The concept of lived experience plays a significant role in the social sciences in general and in philosophy in particular. The idea of lived experience as a social reality has been philosophized and given prime importance in the phenomenological tradition of philosophy. However, the work of Alfred Schutz, one of the phenomenologists on lived experience, has not been given adequate attention by either sociologists or philosophers. This paper attempts to understand how lived experiences are not merely individual or subjective (...) experiences but are also collective or social. Firstly, it suggests that lived experiences need to be understood in a broader context, namely that of the social and inter-subjectivity, which can be found in the Philosophy of Alfred Schutz. Thus, there is an attempt to show how Schutz’s philosophy broadly helps us understand lived experience as a social reality. Secondly, to justify how lived experiences are social and collective experiences, illustrations of the studies related to race, gender, and caste are given as a marker for lived experiences throughout the paper. So far, lived experience has been understood within the domain of individual or the subjective or merely in the first-person perspective. Hence, this paper provides some insights into investigating lived experience from the third-person perspective. (shrink)

Cognition is predominantly seen as information processing in multidisciplinary landscape of cognition studies, despite having had a formidable opposition from embodied and embedded perspectives in the last few decades. This paper analyses cognitive processes involved in different task domains of Kashmiri carpet weaving practice from the theoretical frameworks of cognitivism and situated-distributed cognition. After introducing the practice and its task domains (Section −1), paradigmatic cognitive activities involved in them are discussed and how these are explained by the two theoretical paradigms (...) and why the latter framework explains these processes better (Section-2). During this discussion (Section – 3), a way via culture impacts the practice and resultant cognitive operations turns out to be workspace of the actors. (shrink)

Consumer complaining behavior (CCB) is an important stream of research and practice, as it links the domains of service failure and service recovery. CCB research, although extensive and temporally wide, exhibits a lack of concern for the underlying assumptions of scholarly inquiry. Researchers neither explicitly mention, nor consciously indicate their ontological, epistemological, and methodological assumptions. We systematically identify the extant CCB literature and map it to two well-accepted paradigmatic classifications (Burrell and Morgan 1979; Deetz Organization Science 7(2): 191–207, 1996). Normative (...) or functionalist paradigm with the assumptions of an objective external reality, a positivist epistemology, a determinist view of human nature, and nomothetic methodology emerges as the dominant CCB research paradigm. The implications of this dominance are discussed as a barometer of the future of CCB research and practice. (shrink)

The squared paper or graphs are grid-based design representations used in engineering, industrial and craft design practices wherein designs are drawn over symmetrical grids. This paper reports grid-processing strategies undertaken by actors in a native craft practice, viz. Kashmiri carpet-weaving having three task contexts: (1) _design_, wherein designs are drawn on graph sheets and color scheme given by assigning practice-specific symbolic codes to the motifs by designers; (2) _coding_, wherein a cryptic script, called _talim_, is generated from these encoded graphs (...) by talim-writers; and (3) _weaving_, wherein this script is read, decoded and communicated in an equally cryptic trade-language by weavers among their teams. 32 video-recorded think-aloud sessions were conducted with talim-writers to understand their graph-processing and resultant coding strategies. The observations and qualitative analysis of transcripts revealed highly _situated_ strategies undertaken by experts keen on imbibing visuality of design embedded in the grids, in contrast to _embodied_ strategies undertaken by less-experienced coders keen on processing grids’ structural features and getting overwhelmed by grid-clutter in the process. In this landscape, the findings revealed a negotiated nature of errors instead of being deviations from the underlying grid and thereby objectively assessable. The paper makes recommendations for CAD systems to include more of those grid features exploited by experts during their graph processing to facilitate designers. (shrink)

There is a growing understanding that addressing the global crisis facing humanity will require new methods for knowing, understanding, and valuing the world. Narrow, disciplinary, and reductionist perceptions of reality are proving inadequate for addressing the complex, interconnected problems of the current age. The pervasive Cartesian worldview, which is based on the metaphor of the universe as a machine, promotes fragmentation in our thinking and our perception of the cosmos. This divisive, compartmentalized thinking fosters alienation and self-focused behavior. I aim (...) to show in this essay that healing the fragmentation that is at the root of the current world crises requires an integrated epistemology that embraces both the rational knowledge of scientific empiricism and the inner knowledge of spiritual experience. This “deep science‘ transcends the illusion of separateness to discern the unity, the unbroken wholeness, that underlies the diverse forms of the universe. Our perception of connectedness, of our integral place in the web of life, emerges as an attribute of our connection with the eternal, beatific source of all existence. This awakened spiritual vision “widens our circle of understanding and compassion, to embrace all living creatures in the whole of nature‘ (Einstein, quoted in Goldstein [1976] 1987). Our behavior, as it emerges naturally out of our perception of the sacredness of the natural world, will naturally embody love and respect for all life forms. This vision promotes the healing of our long-standing alienation from the natural world and offers hope for renewal in the midst of widespread cultural deterioration and environmental destruction. (shrink)

Independent philosopher Lee (recently of the U. of Manchester) attends to the deeper implications of ecologically insensitive technology beyond its polluting effects. Contrasting modern with premodern worldviews provides the context for exploring how new sciences like biotechnology require an expanded environmental ethos encompassing both the biotic and the abiotic. The author considers misconceived the notions of nature as either a work of art or a mere social construct per some postmodern thinking. Annotation copyrighted by Book News, Inc., Portland, OR.

