Philosophers interested in the theoretical consequences of predictive processing often assume that predictive processing is an inferentialist and representationalist theory of cognition. More specifically, they assume that predictive processing revolves around approximated Bayesian inferences drawn by inverting a generative model. Generative models, in turn, are said to be structural representations: representational vehicles that represent their targets by being structurally similar to them. Here, I challenge this assumption, claiming that, at present, it lacks an adequate justification. I examine (...) the only argument offered to establish that generative models are structural representations, and argue that it does not substantiate the desired conclusion. Having so done, I consider a number of alternative arguments aimed at showing that the relevant structural similarity obtains, and argue that all these arguments are unconvincing for a variety of reasons. I then conclude the paper by briefly highlighting three themes that might be relevant for further investigation on the matter. (shrink)

A probabilistic Chomsky–Schützenberger hierarchy of grammars is introduced and studied, with the aim of understanding the expressive power of generative models. We offer characterizations of the distributions definable at each level of the hierarchy, including probabilistic regular, context-free, (linear) indexed, context-sensitive, and unrestricted grammars, each corresponding to familiar probabilistic machine classes. Special attention is given to distributions on (unary notations for) positive integers. Unlike in the classical case where the "semi-linear" languages all collapse into the regular languages, using analytic (...) tools adapted from the classical setting we show there is no collapse in the probabilistic hierarchy: more distributions become definable at each level. We also address related issues such as closure under probabilistic conditioning. (shrink)

This paper presents a version of neurophenomenology based on generative modelling techniques developed in computational neuroscience and biology. Our approach can be described as _computational phenomenology_ because it applies methods originally developed in computational modelling to provide a formal model of the descriptions of lived experience in the phenomenological tradition of philosophy (e.g., the work of Edmund Husserl, Maurice Merleau-Ponty, etc.). The first section presents a brief review of the overall project to naturalize phenomenology. The second section presents (...) and evaluates philosophical objections to that project and situates our version of computational phenomenology with respect to these projects. The third section reviews the generative modelling framework. The final section presents our approach in detail. We conclude by discussing how our approach differs from previous attempts to use generative modelling to help understand consciousness. In summary, we describe a version of computational phenomenology which uses generative modelling to construct a computational model of the inferential or interpretive processes that best explain this or that kind of lived experience. (shrink)

ABSTRACT How can imagination generate knowledge when its contents are voluntarily determined? Several philosophers have recently answered this question by pointing to the constraints that underpin imagination when it plays knowledge-generating roles. Nevertheless, little has been said about the nature of these constraints. In this paper, I argue that the constraints that underpin sensory imagination come from the structure of causal probabilistic generative models, a construct that has been highly influential in recent cognitive science and machine learning. I highlight (...) several attractions of this account, and I favourably contrast it with Peter Langland-Hassan’s account of sensory imagination in terms of the forward models exploited in sensorimotor control. (shrink)

The Bayesian brain hypothesis, predictive processing, and variational free energy minimisation are typically used to describe perceptual processes based on accurate generative models of the world. However, generative models need not be veridical representations of the environment. We suggest that they can be used to describe sensorimotor relationships relevant for behaviour rather than precise accounts of the world.

Generative models have been proposed as a new type of non-representational scientific models recently. A generative model is characterized with the capacity of producing new models on the basis of the existing one. The current accounts do not explain sufficiently the mechanism of the generative capacity of a generative model. I attempt to accomplish this task in this paper. I outline two antecedent accounts of generative models. I point out that both types of (...) generative models function to generate new homogenous models in the sense that the latter is a straightforward derivative of the former, both of which share many similar features. Unfortunately, both accounts are implicit about the generative capacity of generative models. Using a case study, I articulate that a two-staged process of abstraction and idealization in modeling may contribute to the generative capacity of a scientific model. I also demonstrate that this two-staged process may go beyond the capacity of generating new homogenous models to generating new heterogeneous models. (shrink)

Does the material basis of conscious experience extend beyond the boundaries of the brain and central nervous system? In Clark 2009 I reviewed a number of ‘enactivist’ arguments for such a view and found none of them compelling. Ward (2012) rejects my analysis on the grounds that the enactivist deploys an essentially world-involving concept of experience that transforms the argumentative landscape in a way that makes the enactivist conclusion inescapable. I present an alternative (prediction-and-generative-model-based) account that neatly accommodates (...) all the positive evidence that Ward cites on behalf of this enactivist conception, and that (I argue) makes richer and more satisfying contact with the full sweep of human experience. (shrink)

