Artificial Intelligence and the New Issue of Cognitive Subject

The emergence of artificial intelligence (AI) technology renders the issue of the cognitive subject in epistemology a prominent one. It challenges the epistemological consensus that humankind is the only cognitive subject. With the development from weak AI to strong AI, the significance of humankind being the cognitive subject will undergo new changes. At present, AI technology does not possess the cognitive ability that humans do. As a result, considering the existing AI technology, the issue related to the cognitive subject to which we must give more attention ultimately concerns the epistemological labor division between human and machine. This division of labor requires the foundation that the technical possibility is governed by the value orientation. From the value perspective of regarding human beings as the purpose, machine intelligence, and the direction and limit of the power that technology brings to cognitive subjects will be clarified. This will let the information technology do what it is meant to do, while people will always be able to reserve the subject status for themselves.     

Representationalism and the Metonymic Fallacy

Representationalism in cognitive science holds that semantic meaning should be explained by representations in the mind or brain. In this paper it is argued that semantic meaning should instead be explained by an abstract theory of semantic machines -- machines with predicative capability. The concept of a semantic machine (like that of a Turing machine or of Dennett's intentional systems) is not a physical concept -- although it has physical implementations. The predicative competence of semantic machines is defined in terms of independent agreement alone (cf. independent, and yet synchronised, clocks). Abstract theories are analysed as systems of quasi-apriori rules for abstract predicates. A relatively limited number of such theories and a few fundamental dimensions (space, time, mass, etc.) are today assumed to exhaust physical reality. However, that assumption need not be in conflict with predicates that cannot be defined in physical terms – for instance the functional and intentional terms that are crucial for cognitive science.     

Against the iDoctor: why artificial intelligence should not replace physician judgment

Experts in medical informatics have argued for the incorporation of ever more machine-learning algorithms into medical care. As artificial intelligence (AI) research advances, such technologies raise the possibility of an “iDoctor,” a machine theoretically capable of replacing the judgment of primary care physicians. In this article, I draw on Martin Heidegger’s critique of technology to show how an algorithmic approach to medicine distorts the physician–patient relationship. Among other problems, AI cannot adapt guidelines according to the individual patient’s needs. In response to the objection that AI could develop this capacity, I use Hubert Dreyfus’s analysis of AI to argue that attention to the needs of each patient requires the physician to attune his or her perception to the patient’s history and physical exam, an ability that seems uniquely human. Human physician judgment will remain better suited to the practice of primary care despite anticipated advances in AI technology.     

Kill the Song—Steal the Show: What Does Distinguish Predicative Metaphors From Decomposable Idioms?

This study examined the semantic processing difference between decomposable idioms and novel predicative metaphors. It was hypothesized that idiom comprehension results from the retrieval of a figurative meaning stored in memory, that metaphor comprehension requires a sense creation process and that this process difference affects the processing time of idiomatic and metaphoric expressions. In the first experiment, participants read sentences containing decomposable idioms, predicative metaphors or control expressions and performed a lexical decision task on figurative targets presented 0, 350, and 500 ms, or 750 after reading. Results demonstrated that idiomatic expressions were processed sooner than metaphoric ones. In the second experiment, participants were asked to assess the meaningfulness of idiomatic, metaphoric and literal expressions after reading a verb prime that belongs to the target phrase (identity priming). The results showed that verb identity priming was stronger for idiomatic expressions than for metaphor ones, indicating different mental representations.     

Being articulate: Rede in Heidegger’s Being and Time

Being and Time’s emphasis on practical activities has attracted much attention as an approach to meaning not modelled exclusively on language. However, understanding this emphasis is made more difficult by Heidegger’s notion of Rede (‘Articulacy’ or ‘Discourse’), which he routinely characterizes as both language-like and basic to all disclosure. This paper assesses whether this notion can be both interpreted coherently and reconciled with Heidegger’s emphasis on intelligent nonlinguistic behaviours. It begins by identifying two functions of Articulacy – the demonstrative and articulatory – and a potential source of incoherence in Heidegger’s analysis. Having reviewed some standard approaches to Heideggerian Articulacy, I show how Heidegger’s discussion of predicative judgements (‘Statements’) implies that language can be linked with different kinds of content. This allows Heidegger’s analysis to be read coherently, provided Articulacy is understood as having distinct purposive and predicative modes. The final section shows how this reading preserves a close connection between Articulacy and language while accommodating intelligent nonlinguistic behaviours.     

Mechanistic versus phenomenal embodiment: Can robot embodiment lead to strong AI?

