Some philosophers suggest that the development of scientificknowledge is a kind of Darwinian process. The process of discovery,however, is one problematic element of this analogy. I compare HerbertSimon's attempt to simulate scientific discovery in a computer programto recent connectionist models that were not designed for that purpose,but which provide useful cases to help evaluate this aspect of theanalogy. In contrast to the classic A.I. approach Simon used, ``neuralnetworks'' contain no explicit protocols, but are generic learningsystems built on the model of (...) the interconnections of neurons in thebrain. I describe two cases that take the connectionist approach a stepfurther by using genetic algorithms, a form of evolutionary computationthat explicitly models Darwinian mechanisms. These cases show thatDarwinian mechanisms can make novel discoveries of complex, previouslyunknown patterns. With some caveats, they lend support to evolutionaryepistemology. (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)

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)

Rayleigh and Ramsay discovered the inert gas argon in the atmospheric air in 1895 using a carefully designed sequence of experiments guided by an informal statistical analysis of the resulting data. The primary objective of this article is to revisit this remarkable historical episode in order to make a case that the error‐statistical perspective can be used to bring out and systematize (not to reconstruct) these scientists' resourceful ways and strategies for detecting and eliminating error, as well as dealing with (...) Duhemian ambiguities and underdetermination problems as they arose in the context of their local research settings. *Received December 2009; revised January 2010. †To contact the author, please write to: Department of Economics, 3016 Pamplin Hall, Virginia Tech, Blacksburg, VA 24061; e‐mail: [email protected]. (shrink)

Research in education and cognitive development suggests that explaining plays a key role in learning and generalization: When learners provide explanations—even to themselves—they learn more effectively and generalize more readily to novel situations. This paper proposes and tests a subsumptive constraints account of this effect. Motivated by philosophical theories of explanation, this account predicts that explaining guides learners to interpret what they are learning in terms of unifying patterns or regularities, which promotes the discovery of broad generalizations. Three (...) experiments provide evidence for the subsumptive constraints account: prompting participants to explain while learning artificial categories promotes the induction of a broad generalization underlying category membership, relative to describing items (Exp. 1), thinking aloud (Exp. 2), or free study (Exp. 3). Although explaining facilitates discovery, Experiment 1 finds that description is more beneficial for learning item details. Experiment 2 additionally suggests that explaining anomalous observations may play a special role in belief revision. The findings provide insight into explanation’s role in discovery and generalization. (shrink)

In this paper we take a fresh look at the information retrieval problem of balancing recall with precision in electronic document extraction. We examine the IR constructs of uncertainty, context and relevance, proposing a new process model for context learning, and introducing a new IT artifact designed to support user driven learning by leveraging explicit knowledge to discover implicit knowledge within a corpus of documents. The IT artifact is a prototype designed to present a small set of extracted (...) documents from a targeted corpus based upon user inputted criteria. The prototype provides the user with the opportunity to balance exploration and exploitation, via iterative relevance feedback to address the problem of imprecision resulting from uncertainty. We model the problem as an exploration–exploitation dilemma and apply it to a specific case of IR called eDiscovery. We conduct a series of behavioral experiments to evaluate the model and the artifact. Our initial findings indicate that the proposed model and the artifact improve performance in the IR result. (shrink)

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)

A considerable number of areas of bioscience, including gene and drug discovery, metabolic engineering for the biotechnological improvement of organisms, and the processes of natural and directed evolution, are best viewed in terms of a ‘landscape’ representing a large search space of possible solutions or experiments populated by a considerably smaller number of actual solutions that then emerge. This is what makes these problems ‘hard’, but as such these are to be seen as combinatorial optimisation problems that are best attacked (...) by heuristic methods known from that field. Such landscapes, which may also represent or include multiple objectives, are effectively modelled in silico, with modern active learning algorithms such as those based on Darwinian evolution providing guidance, using existing knowledge, as to what is the ‘best’ experiment to do next. An awareness, and the application, of these methods can thereby enhance the scientific discovery process considerably. This analysis fits comfortably with an emerging epistemology that sees scientific reasoning, the search for solutions, and scientific discovery as Bayesian processes. (shrink)

This paper presents an implemented computational model of word acquisition which learns directly from raw multimodal sensory input. Set in an information theoretic framework, the model acquires a lexicon by finding and statistically modeling consistent cross‐modal structure. The model has been implemented in a system using novel speech processing, computer vision, and machine learning algorithms. In evaluations the model successfully performed speech segmentation, word discovery and visual categorization from spontaneous infant‐directed speech paired with video images of single objects. These (...) results demonstrate the possibility of using state‐of‐the‐art techniques from sensory pattern recognition and machine learning to implement cognitive models which can process raw sensor data without the need for human transcription or labeling. (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)

Consider the problem of learning, from non-experimental data, the causal (Markov equivalence) structure of the true, unknown causal Bayesian network (CBN) on a given, fixed set of (categorical) variables. This learning problem is known to be very hard, so much so that there is no learning algorithm that converges to the truth for all possible CBNs (on the given set of variables). So the convergence property has to be sacrificed for some CBNs—but for which? In response, the (...) standard practice has been to design and employ learning algorithms that secure the convergence property for at least all the CBNs that satisfy the famous faithfulness condition, which implies sacrificing the convergence property for some CBNs that violate the faithfulness condition (Spirtes, Glymour, and Scheines, 2000). This standard design practice can be justified by assuming—that is, accepting on faith—that the true, unknown CBN satisfies the faithfulness condition. But the real question is this: Is it possible to explain, without assuming the faithfulness condition or any of its weaker variants, why it is mandatory rather than optional to follow the standard design practice? This paper aims to answer the above question in the affirmative. We first define an array of modes of convergence to the truth as desiderata that might or might not be achieved by a causal learning algorithm. Those modes of convergence concern (i) how pervasive the domain of convergence is on the space of all possible CBNs and (ii) how uniformly the convergence happens. Then we prove a result to the following effect: for any learning algorithm that tackles the causal learning problem in question, if it achieves the best achievable mode of convergence (considered in this paper), then it must follow the standard design practice of converging to the truth for at least all CBNs that satisfy the faithfulness condition—it is a requirement, not an option. (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)

In this paper, I develop an epistemological argument for freedom from poverty, building on Gerald Gaus’ work on political and moral disagreement in New Diversity Theory (NDT). NDT argues that diversity and disagreement are fundamental to political and moral learning. In this paper, I address Gaus’ central arguments in NDT, and focus on what I argue to be the key epistemological distinction of his account—namely, the argument that the relevant diversity, which is conducive to political and moral learning (...) and should therefore be protected by an institutional order, should not be defended or defined by reference to its performance with regards to discovery of true beliefs, but by reference to the independent epistemic value of protection of disagreeing, redundant and distant investigators. I develop a restatement of this argument from the perspective of institutional epistemology, and show that (along with constitutional liberties, polycentric self-governance and markets) freedom from poverty must be considered an institutional “device” conducive to such a conception of diversity. While Gaus rejects redistributive policies at the level of an institutional order, it is precisely his account of the epistemic powers of diversity and disagreement which provides the strongest defense of minimal redistribution. (shrink)

