The article reports the results from the developmentof four data-driven discovery systems, operating inlinguistics. The first mimics the induction methods ofJohn Stuart Mill, the second performs componentialanalysis of kinship vocabularies, the third is ageneral multi-class discrimination program, and thefourth finds logical patterns in data. These systemsare briefly described and some arguments are offeredin favour of machine linguistic discovery. Thearguments refer to the strength of machines incomputationally complex tasks, the guaranteedconsistency of machine results, the portability ofmachine (...) methods to new tasks and domains, and thepotential machines provide for our gaining newinsights. (shrink)

ABSTRACTIn Critical Discourse Studies and in other linguistics oriented scholarly journals we now see more research which draws upon multimodality as part of carrying out analyses of how text...

Given a set of objects characterized by a number of attributes, hidden patterns can be discovered in them for the grouping of similar objects into clusters. If each of these clusters can be considered as exemplifying a certain concept, then the problem concerned can be referred to as a concept discovery problem. This concept discovery problem can be solved to some extent by existing data clustering techniques. However, they may not be applicable when the concept involved is vague (...) in nature or when the attributes characterizing the objects can be qualitative, quantitative, and fuzzy at the same time. To discover such concepts from objects with such characteristics, we propose a Genetic-Algorithm-based technique. By encoding a specific object grouping in a chromosome and a fitness measure to evaluate the cluster quality, the proposed technique is able to discover meaningful fuzzy clusters and assign membership degrees to objects that do not fully exemplify a certain concept. For evaluation, we tested the proposed technique with simulated and real data and the results are found to be very promising. (shrink)

Human and machine discovery are gradual problem-solving processes of searching large problem spaces for incompletely defined goal objects. Research on problem solving has usually focused on search of an instance space (empirical exploration) and a hypothesis space (generation of theories). In scientific discovery, search must often extend to other spaces as well: spaces of possible problems, of new or improved scientific instruments, of new problem representations, of new concepts, and others. This paper focuses especially on the processes (...) for finding new problem representations and new concepts, which are relatively new domains for research on discovery.Scientific discovery has usually been studied as an activity of individual investigators, but these individuals are positioned in a larger social structure of science, being linked by the blackboard of open publication (as well as by direct collaboration). Even while an investigator is working alone, the process is strongly influenced by knowledge and skills stored in memory as a result of previous social interaction. In this sense, all research on discovery, including the investigations on individual processes discussed in this paper, is social psychology, or even sociology. (shrink)

The focus of this article is a question that has been neglected in debates about digitalization: Could machines replace human scientists? To provide an intelligible answer to it, we need to answer a further question: What is it that makes (or constitutes) a scientist? I offer an answer to this question by proposing a new demarcation criterion for science which I call “the discoverability criterion”. I proceed as follows: (1) I explain why the target question of this article is important, (...) and (2) show that it leads to a variant of the demarcation problem of science. (3) By arguing that it is probably an essential feature of science that we can make scientific discoveries, I suggest a novel way of dealing with this problem by proposing a new demarcation criterion. Before introducing it, (4) I analyze an exemplary case of a real scientific discovery, and (5) argue that scientific discovery processes have a general underlying structure. (6) I introduce my discoverability criterion for science and present my master argument that helps us understand which criteria have to be fulfilled in order to decide whether machines can replace human scientists or not. (7) I conclude by answering the article’s target question and bringing forward a take-home message. (shrink)

Philosophy Compass, Volume 17, Issue 1, January 2022.