In this paper, we consider how certain longstanding philosophical questions about mental representation may be answered on the assumption that cognitive and perceptual systems implement hierarchical generative models, such as those discussed within the prediction error minimization framework. We build on existing treatments of representation via structural resemblance, such as those in Gładziejewski :559–582, 2016) and Gładziejewski and Miłkowski, to argue for a representationalist interpretation of the PEM framework. We further motivate the proposed approach to content by arguing that (...) it is consistent with approaches implicit in theories of unsupervised learning in neural networks. In the course of this discussion, we argue that the structural representation proposal, properly understood, has more in common with functional-role than with causal/informational or teleosemantic theories. In the remainder of the paper, we describe the PEM framework for approximate Bayesian inference in some detail, and discuss how structural representations might arise within the proposed Bayesian hierarchies. After explicating the notion of variational inference, we define a subjectively accessible measure of misrepresentation for hierarchical Bayesian networks by appeal to the Kullbach–Leibler divergence between posterior generative and approximate recognition densities, and discuss a related measure of objective misrepresentation in terms of correspondence with the facts. (shrink)

According to the parallel architecture, syntactic and semantic information processing are two separate streams that interact selectively during language comprehension. While considerable effort is put into psycho- and neurolinguistics to understand the interchange of processing mechanisms in human comprehension, the nature of this interaction in recent neural Large Language Models remains elusive. In this article, we revisit influential linguistic and behavioral experiments and evaluate the ability of a large language model, GPT-3, to perform these tasks. The model can (...) solve semantic tasks autonomously from syntactic realization in a manner that resembles human behavior. However, the outcomes present a complex and variegated picture, leaving open the question of how Language Models could learn structured conceptual representations. (shrink)

Determining the semantic role of sentence constituents is a key task in determining sentence meanings lying behind a veneer of variant syntactic expression. We present a model of natural language generation from semantics using the FrameNet semantic role and frame ontology. We train the model using the FrameNet corpus and apply it to the task of automatic semantic role and frame identification, producing results competitive with previous work (about 70% role labeling accuracy). Unlike previous models used for this (...) task, our model does not assume that the frame of a sentence is known, and is able to identify null- instantiated roles, which commonly occur in our corpus and whose identification is crucial to natural language interpretation. (shrink)

The spread of aggressive memes on social media is a major problem, demanding solutions for the automatic detection of these undesired memes. However, the line between freedom of expression and the dissemination of aggressive messages is very blurred and difficult to define. Moreover, the detection of aggressive elements in memes presents a significant challenge because memes are mostly multimodal, e.g., a composition of an image with a concise textual content which often lacks a direct connection or correlation. The advent of (...) Generative Models presents an interesting alternative to unravel the inherent meaning of aggressive memes by allowing a fully multimodal analysis. This paper examines the use of Generative Models to detect aggressive memes. The proposed methodology groups aggressive memes from existing datasets according to levels of multimodal analysis difficulty and, together with the creation of prompts via zero-shot, tests and verifies how well these models perform in the task of detecting this type of content and where failures may occur. To validate the methodology, a series of experiments using three models and five datasets was conducted. According to manual annotation, these data were divided into three datasets representing three multimodality reasoning levels, which were submitted to the models to perform the task of identifying the presence of aggressive content. The results show the viability of using generative models in this task, as well as highlight the better performance of some models compared to others. Moreover, a comparison with a non-generative model specially trained on the meme datasets shows that generative models can be competitive in the task of detecting memes with aggressive content as long as the multimodality reasoning level required is not very high. The findings can help academics, managers, and decision-makers develop more effective tools for detecting aggressive content while balancing freedom of expression. The research also serves as a reference for improving moderation systems and guiding future policies in online communication. (shrink)

There are various philosophical interpretations of the account of consciousness associated with the temporal depth of generative models under the Free Energy Principle. This paper strives to develop a new philosophical interpretation of the free energy account of consciousness along the lines of intentionalism.

