MERLEAU-PONTY,EMBODIED COGNITION,AND THE PROBLEM OF INTENTIONALITY

Because embodied cognition is a new approach in cognitive science and artificial intelligence, it is in need of a well-formulated theoretical foundation. We believe that Merleau-Ponty's work will prove useful in this endeavor because of the similarities between his work and embodied cognition. We will briefly sketch out some of these similarities and will show how Merleau-Ponty's work can be coupled with embodied cognition in order solve the problem of intentionality.     

Enaction-Based Artificial Intelligence: Toward Co-evolution with Humans in the Loop

This article deals with the links between the enaction paradigm and artificial intelligence. Enaction is considered a metaphor for artificial intelligence, as a number of the notions which it deals with are deemed incompatible with the phenomenal field of the virtual. After explaining this stance, we shall review previous works regarding this issue in terms of artificial life and robotics. We shall focus on the lack of recognition of co-evolution at the heart of these approaches. We propose to explicitly integrate the evolution of the environment into our approach in order to refine the ontogenesis of the artificial system, and to compare it with the enaction paradigm. The growing complexity of the ontogenetic mechanisms to be activated can therefore be compensated by an interactive guidance system emanating from the environment. This proposition does not however, resolve that of the relevance of the meaning created by the machine (sense-making). Such reflections lead us to integrate human interaction into this environment in order to construct relevant meaning in terms of participative artificial intelligence. This raises a number of questions with regards to setting up an enactive interaction. The article concludes by exploring a number of issues, thereby enabling us to associate current approaches with the principles of morphogenesis, guidance, the phenomenology of interactions and the use of minimal enactive interfaces in setting up experiments which will deal with the problem of artificial intelligence in a variety of enaction-based ways.     

从知识表示到表示:人工智能认识论上的进步

知识表示是对智能进行模拟的一个数学模型,然而它可以不是一个对智能本质的描述,特别是传统的符号主义知识表示离揭示人的智能行为发生的内在过程还有很大的差距,在神经科学和心理学的指导下,通过对智能行为的生理基础和心理过程的研究,遵循“解释智能”的思想,可以得到对知识的心智表示的新认识,这种表示观的不同,预示着人工智能方法论上的进步。     

You and I, robot

I address a number of issues related to building an autonomous social robot. I review different approaches to social cognition and ask how these different approaches may inform the design of social robots. I argue that regardless of which theoretical approach to social cognition one favors, instantiating that approach in a workable robot will involve designing that robot on enactive principles.     

DAO to HANOI via DeSci: AI Paradigm Shifts from AlphaGo to ChatGPT

From AlphaGo to ChatGPT,the field of AI has launched a series of remarkable achievements in recent years.Analyzing,comparing,and summarizing these achievements at the paradigm level is important for future AI innovation,but has not received sufficient attention.In this paper,we give an overview and perspective on machine learning paradigms.First,we propose a paradigm taxonomy with three levels and seven dimensions from a knowledge perspective.Accordingly,we give an overview on three basic and tw...     

Interactivist–constructivist foundations for embodying attention

This article discusses the increasing use of ideas from, and consistency with, the embodiment program in artificial intelligence (AI) and cognitive science. A model is embodied if it is (1) functionally grounded in behavioral regulation and (2) strongly constrained by both the physical and computational parameters of its implementation. The main goal of this is to put forward some dynamical foundations for embodying attention. A recent embodied model of attention is discussed in this context. It is shown that although the model's framework qualifies as embodied, its specific implementation does not. From here, its framework is used to develop a reconstruction of the model's dynamics, from which a new implementation which fits more closely into the embodiment program can be developed in the future. A general strategy of using dynamic considerations to constrain the development of embodied models is in this way advanced.     