One important problem in the philosophy of science is whether there can be a normative theory of discovery, as opposed to a normative theory of justification. Although the possibility of developing a logic of scientific discovery has been often doubted by philosophers, it is particularly interesting to consider how the basic insights of a normative theory of discovery have been turned into an effective research program in computer science, namely the research field of machine learning. In (...) this paper, I introduce some current research on statistical models to a philosophical audience. In particular, I will stress those features of statistical models that make them plausible computational counterparts of scientific theories. After noticing how these models allow for the main kinds of inference that are a trademark of scientific theories, I will focus on the problem of learning statistical models from data. The analysis will show how machine learning is casting new light on traditional problems in the philosophy of science. More precisely, I will explore the implications of some results in statistical learning with respect to the role of simplicity in theory choice and to the role of scalability (as a formal property of induction) in making scientific discovery effective. (shrink)

When philosophers discuss the possibility of machines making scientific discoveries, they typically focus on discoveries in physics, biology, chemistry and mathematics. Observing the rapid increase of computer-use in science, however, it becomes natural to ask whether there are any scientific domains out of reach for machine discovery. For example, could machines also make discoveries in qualitative social science? Is there something about humans that makes us uniquely suited to studying humans? Is there something about machines that would bar (...) them from such activity? A close look at the methodology of interpretive social science reveals several abilities necessary to make a social scientific discovery, and one capacity necessary to possess any of them is imagination. For machines to make discoveries in social science, therefore, they must possess imagination algorithms. (shrink)

During my recent work on machine translation, I have come across many problems of a linguistic nature that should be of general methodological interest. Some of these problems have never been treated extensively before. Others that have been discussed previously appear now in a different and rather interesting light.

Within some online communities, discussion often centers on issues on which writers take sides, and within some subset of those debate-prone communities, we find over time that particular sets of writers almost always end up on the same side of an issue. These sets we call factions. In this paper, we describe a tool to perform what we call faction discovery on online communities. Generalizing methods developed in the bibliometrics and information retrieval literature, we define a network determined by (...) similarities of content in a community of users and add in direct evidence of online ties between users. We then perform community detection on the network to find factions. Using a set of data collected from science and fantasy blogs, we show that the discovered factions accurately reflect an active conflict in the community leading to significant, politically related social fracture. (shrink)

Volume 97, Issue 3, September 2019, Page 625-628.

This article argues that Iris Murdoch, who was supervised by John Wisdom during her 1947–48 fellowship at Newnham College Cambridge, went on to practice philosophy in a recognizably Wisdomian manner in her earliest paper, “Thinking and Language” (1951). To do so, I first describe how Wisdom understood philosophical perplexity and paradox. One task that linguistic philosophers should take up is to investigate the concrete cases that give paradoxical philosophical statements their sense and to sift the truth they contain from (...) the distortion. I then show how this vision informed his critical reception of Gilbert Ryle's The Concept of Mind as well as his own investigation of the Hidden Stream Paradox. Finally, I trace a similar approach to Murdoch's discussions of a paradox I call the Coarse Net Paradox. Recognizing Murdoch's intellectual inheritance from Wisdom enables us to see a thus‐far overlooked connection between Murdoch and the tradition of linguistic philosophy. (shrink)

This paper aims to consider the main features of the philosophy of linguistics proposed by Deleuze and Guattari, which emerges from the criticisms directed at what in A Thousand Plateaus they call ‘postulates of linguistics’. The paper focuses on the transition from the Saussurean concept of system and from the connected notion of structure to Deleuze and Guattari's concept of machine. More precisely, the purpose of the paper lies, on the one hand, in showing in which sense Deleuze and (...) Guattari claim that language is not a ‘structure’ but a ‘machine’ and why, accordingly, they maintain that the mentioned ‘postulates of linguistics’ must be refused; on the other hand, the paper represents an attempt at placing Deleuze and Guattari's position in the context of contemporary linguistics. (shrink)

