University of Tübingen, Carl Friedrich von Weizsäcker Center（图宾根大学，卡尔·弗里德里希·冯·魏茨泽克中心）；University of Geneva, Department of Philosophy（日内瓦大学哲学系）；University of Zurich, Department of Philosophy（苏黎世大学哲学系）
The concepts of blameworthiness and wrongness are of fundamental importance in human moral life. But to what extent are humans disposed to blame artificially intelligent agents, and to what extent will they judge their actions to be morally wrong? To make progress on these questions, we adopted two novel strategies. First, we break down attributions of blame and wrongness into more basic judgments about the epistemic and conative state of the agent, and the consequences of the agent's actions. In this way, we are able to examine any differences between the way participants treat artificial agents in terms of differences in these more basic judgments. our second strategy is to compare attributions of blame and wrongness across human, artificial, and group agents (corporations). Others have compared attributions of blame and wrongness between human and artificial agents, but the addition of group agents is significant because these agents seem to provide a clear middle-ground between human agents (for whom the notions of blame and wrongness were created) and artificial agents (for whom the question remains open).
Feeling Guilty Being a c(ai)borg: Navigating the Tensions Between Guilt and Empowerment in AI Use
KGSt - Kommunale Gemeinschaftsstelle für Verwaltungsmanagement, Germany（德国公共行政管理联合机构）；University of Siegen, Germany（德国施派堡大学）；Clausthal University of Technology, Germany（德国克莱斯特大学）
Universidad Autonoma de Madrid（马德里自治大学）；Universidad Politecnica de Madrid（马德里理工大学）
This work seeks to study the beneficial properties that an autonomous agent can obtain by implementing a cognitive architecture similar to the one of conscious beings. Along this document, a conscious model of autonomous agent based in a global workspace architecture is presented. We describe how this agent is viewed from different perspectives of philosophy of mind, being inspired by their ideas. The goal of this model is to create autonomous agents able to navigate within an environment composed of multiple independent magnitudes, adapting to its surroundings in order to find the best possib…
Seoul National University（首尔大学）；Surromind Robotics（Surromind机器人公司）
Goal-oriented dialog has been given attention due to its numerous applications in artificial intelligence. Goal-oriented dialogue tasks occur when a questioner asks an action-oriented question and an answerer responds with the intent of letting the questioner know a correct action to take. To ask the adequate question, deep learning and reinforcement learning have been recently applied. However, these approaches struggle to find a competent recurrent neural questioner, owing to the complexity of learning a series of sentences. Motivated by theory of mind, we propose "Answerer in Questioner's M…
We present DeepMind Lab2D, a scalable environment simulator for artificial intelligence research that facilitates researcher-led experimentation with environment design. DeepMind Lab2D was built with the specific needs of multi-agent deep reinforcement learning researchers in mind, but it may also be useful beyond that particular subfield.
The Hive Mind is a Single Reinforcement Learning Agent
MIST Lab, Department of Computer and Software Engineering（MIST实验室，计算机与软件工程系）；Polytechnique Montréal（蒙特利尔大学）；Mila - Quebec AI Institute（魁北克人工智能研究所）；Department of Computer and Information Science（计算机与信息科学系）；University of Konstanz（康斯坦茨大学）
The rise of deep learning has brought artificial intelligence (AI) to the forefront. The ultimate goal of AI is to realize machines with human mind and consciousness, but existing achievements mainly simulate intelligent behavior on computer platforms. These achievements all belong to weak AI rather than strong AI. How to achieve strong AI is not known yet in the field of intelligence science. Currently, this field is calling for a new paradigm, especially Theory of Cognitive Relativity (TCR). The TCR aims to summarize a simple and elegant set of first principles about the nature of intelligen…
University of Minnesota（明尼苏达大学）；University of Texas Southwestern Medical Center（德克萨斯大学西南医学中心）；Nerves Incorporated（Nerves公司）；Fasikl Incorporated（Fasikl公司）
Objective: The next generation prosthetic hand that moves and feels like a real hand requires a robust neural interconnection between the human minds and machines. Methods: Here we present a neuroprosthetic system to demonstrate that principle by employing an artificial intelligence (AI) agent to translate the amputee's movement intent through a peripheral nerve interface. The AI agent is designed based on the recurrent neural network (RNN) and could simultaneously decode six degree-of-freedom (DOF) from multichannel nerve data in real-time. The decoder's performance is characterized in motor …
Artificial Authority: From Machine Minds to Political Alignments. An Experimental Analysis of Democratic and Autocratic Biases in Large-Language Models
Jagiellonian University（雅盖隆大学）；AGH University of Kraków（克拉科夫AGH大学）；NASK National Research Institute（NASK国家研究院）
Mind the Gap! Bridging Explainable Artificial Intelligence and Human Understanding with Luhmann's Functional Theory of Communication
School of Law, Birkbeck, University of London（伦敦大学伯贝克学院法学院）；Department of Computer Science, ETH Zurich（苏黎世联邦理工学院计算机科学系）；ARC Centre of Excellence for Automated Decision-Making and Society, RMIT University（皇家墨尔本理工大学自动化决策与社会ARC卓越研究中心）
We seek causes through science, religion, and in everyday life. We get excited when a big rock causes a big splash, and we get scared when it tumbles without a cause. But our causal cognition is usually biased. The 'why' is influenced by the 'who'. It is influenced by the 'self', and by 'others'. We share rituals, we watch action movies, and we influence each other to believe in the same causes. Human mind is packed with subjectivity because shared cognitive biases bring us together. But they also make us vulnerable. An artificial mind is deemed to be more objective than the human mind. After …
Generativism: Toward a Learning Theory for the Age of Generative Artificial Intelligence
Department of Education and Human Services, College of Education, Lehigh University（教育与人类服务系，教育学院，莱维大学）；Department of Community and Global Health, College of Health, Lehigh University（社区与全球健康系，健康学院，莱维大学）
In 1950, Alan Turing proposed an imitation game as the ultimate test of whether a machine was intelligent: could a machine imitate a human so well that its answers to questions indistinguishable from a human. Ever since, creating intelligence that matches human intelligence has implicitly or explicitly been the goal of thousands of researchers, engineers, and entrepreneurs. The benefits of human-like artificial intelligence (HLAI) include soaring productivity, increased leisure, and perhaps most profoundly, a better understanding of our own minds. But not all types of AI are human-like. In fac…