Generative models, with their ability to create new data, have significantly advanced machine learning. However, these models can also produce biased outputs, threatening representational fairness. Current literature mainly addresses biases in synthetic data that amplify social biases, such as racial and gender stereotypes, alongside underrepresentation and misrepresentation. In this paper, we argue for the existence of previously unexplored biases that generative models can produce on a massive scale. Using Generative Adversarial Networks as a case study, we show (...) that these unexpected and hard to interpret biases can systematically marginalize and denigrate minoritized communities. Importantly, these biases are not merely a reflection of human biases in the training data but a consequence of machine learning processes that diverge from the human perspective. We contend that current debiasing strategies are inadequate, as they fail to fully grasp the epistemic challenges and underestimate the moral risks of generative models. We conclude by suggesting a novel direction to ameliorate their harm. (shrink)

Although discriminative deep neural networks are currently dominant in cognitive modeling, we suggest that capacity-limited, generative models are a promising avenue for future work. Generative models tend to learn both local and global features of stimuli and, when properly constrained, can learn componential representations and response biases found in people's behaviors.

A successful vision system must solve the problem of deriving geometrical information about three-dimensional objects from two-dimensional photometric input. The human visual system solves this problem with remarkable efficiency, and one challenge in vision research is to understand howneural representations of objects are formed and what visual information is used to form these representations. Ideal observer analysis has demonstrated the advantages of studying vision from the perspective of explicit generative models and a specified visual task, which divides the causes (...) of image variations into the separate categories of signal and noise. Classification image techniques estimate the visual information used in a task from the properties of “noise” images that interact most strongly with the task. Both ideal observer analysis and classification image techniques rely on the assumption of a generative model. We show here how the ability of the classification image approach to understand how an observer uses visual information can be improved by matching the type and dimensionality of the model to that of the neural representation or internal template being studied. Because image variation in real world object tasks can arise from both geometrical shape and photometric (illumination or material) changes, a realistic image generation process should model geometry as well as intensity. A simple example is used to demonstrate what we refer to as a “classification object” approach to studying three-dimensional object representations. (shrink)

Automatic summarization of legal case judgements, which are known to be long and complex, has traditionally been tried via extractive summarization models. In recent years, generative models including abstractive summarization models and Large language models (LLMs) have gained huge popularity. In this paper, we explore the applicability of such models for legal case judgement summarization. We applied various domain-specific abstractive summarization models and general-domain LLMs as well as extractive summarization models over two sets of legal case judgements – from (...) the United Kingdom (UK) Supreme Court and the Indian Supreme Court – and evaluated the quality of the generated summaries. We also perform experiments on a third dataset of legal documents of a different type – Government reports from the United States. Results show that abstractive summarization models and LLMs generally perform better than the extractive methods as per traditional metrics for evaluating summary quality. However, detailed investigation shows the presence of inconsistencies and hallucinations in the outputs of the generative models, and we explore ways to reduce the hallucinations and inconsistencies in the summaries. Overall, the investigation suggests that further improvements are needed to enhance the reliability of abstractive models and LLMs for legal case judgement summarization. At present, a human-in-the-loop technique is more suitable for performing manual checks to identify inconsistencies in the generated summaries. (shrink)

This paper provides a brief review of artificial intelligence (AI) methods for sustainable aerospace systems, focusing on predictive and generative models that enable innovation in Industry 4.0 and Industry 5.0. Predictive AI models are analyzed in terms of their capacity to estimate remaining useful life (RUL), optimize maintenance planning, and enhance safety management of critical aerospace components, such as turbofan engines and aircraft bearings. Generative models, including GANs, VAEs, and diffusion-based approaches, are examined as enablers of aerodynamic design (...) optimization, structural reliability assessment, and digital twin integration, significantly reducing costs of physical testing and accelerating certification. The review highlights how physics-informed neural networks (PINNs) integrate physical laws into machine learning, ensuring reliability and interpretability in real-world aerospace applications. Key challenges such as data scarcity, algorithmic transparency, and compliance with certification standards are addressed, alongside perspectives for hybrid AI approaches combining symbolic reasoning, neural networks, and digital twins. The findings demonstrate that the integration of predictive and generative AI methods not only supports sustainable development goals by reducing emissions, resource consumption, and downtime, but also strengthens the competitiveness of the aerospace sector in the European digital transition. (shrink)

This paper presents a novel method for enhancing generative models by leveraging **probabilistic top-k feature extraction** combined with a **Transformer** applied to the most informative features. Inspired by the concept of **Markov blankets**, our approach identifies and boosts the features that most influence output generation, allowing improved sample quality in Autoencoder-based frameworks. We provide an information-theoretic proof demonstrating that our method maximizes mutual information between selected latent features and the target output, reducing redundancy compared to standard Autoencoders (AEs) and (...) Variational Autoencoders (VAEs). Experiments on the MNIST dataset show improved reconstruction quality while maintaining computational efficiency. (shrink)

We propose, following Clark, that generative models also play a central role in the perception and interpretation of linguistic signals. The data explanation approach provides a rationale for the role of prediction in language processing and unifies a number of phenomena, including multiple-cue integration, adaptation effects, and cortical responses to violations of linguistic expectations.