In this essay, our goal is to discover science in Martin Heidegger's Introduction to Metaphysics, lecture notes for his 1935 summer semester course, because, after all, his subject is metaphysica generalis, or ontology, and this could be construed as a theory of the human brain. Here, by means of verbatim quotes from his text, we attempt to show that indeed these lectures can be viewed as suggestion for an objective scientific theory of human perception, the human capacity for deciphering phenomena, (...) ie. hermeneutics in its broadest sense. His added notes from the 1953 edition, all of which are comments, not corrections, imply that he never abandoned these thoughts on metaphysics, despite all of his utterances about a need to overcome it, and their popular interpretations to that effect. In his presentation, he further develops the colorful and intuitive style, an hermeneutic language, that he had created in his earlier work Being and Time. Of human existence he speaks in such expressions as being-there -- ``Dasein" -- and being-in-the-world, the latter term no longer explicit in the 1935 lectures. The lectures can be read as a pre-scientific analysis of how man, being-there, perceives his environment, artfully managing to deal with the manifold challenges posed by the phenomenal structures of the universe. Man attentively recognizes and deciphers their pre-linguistic contents and, while trying to make sense of it all, puts into words his awareness. There are numerous passages in Heidegger's work where he in fairly concrete terms provides us with hints for a perceptive science that, as it turns out and is demonstrated below in this essay, can be explained in terms of recent advances in neuroscience. It would be implausible, even unthinkable, unimaginable, for there not to be found essential agreement between on one hand his philosophical insights gained by hermeneutics of Dasein, into how our brain functions to let us know what's happening & on the other hand, scientific discoveries obtained through anatomy, EEG, fMRI, TMS, electrode implants, etc, of brain structures & functions employed in perception of the momentary scene. A nexus between science and philosophy is provided in Heidegger's definitive treatment of the Logos, here interpreted as a structural version of Platonic forms (Burchard, 2014). This allows for neurolinguistic brain functions to serve as meta-context for Heidegger's being-in-the-world. The logical functions of Dasein's anatomical brain are performed by the logos machine, formerly the human soul, using top-down processing based on a global context, the noumenal cosmos which humans maintain internally. Heidegger's 1942/43 winter semester lectures Parmenides extend in unbroken fashion his 1935 work, proving that he never abandoned, as is widely claimed, his metaphysical avenue of thought. (shrink)

Machine learning systems have shown great potential for performing or supporting inferential reasoning through analyzing large data sets, thereby potentially facilitating more informed decision-making. However, a hindrance to such use of ML systems is that the predictive models created through ML are often complex, opaque, and poorly understood, even if the programs “learning” the models are simple, transparent, and well understood. ML models become difficult to trust, since lay-people, specialists, and even researchers have difficulties gauging the reasonableness, correctness, and (...) reliability of the inferences performed. In this article, we argue that bridging this gap in the understanding of ML models and their reasonableness requires a focus on developing an improved methodology for their creation. This process has been likened to “alchemy” and criticized for involving a large degree of “black art,” owing to its reliance on poorly understood “best practices”. We soften this critique and argue that the seeming arbitrariness often is the result of a lack of explicit hypothesizing stemming from an empiricist and myopic focus on optimizing for predictive performance rather than from an occult or mystical process. We present some of the problems resulting from the excessive focus on optimizing generalization performance at the cost of hypothesizing about the selection of data and biases. We suggest embedding ML in a general logic of scientific discovery similar to the one presented by Charles Sanders Peirce, and present a recontextualized version of Peirce’s scientific hypothesis adjusted to ML. (shrink)

This chapter describes how the core principles of generative linguistics, which were outlined by Chomsky in the 1950s and 1960s, yielded a research methodology whose core features guarantee quick and fruitful syntactic research. Although generative linguistics is predominantly a syntax‐focused program, the methodology is intended for use in all linguistic subfields. The discovery that the establishment of a nonlocal dependency rests on hierarchical relations between words and phrases rather than on linear relations represents a watershed moment for generative (...) syntax. The ubiquity of gaps in natural language has prompted extensive research into the precise status of gaps and whether all gaps have the same status. The chapter also discusses the enduring discoveries made about the hierarchical syntactic structures on which nonlocal dependencies are instantiated. It focuses on so‐called gaps or empty categories, which frequently feature as members in nonlocal dependencies. (shrink)