Human participants and recurrent (“connectionist”) neural networks were both trained on a categorization system abstractly similar to natural language systems involving irregular (“strong”) classes and a default class. Both the humans and the networks exhibited staged learning and a generalization pattern reminiscent of the Elsewhere Condition (Kiparsky, 1973). Previous connectionist accounts of related phenomena have often been vague about the nature of the networks’ encoding systems. We analyzed our network using dynamical systems theory, revealing topological and geometric properties that (...) can be directly compared with the mechanisms of non-connectionist, rule-based accounts. The results reveal that the networks “contain” structures related to mechanisms posited by rule-based models, partly vindicating the insights of these models. On the other hand, they support the one mechanism (OM), as opposed to the more than one mechanism (MOM), view of symbolic abstraction by showing how the appearance of MOM behavior can arise emergently from one underlying set of principles. The key new contribution of this study is to show that dynamical systems theory can allow us to explicitly characterize the relationship between the two perspectives in implemented models. (shrink)

Computational aesthetics is a relatively new subfield of artificial intelligence (AI). It includes research that enables computers to "recognize" (and evaluate) beauty in various domains such as visual art, music, and games. Aside from the benefit this gives to humans in terms of creating and appreciating art in these domains, there are perhaps also philosophical implications about the nature and "mechanics" of aesthetic perception in humans. We can, potentially, learn more about ourselves as we replicate or simulate this ability in (...) machines. My original intention or objective was to develop an aesthetics model or method for computers to recognize beauty in the game of chess since it is greatly appreciated by .. (shrink)

Philosophical analyses of scientific methodology have long understood intuition to be incompatible with a rule based reasoning that is often considered necessary for a rational scientific method. This paper seeks to challenge this contention by highlighting the indispensable role that intuition plays in the application of methodologies for scientific discovery. In particular, it seeks to outline a positive role for intuition and personal judgment in scientific discovery by exploring a comparison between the use of heuristic reasoning in scientific practice and (...) engineering design. While these discussions share many features, it will also be shown that the successful use of heuristics in engineering design is often considered to depend on a crucial factor that is markedly absent from accounts of the use of heuristics in scientific discovery; experienced judgment. In the final sections of this paper, I will compare attitudes to the role of computer analysis in scientific and engineering practices, with the aim of showing how the limitations of scientific discovery machines reveal the need for including intuition in philosophical accounts of heuristic reasoning in scientific discovery. (shrink)

At the dawn of the Third Millennium, philosophy is at an important crossroads in its role as paideia—philosophy educating humanity. A major challenge for philosophy today is to mediate the emergmg science-religion dialogue, and enhance understanding of the relationship between science, ethics, and faith. Curiously, the methodological dilemmas and thorny issues of demarcation between science and religion reflect a new awareness regarding metascientific questions posed by science itself. We are at the threshold of a new Golden Age of scientific discoveries (...) and faith-informed, interdisciplinary, and liberal learning interconnecting once more all areas of knowledge with ethics and faith. The likely key to new discoveries is an interdisciplinary approach seeking interrelatedness between all phenomena. This means also that the restoration of philosqphy in the classic sense as sophia or the love of wisdom can only be achieved within the larger framework of dialogue among all disciplines in the quest for truth. (shrink)

Deep learning (DL) is a statistical technique for pattern classification through which AI researchers train artificial neural networks containing multiple layers that process massive amounts of data. I present a three-level account of explanation that can be reasonably expected from DL models in cognitive neuroscience and that illustrates the explanatory dynamics within a future-biased research program (Feest Philosophy of Science 84:1165–1176, 2017 ; Doerig et al. Nature Reviews: Neuroscience 24:431–450, 2023 ). By relying on the mechanistic framework (Craver Explaining (...) the brain: Mechanisms and the mosaic unity of neuroscience. Clarendon, 2007 ; Stinson Eppuor si muove: Doing history and philosophy of Science with Peter Machamer. Springer, 2017, The Routledge Handbook of the computational mind. Routledge, 2018 ), I develop an account that corresponds to the stages of mechanism discovery, i.e., our shifting epistemic position and epistemic goals, and propose a representative model for each level. Generic, theoretical DL models at _Level 1_ address the general features of a cognitive phenomenon through exploration and provide us with how-possibly explanations. On the other hand, DL models at _Level 2_ either identify the interaction between the features or represent the epistemic stage when the researcher is still unsure if the modeled features are crucial or arbitrary. These models should provide us with how-plausibly explanations. Finally, specific DL models of specific brain areas, i.e., ersatz models filled with relevant cognitive and neuroscientific details, are at _Level 3_. At this level, a researcher can advance how-actually explanations of cognitive phenomena. The main strength of this account is that it elucidates both _global explanatory dynamics_ and _local explanatory dynamics_ (cf. Dresow Synthese 199:10441–10474, 2021 ). The former occurs when the transition between levels happens in accordance with the process of obtaining more details about a particular cognitive phenomenon through multiple DL models. The latter, meanwhile, involves cases in which the transition between levels takes place within a single DL model by elucidating internal mechanisms (e.g., using Explainable AI techniques for rendering models more transparent). (shrink)

Frank Mitjans is an architect who has worked in London since 1976. He was introduced to the significance of the figure of St. Thomas More by Andrés Vázquez de Prada, author of the biography, Sir Tomás Moro, Lord Canciller de Inglaterra. In 1977 Vázquez de Prada invited Mitjans to visit with him the Thomas More Exhibition at the National Portrait Gallery, which stimulated his interest in representations of More, his family and his friends. Since August 2002 he has given many (...) presentations and talks on the topography of More’s London to groups of students and other interested people in Britain, Ireland, and Sweden. Frank Mitjans, architecte qui travaille à Londres depuis 1976, fut initié au personnage de Saint Thomas More par Andrés Vázquez de Prada, auteur de la biographie Sir Tomás Moro, Lord Canciller de Inglaterra. En 1977 Vázquez de Prada l’invita à l’accompagner dans sa visite de l’exposition Thomas More à la National Portrait Gallery, évènement qui aiguisa son intérêt pour les représentations de More, de sa famille et de ses amis. Depuis août 2002 il a régulièrement fait des présentations et des exposés à des groupes d’étudiants et d’autres en Grande-Bretagne, Irlande et Suède au sujet de la topographie du Londres de Thomas More. The recent exhibitions of the works of Holbein in London and Basel, and scholarly publications such as Holbein and England, have rekindled interest in the Portrait of Thomas More and his Family, and in the late sixteenth-century versions of Holbein’s presumed lost original. The present paper analyses the differences between the sixteenth-century versions and Holbein’s 1527 composition sketch, and concludes that the sketch is a more reliable witness to Holbein’s lost painting than the later versions, as well as the only authentic witness to More’s intentions; though anything we learn from the drawing must be checked against what we know from More’s letters and writings and other biographical data. From the later versions, however, and in particular from the references to Seneca on them, we can learn about those who commissioned them and England’s peculiar historical circumstances. (shrink)

