As we write this research paper, we notice an explosion in popularity of machine learning in numerous fields (ranging from governance, education, and management to criminal justice, fraud detection, and internet of things). In this contribution, rather than focusing on any of those fields, which have been well-reviewed already, we decided to concentrate on a series of more recent applications of deep learning models and technologies that have only recently gained significant track in the relevant literature. These applications are concerned (...) with artistic production (Sect. 2.1), the writing process (Sect. 2.2), music production (Sect. 2.3), text recognition and attribution (Sect. 2.4). After reviewing and analyzing the positive contributions as well as some of the major limitations of these technologies in each of those fields, we critically reflect (Sect. 3) on how their widespread implementation may affect humans and their creativity. In Sect. 4, we notice that deep learning models are here to stay; so, rather than embracing a negative or pessimistic stance with respect to their future applications in creative domains, we suggest a balanced approach for their assessment and beneficial usage. Finally (Sect. 5), we conclude by summarising what we have achieved and by pointing out possible future research directions. (shrink)

H.-J. Glock has made important contributions to discussions of rationality, reasons, and rules. This chapter addresses four conceptions of rationality that Glock identifies. One of these conceptions of rationality is that rationality consists in responsiveness to reasons. This chapter goes on to consider the idea that reasons became prominent in normative ethics because of their usefulness in articulating moral pluralism. The final section of the chapter connects reasons and rules and contends that both are ineliminable.

Large-scale language technologies are increasingly used in various forms of communication with humans across different contexts. One particular use case for these technologies is conversational agents, which output natural language text in response to prompts and queries. This mode of engagement raises a number of social and ethical questions. For example, what does it mean to align conversational agents with human norms or values? Which norms or values should they be aligned with? And how can this be accomplished? In this (...) paper, we propose a number of steps that help answer these questions. We start by developing a philosophical analysis of the building blocks of linguistic communication between conversational agents and human interlocutors. We then use this analysis to identify and formulate ideal norms of conversation that can govern successful linguistic communication between humans and conversational agents. Furthermore, we explore how these norms can be used to align conversational agents with human values across a range of different discursive domains. We conclude by discussing the practical implications of our proposal for the design of conversational agents that are aligned with these norms and values. (shrink)

This entry provides a substantive overview of research and debates concerning creativity in philosophy and related fields. Topics covered include definitions of creativity, whether creativity can be learned, whether it can be explained, attempts to explain creativity in cognitive science, and whether computer programs or AI systems can be creative.

What is the essential ingredient of creativity that only humans – and not machines – possess? Can artificial intelligence help refine the notion of creativity by reference to that essential ingredient? How / do we need to redefine our conceptual and legal frameworks for rewarding creativity because of this new qualifying – actually creatively significant – factor? -/- Those are the questions tackled in this essay. The author’s conclusion is that consciousness, experiential states (such as a raw feel of what (...) is like to be creating) and propositional attitudes (such as intention to instigate change by creating) appear pivotal to qualifying an exploratory effort as creativity. Artificial intelligence systems would supposedly be capable of creativity if they could exhibit such states, which philosophers and computer scientists posit as conceptually admissible and practically possible. -/- The existing legal framework rewards creative endeavours by reference to the novelty or originality of the end result. But this bar is not insurmountable for artificial intelligence. Technically speaking, artificial intelligence systems can create works that are novel and/or original. Are we then prepared to grant to those systems the legal status of “creators” in their own right? Whom should the associated benefits and rewards be assigned to? How does the position change (or not) based on the qualifying factors set out above? Should – and if, how – the general public benefit from inventions / creative works of artificial intelligence systems if troves of personal data are the key component that fueled and informed creative choices? (shrink)

