伦理智能体及其设计:现状和展望

智能体一直是人工智能的主要研究领域之一,任何独立的能够同环境交互并自主决策的实体都可以抽象为智能体.随着人工智能从计算智能到感知智能,再到认知智能的发展,智能体已逐步渗透到无人驾驶、服务机器人、智能家居、智慧医疗、战争武器等人类生活密切相关的领域.这些应用中,智能体与环境、尤其是与人类和社会的交互愈来愈突出,其中的伦理...     

Autonomous Vehicles and Embedded Artificial Intelligence: The Challenges of Framing Machine Driving Decisions

With the advent of autonomous vehicles society will need to confront a new set of risks which, for the first time, includes the ability of socially embedded forms of artificial intelligence to make complex risk mitigation decisions: decisions that will ultimately engender tangible life and death consequences. Since AI decisionality is inherently different to human decision-making processes, questions are therefore raised regarding how AI weighs decisions, how we are to mediate these decisions, and what such decisions mean in relation to others. Therefore, society, policy, and end-users, need to fully understand such differences. While AI decisions can be contextualised to specific meanings, significant challenges remain in terms of the technology of AI decisionality, the conceptualisation of AI decisions, and the extent to which various actors understand them. This is particularly acute in terms of analysing the benefits and risks of AI decisions. Due to the potential safety benefits, autonomous vehicles are often presented as significant risk mitigation technologies. There is also a need to understand the potential new risks which autonomous vehicle driving decisions may present. Such new risks are framed as decisional limitations in that artificial driving intelligence will lack certain decisional capacities. This is most evident in the inability to annotate and categorise the driving environment in terms of human values and moral understanding. In both cases there is a need to scrutinise how autonomous vehicle decisional capacity is conceptually framed and how this, in turn, impacts a wider grasp of the technology in terms of risks and benefits. This paper interrogates the significant shortcomings in the current framing of the debate, both in terms of safety discussions and in consideration of AI as a moral actor, and offers a number of ways forward.     

人工智能在计算机网络技术中的应用分析

随着信息技术的快速发展,人工智能已成为引领新一轮科技革命和产业变革的战略性技术。现阶段,各个国家都在争先布局和发展人工智能,以期能在未来科技革命中抢占高点和先机。人工智能是一种模拟人脑工作的技术形式,它包含系统推荐、人工神经网络、语言处理、机器学习等方面的内容。将人工智能应用于计算机网络技术,可以节省人力资源、提升效率,可较好地弥补当前计算机网络技术在运用过程中存在的不足,进一步提升计算机网络技术水平。     

可信人工智能系统的质量属性与实现: 三级研究

人工智能系统以一种前所未有的方式,被广泛地用于解决现实世界的各种挑战,其已然成为推动人类社会发展的核心驱动力.随着人工智能系统在各行各业的迅速普及,人们对人工智能系统的可信性愈发感到担忧,其主要原因在于,传统软件系统的可信性已不足以完全描述人工智能系统的可信性.对于人工智能系统的可信性的研究,具有迫切的需要.目前已有大量相关研究,且各有侧重,但缺乏一个整体性、系统性的认识.本研究是一项以现有二级研究为研究对象的三级研究,旨在揭示人工智能系统的可信性相关的质量属性和实践的研究现状,建立一个更加全面的可信人工智能系统质量属性框架.本研究收集、整理和分析了2022年3月前发表的34项二级研究,识别了21种与可信性相关的质量属性及可信性的度量方法和保障实践.研究发现,现有研究主要关注在安全性和隐私性上,对于其它质量属性缺乏广泛且深入的研究.对于需要跨学科协作的两个研究方向,需要在未来的研究中引起重视,一方面是人工智能系统本质上还是一个软件系统,其作为一个软件系统的可信值得人工智能和软件工程专家合作研究;另一方面,人工智能是人类对于机器拟人化的探索,如何从系统层面保障机器在社会环境下的可信,如怎样满足人本主义,值得人工智能和社会科学专家合作研究.     

Learning strategies for task delegation in norm-governed environments

How do I choose whom to delegate a task to? This is an important question for an autonomous agent collaborating with others to solve a problem. Were similar proposals accepted from similar agents in similar circumstances? What arguments were most convincing? What are the costs incurred in putting certain arguments forward? Can I exploit domain knowledge to improve the outcome of delegation decisions? In this paper, we present an agent decision-making mechanism where models of other agents are refined through evidence from past dialogues and domain knowledge, and where these models are used to guide future delegation decisions. Our approach combines ontological reasoning, argumentation and machine learning in a novel way, which exploits decision theory for guiding argumentation strategies. Using our approach, intelligent agents can autonomously reason about the restrictions (e.g., policies/norms) that others are operating with, and make informed decisions about whom to delegate a task to. In a set of experiments, we demonstrate the utility of this novel combination of techniques. Our empirical evaluation shows that decision-theory, machine learning and ontology reasoning techniques can significantly improve dialogical outcomes.     