Curiosity motivates the search for missing information, driving learning, scientific discovery, and innovation. Yet, identifying that there is a gap in one's knowledge is itself a critical step, and may demand that one formulate a question to precisely express what is missing. Our work captures the integral role of self‐generated questions during the acquisition of new information, which we refer to as active‐curiosity‐driven learning. We tested active‐curiosity‐driven learning using our “Curiosity Question & Answer Task” paradigm, where participants (...) (N=135) were asked to generate questions in response to novel, incomplete factual statements and provided the opportunity to forage for answers. We also introduce new measures of question quality that express how well questions capture stimulus and foraging information. We hypothesized that active question asking should influence behavior across the stages of our task by increasing the probability that participants express curiosity, forage for answers, and remember what they had thereby discovered. We found that individuals who asked a high number of quality questions experienced elevated curiosity, were more likely to pursue missing information that was semantically related to their questions, and more likely to retain the information on a later cued recall test. Additional analyses revealed that curiosity played a predominant role in motivating participants to forage for missing information, and that both curiosity and satisfaction with the acquired information boosted memory recall. Overall, our results suggest that asking questions enhances the value of missing information, with important implications for learning and discovery of all forms. (shrink)

It is shown how mathematical discoveries such as De Moivre's theorem can result from patterns among the symbols of existing formulae and that significant mathematical analogies are often syntactic rather than semantic, for the good reason that mathematical proofs are always syntactic, in the sense of employing only formal operations on symbols. This radically extends the Lakatos approach to mathematical discovery by allowing proof-directed concepts to generate new theorems from scratch instead of just as evolutionary modifications to some existing theorem. (...) The emphasis upon syntax and proof permits discoveries to go beyond the limits of any prevailing semantics. It also helps explain the shortcomings of inductive AI systems of mathematics learning such as Lenat's AM, in which proof has played no part in the formation of concepts and conjectures. (shrink)

This work suggests how much human sciences can learn from a booklet almost all cultures know, The Little Prince. It stimulates to read them in their anthropological aspects of behavior facing alterity, highlighting its value without proposing it as educational element to homogenize the different. The reflection is thus focused on the demolishing and lasting effects of European culture during its colonization of American and African people. The contribution ends with Ferraioli and De Vitoria's considerations on a society based on (...) the axiom of the free circulation of men and goods, and the legitimization of thinking given by natural law. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:Reviewed by:Theology as Interdisciplinary Inquiry: Learning with and from the Natural and Human Sciences eds. by Robin W. Lovin and Joshua MauldinSara A. WilliamsTheology as Interdisciplinary Inquiry: Learning with and from the Natural and Human Sciences Edited by Robin W. Lovin and Joshua Mauldin grand rapids, mi: eerdmans, 2017. 202 pp. $32.00How can Christian theology engage in fruitful dialogue with fields of inquiry such as cognitive science, (...) anthropology, and law? Might discoveries in the natural and human sciences open creative possibilities for theology, and vice versa? Can Christian theology maintain its integrity while integrating insights from other disciplines? Theology as Interdisciplinary Inquiry takes up these questions as its central aim. This volume of six essays emerges from a three-year Templeton Foundation initiative that brought an interdisciplinary set of scholars together at the Center for Theological Inquiry to explore how their research might collaboratively push theology in new directions. These conversations centered on three themes: evolution and human nature, religious experience and moral identity, and law and religious freedom.In the first essay, Celia Deane-Drummond proposes an alternative approach to traditional readings of the Genesis creation account that looks to "resonances" between von Balthasar's notion of "theo-drama" and niche construction theory. Her argument allows for human agency while drawing theology into a complementary, rather than conflictual, relationship with evolutionary science. The next three essays take up the theme of religious experience and moral identity. Michael Spezio proposes that both theology and cognitive science look to moral exemplars as the phenomenological basis for more realistic and mutually informative moral theology and cognitive models of learning. Colleen Shantz draws on cognitive science to demonstrate how the material [End Page 192] and relational are constitutive of cognitive processes, arguing that it is precisely through "development and change" that we bear the imago Dei (66). Andrea Hollingsworth offers a rereading of Nicholas of Cusa's De visione Dei to demonstrate how a medieval mystical text echoes elements of contemporary neurocognitive processes of self-transformation. The final two essays center on law and religious freedom. John Burgess examines the privileging of religious freedom for canonizing martyrs in post-Soviet Russian Orthodoxy, arguing that this provides theological grounding for the legal right to religious freedom. Mary Ellen O'Connell proposes aesthetics as a more adequate modern grounding for legal authority than science. She argues that aesthetics takes us beyond the limits of self-interest, drawing us into concern for the good of others through the contemplation of beauty.What holds these essays together is the pursuit of "transdisciplinarity": "paying close attention to the other discipline(s) in a way that has a substantial and mutual impact, but at the same time retaining a clear sense of disciplinary integrity" (14–15). Lovin and Mauldin argue for humility and hope as virtues that enable us to navigate the tension between openness and integrity. While humility enables us to recognize the limits of disciplinary boundaries and assumptions, hope propels us forward, promising that however incomplete our knowledge remains, by the grace of God we will make advances in understanding our world and ourselves.Just as Christian Scharen and Aana Marie Vigen's Ethnography as Christian Theology and Ethics argues for the value of engagement with ethnography and its related disciplines to produce theology and ethics grounded in lived reality, this volume argues for the value of engagement with the natural and human sciences to produce "theological realism" that accounts for the complexity of "human personality and community" (xviii). It is unsurprising, then, that both volumes respond to objections from the neo-Radical Reformation and Radical Orthodoxy camps. In his conclusion, Douglas Ottati argues that these camps cannot account for the ways in which theological production inevitably interacts with natural and cultural processes. He argues further that insulating Christian theology is a form of cultural accommodation because it mirrors the formation of discrete modern disciplines, the very limitation these essays seek to transcend.This volume advances the live discussion in Christian ethics about the promise and risks of engaging natural and social sciences. It is a valuable resource on this topic, and its essays are suitable for use in seminary course discussions... (shrink)