Current developments in fields such as quantum physics, fine arts, robotics, cognitive sciences or defense and security indicate the emergence of creative systems capable of producing new and innovative solutions through combinations of machine learning algorithms. These systems, called machine invention systems, challenge the established invention paradigm in promising the automation of – at least parts of – the innovation process. This paper’s main contribution is twofold. Based on the identified state-of-the-art examples in the above mentioned fields, key (...) components for machine invention systems and their relations are identified, creating a conceptual model as well as proposing a working definition for machine invention systems. The differences and delimitations to other concepts in the field of machine learning and artificial intelligence, such as machine discovery systems are discussed as well. Furthermore, the paper briefly addresses the social and societal implications and limitations that come with the adoption of the technology. Because of their revolutionizing potential, there are widespread implications to consider from ethical and moral implications to policymaking and societal changes, like changes in the job structure. The discussion part approaches some of these implications, as well as solutions to some of the proposed challenges. The paper concludes by discussing some of the systemic benefits that can be accessed through machine invention. (shrink)

Scientists routinely solve the problem of supplementing one’s store of variables with new theoretical posits that can explain the previously inexplicable. The banality of success at this task obscures a remarkable fact. Generating hypotheses that contain novel variables and accurately project over a limited amount of additional data is so difficult—the space of possibilities so vast—that succeeding through guesswork is overwhelmingly unlikely despite a very large number of attempts. And yet scientists do generate hypotheses of this sort in very few (...) tries. I argue that this poses a dilemma: either the long history of scientific success is a miracle, or there exists at least one method or algorithm for generating novel hypotheses with at least limited projectibility on the basis of what’s available to the scientist at a time, namely a set of observations, the history of past conjectures, and some prior theoretical commitments. In other words, either ordinary scientific success is miraculous or there exists a logic of discovery at the heart of actual scientific method. (shrink)

Decisions based on algorithmic, machine learning models can be unfair, reproducing biases in historical data used to train them. While computational techniques are emerging to address aspects of these concerns through communities such as discrimination-aware data mining and fairness, accountability and transparency machine learning, their practical implementation faces real-world challenges. For legal, institutional or commercial reasons, organisations might not hold the data on sensitive attributes such as gender, ethnicity, sexuality or disability needed to diagnose and mitigate emergent indirect (...) discrimination-by-proxy, such as redlining. Such organisations might also lack the knowledge and capacity to identify and manage fairness issues that are emergent properties of complex sociotechnical systems. This paper presents and discusses three potential approaches to deal with such knowledge and information deficits in the context of fairer machine learning. Trusted third parties could selectively store data necessary for performing discrimination discovery and incorporating fairness constraints into model-building in a privacy-preserving manner. Collaborative online platforms would allow diverse organisations to record, share and access contextual and experiential knowledge to promote fairness in machine learning systems. Finally, unsupervised learning and pedagogically interpretable algorithms might allow fairness hypotheses to be built for further selective testing and exploration. Real-world fairness challenges in machine learning are not abstract, constrained optimisation problems, but are institutionally and contextually grounded. Computational fairness tools are useful, but must be researched and developed in and with the messy contexts that will shape their deployment, rather than just for imagined situations. Not doing so risks real, near-term algorithmic harm. (shrink)

Philosophers have recently focused on critical, epistemological challenges that arise from the opacity of deep neural networks. One might conclude from this literature that doing good science with opaque models is exceptionally challenging, if not impossible. Yet, this is hard to square with the recent boom in optimism for AI in science alongside a flood of recent scientific breakthroughs driven by AI methods. In this paper, I argue that the disconnect between philosophical pessimism and scientific optimism is driven by a (...) failure to examine how AI is actually used in science. I show that, in order to understand the epistemic justification for AI-powered breakthroughs, philosophers must examine the role played by deep learning as part of a wider process of discovery. The philosophical distinction between the 'context of discovery' and the 'context of justification' is helpful in this regard. I demonstrate the importance of attending to this distinction with two cases drawn from the scientific literature, and show that epistemic opacity need not diminish AI's capacity to lead scientists to significant and justifiable breakthroughs. (shrink)