Embodiment has become the raison d’etre for much of the new ‘cognitive robotics’. It fills a gap in the non-interactivist approach of traditional artificial intelligence (AI) in which ‘intelligence’ is viewed as the manipulation of symbols in a vacuum. However, a foundational question for the new AI is, can embodiment lead to a strong AI, i.e. a robot mind? To address this question, two extreme poles of embodiment are distinguished here, mechanistic and phenomenal. A detailed exploration of each type of embodiment is provided together with an appraisal of whether strong embodiment is possible for robotics, or whether robotics merely provides a tool for scientific exploration and modelling, i.e. weak embodiment? It is argued that strong embodiment, either mechanistic or phenomenal, is not possible for present day robots. However, weak embodiment may provide an enlightened approach to using robots for modelling cognition.     

Has Artificial Intelligence Contributed to an Understanding of the Human Mind?: A Critique of Arguments For and Against

This essay examines arguments for and against the proposition that Artificial Intelligence (AI) research makes an important contribution to the understanding of the human mind. A number of recent articles have seemed to question the value of Al ideas in specific domains (e.g., language. mental imagery, problem solving). In the present paper, it is argued that the real disagreement concerns the form of a scientific psychology. The critics of Artificial Intelligence believe that many acceptable psychological theories exist and the important task of the scientist is to show that certain of these theories are true. They criticize AI research because it has not produced theories whose adequacy can be tested by empirical research. The supporters of AI research believe that no adequate psychological theories exist. They claim that the important task is to develop adequate theories and until such theories have been constructed, it is pointless to worry about empirical tests. A number of major arguments attacking and defending AI research are examined in order to illustrate that the real issue is methodological. It is concluded that the present debate is counterproductive since both sides assume that the methodological premise of the other side is wrong without attempting to refute it. A more constructive debate would address directly the more fundamental question of what methods offer the greatest promise of solving the major problems of psychology.     

人工智能决策的公平感知

不平等问题是全球社会和经济发展需要应对的首要挑战, 也是实现全球可持续发展目标的核心障碍。人工智能(artificial intelligence, AI)为缓解不平等、促进社会公平提供了新的途径。然而, 新近研究发现, 即使客观上AI决策具有公平性和准确性, 个体仍可能对AI决策的公平感知较低。因此, 近年来越来越多的研究开始关注AI决策公平感知的影响因素。然而, 目前研究较为分散, 呈现出研究范式不统一、理论不清晰和机制未厘清等特征。这既不利于跨学科的研究对话, 也不利于研究者和实践者对AI决策公平感知形成系统性理解。基于此, 通过系统的梳理, 现有研究可以划分为两类: (1) AI单一决策的公平感知研究, 主要聚焦于AI特征和个体特征如何影响个体对AI决策的公平感知; (2) AI-人类二元决策的公平感知研究, 主要聚焦于对比个体对AI决策与人类决策公平感知的差异。在上述梳理基础上, 未来研究可以进一步探索AI决策公平感知的情绪影响机制等方向。     

A critical examination of Michael Polanyi's conception of skills

In this paper I will consider some of the main issues in Michael Polanyi's discussion of ‘Skills’ from Chapter 4 of his book Personal Knowledge published in 1958.The concept of ‘skill’ features prominently in psychological theories of human performance in activities such as games, gymnastics, swimming and dance; such theories are often applied to other spheres of human activity in which issues about skill acquisition arise. It is not surprising to discover that skill theory is considered to be essential to the study of human movement and physical education. Physical education teachers in schools have consistently maintained that ‘skill acquisition’ is a major objective for the P.E. curriculum — see Kane 1974. Yet concepts such as ‘skill’, ‘ability’ and ‘know-how’ are constituent features of practical knowledge therefore the notion of ‘skill acquisition’ on its own will not do as a curriculum objective since all instances of skill and practical knowledge are specific to their contexts, namely, to different practical activities. If particular activities are valued in schools in the pursuit of children's learning and education it seems necessary to clarify the epistemological features of such activities in order to understand what it means to teach them and what it means for children to learn and know-how to perform them successfully. It is in these respects that the concept of ‘skill’ in different human activities should attract the interest of physical education students. In order to draw attention to major features of Polanyi's thesis on the nature of ‘skill’ I shall consider the relevance of his ideas about skill and knowledge in relation to human action theory. I suggest that misconceptions may arise in teaching and learning theories relating to ‘playing soccer’ if the basic underlying ideas about ‘skill’ in human activities are inconsistent with ideas about human action or, say, practical reasoning. It is this issue that is not, in my view, attended to by Polanyi in his discussion of ‘skill’. Thus, if my criticisms of Polanyi's ideas are shown to be valid it will be necessary, by implication that is, to exercise caution before any attempt is made to use his thesis as a basis from which to formulate ideas about teaching skills and skill acquisition. The extensive use I make of quotations from Polanyi's writings is necessary because it is towards Polanyi's use of language in his explanation of ‘skills’ that much of my criticism is directed. It is hoped that what follows may provide students of human movement and physical education with an insight into Polanyi's view of ‘skills’ in particular and to issues related to skill theory and human action in general.     