Storytellers and their audiences over many millennia have thought that we can learn from fiction. Philosopher Gregory Currie challenges that supposition. He doubts knowing can be founded on imagining, and claims that what we think we learn from fiction is not reli­able in the way science or philosophy is, because not tested through peerreview, experi­ment, and argument. He underrates the role of the imagination in understanding all hu­man language, in fictionality outside formal fictions, and in science. Science is not “reliabilist” (...) as Currie assumes: it aims at bold imaginative discoveries that often overturn what had previously been thought secure and may well be displaced by still newer discov­eries. Fiction may not have peer review, but it is tested on the highly developed intuitions of audiences, on the expertise of critics, and through the corrective competition and inno­vations of other storytellers, as Joyce challenges Homer, or David Sloan Wilson’s recent Atlas Hugged challenges Ayn Rand’s Atlas Shrugged. There are strong reasons for predicting that fiction has a prosocial bias from which humans over many millennia have learned to expand their sociality. That does not mean that all exposure to fiction is beneficial. (shrink)

Schyns et al. make a strong case for context-dependent feature discovery. The features computed from specialized and diverse data-sets help to coordinate their activity by adapting so as to emphasize what is related across sets. Their perspective can be strengthened and extended by formal arguments for the contextual guidance of learning and processing and by neurobiological and psychological evidence of structures and processes that implement this guidance.

How do major scientific discoveries reshape their originators’, and our own, sense of reality and concept of the physical world? The Scientist as Philosopher explores the interaction between physics and philosophy. Clearly written and well illustrated, the book first places the scientist-philosophers in the limelight as we learn how their great scientific discoveries forced them to reconsider the time-honored notions with which science had described the natural world. Then, the book explains that what we understand by nature and science have (...) undergone fundamental conceptual changes as a result of the discoveries of electromagnetism, thermodynamics and atomic structure. Even more dramatically, the quantum theory and special theory of relativity questioned traditional assumptions about causation and the passage of time. The author concludes that the dance between science and philosophy is an evolutionary process, which will keep them forever entwined. (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)

Any study of the 'Scientific Revolution' and particularly Descartes' role in the debates surrounding the conception of nature (atoms and the void v. plenum theory, the role of mathematics and experiment in natural knowledge, the status and derivation of the laws of nature, the eternality and necessity of eternal truths, etc.) should be placed in the philosophical, scientific, theological, and sociological context of its time. Seventeenth-century debates concerning the nature of the eternal truths such as '2 + 2 = 4' (...) or the law of inertia turn on the question of whether these truths were created along with nature, or were uncreated and subsisting in God's mind. One's answer to that question has direct consequences for conceptions of the necessity/contingency of mathematical and natural knowledge, how knowledge of such truths is accomplished by humans, and what grounds these truths. In this paper, I review the positions of four successors to Descartes' philosophy on the question of the eternal truths to illustrate how in specific ways that question with its theological, metaphysical, modal, and epistemological dimensions concerned the objectivity and certainty of the discoveries of the new science. Author Recommends: Clarke, Desmond. Descartes' Philosophy of Science . University Park, Penn State Press, 1982. This work provides an account of Descartes as a practicing scientist whose rationalism is mitigated by reliance on experiment and experience. Author re-examines Descartes' philosophical and scientific works in this new light. Dear, Peter. Revolutionizing the Sciences: European Knowledge and its Ambitions, 1500–1700 . Princeton, Princeton University Press, 2001. This work provides a useful overview of the issues and thinkers of the Scientific Revolution. Of particular relevance is chapter 8 on Cartesian and Newtonian science. Funkenstein, Amos. Theology and the Scientific Imagination from the Middle Ages to the Seventeenth Century . Princeton, Princeton University Press, 1986. This work is an advanced study of the theological and metaphysical foundations of early modern science. Discussions include questions of God's nature, God's knowledge in relation to human knowledge, providence, the laws of nature, and the truths of mathematics. In particular, chapter 3 discusses Descartes' account of the eternal truths and divine omnipotence. Garber, Daniel. Descartes' Metaphysical Physics . Chicago, University of Chicago Press, 1992. This work examines how Descartes' metaphysical doctrines of God, soul, and body set the groundwork for his physics. It includes a study of God and the grounds for the laws of physics (chapter 9). Henry, John. The Scientific Revolution and the Origins of Modern Science . 3rd ed. New York, Palgrave, Macmillan Press, 2008. This work provides a brief, general, and informative overview of the Scientific Revolution, including the themes of method, magic, religion, and culture. Osler, Margaret J. Divine Will and the Mechanical Philosophy: Gassendi and Descartes on Contingency and Necessity in the Created World . Cambridge, Cambridge University Press, 1994. This work is an examination and comparison of the mechanical philosophies of Gassendi and Descartes. It offers in-depth discussion of the issue of voluntarism and intellectualism in the period and how that related to conceptions of laws of nature and the eternal truths. Shapin, Steven. The Scientific Revolution . Chicago, University of Chicago Press, 1996. This work provides a critical synthesis of as well as a guide to recent scholarship in the history of science for a general readership. Online Materials Dr. Robert A. Hatch's Scientific Revolution Website: http://web.clas.ufl.edu/users/rhatch/pages/03-Sci-Rev/SCI-REV-Home/ A compendium of resources for the study of Scientific Revolution. Early English Books Online: http://eebo.chadwyck.com/home Early English Books Online (EEBO) contains digital facsimile page images of virtually every work printed in England, Ireland, Scotland, Wales and British North America and works in English printed elsewhere from 1473 to 1700. Early Modern Resources: http://www.earlymodernweb.org.uk/emr/ Early Modern Resources is a gateway for all those interested in finding electronic resources relating to the early modern period in history. Gallica, the Digital Library of the Bibliothèque Nationale de France: http://gallica.bnf.fr/ An ever-growing digital library which includes numerous primary and secondary texts of relevance to Descartes and his role in Scientific Revolution. Hatfield, Gary, 'René Descartes', The Stanford Encyclopedia of Philosophy. Spring 2009 ed. Ed. Edward N. Zalta; URL: http://plato.stanford.edu/archives/spr2009/entries/descartes/ Slowik, Edward, 'Descartes' Physics', The Stanford Encyclopedia of Philosophy. Winter 2008 ed. Ed. Edward N. Zalta; URL: http://plato.stanford.edu/archives/win2008/entries/descartes-physics/ Syllabus Sample Syllabus: Cartesian Science The following is five weeks covering Cartesian Science in a course on Descartes or the Scientific Revolution, or 17th-century theories of matter, or related themes on early modern truth and method, especially on the continent. This material is best suited to a graduate level audience, but it could be modified to suit an upper-division undergraduate course, as the readings are basically primary texts whose context and background can be explained in lectures. Week 1: Cartesian Revolution in France • Scientific method • Role of mathematics and experiment • Certainty of scientific knowledge Readings: Hatfield, Gary, 'René Descartes', The Stanford Encyclopedia of Philosophy. Spring 2009 ed. Ed. Edward N. Zalta; URL: http://plato.stanford.edu/archives/spr2009/entries/descartes/ Descartes, Discourse on Method , Parts 1–3 Descartes, Meditations on First Philosophy , First Meditation. Week 2: Descartes' Scientific Treatises • Mechanization and mathematization of nature • Primary–secondary quality distinction Readings: Discourse on Method, Parts 4–6 Selections from Descartes' Scientific Essays: The World or Treatise on Light (ATXI 3–48); Treatise on Man (ATXI 119–202); Optics (ATVI 82–147). Slowik, Edward, 'Descartes' Physics', The Stanford Encyclopedia of Philosophy. Winter 2008 ed. Ed. Edward N. Zalta; URL: http://plato.stanford.edu/archives/win2008/entries/descartes-physics/ Henry, John, 'The Mechanical Philosophy,' chapter 5. The Scientific Revolution and the Origins of Modern Science . 3rd ed. Macmillan, 2008. Week 3: Descartes' Theory of Nature • Descartes' derivation of the law of conservation and the three laws of motion • God's role in the metaphysics and physics of nature Readings: Selections from Principles of Philosophy, Preface (all); Letter to Elizabeth; Part I: 1–8; Part II: 1–45, 55, 64; Part III: 1–4, 15–19, 45–47; Part IV: 187–207. John Henry, 'Religion and Science,' chapter 6. The Scientific Revolution and the Origins of Modern Science . 3rd ed. Macmillan, 2008. Week 4: Post-1650 Cartesian Science: Necessity and Contingency in Nature • Debates on God, Creation, and Causes Readings: Easton, Patricia, 'What is at Stake in the Cartesian Debates on the Eternal Truths?' Philosophy Compass 4.2 (2009): 348–62. Malebranche, Nicolas, 'Elucidation 10', from The Search after Truth (1674). Note: All selections available in Nicolas Malebranche (1992). Philosophical Selections , edited by S. Nadler, Hackett. Gottfried Leibniz (1714) Monadology . Week 5: Causes in Nature and Morals • Theodicy as an explanation of defect and evil in a lawful universe: Malebranche v. Leibniz Readings: Nicolas Malebranche, Elucidation XVI (on occasionalism), and Treatise on Nature and Grace, Discourse One, Part 1. Gottfried Leibniz (1706), Theodicy. Focus Questions Weekly questions can be used to focus the readings. This can be done in a web or e-mail discussion thread, as a weekly assignment, or for in class discussion. I require students to post a short paragraph in response to the question or some posting by a classmate on the question. Students are required to post by 10 a.m. the day before we meet for class on a course website. Week 1: According to Descartes, what role does skepticism play in scientific reasoning? Week 2: Comment on the following: 'But I am supposing this machine to be made by the hands of God, and so I think you may reasonably think it capable of a greater variety of movements than I could possibly imagine in it, and of exhibiting more artistry than I could possibly ascribe to it' [ Treatise on Man ; ATXI 120]. Week 3: What is Descartes' conception of the relation between the metaphysics and physics of nature? Week 4: Critically discuss the positions of Descartes, Malebranche, and Leibniz on what provides the foundation for the certitude of natural knowledge? Week 5: Explain why both Malebranche and Leibniz consider moral sin to be analogous to natural defect? Seminar/Project Idea Hold a debate on the question of the status of the eternal truths. The proposition will be Descartes' position: 'Eternal truths must be both created and necessary if certainty in science is to be possible'. Format: 1. At the beginning of the 5-week module, students will be assigned to one of three roles: Team A, Team B, and judge's panel. Students will be given the debate proposition, but will not be told which team will take the affirmative and which team the negative until the time of the debate. 2. Recommend a variation on the Classic Debate Format to encourage the development of argument: sequence begins with affirmative construction (8 minutes), negative construction (8 minutes), second affirmative construction (8 minutes), second negative construction (8 minutes), first negative rebuttal (4 minutes), first affirmative rebuttal (4 minutes), final negative rebuttal (4 minutes) and final affirmative rebuttal (4 minutes). 3. Judges Panel: will consist of 3–4 judges who will assess the performance of Teams A and B. Judgment should be based on the persuasiveness of the team position. 4. Debate will be held at the end of the fifth week, or semester, whichever makes most sense given the course length and structure. Acknowledgements The author gratefully acknowledges the immensely helpful comments and suggestions by the participants in her graduate seminar on the Scientific Revolution: Benjamin Chicka, Sarah Jacques-Ross, Richard Ross, Marcella Stockstill, and Zohra Wolters. (shrink)

