Intelligence is a fundamental part of all living things, as well as the foundation for Artificial Intelligence. In this primer we explore the ideas associated with intelligence and, by doing so, understand the implications and constraints and potentially outline the capabilities of future systems. Artificial Intelligence, in the form of Machine Learning, has already had a significant impact on our lives. As an exploration, we journey into different parts of intelligence that appear essential. We hope that people find this helpful in determining the future. Also, during the exploration, we hope to create new thought-provoking questions. Intelligence is not a single weighable quantity but a subject that spans Biology, Physics, Philosophy, Cognitive Science, Neuroscience, Psychology, and Computer Science. The historian Yuval Noah Harari pointed out that engineers and scientists in the future will have to broaden their understandings to include disciplines such as Psychology, Philosophy, and Ethics. Fiction writers have long portrayed engineers and scientists as deficient in these areas. Today, in modern society, the emergence of Artificial Intelligence and legal requirements act as forcing functions to push these broader subjects into the foreground. We start with an introduction to intelligence and move quickly to more profound thoughts and ideas. We call this a Life, the Universe, and Everything primer, after the famous science fiction book by Douglas Adams. Forty-two may be the correct answer, but what are the questions?
In September 2019, the Council of Europe's Committee of Ministers adopted the terms of reference for the Ad Hoc Committee on Artificial Intelligence (CAHAI). The CAHAI is charged with examining the feasibility and potential elements of a legal framework for the design, development, and deployment of AI systems that accord with Council of Europe standards across the interrelated areas of human rights, democracy, and the rule of law. As a first and necessary step in carrying out this responsibility, the CAHAI's Feasibility Study, adopted by its plenary in December 2020, has explored options for …
We are at the cusp of a transition from "learning from data" to "learning what data to learn from" as a central focus of artificial intelligence (AI) research. While the first-order learning problem is not completely solved, large models under unified architectures, such as transformers, have shifted the learning bottleneck from how to effectively train our models to how to effectively acquire and use task-relevant data. This problem, which we frame as exploration, is a universal aspect of learning in open-ended domains, such as the real world. Although the study of exploration in AI is largel…
University of Cambridge（剑桥大学）；Intel Labs（英特尔实验室）；Julius-Maximilians-Universität Würzburg（维尔茨堡朱利叶斯-马克西米利安大学）
There is a concerted effort to build domain-general artificial intelligence in the form of universal neural network models with sufficient computational flexibility to solve a wide variety of cognitive tasks but without requiring fine-tuning on individual problem spaces and domains. To do this, models need appropriate priors and inductive biases, such that trained models can generalise to out-of-distribution examples and new problem sets. Here we provide an overview of the hallmarks endowing biological neural networks with the functionality needed for flexible cognition, in order to establish …
Self-Emotion-Mediated Exploration in Artificial Intelligence Mirrors: Findings from Cognitive Psychology
University of Coimbra（科英布拉大学）；Institute for Biomedical Imaging and Translational Research（生物医学影像与转化研究 institutes）；Institute of Systems and Robotics（系统与机器人研究所）
Breast Cancer Diagnosis: A Comprehensive Exploration of Explainable Artificial Intelligence (XAI) Techniques
Canadian Institute of Cybersecurity (CIC)（加拿大网络安全研究所）；University of New Brunswick（新不伦瑞克大学）；Dipartimento di Informatica, Università di Verona（威尼斯大学信息学院）；Università di Verona（威尼斯大学）；Department of Computer Science, School of Mathematics and Computer Science, Institute of Business Administration（计算机科学系，数学与计算机科学学院，商学院）；LIRIS, CNRS, Université Claude Bernard Lyon 1（LIRIS，国家科学研究中心，克莱尔伯恩大学 Lyon 1 分校）；LHC, UMR5516, Université de Saint-Etienne（LHC，UMR5516，圣艾蒂安大学）
Sparks of Artificial General Intelligence(AGI) in Semiconductor Material Science: Early Explorations into the Next Frontier of Generative AI-Assisted Electron Micrograph Analysis