The Ways of ‘Being’ and ‘Becoming’ Human

We argue that developing integrative models of psychological phenomenon require dealing simultaneously with information-theoretic and meaning making processes at the individual and group levels. Attempts to fuse these across levels based on intentionality become difficult because phenomena at the lower level organisms are conceptualized employing concrete systems language, while in case of human and social systems both concrete and abstracted systems concepts are used. Intentionality also needs to be looked at as arising out of the processes of both being and becoming. Fusing of psycho-and socio-logics necessarily requires taking into consideration psychological processes and notions of intentionality and future within cultural contexts in a more holistic manner. It is also argued that both at personal and collective levels, human intentionality operate as bounded intentionality alternating between states of being and becoming.     

Language as shaped by the brain

It is widely assumed that human learning and the structure of human languages are intimately related. This relationship is frequently suggested to derive from a language-specific biological endowment, which encodes universal, but communicatively arbitrary, principles of language structure (a Universal Grammar or UG). How might such a UG have evolved? We argue that UG could not have arisen either by biological adaptation or non-adaptationist genetic processes, resulting in a logical problem of language evolution. Specifically, as the processes of language change are much more rapid than processes of genetic change, language constitutes a "moving target" both over time and across different human populations, and, hence, cannot provide a stable environment to which language genes could have adapted. We conclude that a biologically determined UG is not evolutionarily viable. Instead, the original motivation for UG--the mesh between learners and languages--arises because language has been shaped to fit the human brain, rather than vice versa. Following Darwin, we view language itself as a complex and interdependent "organism," which evolves under selectional pressures from human learning and processing mechanisms. That is, languages themselves are shaped by severe selectional pressure from each generation of language users and learners. This suggests that apparently arbitrary aspects of linguistic structure may result from general learning and processing biases deriving from the structure of thought processes, perceptuo-motor factors, cognitive limitations, and pragmatics.     

In Babel

In discussions on the plurality or the pluralism of different theoretical approaches in psychoanalysis there is often talk about “Babylonian” circumstances, at times connected with the hope of being able to leave language confusion and communication problems behind. The author takes up the metaphor “Babel”, describes constructive and destructive facets of the actual state inside the psychoanalytic Babel and illustrates with examples some problems but also the potentials of the analytic discourse. She pleads for giving the potentials inside Babel a chance before looking for solutions beyond Babel. In the field of tension between inside and beyond Babel approaches are described how to tolerate the diversity of other language games, to acknowledge it and to deal with it.     

Novelty and processing demands in conceptual combination

In this article, we sought to isolate the processing demands of combining the concepts of modifier‐noun phrases from those of other language comprehension processes. Probe reaction time (RT) was used as an indication of the processing resources required for combining concepts. Phrase frequency (as measured by Google hit rates) was used as a metric of the degree of conceptual combination required for each phrase. Participants were asked to interpret modifier‐noun phrases using a sense‐nonsense decision (Experiment 1) and a phrase meaning access task (Experiment 2). Experiment 2 also used a lexical decision task to activate the word's individual meanings. Regression analyses for both experiments indicated that phrase frequency (indicating novelty) predicts a significant portion of the probe RT variance, such that low‐frequency phrases required more processing resources than high‐frequency phrases, when controlling for associative strength, word frequency, letter length, and lexical‐semantic activation. Overall, this study indicates that conceptual combination requires processing resources beyond those of other language processes.     

ENCOUNTERS WITH EMERGENT DEITIES: ARTIFICIAL INTELLIGENCE IN SCIENCE FICTION NARRATIVE

In the mid-twentieth century, theorists began seriously forecasting possibilities for artificial intelligence (AI). As related research gathered momentum and resources, the topic made impressions on public discourse. One effect was increasingly pointed emphasis on AI in popular narratives. Although considerably earlier thematic examples may be located, we can observe swelling and generally pessimistic threads of speculation in science fiction of the 1950s and 1960s. This discussion identifies some pertinent science fiction texts from that period, alongside public discussion arising from contemporary research. One consistent theme is human receptiveness to the numinous, and the capacity to ascribe personality and even divinity to sufficiently impressive manifestations, even artificial ones. Science fiction has long contemplated such reactions, prefiguring today's anticipations of AIs that might abruptly develop themselves beyond any possible human comprehension or control. This body of exploratory projections is a useful resource for the engineers and philosophers currently grappling with realistic prospects for Western humanity's shifting conception of itself.     