Using the Chinese Ring Puzzle (Kotovsky & Simon, Citation1990; P. J. Reber & Kotovsky, Citation1997), we studied the effect on rule discovery of having to plan actions or not in order to reach a goal state. This was done by asking participants to predict legal moves as in implicit learning tasks (Experiment 1) and by asking participants to make legal moves as in problem-solving tasks (Experiment 2). Our hypothesis was that having a specific goal state to reach has a (...) dual effect on rule discovery. The first effect is positive and related to feedback from moves done in order to attain the goal: generalising the results of action and associating them to the conditions in which they were obtained allows discovery of the rule and learning it. The second effect is negative. In attempting to reach a specific goal, participants first tend to reduce the distance that separates the current state from the goal state (hill climbing) and so neglect the kind of exploration that facilitates rule and procedure discovery because this would seem to be a detour from the goal. Results show that having to plan actions improved performance in implicit learning tasks (Experiment 1), yet it impaired performance in problem-solving tasks (Experiment 2). Although implicit learning and problem solving are based on rule discovery, and entail noticing regularities in the material, in both cases, rule discovery processes appear to be task-dependent. (shrink)

Formal learning theory is an approach to the study of inductive inference that has been developed by computer scientists. In this paper, I discuss the relevance of formal learning theory to such standard topics in the philosophy of science as underdetermination, realism, scientific progress, methodology, bounded rationality, the problem of induction, the logic of discovery, the theory of knowledge, the philosophy of artificial intelligence, and the philosophy of psychology.