Machine intelligence is very successful at standard recognition tasks when having high-quality training data. There is still a significant gap between machine-level pattern recognition and human-level concept learning. Humans can learn under uncertainty from only a few examples and generalize these concepts to solve new problems. The growing interest in explainable machine intelligence, requires experimental environments and diagnostic tests to analyze weaknesses in existing approaches to drive progress in the field. In this paper, we discuss existing diagnostic tests and test data sets such a…
City, University of London（伦敦城市大学）；Fujitsu Laboratories of Europe（富士通欧洲实验室）；Intesa Sanpaolo S.p.A.（意大利联合圣保罗银行）
With Artificial intelligence (AI) to aid or automate decision-making advancing rapidly, a particular concern is its fairness. In order to create reliable, safe and trustworthy systems through human-centred artificial intelligence (HCAI) design, recent efforts have produced user interfaces (UIs) for AI experts to investigate the fairness of AI models. In this work, we provide a design space exploration that supports not only data scientists but also domain experts to investigate AI fairness. Using loan applications as an example, we held a series of workshops with loan officers and data scienti…
National Institutes of Health Clinical Center（美国国立卫生研究院临床中心）
Artificial intelligence has made great strides since the deep learning revolution, but AI systems still struggle to extrapolate outside of their training data and adapt to new situations. For inspiration we look to the domain of science, where scientists have been able to develop theories which show remarkable ability to extrapolate and sometimes predict the existence of phenomena which have never been observed before. According to David Deutsch, this type of extrapolation, which he calls "reach", is due to scientific theories being hard to vary. In this work we investigate Deutsch's hard-to-v…
Applications of Explainable artificial intelligence in Earth system science
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)（南方海洋科学与工程广东省实验室（珠海））；Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies（广东省气候变化与自然灾害研究重点实验室）；Max Planck Institute for Biogeochemistry（马克斯·普朗克生物地球化学研究所）；Shanwei Meteorological Service（汕尾市气象局）；College of Computer Science and Technology, Changchun Normal University（长春师范大学计算机科学与技术学院）；ELLIS Unit Jena（ELLIS耶拿单元）
Over the past decade, the NASA Autonomous Systems and Operations (ASO) project has developed and demonstrated numerous autonomy enabling technologies employing AI techniques. Our work has employed AI in three distinct ways to enable autonomous mission operations capabilities. Crew Autonomy gives astronauts tools to assist in the performance of each of these mission operations functions. Vehicle System Management uses AI techniques to turn the astronaut's spacecraft into a robot, allowing it to operate when astronauts are not present, or to reduce astronaut workload. AI technology also enables …
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 …
Imperial College London（帝国理工学院）；University of Bayreuth（拜罗伊特大学）
An appropriate ethical framework around the use of Artificial Intelligence (AI) in healthcare has become a key desirable with the increasingly widespread deployment of this technology. Advances in AI hold the promise of improving the precision of outcome prediction at the level of the individual. However, the addition of these technologies to patient-clinician interactions, as with any complex human interaction, has potential pitfalls. While physicians have always had to carefully consider the ethical background and implications of their actions, detailed deliberations around fast-moving techn…
Tsinghua University（清华大学）；Wuxi Power Supply Company of State Grid（国家电网无锡供电公司）
With the rapid development of artificial intelligence (AI), it is foreseeable that the accuracy and efficiency of dynamic analysis for future power system will be greatly improved by the integration of dynamic simulators and AI. To explore the interaction mechanism of power system dynamic simulations and AI, a general design of an AI-oriented power system dynamic simulator is proposed, which consists of a high-performance simulator with neural network supportability and flexible external and internal application programming interfaces (APIs). With the support of APIs, simulation-assisted AI an…
Psychological curiosity plays a significant role in human intelligence to enhance learning through exploration and information acquisition. In the Artificial Intelligence (AI) community, artificial curiosity provides a natural intrinsic motivation for efficient learning as inspired by human cognitive development; meanwhile, it can bridge the existing gap between AI research and practical application scenarios, such as overfitting, poor generalization, limited training samples, high computational cost, etc. As a result, curiosity-driven learning (CDL) has become increasingly popular, where agen…
