Artificial intelligence (AI) has become a part of everyday conversation and our lives. It is considered as the new electricity that is revolutionizing the world. AI is heavily invested in both industry and academy. However, there is also a lot of hype in the current AI debate. AI based on so-called deep learning has achieved impressive results in many problems, but its limits are already visible. AI has been under research since the 1940s, and the industry has seen many ups and downs due to over-expectations and related disappointments that have followed.
The purpose of this book is to give a realistic picture of AI, its history, its potential and limitations. We believe that AI is a helper, not a ruler of humans. We begin by describing what AI is and how it has evolved over the decades. After fundamentals, we explain the importance of massive data for the current mainstream of artificial intelligence. The most common representations for AI, methods, and machine learning are covered. In addition, the main application areas are introduced. Computer vision has been central to the development of AI. The book provides a general introduction to computer vision, and includes an exposure to the results and applications of our own research. Emotions are central to human intelligence, but little use has been made in AI. We present the basics of emotional intelligence and our own research on the topic. We discuss super-intelligence that transcends human understanding, explaining why such achievement seems impossible on the basis of present knowledge,and how AI could be improved. Finally, a summary is made of the current state of AI and what to do in the future. In the appendix, we look at the development of AI education, especially from the perspective of contents at our own university.
MME-Emotion: A Holistic Evaluation Benchmark for Emotional Intelligence in Multimodal Large Language Models
The Chinese University of Hong Kong（香港中文大学）；Tongyi Lab（通义实验室）；SZTU（深圳技术大学）；Peking University（北京大学）；Tongji University（同济大学）；CASIA（中国科学院自动化所）；Tencent（腾讯）；UCLA（加州大学洛杉矶分校）；Westlake University（西湖大学）；NTU（国立大学）
Research on emotionally intelligent dialogue generation based on automatic dialogue system
University of the People（人民大学）；Sehan University（世翰大学）；Indian Institute of Technology Guwahati（印度理工学院古瓦哈提分校）；Universidad Internacional Isabel I de Castilla（卡斯蒂利亚伊莎贝尔一世国际大学）；AMA University（AMA大学）
Veracity is an essential key in research and development of innovative products. Live Emotion analysis and verification nullify deceit made to complainers on live chat, corroborate messages of both ends in messaging apps and promote an honest conversation between users. The main concept behind this emotion artificial intelligent verifier is to license or decline message accountability by comparing variegated emotions of chat app users recognized through facial expressions and text prediction. In this paper, a proposed emotion intelligent live detector acts as an honest arbiter who distributes …
Emotional intelligence in large language models is fragmented across perception, cognition, and interaction
X-LANCE Lab（X-LANCE实验室）；School of Computer Science, Shanghai Jiao Tong University（上海交通大学计算机科学学院）；MoE Key Lab of Artificial Intelligence（人工智能MOE重点实验室）；Jiangsu Key Lab of Language Computing（江苏省语言计算重点实验室）；Beijing Key Laboratory of Applied Experimental Psychology（北京应用实验心理学重点实验室）；National Demonstration Center for Experimental Psychology Education, Faculty of Psychology, Beijing Normal University（北京师范大学实验心理学教育国家级示范中心，心理学学院）
Level of agreement between emotions generated by Artificial Intelligence and human evaluation: a methodological proposal
Facultad de Ingeniería y Ciencias（工程与科学学院）；Universidad Adolfo Ibañez（阿道夫·伊巴涅斯大学）；Faculty of Education Sciences and Psychology（教育科学与心理学院）；University of Cordoba（科尔多瓦大学）；Faculty of Law, Economics and Business（法律、经济与商学院）
Intelligent Agents with Emotional Intelligence: Current Trends, Challenges, and Future Prospects
School of Computer Engineering, Iran University of Science and Technology（伊朗科学技术大学计算机工程学院）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算机与数据科学学院）
EmoBench-M: Benchmarking Emotional Intelligence for Multimodal Large Language Models
Shenzhen University（深圳大学）；Guangdong Laboratory of Artificial Intelligence（广东人工智能与数字经济实验室）；Auckland University of Technology（奥克兰理工大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；SKL-IOTSC, CIS, University of Macau（澳门科学技术大学SKL-IOTSC、CIS）
Exploring Teachers' Perception of Artificial Intelligence: The Socio-emotional Deficiency as Opportunities and Challenges in Human-AI Complementarity in K-12 Education
Michigan State University（密歇根州立大学）；University of Pittsburgh（匹兹堡大学）
EmotionQueen: A Benchmark for Evaluating Empathy of Large Language Models
Fudan University（复旦大学）；School of Computer Science, Fudan University（复旦大学计算机科学学院）；Beijing Yuandian Technology（北京原点科技）；School of Engineering and Applied Science, University of Pennsylvania（宾夕法尼亚大学工程与应用科学学院）
The TriRhenaTech alliance presents a collection of accepted papers of the cancelled tri-national 'Upper-Rhine Artificial Inteeligence Symposium' planned for 13th May 2020 in Karlsruhe. The TriRhenaTech alliance is a network of universities in the Upper-Rhine Trinational Metropolitan Region comprising of the German universities of applied sciences in Furtwangen, Kaiserslautern, Karlsruhe, and Offenburg, the Baden-Wuerttemberg Cooperative State University Loerrach, the French university network Alsace Tech (comprised of 14 'grandes écoles' in the fields of engineering, architecture and managemen…
Thanks to rapid development of artificial intelligence technology in recent years, the current artificial intelligence technology is contributing to many part of society. Education, environment, medical care, military, tourism, economy, politics, etc. are having a very large impact on society as a whole. For example, in the field of education, there is an artificial intelligence tutoring system that automatically assigns tutors based on student's level. In the field of economics, there are quantitative investment methods that automatically analyze large amounts of data to find investment laws …
The 2021 Drexel Society of Artificial Intelligence Research Conference highlights papers focused on a broad set of papers in machine learning. This was our organizations' first annual conference. It was conducted virtually via Zoom. The highlights are currently posted on YouTube.