Lady Lovelace’s notes on Babbage’s Analytical Engine never refer to the concept of surprise. Having some pretension to ‘originate’ something—unlike the Analytical Engine—is neither necessary nor sufficient to being able to surprise someone. Turing nevertheless translates Lovelace’s ‘this machine is incapable of originating something’ in terms of a hypothetical ‘computers cannot take us by surprise’ objection to the idea that machines may be deemed capable of thinking. To understand the contemporary significance of what is missed in Turing’s ‘surprise’ translation of (...) Lovelace’s insight, one needs to distinguish between trivial surprises and those events, propositions or encounters that lead us to question our understanding of ourselves or what surrounds us. Only some of these non-trivial surprises are the product of originality endeavours. Not only is it uncommon for surprises to track such endeavours, the type of autonomy that would be required on the part of ‘digital computers’ for originality and surprise to intersect in that way goes far beyond the operational autonomy that can be achieved by ‘learning machines’. This paper argues that a salient translation of Lovelace’s originality insight—for contemporary and future ‘learning machines’—is an upside-down version of Turing’s surprise question: can computers be surprised by us in a non-trivial, ‘co-produced’ way? (shrink)

Comparatively easy questions we might ask about creativity are distinguished from the hard question of explaining transformative creativity. Many have focused on the easy questions, offering no reason to think that the imagining relied upon in creative cognition cannot be reduced to more basic folk psychological states. The relevance of associative thought processes to songwriting is then explored as a means for understanding the nature of transformative creativity. Productive artificial neural networks—known as generative antagonistic networks (GANs)—are a recent example of (...) how a system’s ability to generate novel products can both be finely tuned by prior experience and grounded in strategies that cannot be articulated by the system itself. Further, the kinds of processes exploited by GANs need not be seen as incorporating something akin to sui generis imaginative states. The chapter concludes with reflection on the added relevance of personal character to explanations of creativity. [This is Chapter 12 of the book Explaining Imagination.]. (shrink)

This paper presents a practical case study showing how, despite the nowadays limited collaboration between AI and Cognitive Science (CogSci), cognitive research can still have an important role in the development of novel AI technologies. After a brief historical introduction about the reasons of the divorce between AI and CogSci research agendas (happened in the mid’80s of the last century), we try to provide evidence of a renewed collaboration by showing a recent case study on a commonsense reasoning system, built (...) by using insights from cognitive semantics. (shrink)

​Imagination will remain a mystery—we will not be able to explain imagination—until we can break it into parts we already understand. Explaining Imagination is a guidebook for doing just that, where the parts are other ordinary mental states like beliefs, desires, judgments, and decisions. In different combinations and contexts, these states constitute cases of imagining. This reductive approach to imagination is at direct odds with the current orthodoxy, according to which imagination is a sui generis mental state or process—one with (...) its own inscrutable principles of operation. Explaining Imagination upends that view, showing how, on closer inspection, the imaginings at work in hypothetical reasoning, pretense, the enjoyment of fiction, and creativity are reducible to other familiar mental states—judgments, beliefs, desires, and decisions among them. Crisscrossing contemporary philosophy of mind, cognitive science, and aesthetics, Explaining Imagination argues that a clearer understanding of imagination is already well within reach. (shrink)

In this paper we propose a computational framework aimed at extending the problem solving capabilities of cognitive artificial agents through the introduction of a novel, goal-directed, dynamic knowledge generation mechanism obtained via a non monotonic reasoning procedure. In particular, the proposed framework relies on the assumption that certain classes of problems cannot be solved by simply learning or injecting new external knowledge in the declarative memory of a cognitive artificial agent but, on the other hand, require a mechanism for the (...) automatic and creative re-framing, or re-formulation, of the available knowledge. We show how such mechanism can be obtained trough a framework of dynamic knowledge generation that is able to tackle the problem of commonsense concept combination. In addition, we show how such a framework can be employed in the field of cognitive architectures in order to overcome situations like the impasse in SOAR by extending the possible options of its subgoaling procedures. (shrink)

We propose a nonmonotonic Description Logic of typicality able to account for the phenomenon of the combination of prototypical concepts. The proposed logic relies on the logic of typicality ALC + TR, whose semantics is based on the notion of rational closure, as well as on the distributed semantics of probabilistic Description Logics, and is equipped with a cognitive heuristic used by humans for concept composition. We first extend the logic of typicality ALC + TR by typicality inclusions of the (...) form p :: T(C) v D, whose intuitive meaning is that “we believe with degree p about the fact that typical Cs are Ds”. As in the distributed semantics, we define different scenarios containing only some typicality inclusions, each one having a suitable probability. We then exploit such scenarios in order to ascribe typical properties to a concept C obtained as the combination of two prototypical concepts. We also show that reasoning in the proposed Description Logic is EXPTIME-complete as for the underlying standard Description Logic ALC. (shrink)