NIMS-OS: An automation software to implement a closed loop between artificial intelligence and robotic experiments in materials science
Center for Basic Research on Materials（材料基础研究中心）；National Institute for Materials Science（国家材料科学研究所）；Graduate School of Frontier Sciences, The University of Tokyo（东京大学前沿科学研究生院）；Research Center for Energy and Environmental Materials(GREEN)（能源与环境材料研究中心（GREEN））；Center for Advanced Battery Collaboration, Research Center for Energy and Environmental Materials (GREEN)（先进电池协作中心，能源与环境材料研究中心（GREEN））
The exploration of geometrical patterns stimulates imagination and encourages abstract reasoning which is a distinctive feature of human intelligence. In cognitive science, Gestalt principles such as symmetry have often explained significant aspects of human perception. We present a computational technique for building artificial intelligence (AI) agents that use symmetry as the organizing principle for addressing Dehaene's test of geometric intelligence \cite{dehaene2006core}. The performance of our model is on par with extant AI models of problem solving on the Dehaene's test and seems corre…
In this work, we analyze the happiness levels of countries using an unbiased emotion detector, artificial intelligence (AI). To date, researchers proposed many factors that may affect happiness such as wealth, health and safety. Even though these factors all seem relevant, there is no clear consensus between sociologists on how to interpret these, and the models to estimate the cost of these utilities include some assumptions. Researchers in social sciences have been working on determination of the happiness levels in society and exploration of the factors correlated with it through polls and …
Simon Fraser University（西蒙弗雷泽大学）；University of British Columbia（不列颠哥伦比亚大学）
The common view that our creativity is what makes us uniquely human suggests that incorporating research on human creativity into generative deep learning techniques might be a fruitful avenue for making their outputs more compelling and human-like. Using an original synthesis of Deep Dream-based convolutional neural networks and cognitive based computational art rendering systems, we show how honing theory, intrinsic motivation, and the notion of a 'seed incident' can be implemented computationally, and demonstrate their impact on the resulting generative art. Conversely, we discuss how explo…
Donders Institute for Brain, Cognition, and Behaviour（唐德斯大脑、认知与行为研究所）；Radboud University（拉德堡德大学）；Joint Research Centre, European Commission（欧盟委员会联合研究中心）；Ghent University（根特大学）；imec（微电子研究中心）；Instituto Superio Técnico（高等技术学院）；University of Lisbon（里斯本大学）
Understanding and modelling children's cognitive processes and their behaviour in the context of their interaction with robots and social artificial intelligence systems is a fundamental prerequisite for meaningful and effective robot interventions. However, children's development involve complex faculties such as exploration, creativity and curiosity which are challenging to model. Also, often children express themselves in a playful way which is different from a typical adult behaviour. Different children also have different needs, and it remains a challenge in the current state of the art t…
Prospects for Using Artificial Intelligence to Understand Intrinsic Kinetics of Heterogeneous Catalytic Reactions
organization= School of Chemical \& Biomolecular Engineering, Georgia Institute of Technology , addressline= 311 Ferst Drive NW , city= Atlanta , postcode= 30332 , state= GA , country= USA；organization= Department of Chemical Engineering, Carnegie Mellon University , addressline= 5000 Forbes Street , city= Pittsburgh , postcode= 15213 , state= PA , country= USA
Sustainable Materials Discovery in the Era of Artificial Intelligence
Department of Civil and Environmental Engineering, Indian Institute of Technology Delhi（印度理工学院德里分校土木与环境工程系）；Department of Civil and Environmental Engineering, Imperial College London（帝国理工学院伦敦分校土木与环境工程系）；Department of Metallurgical and Materials Engineering, Indian Institute of Technology Madras（印度理工学院马德拉斯分校冶金与材料工程系）；Department of Computer Science and Engineering, Chalmers University of Technology & University of Gothenburg（查尔姆斯理工大学与哥德堡大学计算机科学与工程系）；Yardi School of Artificial Intelligence, Indian Institute of Technology Delhi（印度理工学院德里分校亚里学校人工智能系）