In this chapter, Samantha Copeland explores the relationship between ethics practice and theory and recent work in modelling theory. Starting with model-based reasoning as theorized by Magnani and Nersessian, Copeland draws from recent work on normative modelling as well as recent work on participatory multi-modelling. The parallels reveal both descriptive commonalities as well as grounding normative advice for the practice of ethics in the contemporary world.

There has been a notable increase in the use of generative models over the past few years. However, there is still a lack of full exploitation due to the difficulty of their implementation, particularly in the medical field. This work presents a tool for generating synthetic videos of embryo development and an analysis to identify the most suitable generative model for this task. The proposed tool streamlines the use of generative models for technical personnel by enabling (...) the generation of multiple video versions for the same case, automatically evaluating their quality, and assigning scores. In addition to generating synthetic videos, this tool provides automatic analysis of the output, ranking the videos based on their resemblance to real cases and absence of inconsistencies or noise. This automatic analysis ensures a higher quality and more realistic portrayal of embryo development, while making advanced generative models more accessible to a wider audience. Moreover, a comprehensive examination of two distinct generative models, one based on Generative Adversarial Networks and another based on Diffusion Models, reveals that the Diffusion Model excels at precisely locating embryo stages in more accurate positions, resulting in more realistic videos. (shrink)

We describe a deep generative model in which the lowest layer represents the word-count vector of a document and the top layer represents a learned binary code for that document. The top two layers of the generative model form an undirected associative memory and the remaining layers form a belief net with directed, top-down connections. We present efficient learning and inference procedures for this type of generative model and show that it allows more accurate (...) and much faster retrieval than latent semantic analysis. By using our method as a filter for a much slower method called TF-IDF we achieve higher accuracy than TF-IDF alone and save several orders of magnitude in retrieval time. By using short binary codes as addresses, we can perform retrieval on very large document sets in a time that is independent of the size of the document set using only one word of memory to describe each document. (shrink)

Long-range correlation, a property of time series exhibiting long-term memory, is mainly studied in the statistical physics domain and has been reported to exist in natural language. Using a state-of-the-art method for such analysis, long-range correlation is first shown to occur in long CHILDES data sets. To understand why, Bayesian generative models of language, originally proposed in the cognitive scientific domain, are investigated. Among representative models, the Simon model was found to exhibit surprisingly good long-range correlation, but {\em (...) not} the Pitman-Yor model. Since the Simon model is known not to correctly reflect the vocabulary growth of natural language, a simple new model is devised as a conjunct of the Simon and Pitman-Yor models, such that long-range correlation holds with a correct vocabulary growth rate. The investigation overall suggests that uniform sampling is one cause of long-range correlation and could thus have a relation with actual linguistic processes. (shrink)

Recent advances in Generative Language Models (GLMs) have renewed focus on promising results in zero-shot text classification. However, their off-the-shelf performance on unfamiliar and domain specific tasks remains uncertain. In this legal clause classification task we evaluate a plug-and-play zero-shot prompting strategy for OpenAI’s GPT-4 GLM on a contract clause dataset. We introduce the new CUAD-SL dataset that has been refactored as a single label classification problem as a fairer and more robust legal classification benchmark. In a comparative study, (...) we show that fine-tuning on legal domain data adapts smaller, less complex models to the task at hand, with significant classification accuracy improvement of up to 20.6%, with a best overall performance of 87.8% for the DeBERTa Transformer model compared to GPT-4's 67.2% performance. This study also takes the novel approach of assessing the business feasibility of deploying each of these machine learning models through a detailed cost–benefit analysis that measures the trade-off between performance metrics and low and high usage running costs. (shrink)

In this paper, I address the issue of scientific modelling in contemporary linguistics, focusing on the generative tradition. In so doing, I identify two common varieties of linguistic idealisation, which I call determination and isolation respectively. I argue that these distinct types of idealisation can both be described within the remit of Weisberg’s :639–659, 2007) minimalist idealisation strategy in the sciences. Following a line set by Blutner :27–35, 2011), I propose this minimalist idealisation analysis for a broad construal of (...) the generative linguistic programme and thus cite examples from a wide range of linguistic frameworks including early generative syntax, Minimalism, the parallel architecture and optimality theory. Lastly, I claim that from a modelling perspective, the dynamic turn in syntax can be explained as a continuation, as opposed to a marked shift, of the generative modelling paradigm. Seen in this light, my proposal is an even broader construal of the generative tradition, along scientific modelling lines. Thus, I offer a lens through which to appreciate the scientific contribution of generative grammar, amid an increased resistance to some of its core theoretical posits, in terms of a brand of structural realism in the philosophy of science and specifically scientific modelling. (shrink)