Abstract. In this essay I take Michael Polanyi's analysis of scientific discovery and extend it to encompass fresh encounters with the living God. Given the embodied character of all human knowing, Polanyi challenged objectivism and positivism as untenable. In its place, Polanyi noted that the tacit skills established when a physicist learns to detect radio waves has its counterpart in a Christian's being trained to find God. Once trained, stubborn organismic habits constrain both physicist and believer within a socially approved (...) heuristic circle that can be broken only by the act of discovery. The puzzlement that erupts at the onset of an inquiry ultimately finds relief only in an expanded encounter with the realities that one has been trained to serve. Thus, the act of discovery not only serves to disrupt the tradition as it has been received but also reveals that the realities being served make themselves known in novel ways. The lifelong pursuit of God and the lifelong pursuit of novel manifestations of radio waves thus share a common epistemological and phenomenological underpinning. (shrink)

Technology has substantially transformed the way legal services operate in many different countries. With a large and complex collection of digitized legal documents, the judiciary system worldwide presents a promising scenario for the development of intelligent tools. In this work, we tackle the challenging task of organizing and summarizing the constantly growing collection of legal documents, uncovering hidden topics, or themes that later can support tasks such as legal case retrieval and legal judgment prediction. Our approach to this problem relies (...) on topic discovery techniques combined with a variety of preprocessing techniques and learning-based vector representations of words, such as Doc2Vec and BERT-like models. The proposed method was validated using four different datasets composed of short and long legal documents in Brazilian Portuguese, from legal decisions to chapters in legal books. Analysis conducted by a team of legal specialists revealed the effectiveness of the proposed approach to uncover unique and relevant topics from large collections of legal documents, serving many purposes, such as giving support to legal case retrieval tools and also providing the team of legal specialists with a tool that can accelerate their work of labeling/tagging legal documents. (shrink)

The idea of entrepreneurial discovery of profitable opportunities neglected by others as the driving force of the market process is the key contribution of Kirzner to economics. However, to enrich our understanding of the process of entrepreneurial discovery and to derive testable implications we need something more concrete than Kirzner’s alertness. The paper builds on Kirzner’s distinction between the logic of choice and perception, by arguing that the essence of decision making is coming to an understanding, and learning how (...) to act. A set of behavioral implications follows as patterns of learning in different contexts. Further, conventions emerge as a means of stabilizing mutual expectations. Stability of conventions on the one hand and learning over time on the other creates possibility of entrepreneurial discovery and endogenous process of change.L’idée de découverte entrepreneuriale des opportunités profitables ignorées par les autres en tant que force motrice du processus marchand représente la contribution fondamentale de Kirzner à la science économique. Cependant, nous devons disposer de quelque chose de plus concret que la vigilance kirznerienne si nous voulons approfondir notre compréhension du processus de découverte entrepreneuriale et en déduire des implications testables. L’article se fonde sur la distinction qu’opère Kirzner entre la logique des choix et la perception et propose que l’essence de la prise de décision consiste à parvenir à une compréhension et à apprendre comment agir. Un ensemble d’implications au niveau des comportements en découle comme schème d’ apprentissage dans des contextes différents. De plus, les conventions émergent en tant que moyens de stabilisation des anticipations mutuelles. La stabilité des conventions, d’un côté, et l’apprentissage dans le temps, de l’autre, créent la possibilité de découverte entrepreneuriale et de processus de changement endogènes. (shrink)

Machine learning (ML) has been praised as a tool that can advance science and knowledge in radical ways. However, it is not clear exactly how radical are the novelties that ML generates. In this article, I argue that this question can only be answered contextually, because outputs generated by ML have to be evaluated on the basis of the theory of the science to which ML is applied. In particular, I analyze the problem of novelty of ML outputs in (...) the context of molecular biology. In order to do this, I first clarify the nature of the models generated by ML. Next, I distinguish three ways in which a model can be novel (from the weakest to the strongest). Third, I dissect the way ML algorithms work and generate models in molecular biology and genomics. On these bases, I argue that ML is either a tool to identify instances of knowledge already present and codified, or to generate models that are novel in a weak sense. The notable contribution of ML to scientific discovery in the context of biology is that it can aid humans in overcoming potential bias by exploring more systematically the space of possible hypotheses implied by a theory. (shrink)

A great deal of research has demonstrated that learning is influenced by the learner’s prior background knowledge (e.g. Murphy, 2002; Keil, 1990), but little is known about the processes by which prior knowledge is deployed. We explore the role of explanation in deploying prior knowledge by examining the joint effects of eliciting explanations and providing prior knowledge in a task where each should aid learning. Three hypotheses are considered: that explanation and prior knowledge have independent and additive effects (...) on learning, that their joint effects on learning are subadditive, and that their effects are superadditive. A category learning experiment finds evidence for a superadditive effect: explaining drives the discovery of regularities, while prior knowledge constrains which regularities learners discover. This is consistent with an account of explanation’s effects on learning proposed in Williams & Lombrozo (in press). (shrink)

This chapter presents a new semantics for inductive empirical knowledge. The epistemic agent is represented concretely as a learner who processes new inputs through time and who forms new beliefs from those inputs by means of a concrete, computable learning program. The agent’s belief state is represented hyper-intensionally as a set of time-indexed sentences. Knowledge is interpreted as avoidance of error in the limit and as having converged to true belief from the present time onward. Familiar topics are re-examined (...) within the semantics, such as inductive skepticism, the logic of discovery, Duhem’s problem, the articulation of theories by auxiliary hypotheses, the role of serendipity in scientific knowledge, Fitch’s paradox, deductive closure of knowability, whether one can know inductively that one knows inductively, whether one can know inductively that one does not know inductively, and whether expert instruction can spread common inductive knowledge—as opposed to mere, true belief—through a community of gullible pupils. (shrink)

I have applied a fairly general, learning theoretic perspective to some questions raised by Reichenbach's positions on induction and discovery. This is appropriate in an examination of the significance of Reichenbach's work, since the learning-theoretic perspective is to some degree part of Reichenbach's reliabilist legacy. I have argued that Reichenbach's positivism and his infatuation with probabilities are both irrelevant to his views on induction, which are principally grounded in the notion of limiting reliability. I have suggested that limiting (...) reliability is still a formidable basis for the formulation of methodological norms, particularly when reliability cannot possibly be had in the short run, so that refined judgments about evidential support must depend upon measure-theoretic choices having nothing to do in the short run with the truth of the hypothesis under investigation. To illustrate the generality of Reichenbach's program, I showed how it can be applied to methods that aim to solve arbitrary assessment and discovery problems in various senses. In this generalized Reichenbachian setting, we can characterize the intrinsic complexity of reliable inductive inference in terms of topological complexity. Finally, I let Reichenbach's theory of induction have the last say about hypothetico-deductive method. (shrink)

From an early age, exposure to a spoken language has allowed us to implicitly capture the structure underlying the succession of speech sounds in that language and to segment it into meaningful units. Statistical learning, the ability to pick up patterns in the sensory environment without intention or reinforcement, is thus assumed to play a central role in the acquisition of the rule-governed aspects of language, including the discovery of word boundaries in the continuous acoustic stream. Although extensive evidence (...) has been gathered from artificial languages experiments showing that children and adults are able to track the regularities embedded in the auditory input, as the probability of one syllable to follow another syllable in the speech stream, the developmental trajectory of this ability remains controversial. In this work, we have collected Event-Related Potentials while 5-year-old children and young adults were exposed to a speech stream made of the repetition of eight three-syllable nonsense words presenting different levels of predictability to mimic closely what occurs in natural languages and to get new insights into the changes that the mechanisms underlying auditory statistical learning might undergo through the development. The participants performed the aSL task first under implicit and, subsequently, under explicit conditions to further analyze if children take advantage of previous knowledge of the to-be-learned regularities to enhance SL, as observed with the adult participants. These findings would also contribute to extend our knowledge of the mechanisms available to assist SL at each developmental stage. Although behavioral signs of learning, even under explicit conditions, were only observed for the adult participants, ERP data showed evidence of online segmentation in the brain in both groups, as indexed by modulations in the N100 and N400 components. A detailed analysis of the neural data suggests, however, that adults and children rely on different mechanisms to assist the extraction of word-like units from the continuous speech stream, hence supporting the view that SL with auditory linguistic materials changes through development. (shrink)