This study investigates the performance of 21 monolingual and 56 bilingual children aged 5;6-9;0 on German-LITMUS-sentence-repetition (SRT; Hamann et al., 2013) and nonword-repetition-tasks (NWRT; Grimm et al., 2014), which were constructed according to the LITMUS-principles (Language Impairment Testing in Multilingual Settings; Armon-Lotem et al., 2015). Both tasks incorporate complex structures shown to be cross-linguistically challenging for children with Specific Language Impairment (SLI) and aim at minimizing bias against bilingual children while still being indicative of the presence of language impairment across (...) language combinations. Given the great variability in bilingual language exposure and the potential effect of language experience on language performance in bilingual children, we examined whether background variables related to bilingualism, particularly, the degree language dominance as measured by relative amount of use and exposure, could compromise the diagnostic accuracy of the German-LITMUS-SRT and NWRT. We further investigated whether a combination of the two tasks provides better diagnostic accurracy. To address this, we used an unsupervised machine learning algorithm, the Partitioning-Around-Medoids (PAM), for deriving a clinical category for the children as ±language-impaired based on performance scores on SRT and/or NWRT while withholding information about their clinical status based on standardized assessment in L1/L2. Subsequently, we calculated diagnostic accuracy and investigated which background variables (age of onset, length of exposure, degree of language-dominance, socio-economic-status, and risk factors for SLI) best explained clinical-group-membership yielded from the PAM-analysis based on NWRT and/or SRT scores. Results show that although language-dominance influences the performance of bilingual typically-developing children, especially in the SRT, the diagnostic accuracy of the tools is not compromised by language dominance: while risk factors for SLI were significant predictors for clinical group membership in all models, language dominance did not contribute at all to explaining clinical cluster membership as typically-developing or SLI based on any of the combinations of the SRT and NWRT variables. Additionally, results confirm that a combination of SRT scored by correct target structure and the structurally more complex language-dependent part of the NWRT yields better diagnostic accuracy than single measures and is only sensitive for risk factors for SLI and not to dominance levels or SES. (shrink)

Machine learning and essentialism have been connected in the past by various researchers, in order to state that the main paradigm in machine learning processes is equivalent to choosing the “essential” attributes for the machine to search for. Our goal in this paper is to show that there are connections between machine learning and essentialism, but only for some kinds of machine learning, and often not including deep learning methods. Similarity-based approaches, more connected to the (...) overall prototype theory, spanning from psychology and linguistics, seem more suited for pattern recognition and complex deep-learning issues, while for classification problems, mostly for unsupervised learning, essentialism seems like the best choice. In order to illustrate the difference better, we will connect both paths to their sources in other disciplines and see how human psychology influences our decision in machine-learning modeling as well. This leads to a philosophically very interesting consequence: even in the setting of supervised machine learning, essences are not present in data, but in targets, which in turn means that the categories which purport to be essences are in fact human-made, and hand-coded in the targets. The success of machine learning, therefore, does not give any substantial evidence for the independent existence of essential properties. Our stance here is to state that neither the existence nor the lack of “essential” properties in machine learning can lead to metaphysical, i.e., ontological claims. (shrink)

The World Wide Web has had a notable impact on a variety of epistemically-relevant activities, many of which lie at the heart of the discipline of knowledge engineering. Systems like Wikipedia, for example, have altered our views regarding the acquisition of knowledge, while citizen science systems such as Galaxy Zoo have arguably transformed our approach to knowledge discovery. Other Web-based systems have highlighted the ways in which the human social environment can be used to support the development of intelligent (...) systems, either by contributing to the provision of epistemic resources or by helping to shape the profile of machine learning. In the present paper, such systems are referred to as ‘knowledge machines’. In addition to providing an overview of the knowledge machine concept, the present paper reviews a number of issues that are associated with the scientific and philosophical study of knowledge machines. These include the potential impact of knowledge machines on the theory and practice of knowledge engineering, the role of social participation in the realization of intelligent systems, and the role of standardized, semantically-enriched data formats in supporting the ad hoc assembly of special-purpose knowledge systems and knowledge processing pipelines. (shrink)