Open ended intelligence: the individuation of intelligent agents

Artificial general intelligence is a field of research aiming to distil the principles of intelligence that operate independently of a specific problem domain and utilise these principles in order to synthesise systems capable of performing any intellectual task a human being is capable of and beyond. While “narrow” artificial intelligence which focuses on solving specific problems such as speech recognition, text comprehension, visual pattern recognition and robotic motion has shown impressive breakthroughs lately, understanding general intelligence remains elusive. We propose a paradigm shift from intelligence perceived as a competence of individual agents defined in relation to an a priori given problem domain or a goal, to intelligence perceived as a formative process of self-organisation. We call this process open-ended intelligence. Starting with a brief introduction of the current conceptual approach, we expose a number of serious limitations that are traced back to the ontological roots of the concept of intelligence. Open-ended intelligence is then developed as an abstraction of the process of human cognitive development, so its application can be extended to general agents and systems. We introduce and discuss three facets of the idea: the philosophical concept of individuation, sense-making and the individuation of general cognitive agents. We further show how open-ended intelligence can be framed in terms of a distributed, self-organising network of interacting elements and how such process is scalable. The framework highlights an important relation between coordination and intelligence and a new understanding of values.     

人工智能领域知识图谱构建与分析

近年来人工智能技术成为学术界和产业界的研究焦点,基于领域科技文献对人工智能相关技术脉络的发展进行分析和研究有助于科研人员掌握相关技术发展方向,同时为国家制定相关政策措施提供了大数据支撑。美国人工智能协会年会(AAAI)和人工智能国际联合大会(IJCAI)是人工智能领域最主要的学术会议,众多领先的AI科技成果在上述会议期间被提出。论文对最近十余年的AAAI和IJCAI会议中的论文集进行了整理分析和挖掘,构建了包含500000个反映研究主题、研究人员等实体及其关系的三元组的人工智能领域知识图谱,并在此基础上对人工智能领域的研究热点和发展趋势进行了分析和讨论。     

美国国防部联合人工智能中心建设情况及经验分析

联合人工智能中心是美国国防部推动人工智能发展和军事应用的核心机构,中心下设战略政策部、能力发展部、任务部、规划预算采办部等业务部门,中心的运行贯穿了人工智能应用全生命周期,重点聚焦短期项目执行与人工智能技术应用工作,经过多年发展,中心在跨机构协调、业务管理模式优化、技术环境和工具手段建设、内部文化建设、高端人才引进等方面积累了诸多经验,值得分析和借鉴。     

Progress and Challenge of Artificial Intelligence

Artificial Intelligence （AI） is generally considered to be a subfield of computer science, that is concerned to attempt simulation, extension and expansion of human intelligence. Artificial intelligence has enjoyed tremendous success over the last fifty years. In this paper we only focus on visual perception, granular computing, agent computing, semantic grid. Human-level intelligence is the long-term goal of artificial intelligence. We should do joint research on basic theory and technology of intelligence by brain science, cognitive science, artificial intelligence and others. A new cross discipline intelligence science is undergoing a rapid development. Future challenges are given in final section.     

AI Decision Making with Dignity? Contrasting Workers’ Justice Perceptions of Human and AI Decision Making in a Human Resource Management Context

Information Systems Frontiers - Using artificial intelligence (AI) to make decisions in human resource management (HRM) raises questions of how fair employees perceive these decisions to be and...     

Prolegomena to any future artificial moral agent

As artificial intelligence moves ever closer to the goal of producing fully autonomous agents, the question of how to design and implement an artificial moral agent (AMA) becomes increasingly pressing. Robots possessing autonomous capacities to do things that are useful to humans will also have the capacity to do things that are harmful to humans and other sentient beings. Theoretical challenges to developing artificial moral agents result both from controversies among ethicists about moral theory itself, and from computational limits to the implementation of such theories. In this paper the ethical disputes are surveyed, the possibility of a ‘moral Turing Test’ is considered and the computational difficulties accompanying the different types of approach are assessed. Human-like performance, which is prone to include immoral actions, may not be acceptable in machines, but moral perfection may be computationally unattainable. The risks posed by autonomous machines ignorantly or deliberately harming people and other sentient beings are great. The development of machines with enough intelligence to assess the effects of their actions on sentient beings and act accordingly may ultimately be the most important task faced by the designers of artificially intelligent automata.     

大数据时代下人工智能在计算机网络技术中的应用

现阶段,我国人工智能技术研究不断深入,促进了计算机网络技术的不断革新。在此背景下,人们的生活变得更加便捷和智能化。人工智能作为现阶段高科技发展的产物,对计算机网络技术的发展起到了一定引领作用。人工智能技术是当前备受关注的高新科技,其发展不仅加快了我国社会的智能化进程,而且提高了计算机的技术水平,能够促进计算机网络技术更好地为社会服务。     

An Ethics Evaluation Tool for Automating Ethical Decision-Making in Robots and Self-Driving Cars

As we march down the road of automation in robotics and artificial intelligence, we will need to automate an increasing amount of ethical decision-making in order for our devices to operate independently from us. But automating ethical decision-making raises novel questions for engineers and designers, who will have to make decisions about how to accomplish that task. For example, some ethical decision-making involves hard moral cases, which in turn requires user input if we are to respect established norms surrounding autonomy and informed consent. The author considers this and other ethical considerations that accompany the automation of ethical decision-making. He proposes some general ethical requirements that should be taken into account in the design room, and sketches a design tool that can be integrated into the design process to help engineers, designers, ethicists, and policymakers decide how best to automate certain forms of ethical decision-making.     