机制主义人工智能理论——一种通用的人工智能理论

现行人工智能研究取得了许多进展,但存在“深度上浅层化、广度上碎片化和体系上封闭化”的重要缺陷。这不是改进算法或者提高硬件性能所能解决的问题,而是要在科学观方法论上寻找根源。本文依据“科学观→方法论→研究模型→研究途径→基本概念→基本原理”这个顶天立地的研究纲领,总结了信息科学的科学观,提炼了信息生态方法论;在新的科学观和方法论指导下构筑了体现智能生长全过程的研究模型,发现了智能生长的共性机制,确立了机制主义研究途径,进而澄清和匡正了信息（特别是语义信息）、感知、知识、认知、基础意识、情感、理智、综合决策等一系列基础概念,总结了实现信息－知识－智能转换的一组基本原理,创建了机制主义人工智能理论。而且证明了:长期三分而立的结构主义（人工神经网络）、功能主义（专家系统）、行为主义（感知动作系统）三大人工智能理论可在机制主义人工智能理论框架内实现和谐统一;机制主义是生成基础意识、情感、理智三位一体高等人工智能的科学途径;机制主义人工智能理论是通用型的人工智能理论。     

Design principles for autonomous agents: A case study of classification

Building artificial lifelike autonomous agents is still considered an art, rather than a science. A generally accepted precise methodology is missing, and—given the properties of the real world—it is doubtful whether such a methodology will ever be developed. Nevertheless, it is possible to define criteria and provide heuristics for good designs. We have developed a number of design principles which, when applied, lead to what we would consider good designs from a cognitive science or artificial life (ALife) perspective. The paper illustrates some of these principles using a case study of classification. Presented at the Internatial Symposium on Artificial Life and Robotics, Oita, Japan, February 18–20, 1996     

Computationalism

Computationalism, the notion that cognition is computation, is a working hypothesis of many AI researchers and Cognitive Scientists. Although it has not been proved, neither has it been disproved. In this paper, I give some refutations to some well-known alleged refutations of computationalism. My arguments have two themes: people are more limited than is often recognized in these debates; computer systems are more complicated than is often recognized in these debates. To underline the latter point, I sketch the design and abilities of a possible embodied computer system.     

当前人工智能技术创新特征和演进趋势

近年来，全球人工智能发展进入新一轮技术创新活跃期，新的理论、模型、算法快速迭代。本文从模型算法、软硬件实现以及技术形态等角度分析了当前全球人工智能技术的主要特征，总结了国内外人工智能前沿研究的一些创新热点和新动向，并从基础理论突破、底层计算模式创新、模型算法演进等方面，展望和探讨了未来人工智能技术进一步突破的几个可能趋势。     

Visual language integration: A survey and open challenges

With the recent development of deep learning technology comes the wide use of artificial intelligence (AI) models in various domains. AI shows good performance for definite-purpose tasks, such as image recognition and text classification. The recognition performance for every single task has become more accurate than feature engineering, enabling more work that could not be done before. In addition, with the development of generation technology (e.g., GPT-3), AI models are showing stable performances in each recognition and generation task. However, not many studies have focused on how to integrate these models efficiently to achieve comprehensive human interaction. Each model grows in size with improved performance, thereby consequently requiring more computing power and more complicated designs to train than before. This requirement increases the complexity of each model and requires more paired data, making model integration difficult. This study provides a survey on visual language integration with a hierarchical approach for reviewing the recent trends that have already been performed on AI models among research communities as the interaction component. We also compare herein the strengths of existing AI models and integration approaches and the limitations they face. Furthermore, we discuss the current related issues and which research is needed for visual language integration. More specifically, we identify four aspects of visual language integration models: multimodal learning, multi-task learning, end-to-end learning, and embodiment for embodied visual language interaction. Finally, we discuss some current open issues and challenges and conclude our survey by giving possible future directions.     

Designing reality-based interfaces for experiential bio-design

Reality-based interfaces (RBIs) such as tabletop and tangible user interfaces draw upon ideas from embodied cognition to offer a more natural, intuitive, and accessible form of interaction that reduces the mental effort required to learn and operate computational systems. However, to date, little research has been devoted to investigating the strengths and limitations of applying reality-based interaction for promoting learning of complex scientific concepts at the college level. We propose that RBIs offer unique opportunities for enhancing college-level science education. This paper presents three main contributions: (1) design considerations and participatory design process for enhancing college-level science education through reality-based interaction, (2) reflections on the design, implementation, and validation of two case studies—RBIs for learning synthetic biology, and (3) discussion of opportunities and challenges for advancing learning of college-level sciences through next-generation interfaces.     