Neuro-Symbolic Artificial Intelligence -- the combination of symbolic methods with methods that are based on artificial neural networks -- has a long-standing history. In this article, we provide a structured overview of current trends, by means of categorizing recent publications from key conferences. The article is meant to serve as a convenient starting point for research on the general topic.
Recent research in artificial intelligence and machine learning has largely emphasized general-purpose learning and ever-larger training sets and more and more compute. In contrast, I propose a hybrid, knowledge-driven, reasoning-based approach, centered around cognitive models, that could provide the substrate for a richer, more robust AI than is currently possible.
We propose that intelligently combining models from the domains of Artificial Intelligence or Machine Learning with Physical and Expert models will yield a more "trustworthy" model than any one model from a single domain, given a complex and narrow enough problem. Based on mean-variance portfolio theory and bias-variance trade-off analysis, we prove combining models from various domains produces a model that has lower risk, increasing user trust. We call such combined models - physics enhanced artificial intelligence (PEAI), and suggest use cases for PEAI.
This volume represents the accepted submissions from the AAAI-2019 Workshop on Games and Simulations for Artificial Intelligence held on January 29, 2019 in Honolulu, Hawaii, USA. https://www.gamesim.ai
The application of "machine learning" and "artificial intelligence" has become popular within the last decade. Both terms are frequently used in science and media, sometimes interchangeably, sometimes with different meanings. In this work, we aim to clarify the relationship between these terms and, in particular, to specify the contribution of machine learning to artificial intelligence. We review relevant literature and present a conceptual framework which clarifies the role of machine learning to build (artificial) intelligent agents. Hence, we seek to provide more terminological clarity and…
Complex systems fail. I argue that failures can be a blueprint characterizing living organisms and biological intelligence, a control mechanism to increase complexity in evolutionary simulations, and an alternative to classical fitness optimization. Imitating biological successes in Artificial Life and Artificial Intelligence can be misleading; imitating failures offers a path towards understanding and emulating life it in artificial systems.
In this article, we first give an introduction to artificial intelligence and its applications in biology and medicine in Section 1. Deep learning methods are then described in Section 2. We narrow down the focus of the study on textual data in Section 3, where natural language processing and its applications in the biomedical domain are described. In Section 4, we give an introduction to explainable artificial intelligence and discuss the importance of explainability of artificial intelligence systems, especially in the biomedical domain.
Artificial Intelligence has been a growth catalyst to our society and is cosidered across all idustries as a fundamental technology. However, its development has been limited to the signal processing domain that relies on the generated and collected data from other sensors. In recent research, concepts of Digital Artificial Intelligence and Physicial Artifical Intelligence have emerged and this can be considered a big step in the theoretical development of Artifical Intelligence. In this paper we explore the concept of Physicial Artifical Intelligence and propose two subdomains: Integrated Phy…
Skolkovo Institute of Science and Technology（斯科尔科沃科学技术研究院）
This article discusses some trends and concepts in developing new generation of future Artificial General Intelligence (AGI) systems which relate to complex facets and different types of human intelligence, especially social, emotional, attentional and ethical intelligence. We describe various aspects of multiple human intelligences and learning styles, which may impact on a variety of AI problem domains. Using the concept of 'multiple intelligences' rather than a single type of intelligence, we categorize and provide working definitions of various AGI depending on their cognitive skills or ca…
In September 2016, Stanford's "One Hundred Year Study on Artificial Intelligence" project (AI100) issued the first report of its planned long-term periodic assessment of artificial intelligence (AI) and its impact on society. It was written by a panel of 17 study authors, each of whom is deeply rooted in AI research, chaired by Peter Stone of the University of Texas at Austin. The report, entitled "Artificial Intelligence and Life in 2030," examines eight domains of typical urban settings on which AI is likely to have impact over the coming years: transportation, home and service robots, healt…
Nanjing University of Science and Technology（南京理工大学）；Dalhousie University（达尔豪斯大学）
Taking advantage of social media platforms, such as Twitter, this paper provides an effective framework for emotion detection among those who are quarantined. Early detection of emotional feelings and their trends help implement timely intervention strategies. Given the limitations of medical diagnosis of early emotional change signs during the quarantine period, artificial intelligence models provide effective mechanisms in uncovering early signs, symptoms and escalating trends. Novelty of the approach presented herein is a multitask methodological framework of text data processing, implement…
Large Language Models Understand and Can be Enhanced by Emotional Stimuli
Institute of Software, CAS（中国科学院软件研究所）；Microsoft（微软）；William&Mary（威廉与玛丽学院）；Department of Psychology, Beijing Normal University（北京师范大学心理学系）；HKUST（香港科技大学）
A short review of the literature on measurement and detection of artificial general intelligence is made. Proposed benchmarks and tests for artificial general intelligence are critically evaluated against multiple criteria. Based on the findings, the most promising approaches are identified and some useful directions for future work are proposed.