Acquiring verbal reference: The interplay of cognitive,linguistic, and general learning capacities

Verbal reference is the ability to use language to communicate about objects, events, or ideas, even if they are not witnessed directly, such as past events or faraway places. It rests on a three-way link between words, their referents, and mental representations of those referents. A foundational human capacity, verbal reference extends the communicative power of language beyond the here-and-now, enabling access to language-mediated learning and thus fueling cognitive development. In the current review, we consider how and when verbal reference develops. The existing literature suggests that verbal reference emerges around infants’ first birthdays and becomes increasingly robust by their second. In discussing the powerful developmental advantages of acquiring verbal reference we propose that this achievement requires a dynamic interplay among infants’ cognitive and language development, fueled by general learning capacities. We close by describing new research directions, aimed at advancing our understanding of how verbal reference emerges.     

Building human-like communicative intelligence: A grounded perspective

Modern Artificial Intelligence (AI) systems excel at diverse tasks, from image classification to strategy games, even outperforming humans in many of these domains. After making astounding progress in language learning in the recent decade, AI systems, however, seem to approach the ceiling that does not reflect important aspects of human communicative capacities. Unlike human learners, communicative AI systems often fail to systematically generalize to new data, suffer from sample inefficiency, fail to capture common-sense semantic knowledge, and do not translate to real-world communicative situations. Cognitive Science offers several insights on how AI could move forward from this point. This paper aims to: (1) suggest that the dominant cognitively-inspired AI directions, based on nativist and symbolic paradigms, lack necessary substantiation and concreteness to guide progress in modern AI, and (2) articulate an alternative, “grounded”, perspective on AI advancement, inspired by Embodied, Embedded, Extended, and Enactive Cognition (4E) research. I review results on 4E research lines in Cognitive Science to distinguish the main aspects of naturalistic learning conditions that play causal roles for human language development. I then use this analysis to propose a list of concrete, implementable components for building “grounded” linguistic intelligence. These components include embodying machines in a perception–action cycle, equipping agents with active exploration mechanisms so they can build their own curriculum, allowing agents to gradually develop motor abilities to promote piecemeal language development, and endowing the agents with adaptive feedback from their physical and social environment. I hope that these ideas can direct AI research towards building machines that develop human-like language abilities through their experiences with the world.     

Models,Models, and Models

Michael Dummett famously maintained that analytic philosophy was simply philosophy that followed Frege in treating the philosophy of language as the basis for all other philosophy (1978, 441). But one important insight to emerge from computer science is how difficult it is to animate the linguistic artifacts that the analysis of thought produces. Yet, modeling the effects of thought requires a new skill that goes beyond analysis: procedural literacy. Some of the most promising research in philosophy makes use of a variety of modeling techniques that go beyond basic logic and elementary probability theory. What unifies this approach is a focus on what Alan Perlis called procedural literacy. This essay argues that the future spoils in philosophical research will disproportionally go to those who are procedurally literate.     

Epistemology of AI Revisited in the Light of the Philosophy of Information

Artificial intelligence has often been seen as an attempt to reduce the natural mind to informational processes and, consequently, to naturalize philosophy. The many criticisms that were addressed to the so-called “old-fashioned AI” do not concern this attempt itself, but the methods it used, especially the reduction of the mind to a symbolic level of abstraction, which has often appeared to be inadequate to capture the richness of our mental activity. As a consequence, there were many efforts to evacuate the semantical models in favor of elementary physiological mechanisms simulated by information processes. However, these views, and the subsequent criticisms against artificial intelligence that they contain, miss the very nature of artificial intelligence, which is not reducible to a “science of the nature”, but which directly impacts our culture. More precisely, they lead to evacuate the role of the semantic information. In other words, they tend to throw the baby out with the bath-water. This paper tries to revisit the epistemology of artificial intelligence in the light of the opposition between the “sciences of nature” and the “sciences of culture”, which has been introduced by German neo-Kantian philosophers. It then shows how this epistemological view opens on the many contemporary applications of artificial intelligence that have already transformed—and will continue to transform—all our cultural activities and our world. Lastly, it places those perspectives in the context of the philosophy of information and more particularly it emphasizes the role played by the notions of context and level of abstraction in artificial intelligence.     

Generation and evaluation of user tailored responses in multimodal dialogue

When people engage in conversation, they tailor their utterances to their conversational partners, whether these partners are other humans or computational systems. This tailoring, or adaptation to the partner takes place in all facets of human language use, and is based on a mental model or a user model of the conversational partner. Such adaptation has been shown to improve listeners’ comprehension, their satisfaction with an interactive system, the efficiency with which they execute conversational tasks, and the likelihood of achieving higher level goals such as changing the listener’s beliefs and attitudes. We focus on one aspect of adaptation, namely the tailoring of the content of dialogue system utterances for the higher level processes of persuasion, argumentation and advice-giving. Our hypothesis is that algorithms that adapt content for these processes, according to a user model, will improve the usability, efficiency, and effectiveness of dialogue systems. We describe a multimodal dialogue system and algorithms for adaptive content selection based on multi-attribute decision theory. We demonstrate experimentally the improved efficacy of system responses through the use of user models to both tailor the content of system utterances and to manipulate their conciseness.