The paper aims to show how by elaborating the Peircean terms used in the title creativity in learning processes and in scientific discoveries can be explained within a semiotic framework. The essential idea is to emphasize both the role of external representations and of experimenting with those representations , and to describe a process consisting of three steps: First, looking at diagrams "from a novel point of view" offers opportunities to synthesize elements of these diagrams which have never been (...) perceived as connected before. Second, by forming those observed syntheses to "new objects" of thinking, and by signifying these objects through new signs , new means of thinking and acting are created . And finally, by applying these new means – in proofs, for instance – the "intelligibility" of new discoveries and their power to explain problematic facts must be tested. (shrink)

An influential view of education is that it prepares young people for adult life, usually in the areas of civic engagement, leisure and contemplation. Employment may be a locus for learning some worthwhile skills and knowledge, but it is not itself the possible locus or one of the possible loci of a worthwhile life. This article disputes that view by drawing attention to those aspects of employment that make it potentially an aspect of a worthwhile life. The exercise and (...) development of one's abilities, co-operation with others, self-discovery and the pursuit of excellence are all identified as potential components of a worthwhile life. If such an aspect of life is worth striving for then education should prepare one for it. Objections to this argument are reviewed and ultimately rejected. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:Jewish Thought and Scientific Discovery in Early Modern EuropeNoah J. EfronAlmost a quarter-century ago Benjamin Nelson published his famous plea for what he called a “differential” and “comparative historical sociology of ‘science’ in civilizational perspective.” 1 Like Max Weber, Robert Merton, and Joseph Needham, Nelson believed that the growth of western science could be better understood when compared to the ways “science” fared in other cultures with other intellectual (...) and institutional and economic sensibilities and structures. A particularly propitious case study for such a differential and comparative historical study of science, as Hillel Levine observed in his 1983 essay about Jewish reactions to heliocentrism, are the Jews of early modern Europe, who constituted “a society geographically and culturally contiguous with those who framed and advanced occidental ‘science.’” 2Since about the time of Nelson’s plea, David B. Ruderman has been writing the history of Jewish attitudes towards, and engagements with, science during the period in which modern western science was constituting itself. It is no exaggeration to say that he established this as a field of study, producing a steady stream of historical studies, initially of Italian Jews who read and wrote about nature and its study, but continually expanding his compass. 3 The field [End Page 719] Ruderman established and the studies he produced have been “repercussive” (to borrow his own adjective) in many directions—shedding light on Jewish-Christian relations, on early-modern Jewish intellectual history, on the deployments of Kabbalah and magic in early modern times, and so on. One of Ruderman’s achievements—perhaps not the most important in his view (for he considers himself a historian of Jewish thought and culture and not a historian of science) but of great importance nonetheless—has been to begin to lay the groundwork for a comparative history of Jewish and Christian attitudes towards and participation in the “new sciences.”Ruderman’s newest book is a big step towards Nelson’s desideratum, using Jewish attitudes as the comparative case. It is a collection of twelve linked essays (five of which have appeared elsewhere in one form or another) framed by a synthetic introduction and epilogue. The essays are individual or group portraits of Jews who evinced interest in natural philosophy or medicine, in a variety of settings and for a variety of reasons, between the sixteenth and eighteenth centuries. Ruderman describes his subject as “three distinct but interrelated groups among early modern Jews”: (1) “converso physicians and other university-trained intellectuals who fled Spain and Portugal in the seventeenth century and settled in Holland, Italy, Germany, England, and even eastern Europe, serving as doctors and purveyors of scientific learning throughout the Jewish communities of Europe, while yielding considerable political and economic power,” (2) “certain circles of Jewish scholars in central and eastern Europe [who] pursued scientific learning, especially astronomy, in more informal settings as a desirable supplement to rabbinic study,” and (3) “the hundreds of Jews who attended Italian medical schools, primarily the University of Padua, from the late sixteenth through the eighteenth centuries.” 4In fact Ruderman covers even more ground than this. As background, he includes a brief essay on the attitudes of tenth- to fifteenth-century Jews towards nature and its study, surely the best survey of the sort available. Of the thirty or so early modern Jews that he describes in the remainder of the book, about twenty fit in at least one of the three camps upon which he focuses. 5 Nine [End Page 720] emerged from different circumstances (though these too were often highly influenced by one or more of the focus populations). This fact does nothing to impugn Ruderman’s typology—the groups he described were, in fact, of particular importance, and his identification of them as the principle populations contemplating and promulgating natural philosophy among Jews is in itself an advance of some moment. But the fact that a third of the cases Ruderman described do not fit into his own typology is testimony both to the complexity of his subject, and to his agility in addressing it.By anthologizing a dozen depictions of different thinkers in different milieus, Ruderman affords what might be called a “hawk... (shrink)

Contemporary historiography holds that it was the practices and technologies underpinning both the Great Voyages and the 'New Science', as opposed to traditional book learning, which led to the major epistemic breakthroughs of early modernity. This study, however, returns to the importance of book-learning by exploring how cosmological and cosmographical 'novelties' were explained and presented in Renaissance texts, and discloses the ways in which the reports presented by sailors, astronomers, and scientists became not only credible but also deeply (...) disturbing for scholars, preachers, and educated laymen in sixteenth- and seventeenth-century France. It is argued here that dialectic--the art of argumentation and reasoning--played a crucial role in articulating and popularizing new learning about the cosmos by providing the argumentative toolkit needed to define, discard, and authorize novelties. The debates that shaped them were not confined to learned circles; rather, they reached a wider audience via early modern vernacular genres such as the essay. Focusing both on major figures such as Montaigne or Descartes, as well as on now-forgotten popularizers such as Belleforest and Binet, this book describes the deployment of dialectic as a means of articulating and disseminating, but also of containing, the disturbance generated by cosmological and cosmographical novelties in Renaissance France, whether for the lay reader in Court or Parliament, for the parishioner at Church, or for the student in the classroom. (shrink)

Narrative accounts misrepresent discovery by reconstructing worlds ordered by success rather than the world as explored. Such worlds rarely contain the personal knowledge that informed actual exploration and experiment. This article describes an attempt to recover situated learning in a material environment, tracing the discovery of the first electromagnetic motor by Michael Faraday in September 1821 to show how he modeled new experience and invented procedures to communicate that novelty. The author introduces a notation to map experiment as an (...) active process in a real-world environment and to display the human agency written out of most narratives. Comparing maps of accounts shows how knowledge-construction depends on narrative reconstruction. It is argued that invention processes can be interpreted in the same way as discovery, and a study is proposed to compare packaging learned skills into demonstration devices with the innovative strategies of inventors such as Edison. If situational knowledge is as important as is claimed, computationalists need to join science studies scholars in coming to grips with nonverbal and procedural aspects of discovery and invention. (shrink)

Gegenwartig sind wir Zeugen einer tief greifenden Sinn- als Vertrauenskrise, die sich weitlaufig im Wirtschafts-, Finanz-, Umwelt- und Bildungssektor bemerkbar macht.