This paper argues that all discoveries, if they can be viewed as autonomous learning from the environment, share a common process. This is the process of model abstraction involving four steps: act, predict, surprise, and refine, all built on top of the discoverer's innate actions, percepts, and mental constructors. The evidence for this process is based on observations on various discoveries, ranging from children playing to animal discoveries of tools, from human problem solving to scientific discovery. Details of this (...) process can be studied with computer simulations of discovery in simulated environments. (shrink)

The paper argues that machines cannot duplicate human linguistic creativity because linguistic meaning is context dependent in a way that eludes any machine.

The book’s core argument is that an artificial intelligence that could equal or exceed human intelligence—sometimes called artificial general intelligence (AGI)—is for mathematical reasons impossible. It offers two specific reasons for this claim: Human intelligence is a capability of a complex dynamic system—the human brain and central nervous system. Systems of this sort cannot be modelled mathematically in a way that allows them to operate inside a computer. In supporting their claim, the authors, Jobst Landgrebe and Barry Smith, marshal evidence (...) from mathematics, physics, computer science, philosophy, linguistics, and biology, setting up their book around three central questions: What are the essential marks of human intelligence? What is it that researchers try to do when they attempt to achieve "artificial intelligence" (AI)? And why, after more than 50 years, are our most common interactions with AI, for example with our bank’s computers, still so unsatisfactory? Landgrebe and Smith show how a widespread fear about AI’s potential to bring about radical changes in the nature of human beings and in the human social order is founded on an error. There is still, as they demonstrate in a final chapter, a great deal that AI can achieve which will benefit humanity. But these benefits will be achieved without the aid of systems that are more powerful than humans, which are as impossible as AI systems that are intrinsically "evil" or able to "will" a takeover of human society. (shrink)

In this paper, we explore the possibility that machine learning approaches to naturallanguage processing being developed in engineering-oriented computational linguistics may be able to provide specific scientific insights into the nature of human language. We argue that, in principle, machine learning results could inform basic debates about language, in one area at least, and that in practice, existing results may offer initial tentative support for this prospect. Further, results from computational learning theory can inform arguments carried on within (...) linguistic theory as well. (shrink)

In this paper, we explore the possibility that machine learning approaches to naturallanguage processing being developed in engineering-oriented computational linguistics may be able to provide specific scientific insights into the nature of human language. We argue that, in principle, machine learning results could inform basic debates about language, in one area at least, and that in practice, existing results may offer initial tentative support for this prospect. Further, results from computational learning theory can inform arguments carried on within (...) linguistic theory as well. (shrink)

Rodolfo Sacco developed the idea of “mute behaviours” during his studies on mute law. The notion of “mute behaviours” denotes an action that is able to mould a legal relationship without any use of language. Certainly, this concept may give rise to some doubts in relation to the attribution—to a behaviour qualified as mute—of the capability to affect dynamics involving a plurality of people. Aiming to clarify the idea of “mute behaviours” by this point of view, the authors analysed the (...) semiotic terminology used by Sacco himself to explain this notion and the issue of its silent nature. Therefore, the terms “signifying signs” and “symptoms”, deemed by Sacco—respectively—incompatible and compatible with the theory of mute behaviours were considered. The former term was traced back to the notion of conventional and arbitrary sign, the notion of the latter was reconstructed linking it to the concept of signs based on signification and suggesting a possible framework in view of the idea of “presentative meanings” developed by the philosopher Guido Morpurgo-Tagliabue. Clarified this dichotomy by a terminological analysis, the authors noticed—as affirmed by Rodolfo Sacco himself—the impossibility to ascribe the notion of gesture to the mute behaviour’s category, as some exegetes proposed. Consequently to this last remark, the authors tried to analyse the relationship between gestures and “mute behaviours”, identifying the category of “sema-pragmatic acts”, that includes gestures and may be seen as an intermediate phase between “mute behaviours” and legally relevant behaviours operated by speaking. (shrink)