The new wave of ‘foundation models’—general-purpose generative AI models, for production of text (e.g., ChatGPT) or images (e.g., MidJourney)—represent a dramatic advance in the state of the art for AI. But their use also introduces a range of new risks, which has prompted an ongoing conversation about possible regulatory mechanisms. Here we propose a specific principle that should be incorporated into legislation: that any organization developing a foundation model intended for public use must demonstrate a reliable detection mechanism (...) for the content it generates, as a condition of its public release. The detection mechanism should be made publicly available in a tool that allows users to query, for an arbitrary item of content, whether the item was generated (wholly or partly) by the model. In this paper, we argue that this requirement is technically feasible and would play an important role in reducing certain risks from new AI models in many domains. We also outline a number of options for the tool’s design, and summarize a number of points where further input from policymakers and researchers would be required. (shrink)

In recent decades, scientists and philosophers have developed several naturalistic theories of consciousness, in which they try to work out some theoretical foundations for a satisfactory solution to the problem of voluntary acts, in particular the genesis of voluntary bodily movements. From the author’s point of view, depending on which concept of consciousness scientists rely on in their empirical studies of voluntary movements, volition can be understood either as a generative act or as a perceptual act. The first part (...) of the article shows that nowadays there are two competing philosophical and scientific models of volition: the generative model (dualistic and casualistic types) and the perceptual model (strong and weak types). The second part of the article deals with some experimental data from cognitive psychology and neuroscience related to the study of voluntary movements and concludes that they are in favor of the perceptive model of volition. (shrink)

Text-to-image (T2I) generative models are largely used in AI-powered real-world applications and value creation. However, their strategic deployment raises critical concerns for responsible AI management, particularly regarding the reproduction and amplification of race- and gender-related stereotypes that can undermine organizational ethics. In this work, we investigate whether such societal biases are systematically encoded within the pretrained latent spaces of state-of-the-art T2I models. We conduct an empirical study across the five most popular open-source models, using ten neutral, profession-related prompts to (...) generate 100 images per profession, resulting in a dataset of 5,000 images evaluated by diverse human assessors representing different races and genders. We demonstrate that all five models encode and amplify pronounced societal skew: caregiving and nursing roles are consistently feminized, while high-status professions such as corporate CEO, politician, doctor, and lawyer are overwhelmingly represented by males and mostly White individuals. We further identify model-specific patterns, such as QWEN-Image’s near-exclusive focus on East Asian outputs, Kandinsky’s dominance of White individuals, and SDXL’s comparatively broader but still biased distributions. These results provide critical insights for AI project managers and practitioners, enabling them to select equitable AI models and customized prompts that generate images in alignment with the principles of responsible AI. We conclude by discussing the risks of these biases and proposing actionable strategies for bias mitigation in building responsible GenAI systems. Code and Data Repository: Link. (shrink)

This paper analyzes the impact of generative large language models on academic integrity as a socio-technical issue, rather than a strictly individual problem. The paper does a cross-analysis of 30 high-level Chinese academic articles indexed by SSCI/CSSCI Core, conducts controlled experiments on the generative ability of four major models (ByteDance Doubao, Tencent Yuanbao, Baidu Wenxin Yiyan, and OpenAI Chat-GPT), and does semi-structured interviews with 18 individuals, including editors, professors, integrity officers, publishers, and AI engineers.This paper finds that “surface (...) originality can successfully conceal derivation to circumvent plagiarism and AIGC detection tools such as PaperPass.” The paper finds three themes: surface originality masking derivative content, length-dependent evasion patterns, and institutional responses to evolving evasion. This paper proposes to “enrich the Technology Acceptance Model by perceived ethical risk, develop a concept of a surface-originality evasion ecology.”. (shrink)