Retrieval of relevant unstructured information from the ever-increasing textual communications of individuals and businesses has become a major barrier to effective litigation/defense, mergers/acquisitions, and regulatory compliance. Such e-discovery requires simultaneously high precision with high recall (high-P/R) and is therefore a prototype for many legal reasoning tasks. The requisite exhaustive information retrieval (IR) system must employ very different techniques than those applicable in the hyper-precise, consumer search task where insignificant recall is the accepted norm. We apply Russell, et al.’s cognitive task (...) analysis of sensemaking by intelligence analysts to develop a semi-autonomous system that achieves high IR accuracy of F1 ≥ 0.8 compared to F1 < 0.4 typical of computer-assisted human-assessment (CAHA) or alternative approaches such as Roitblat, et al.’s. By understanding the ‘Learning Loop Complexes’ of lawyers engaged in successful small-scale document review, we have used socio-technical design principles to create roles, processes, and technologies for scalable human-assisted computer-assessment (HACA). Results from the NIST-TREC Legal Track’s interactive task from both 2008 and 2009 validate the efficacy of this sensemaking approach to the high-P/R IR task. (shrink)

We describe a way of modeling high‐dimensional data vectors by using an unsupervised, nonlinear, multilayer neural network in which the activity of each neuron‐like unit makes an additive contribution to a global energy score that indicates how surprised the network is by the data vector. The connection weights that determine how the activity of each unit depends on the activities in earlier layers are learned by minimizing the energy assigned to data vectors that are actually observed and maximizing the energy (...) assigned to “confabulations” that are generated by perturbing an observed data vector in a direction that decreases its energy under the current model. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:The Originality of Descartes’s Conception of Analysis as DiscoveryBenoît TimmermansAccording to Descartes, his Meditations employ the method of analysis. This method of proof, says Descartes, “shows the true way by means of which the thing in question was discovered methodically and as it were a priori.” 1 Such a definition of analysis poses a problem that seems to have attracted little attention among commentators until now, namely, why Descartes (...) considers analysis a method of discovery whereas the Scholastic tradition asserts just the contrary, that synthesis allows the discovery of new things, whereas analysis is a method for judging findings or verifying them a posteriori. What is more, the definition that Descartes gives of his analysis contains another divergence, this time brought up by the commentators, 2 for he curiously considers analysis an a priori movement and not an a posteriori movement as dictated by tradition.These issues are all the more important as the analytical method acquired particular prestige in the seventeenth century. This prestige concerned not only philosophy (Descartes, Leibniz, Locke, etc.) but science as well. Thus algebra lost some of its rough edges and gradually took on the name of “analysis.” 3 Descartes created a type of geometry that was soon dubbed “analytic,” and Leibniz developed his analysis infinitorum or differential and integral calculus. If the scientific revolution and the new philosophies that were associated with it attached so much importance to a method that had already been known in ancient Greece, was it not due to a change of the meaning of analysis or at least a rearranging of some of its functions? This is the question that we propose to [End Page 433] tackle while trying to determine to what extent Cartesian analysis breaks with the forms of analysis that preceded it. 4To do this we shall first examine the teachings of Scholastic philosophy concerning the connections between analysis and discovery in general and then study the logical possibilities of discovery through analysis that draw their inspiration from Aristotle’s Analytics and that were developed extensively by the School of Padua in the sixteenth century. Finally, we shall attempt to determine whether the mathematical definition of analysis derived from the works of Euclid and Pappus gives grounds for the correlation with discovery.The Link Between Analysis and Discovery According to the ScholasticsOne might well think that Descartes was completely unaware of the contradiction between his definition of analysis and the teachings of the Scholastics. Had not he always exhibited a true disdain for the latter? However, in September 1640, that is, four months before writing his text on analysis, Descartes began girding for the Jesuit Fathers’ objections to his Meditations. This involved looking for an “abstract of the whole of scholastic philosophy, this would save the time it would take to read their huge tomes.” 5 He finally found what he was looking for in the shops of Leiden, namely, Eustachius a Sancto Paulo’s Summa Philosophiae. There is reason to believe that Descartes read this opus with interest, for two months later he referred to it as “the best book of its kind ever made.” 6 Later he said that he would definitely have chosen the Summa Philosophiae as the basis for refuting the School’s doctrine if he had not given up such a project. 7 Now, Eustachius a Sancto Paulo’s Summa Philosophiae is categorical on the subject of analysis versus synthesis. Analysis of a definition “is perfectly compatible with the doctrines for teaching and learning; as for the other disciplines of discovery, the other method, that is the composite or synthetic method, must be used.” 8According to the Scholastic doctrine as stated by Eustachius a Sancto Paulo, analysis thus plays a part in the teaching of well-known things and in all processes of appraisal, assessment, or judgment. This is easy to explain if one considers that, etymologically, analysis breaks links (ana-luein), goes backward, or climbs back up from the consequences to the principles. Starting from an observation, proposition, or question, it wends its way back to the simplest, [End Page 434] surest items of knowledge that shed light on and verify said starting... (shrink)

Listening, seeing and reading Gilles Deleuze has had an influence on my thinking more than most of the economic writings I have consulted over the past quarter of a century. This discovery and furtherance of knowledge enriched my reflection and also allowed me to go beyond the general philosopher, as a philosopher opening the way to new horizons. It makes the researcher aware that the most important thing is not the philosopher man but the man philosopher, i.e. the one who (...) writes something that touches a human being at his deepest level and concerns him in his life every day. New generations of economists should meditate on this by going beyond the chapel quarrels coming from the Schumpeterian dichotomy 'science versus ideology'. To quote one of Deleuze's main ideas, no thinking against anything has been important over a long period; what counts are thoughts for something new that affect people's lives, and which are produced with rigor. This opens the way to a thought for life and not against life, which is in line with the progress of research in methodology, where it is a question of giving more importance to social ontology as a level of analysis and not focusing solely on epistemology in the narrowest sense. (shrink)