In the paper I explore the relations between a relatively new and quickly expanding branch of artificial intelligence –- the automated discovery systems –- and some new views advanced in the old debate over scientific realism. I focus my attention on one such system, GELL-MANN, designed in 1990 at Wichita State University. The program's task was to analyze elementary particle data available in 1964 and formulate an hypothesis (or hypotheses) about a `hidden', more simple structure of matter, or to (...) put it in contemporary terms –- the discovery of quarks. The central thesis of my paper is that systems like GELL-MANN not only discover (or rediscover) the hidden structure of matter, but also provide independent strong evidence in favor of scientific realism about entities involved in that structure. I make an attempt to show how an argument for scientific realism about sub-microscopic entities can be constructed that would parallel Ian Hacking's `argument from coincidence' presented with respect to microscopic objects in his famous book Representing and Intervening. (shrink)

Name der Zeitschrift: Cognitive Linguistics Jahrgang: 27 Heft: 1 Seiten: 1-33.

Over three decades ago, in a brief but provocative essay, Paul Ziff argued for the thesis that robots cannot have feelings because they are "mechanisms, not organisms, not living creatures. There could be a broken-down robot but not a dead one. Only living creatures can literally have feelings."[i] Since machines are not living things they cannot have feelings. In the first half of my paper I review Ziff's arguments against the idea that robots could be conscious, especially his appeal to (...) our linguistic usage. In the second half of the essay I try to provide a deeper ontological understanding of why we ought not attribute minds to nonliving artifacts. I argue that inanimate mechanisms are incapable of genuinely active and purposive behavior. They are importantly different in kind from living human beings and animals. (shrink)

This collection of work addresses the contribution that ethnography and linguistics make to education, and the contribution that research in education makes to anthropology and linguistics.; The first section of the book pinpoints characteristics of anthropology that most make a difference to research in education. The second section describes the perspective that is needed if the study of language is to contribute adequately to problems of education and inequality. Finally, the third section takes up discoveries about narrative, which show that (...) young people's narratives may have a depth of form and skill that has gone largely unrecognized. (shrink)

In The Sleepwalkers and The Act of Creation Arthur Koestler provided pioneering studies of scientific discovery and artistic inspiration, the twin pinnacles of human achievement. The Ghost in the Machine looks at the dark side of the coin: our terrible urge to self-destruction... Could the human species be a gigantic evolutionary mistake? To answer that startling question Koestler examines how experts on evolution and psychology all too often write about people with an 'antiquated slot-machine model based on (...) the naively mechanistic world-view of the nineteenth century. His brilliant polemic helped to instigate a major revolution in the life sciences, yet its 'glimpses of an alternative world-view' form only the background to an even more challenging analysis of the human predicament. Perhaps, he suggests, we are a species in which ancient and recent brain structures - or reason and emotion - are not fully co-ordinated. Such in-built deficiencies may explain the paranoia, violence and insanity that are central strands of human history. And however disturbing we find such issues, Koestler contends, it is only when we face our limitations head-on that we can hope to find a remedy. (shrink)

While collaboration has always played an important role in many cases of discovery and creation, recent developments such as the web facilitate and encourage collaboration at scales never seen before, even in areas such as mathematics, where contributions by single individuals have historically been the norm. This new scenario poses a challenge at the theoretical level, as it brings out the importance of various issues which, as of yet, have not been sufficiently central to the study of problem-solving, (...) class='Hi'>discovery, and creativity. We analyze the case of collective and web-based proof events in mathematics, which share their temporal and social nature with every case of collective problem-solving. We propose that some ideas from cognitive architectures, in particular, the notion of codelet—understood as an agent engaged in one of a multitude of available tasks—can illuminate our understanding of collective problem-solving and act as a natural bridge from some of the theoretical aspects of collective, web-based discovery to the practical concern of designing cognitively inspired systems to support collective problem-solving. We use the Pythagorean Theorem and its many proofs as a case study to illustrate our approach. (shrink)