设计认知过程研究的发展与分析

为把握设计认知研究的发展动态,深入理解设计认知的本质,对设计认知研究的系统化描述阶段和心智活动研究阶段进行综合评述。分析典型的系统化设计认知模式,总结出其三个基本特点和理论缺陷。着重从人工智能和认知科学这两路径,分析面向设计问题的心智活动研究,指出两条研究路径互为依存、相互促进的关系。指出在设计认知过程分解、设计知识表征和心智影像运作方面需要进一步突破。     

深度学习模型可解释性的研究进展

深度学习在很多人工智能应用领域中取得成功的关键原因在于,通过复杂的深层网络模型从海量数据中学习丰富的知识。然而,深度学习模型内部高度的复杂性常导致人们难以理解模型的决策结果,造成深度学习模型的不可解释性,从而限制了模型的实际部署。因此,亟需提高深度学习模型的可解释性,使模型透明化,以推动人工智能领域研究的发展。本文旨在对深度学习模型可解释性的研究进展进行系统性的调研,从可解释性原理的角度对现有方法进行分类,并且结合可解释性方法在人工智能领域的实际应用,分析目前可解释性研究存在的问题,以及深度学习模型可解释性的发展趋势。为全面掌握模型可解释性的研究进展以及未来的研究方向提供新的思路。     

Review of State-of-the-Art in Deep Learning Artificial Intelligence

The current state-of-the-art in Deep Learning (DL) based artificial intelligence (AI) is reviewed. A special emphasis is made to compare the level of a concrete AI system with human abilities to show what remains to be done to achieve human level AI. Several estimates are proposed for comparison of the current “intellectual level” of AI systems with the human level. Among them is relation of Shannon’s estimate for lower bound on human word perplexity to recent progress in natural language AI modeling. Relations between the operation of DL constructions and principles of live neural information processing are discussed. The problem of AI risks and benefits is also reviewed based on arguments from both sides.     

A survey of modern knowledge modeling techniques

A major characteristic regarding developments in the broad field of artificial intelligence (AI) during the 1990s has been an increasing integration of AI with other disciplines. A number of other computer science fields and technologies have been used in developing intelligent systems, starting from traditional information systems and databases, to modern distributed systems and the Internet. This paper surveys the knowledge modeling techniques that have received most attention in recent years among developers of intelligent systems, AI practitioners and researchers. The techniques are described from two perspectives, theoretical and practical. Hence the first part of the paper presents major theoretical and architectural concepts, design approaches, and research issues. The second part deals with several practical systems, applications, and ongoing projects that use and implement the techniques described in the first part.     

Computational Models of Ethical Reasoning: Challenges, Initial Steps, and Future Directions

Computational models of ethical reasoning are in their infancy in the field of artificial intelligence. Ethical reasoning is a particularly challenging area of human behavior for AI scientists and engineers because of its reliance on abstract principles, philosophical theories not easily rendered computational, and deep-seated, even religious, beliefs. A further issue is this endeavor's ethical dimension: Is it even appropriate for scientists to try to imbue computers with ethical-reasoning powers? A look at attempts to build computational models of ethical reasoning illustrates this task's challenges. In particular, the Truth-Teller and SIROCCO programs incorporate AI computational models of ethical reasoning, both of which model the ethical approach known as casuistry. Truth-Teller compares pairs of truth-telling cases; SIROCCO retrieves relevant past cases and principles when presented with a new ethical dilemma. The computational model underlying Truth-Teller could serve as the basis for an intelligent tutor for ethics.This article is part of a special issue on Machine Ethics.     

网络时代人工智能研究与发展

50多年来,人工智能在模式识别、知识工程、机器人等领域已经取得重大成就,但是离真正的人类智能还相差甚远.当今网络时代,人工智能科学要在学科交叉研究中实现人工智能的发展与创新,会更加关注认知科学、脑科学、生物智能、物理学、网络科学、计算机科学与人工智能之间的交叉渗透,重视认知物理学的研究;自然语言是人工智能研究知识表示无法回避的直接对象,要对语言中的概念建立起能够定量表示的不确定性转换模型,发展不确定性人工智能;要利用现实生活中复杂网络的小世界模型和无标度特性,把网络拓扑作为知识表示的一种新方法,研究网络拓扑的演化与网络动力学行为,研究网络智能.对这3个重要方向进行了阐述,并提出了具体建议.