Recent progress in artificial intelligence has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats that of humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking (...) machines will have to reach beyond current engineering trends in both what they learn and how they learn it. Specifically, we argue that these machines should build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; ground learning in intuitive theories of physics and psychology to support and enrich the knowledge that is learned; and harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes toward these goals that can combine the strengths of recent neural network advances with more structured cognitive models. (shrink)

Big Data has the potential to enable unprecedentedly rigorous quantitative modeling of complex human social relationships and social structures. When such models are extended to nonhuman domains, they can undermine anthropocentric assumptions about the extent to which these relationships and structures are specifically human. Discoveries of relevant commonalities with nonhumans may not make us less human, but they promise to challenge fundamental views of what it is to be human.

Over the last four decades arguments for and against the claim that creative hypothesis formation is based on Darwinian ‘blind’ variation have been put forward. This paper offers a new and systematic route through this long-lasting debate. It distinguishes between undirected, random, and unjustified variation, to prevent widespread confusions regarding the meaning of undirected variation. These misunderstandings concern Lamarckism, equiprobability, developmental constraints, and creative hypothesis formation. The paper then introduces and develops the standard critique that creative hypothesis formation is guided (...) rather than blind, integrating developments from contemporary research on creativity. On that basis, I discuss three compatibility arguments that have been used to answer the critique. These arguments do not deny guided variation but insist that an important analogy exists nonetheless. These compatibility arguments all fail, even though they do so for different reasons: trivialisation, conceptual confusion, and lack of evidence respectively. Revisiting the debate in this manner not only allows us to see where exactly a ‘Darwinian’ account of creative hypothesis formation goes wrong, but also to see that the debate is not about factual issues, but about the interpretation of these factual issues in Darwinian terms. (shrink)

Over recent decades there has been a growing interest in the question of whether computer programs are capable of genuinely creative activity. Although this notion can be explored as a purely philosophical debate, an alternative perspective is to consider what aspects of the behaviour of a program might be noted or measured in order to arrive at an empirically supported judgement that creativity has occurred. We sketch out, in general abstract terms, what goes on when a potentially creative program is (...) constructed and run, and list some of the relationships (for example, between input and output) which might contribute to a decision about creativity. Specifically, we list a number of criteria which might indicate interesting properties of a program’s behaviour, from the perspective of possible creativity. We go on to review some ways in which these criteria have been applied to actual implementations, and some possible improvements to this way of assessing creativity. (shrink)

1. In the last decade, technological systems based on Artificial Intelligence (AI) architectures entered our lives at an increasingly fast pace. Virtual assistants facilitate our daily tasks, recom...

Currie’s (2010) argument that “i-desires” must be posited to explain our responses to fiction is critically discussed. It is argued that beliefs and desires featuring ‘in the fiction’ operators—and not sui generis imaginings (or "i-beliefs" or "i-desires")—are the crucial states involved in generating fiction-directed affect. A defense of the “Operator Claim” is mounted, according to which ‘in the fiction’ operators would be also be required within fiction-directed sui generis imaginings (or "i-beliefs" and "i-desires"), were there such. Once we appreciate that (...) even fiction-directed sui generis imaginings would need to incorporate ‘in the fiction’ operators, the main appeal of the idea that sui generis imaginings (or "i-beliefs" or "i-desires") are at work in fiction-appreciation dissipates. [This is Chapter 10 of Explaining Imagination (OUP, 2020)]. (shrink)

In this paper, we look at the possibility of a machine having a sense of humour. In particular, we focus on actual machine utterances in Turing test discourses. In doing so, we do not consider the Turing test in depth and what this might mean for humanity, rather we merely look at cases in conversations when the output from a machine can be considered to be humorous. We link such outpourings with Turing’s “arguments from various disabilities” used against the concept (...) of a machine being able to think, taken from his seminal work of 1950. Finally we consider the role that humour might play in adding to the deception, integral to the Turing test, that a machine in practice appears to be a human. (shrink)