News reporting on generative artificial intelligence (GenAI) and large language models (LLMs) plays a central role in shaping the public epistemology of these technologies, as journalism co-produces public agendas and narratives. Central to these debates are framing practices that portray LLM-based technologies as mentalistic, often through metaphors that attribute human-like cognitive capabilities or even experiences to AI systems (e.g. AI “thinks” or “feels”). While anthropomorphic framing has a long history in technology discourse, such practices carry particular social, cultural, and (...) political weight when they contribute to inflated perceptions of AI capabilities. The present study analyses LLM-centric news framing with a focus on mentalistic representations, examining their thematic contexts, valence, and metaphorical patterns from a comparative, cross-cultural perspective. It combines computational text analysis with manual content analysis to critically explore 18,032 English-language news articles published between 2022 and 2024 across 15 outlets in Europe, Asia, and North America. The findings show that mentalistic framings are present but not uniformly dominant, varying across editorial cultures and journalistic styles. Situated against news media’s growing organisational and economic entanglement with GenAI, the study contributes to debates on AI imaginaries, public epistemology, and journalism’s role in fostering critical AI literacy. (shrink)

Generative AI technologies have the potential to be socially and politically transformative. In this paper, we focus on exploring the potential impacts that Generative AI could have on the functioning of our democracies and the nature of citizenship. We do so by drawing on accounts of deliberative democracy and the deliberative virtues associated with it, as well as the reciprocal impacts that social media and Generative AI will have on each other and the broader information landscape. Drawing (...) on this background theory, we outline some of the key positive and negative impacts that Generative AI is likely to have on democratic citizenship. The political significance of these impacts suggests the need for further regulation. (shrink)

We present a generative distributional model for the unsupervised induction of natural language syntax which explicitly models constituent yields and contexts. Parameter search with EM produces higher quality analyses than previously exhibited by unsupervised systems, giving the best published unsupervised parsing results on the ATIS corpus. Experiments on Penn treebank sentences of comparable length show an even higher F1 of 71% on nontrivial brackets. We compare distributionally induced and actual part-of-speech tags as input data, and examine extensions to (...) the basic model. We discuss errors made by the system, compare the system to previous models, and discuss upper bounds, lower bounds, and stability for this task. (shrink)

The complexity and emergent autonomy of Generative AI systems introduce challenges in predictability and legal compliance. This paper analyses some of the legal and regulatory implications of such challenges in the European Union context, focusing on four areas: liability, privacy, intellectual property, and cybersecurity. It examines the adequacy of the existing and proposed EU legislation, including the Artificial Intelligence Act (AIA), in addressing the challenges posed by Generative AI in general and LLMs in particular. The paper identifies potential (...) gaps and shortcomings in the EU legislative framework and proposes recommendations to ensure the safe and compliant deployment of generative models. (shrink)

The concept of Generative Artificial Intelligence (GenAI) is ubiquitous in the public and semi-technical domain, yet rarely defined precisely. We clarify main concepts that are usually discussed in connection to GenAI and argue that one ought to distinguish between the technical and the public discourse. In order to show its complex development and associated conceptual ambiguities, we offer a historical-systematic reconstruction of GenAI and explicitly discuss two exemplary cases: the generative status of the Large Language Model BERT (...) and the differences between protein structure predictions from AlphaFold 2 and 3. Our analysis shows that there is no unique and unambiguous definition of GenAI based on a purely technical account of the term. Following this conclusion, we argue that the public discourse is not simply a less complex way of speaking, but instead transcends its technical basis. As a means to structure this newly emerging discussion landscape we introduce a non-exhaustive list of four central aspects of GenAI: (multi-)modality, interaction, flexibility, and productivity. These dimensions constitute a first step towards defining GenAI beyond its technical basis. (shrink)

The revolution of artificial intelligence (AI), particularly generative AI, and its implications for human–robot interaction (HRI) opened up the debate on crucial regulatory, business, societal, and ethical considerations. This paper explores essential issues from the anthropomorphic perspective, examining the complex interplay between humans and AI models in societal and corporate contexts. We provided a comprehensive review of existing literature on HRI, with a special emphasis on the impact of generative models such as ChatGPT. The scientometric study posits that (...) due to their advanced linguistic capabilities and ability to mimic human-like behavior, generative AIs like ChatGPT will continue to grow in popularity in pair with human rational empathy, tendency for personification and their advanced linguistic capabilities and ability to mimic human-like behavior. As they blur the boundaries between humans and robots, these models introduce fresh moral and philosophical dilemmas. Our research aims to extrapolate key trends and unique factors in HRI and to elucidate the technical aspects of generative AI that enhance its effectiveness in this field compared to traditional rule-based AI systems. We further discuss the challenges and limitations of applying generative AI in HRI, providing a future research agenda for AI optimization in diverse applications, including education, entertainment, and healthcare. (shrink)