基于生物脑网络连接特性的高效神经网络算法

人工神经网络发展至今,已经在计算机视觉、类脑智能等方面得到广泛应用.在过去几十年中,人们对神经网络的研究注重追求更高的准确率,从而忽略了对网络计算成本的控制.而人脑作为高效且节能的网络,其对人工智能的发展起到了重要启示作用.如何仿真生物脑网络的连接特性,建立超低能耗的人工神经网络模型实现基本相同的目标识别正确率成为当前研究的热点.为建立低能耗的人工神经网络模型,本文结合大脑网络的连接特性,通过改变人工神经网络的连接实现网络的高效性.实验结果表明,结合生物脑网络的连接特性,改变网络的连接,很大程度上减少了网络的计算成本,而网络的性能并没有受到明显影响.     

Application of deep learning algorithms in geotechnical engineering: a short critical review

Artificial Intelligence Review - With the advent of big data era, deep learning (DL) has become an essential research subject in the field of artificial intelligence (AI). DL algorithms are...     

新一代人工智能十问十答

新一代人工智能是我国在全球第一个面向2030年提出的国家重大发展战略。如何认识新一代人工智能和传统人工智能的不同,了解其内涵、外延、技术特征以及发展目标,从而更好地凝聚研发队伍,是实现这一战略的重要保证。本文围绕新一代人工智能的技术内核,提出十个主要问题,自问自答,大问小答,指出新一代人工智能将从传统的计算机智能跃升为无意识的类脑智能,是人类智能的体外延伸,不涉及生命和意识,由人赋予意图,通过有指导的传承学习和自主学习,能够与时俱进地解释、解决新的智力问题,形成有感知、有认知、有行为、可交互、会学习、自成长的新一代人工智能—智能机器。     

人工智能发展预测

目前,人工智能的推理功能已获突破,学习及联想功能正在研究之中,下一步就是模仿人类右脑的模糊处理功能和整个大脑的并行化处理功能。人工神经网络是未来人工智能应用的新领域,未来智能计算机的构成,可能就是作为主机的冯．诺依曼型机与作为智能外围的人工神经网络的结合。     

Imitating the brain with neurocomputer a “new” way towards artificial general intelligence

To achieve the artificial general intelligence (AGI), imitate the intelligence? or imitate the brain? This is the question! Most artificial intelligence (AI) approaches set the understanding of the intelligence principle as their premise. This may be correct to implement specific intelligence such as computing, symbolic logic, or what the AlphaGo could do. However, this is not correct for AGI, because to understand the principle of the brain intelligence is one of the most difficult challenges for our human beings. It is not wise to set such a question as the premise of the AGI mission. To achieve AGI, a practical approach is to build the so-called neurocomputer, which could be trained to produce autonomous intelligence and AGI. A neurocomputer imitates the biological neural network with neuromorphic devices which emulate the bio-neurons, synapses and other essential neural components. The neurocomputer could perceive the environment via sensors and interact with other entities via a physical body. The philosophy under the “new” approach, so-called as imitationalism in this paper, is the engineering methodology which has been practiced for thousands of years, and for many cases, such as the invention of the first airplane, succeeded. This paper compares the neurocomputer with the conventional computer. The major progress about neurocomputer is also reviewed.     

人工智能的监管挑战与路径构建

人工智能有着巨大的深度学习和情感伦理识别的潜力,这给人类带来了根本性的生存危机和颠覆性的冲击。然而,监管技术匮乏、监管法律、机制滞后等问题,都无法有效应对未来人工智能的快速发展。因此,本文从传统的行为监管和技术监管等传统防御性监管的维度之外增加以人工智能的自身监管（智能治理）。同时,在技术层面,让人工智能能自主识别情感,建立公平、透明、问责的“程序伦理”算法监管;在监管的引导下,建立统一的行业标准和自身监管系统。这样才能有效地回应人工智能对内含的安全风险及其引发的监管挑战,消除人类的发展顾虑。     

人工智能与法律结合的现状及发展趋势

人工智能并没有一个统一的定义,但若一个计算机系统能做人需要智能才能做的事,一般便认为这样的计算机系统具有人工智能。因此,人工智能被广泛应用于许多需要人类智能的领域,如法律、医疗、金融、电子商务等,其中法律是当前的一个重要应用领域。因此,文中主要从立法(人工智能系统辅助立法以及立法监管人工智能系统,特别是自主驾驶汽车)、知法守法(法律信息的检索、法律文书的生成和审核)、司法(证据收集、法律推理以及在线纠纷解决)等方面综述了人工智能和法律结合的研究现状以及发展趋势,希望能引导更多人投入这个研究领域。