We examine Gärdenfors’ theory of conceptual spaces, a geometrical form of knowledge representation (Conceptual spaces: The geometry of thought, MIT Press, Cambridge, 2000 ), in the context of the general Creative Systems Framework introduced by Wiggins (J Knowl Based Syst 19(7):449–458, 2006a ; New Generation Comput 24(3):209–222, 2006 b ). Gärdenfors’ theory offers a way of bridging the traditional divide between symbolic and sub-symbolic representations, as well as the gap between representational formalism and meaning as perceived by human minds. We (...) discuss how both these qualities may be advantageous from the point of view of artificial creative systems. We take music as our example domain, and discuss how a range of musical qualities may be instantiated as conceptual spaces, and present a detailed conceptual space formalisation of musical metre. (shrink)

As artificial intelligence (AI) technology advances, we increasingly delegate mental tasks to machines. However, today’s AI systems usually do these tasks with an unusual imbalance of insight and understanding: new, deeper insights are present, yet many important qualities that a human mind would have previously brought to the activity are utterly absent. Therefore, it is crucial to ask which features of minds have we replicated, which are missing, and if that matters. One core feature that humans bring to tasks, when (...) dealing with the ambiguity, emergent knowledge, and social context presented by the world, is reflection. Yet this capability is completely missing from current mainstream AI. In this paper we ask what reflective AI might look like. Then, drawing on notions of reflection in complex systems, cognitive science, and agents, we sketch an architecture for reflective AI agents, and highlight ways forward. (shrink)

In this thesis I propose a new way of understanding depressive illness as not exclusively harmful, but as related to particular, empirically evidenced, epistemic and pragmatic benefits for the subject, alongside the associated costs. For each of the benefits considered, I provide and concisely analyse the empirical evidence both in its favour and against it, suggest ways in which these benefits could apply in the circumstances presented, discuss some outstanding problems for that application as stated, and describe potential implications. The (...) issues discussed involve topics in cognitive psychology as well as in ethics, which I regard as complementary to the debate. I take the view that depression related phenomena such as low mood, low self-esteem, and mood-congruent delusions offer potential benefits to a subject under certain circumstances, but not under others. In discussing the hypotheses presented, I argue for a more complex view of depression, which includes its potential epistemic and pragmatic benefits as well as its costs and I propose possible directions for future research. (shrink)

In cognitive science, creative ideas are defined as original and feasible solutions in response to problems. A common proposal is that creative ideas are generated across dedicated cognitive pathways. Only after creative ideas have emerged, they can be enacted to solve the problem. We present an alternative viewpoint, based upon the dynamic systems approach to perception and action, that creative solutions emerge in the act rather than before. Creative actions, thus, are as much a product of individual constraints as they (...) are of the task and environment constraints. Accordingly, we understand creative motor actions as functional movement patterns that are new to the individual and/or group and adapted to satisfy the constraints on the motor problem at hand. We argue that creative motor actions are promoted by practice interventions that promote exploration by manipulating constraints. Exploration enhances variability of functional movement patterns in terms of either coordination or control solutions. At both levels, creative motor actions can emerge from finding new and degenerate adaptive motor solutions. Generally speaking, we anticipate that in most cases, when exposed to variation in constraints, people are not looking for creative motor actions, but discover them while doing an effort to satisfy constraints. For future research, this paper achieves two important aspects: it delineates how adaptive (movement) variability is at the heart of (motor) creativity, and it sets out methodologies toward stimulating adaptive variability. (shrink)

Creativity is the hallmark of human cognition, yet scientific understanding of creative processes is limited. However, there is increasing interest in revealing the neural correlates of human creativity. Though many of these studies, pioneering in nature, help demystification of creativity, but the field is still dominated by popular beliefs in associating creativity with "right brain thinking", "divergent thinking", "altered states" and so on (Dietrich and Kanso, 2010). In this article, we discuss a computational framework for creativity based on Baars' global (...) workspace theory (Baars, 1988) enhanced with mechanisms based on information theory. Next we propose a neurocognitive architecture of creativity with a strong focus on various facets (i.e., unconscious thought theory, mind wandering, spontaneous brain states) of un/pre-conscious brain responses. Our principal argument is that pre-conscious creativity happens prior to conscious creativity happens prior to conscious creativity and the proposed computational model may provide a mechanism by which this transition is managed. This integrative approach, albeit unconventional, will hopefully stimulate future neuroscientific studies of the inscrutable phenomenon of creativity. (shrink)