Linguistic Issues in Language Technology (LiLT) is an open-access journal that focuses on the relationships between linguistic insights and language technology. In conjunction with machine learning and statistical techniques, deeper and more sophisticated models of language and speech are needed to make significant progress in both existing and newly emerging areas of computational language analysis. The vast quantity of electronically accessible natural language data (text and speech, annotated and unannotated, formal and informal) provides unprecedented opportunities for data-intensive (...) analysis of linguistic phenomena, which can in turn enrich computational methods. Taking an eclectic view on methodology, LiLT provides a forum for this work. In this volume, contributors offer new perspectives on semantic representations for textual inference. (shrink)

The purpose of this work is to analyse the cognitive process of the domain theories in terms of the measurement theory to develop a computational machine learning approach for implementing it. As a result, the relational data mining approach, the authors proposed in the preceding books, was improved. We present the approach as an implementation of the cognitive process as the measurement theory perceived. We analyse the cognitive process in the first part of the paper and present the theory (...) and method of the logically most powerful empirical theory discovery in the second. The theory is based on the notion of law-like rules, which conform to all the properties of laws of nature, namely generality, simplicity, maximum refutability and minimum number of parameters. This notion is defined for deterministic and probabilistic cases. Based on the method, the discovery system is developed. The system was successfully applied to many practical tasks. (shrink)

In recent years, the work of organizations in the area of digitization has intensified significantly. This trend is also evident in the field of recruitment where job application tracking systems (ATS) have been developed to allow job advertisements to be published online. However, recent studies have shown that recruiting in most organizations is not inclusive, being subject to human biases and prejudices. Most discrimination activities appear early but subtly in the hiring process, for instance, exclusive phrasing in job advertisement discourages (...) qualified applicants from minority groups from applying. The existing works are limited to analyzing, categorizing and highlighting the occurrence of bias in the recruitment process. In this paper, we go beyond this and develop machine learning models for identifying and classifying biased and discriminatory language in job descriptions. We develop and evaluate a machine learning system for identifying five major categories of biased and discriminatory language in job advertisements, i.e., masculine-coded, feminine-coded, exclusive, LGBTQ-coded, demographic and racial language. We utilized the combination of linguistic features with recent state-of-the-art word embeddings representations as input features for various machine learning classifiers. Our results show that the machine learning classifiers were able to identify all the five categories of biased and discriminatory language with a decent accuracy. The Random Forest classifier with FastText word embeddings achieved the best performance with tenfolds cross-validation. Our system directly addresses the bias in the attraction phase of hiring by identifying and classifying biased and discriminatory language and thus encouraging recruiters to write more inclusive job advertisements. (shrink)

Intelligent machines are no longer distant fantasies of the future or solely used for industrial purposes; they are real “living” things that operate similarly to humans with verbal and nonverbal communication capabilities. Humans see in such technology the horrifying dangers and the bliss enabled by the saving power. Entrenched in the emotions of hope and fear concerning intelligent machines, humans’ attitudes toward intelligent machines are not free of expectations, judgments, strategies, and selfish agendas. As the discovery of the New (...) Worlds has led to the development of an understanding of self as distinct from the other, narratives on intelligent robots have taken on similar forms of colonial discourse: contradictory manifestations of ethnocentrism (anthropocentrism) and exoticism. Similar to colonial encounters, the racialized Mechanical _Other_ operates like any other marginalized group. Colonial discourse often regards the mechanical _Other_ as both inferior yet exotic, which usually produces a mixed sense of blessing and curse, ambivalence composed of both desire and aversion. In these representations of machinic _otherness,_ one should not miss the fundamental injustice of creating the “Machinic Other” and imposing humans’ viewpoints. The field must expand the purview of intelligent machines to consider the larger historical and cultural forces shaping our understanding of who we are as reflected in the mirror of intelligent machines. (shrink)