact that sampling a large number of ground states over a high-dimensional PES can require a vast number of state preparations. In this work, we propose using a generative quantum machine learning model to prepare quantum states at arbitrary points on the PES. The model is trained using quantum data consisting of ground-state wavefunctions associated with different classical nuclear coordinates. Our approach uses a classical neural network to convert the nuclear coordinates of a molecule into quantum parameters of a variational quantum circuit. The model is trained using a fidelity loss function to optimize the neural network parameters. We show that gradient evaluation is efficient and numerically demonstrate our method’s ability to prepare wavefunctions on the PES of hydrogen chains, water, and beryllium hydride. In all cases, we find that a small number of training points are needed to achieve very high overlap with the groundstates in practice. From a theoretical perspective, we further prove limitations on these protocols by showing that if we were able to learn across an avoided crossing using a small number of samples, then we would be able to violate Grover’s lower bound. Additionally, we prove lower bounds on the amount of quantum data needed to learn a locally optimal neural network function using arguments from quantum Fisher information. This work further identifies that quantum chemistry can be an important use case for quantum machine learning. © 2022, CC BY.

关键词： Molecules

Video Summarization via Cluster-Based Object Tracking and Type-Based Synopsis

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Video Summarization via Cluster-Based Object Tracking and Ty...

IEEE Conference on Multimedia information Processing and Retrieval (MIPR)

作者： Yuxi Li Weiyao Lin Tao Wang Qi Guo Ruijia Yang Shugong Xu Shanghai Institute for Advanced Communication and Data Science Shanghai University China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University China Research & Advanced Technology Division SAIC Motor Corporation Limited China

ISBN: (数字)9781728142722

ISBN: (纸本)9781728142739

In this paper, we construct a trajectory-based system for the synopsis of surveillance video stream. The proposed approach first applies a cluster-based tracking method to extract foreground object from input videos, then extracts the abnormal object and classifies them into different motion patterns. Finally a type-based synopsis scheme is proposed to properly gather the moving object of different pattern types into limited time endurance. As a consequence, this system would be helpful for accurate and fast surveillance videos analysis.

关键词： Trajectory Surveillance Streaming media Object tracking Conferences Feature extraction

Entanglement induced barren plateaus

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arXiv 2020年

作者： Marrero, Carlos Ortiz Kieferová, Mária Wiebe, Nathan Data Sciences and Analytics Group Pacific Northwest National Laboratory RichlandWA99354 United States Centre for Quantum Computation and Communication Technology Centre for Quantum Software and Information University of Technology Sydney NSW2007 Australia Department of Computer Science University of Toronto ONM5S 1A1 Canada

We argue that an excess in entanglement between the visible and hidden units in a Quantum Neural Network can hinder learning. In particular, we show that quantum neural networks that satisfy a volume-law in the entanglement entropy will give rise to models not suitable for learning with high probability. Using arguments from quantum thermodynamics, we then show that this volume law is typical and that there exists a barren plateau in the optimization landscape due to entanglement. More precisely, we show that for any bounded objective function on the visible layers, the Lipshitz constants of the expectation value of that objective function will scale inversely with the dimension of the hidden-subsystem with high probability. We show how this can cause both gradient descent and gradient-free methods to fail. We note that similar problems can happen with quantum Boltzmann machines, although stronger assumptions on the coupling between the hidden/visible subspaces are necessary. We highlight how pretraining such generative models may provide a way to navigate these barren plateaus. Copyright © 2020, The Authors. All rights reserved.

关键词： Gradient methods

Harnessing the Diversity of Ideas for Competitive Advantage

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Insight 2019年第3期22卷 21-25页

作者： Morgan, Johnny D. Senior director and the Data and Analytics Practice lead within the Intelligence and Homeland Security Division inside General Dynamic's Information Technology sector.

The engineering community has made significant advances in traditional diversity measures in the last 50 years, but because the world has become more complex, the engineering community needs to embrace new ideas that are outside the realm of traditional engineering disciplines. This paper identifies that individuals are the sources of new ideas, but it is organizations that transform these ideas into value-delivered innovations. It examines the relationship of maximizing information diversity to promote idea discovery while minimizing work force diversity to promote the sharing and acceptance of these ideas. It then discusses the concept of inclusion as a necessary team skill for organizations to deliver these ideas as innovations. This paper concludes by identifying systems engineering behaviors that create a diverse environment for idea discovery and an inclusive team environment that overcomes challenges and transitions ideas into delivered innovations, thus providing a competitive advantage. © 2019 International Council on Systems Engineering.

关键词： behaviors culture diversity idea inclusion innovation organization perspectives systems engineering