Jon Bing was not only a pioneer in the field of artificial intelligence and law and the legal regulation of technology. He was also an accomplished author of fiction, with an oeuvre spanning from short stories and novels to theatre plays and even an opera. As reality catches up with the imagination of science fiction writers who have anticipated a world shared by humans and non-human intelligences of their creation, some of the copyright issues he has discussed in his academic (...) capacity take on new resonance. How will we regulate copyright when robots are producers and consumers of art? This paper tries to give a sketch of the problem and hints at possible answers that are to a degree inspired by Bing’s academic and creative writing. (shrink)

This paper is presented as a performance between the two authors who are discussing the notion of daydreaming as a transitional space between their research interests in dreams and the semantic associations of conscious thought. The first half concerns the logical, rational awake mind when applied to an understanding of daydreaming as a bridge between one state and another. It investigates the idea of the interactive interface as a parallel with the daydream where both enable a middle ground, or safe (...) space for crossing-over. The difficulty highlighted through the performative reading of the paper is to keep focus without slipping into the interstitial state of a daydream. The paper gently drifts towards the second half, which explores understanding of dreamstates and the neuroscientific models of the unconscious brain. (shrink)

This paper provides an account of the nature of creativity in high-energy physics experiments through an integrated historical and philosophical study of the current and planned attempts to measure the self-coupling of the Higgs boson by two experimental collaborations at the Large Hadron Collider and the planned High Luminosity Large Hadron Collider. A notion of creativity is first identified broadly as an increase in the epistemic value of a measurement outcome from an unexpected transformation, and narrowly as a condition for (...) knowledge of the measurement of the self-coupling of the Higgs. Drawing upon Tal’s model-based epistemology of measurement this paper shows how without change to ‘readings’ a transformation to the model of the measurement process can increase the epistemic value of the measurement outcome. Such transformations are attributed to the creativity of the experimental collaboration. Creativity, in this context, is both a product, a creative and improved model, and the distributed collaborative process of transformation to the model of the measurement process. For the case of the planned measurements at the HL-LHC, where models of the measurement process perform the epistemic function of prediction, creativity is included in the models of the measurement process, both as projected quantified creativity and as an assumed property of the future collaborations. (shrink)

Why does one need creativity? On a personal level, improvisation with available resources is needed for online coping with unforeseen environmental stimuli when existing knowledge and apparent action strategies do not work. On a cultural level, the exploitation of existing cultural means and norms for the deliberate production of novel and valuable artifacts is a basis for cultural and technological development and extension of human action possibilities across various domains. It is less clear, however, how creativity develops and how exactly (...) one arrives at generating new action possibilities and producing multiple alternative action strategies using familiar objects. In this theoretical paper, we first consider existing accounts of the creative process in the Alternative Uses Task and then present an alternative interpretation, drawing on sociocultural views and an embodied cognition approach. We explore similarities between the psychological processes underlying the generation of new uses in the Alternative Uses Task and children’s pretend play. We discuss possible cognitive mechanisms and speculate how the generation of new action possibilities for common objects in pretend play can be related to adults’ ability to generate new action strategies associated with object use. Implications for creativity development in humans and embodied artificial agents are discussed. (shrink)

Artificial general intelligence is not a new notion, but it has certainly been gaining traction in recent years, and academic as well as industry resources are redirected to research in AGI. The main reason for this is that current AI techniques are limited as they are designed to operate in specific problem-domains, following meticulous preparation. These systems cannot operate in an unknown environment or under conditions of uncertainty, reuse knowledge gained in another problem domain, or autonomously learn and understand the (...) problem-domain. We shall call AI systems capable of such feats artificial general intelligent systems. The three tasks of this paper are to provide a working definition of the term AGI, examine the “missing G”, i.e., the set of abilities that current AI systems lack and whose implementation will result in a basic AGI system, and consider different approaches, including a hybrid one, to a comprehensive solution for an AGI. (shrink)