The last century saw two great revolutions in genetics the development of classic Mendelian theory and the discovery and investigation of DNA. Each fundamental scientific discovery in turn generated its own distinctive technology. These two case studies, examined in this text, enable the author to conduct a philosophical exploration of the relationship between fundamental scientific discoveries on the one hand, and the technologies that spring from them on the other. As such it is also an exercise in the philosophy of (...) technology. (shrink)

The Higgs particle has been detected a few years ago, that is what newspapers tell us. For many physicists, the Standard Model of particle physics has accomplished all the jobs. Or to put it simply: The game is over. Is it true? Then some physicists began to ask: can go beyond the Standard Model? Because the supersymmetric extension of the Standard Model has failed. If you feel that theoretical physics is becoming boring, you are not alone. Fortunately, there is good (...) news: a new generation of physicists are doing table-top experiments in their basements. Can we expect new results later? If so, what will the future of physics look like? This article discusses this question, starting with a blunt look at the relationship between mathematics and physical reality, written from the perspectives of a mathematician and a cosmologist. (shrink)

In recent years, the family of algorithms collected under the term ``deep learning'' has revolutionized artificial intelligence, enabling machines to reach human-like performances in many complex cognitive tasks. Although deep learning models are grounded in the connectionist paradigm, their recent advances were basically developed with engineering goals in mind. Despite of their applied focus, deep learning models eventually seem fruitful for cognitive purposes. This can be thought as a kind of biological exaptation, where a physiological structure becomes (...) applicable for a function different from that for which it was selected. In this paper, it will be argued that it is time for cognitive science to seriously come to terms with deep learning, and we try to spell out the reasons why this is the case. First, the path of the evolution of deep learning from the connectionist project is traced, demonstrating the remarkable continuity, and the differences as well. Then, it will be considered how deep learning models can be useful for many cognitive topics, especially those where it has achieved performance comparable to humans, from perception to language. It will be maintained that deep learning poses questions that cognitive sciences should try to answer. One of such questions is the reasons why deep convolutional models that are disembodied, inactive, unaware of context, and static, are by far the closest to the patterns of activation in the brain visual system. (shrink)

Deep ecology's biocentric philosophy rejects the anthropocentrism of mainstream environmentalism. Biocentrism holds that all life has inherent value and, as such, is worthy of respect and protection. Deep ecology's action strategy emerges from disgust with the compromises made by mainstream environmentalism. Deep ecologists tend toward confrontational actions such as blockades, “tree sits,” and “ecotage” (“monkey wrenching” or covert direct action). Earth First! in the United States, and Rainforest Action Network at the international level, are two well-known (...) class='Hi'>deep ecology groups. Bound together in a complex relationship, deep ecology is both dependent on and antagonistic toward the life sciences. As yet, there is no explicit, deep ecological statement for scientific reform. But there have been scientific developments cheering to deep ecologists, including the development and growth of the new field of conservation biology. This article begins to outline the reforms necessary to bring establishment science closer to radical ecological principles. (shrink)

(1996). Deep time and shallow time: Metaphors for conflict and cooperation in the natural sciences. The European Legacy: Vol. 1, Science and Religion in Modern Western Thought, pp. 1752-1763.

We review publications in automated scientific discovery using deep learning, with the aim of shedding light on problems with strong connections to philosophy of science, of physics in particular. We show that core issues of philosophy of science, related, notably, to the nature of scientific theories; the nature of unification; and of causation loom large in scientific deep learning. Therefore, advances in deep learning could, and ideally should, have impact on philosophy of science, and vice versa. We (...) suggest lines of further research, and highlight the role ‘theory-based’ AI could have in future developments of the field. (shrink)

Deep ecology is examined from the perspective of scientific ecology. Two norms, self-realization and biocentric equality, are considered central to deep ecology, and are explored in brief. Concepts of scientific ecology that seem to form a bridge to these norms are ecological hierarchical organization, the exchange of energy, material and information, and the development of species within ecosystems and the biosphere. While semantic problems exist, conceptually it appears that deep ecology norms can be interpreted through scientific ecology.

Deep ecology is examined from the perspective of scientific ecology. Two norms, self-realization and biocentric equality, are considered central to deep ecology, and are explored in brief. Concepts of scientific ecology that seem to form a bridge to these norms are ecological hierarchical organization, the exchange of energy, material and information, and the development of species within ecosystems and the biosphere. While semantic problems exist, conceptually it appears that deep ecology norms can be interpreted through scientific ecology.

In the early twentieth century, with the development of Western scientific imperialism, Asia, South America, and Africa became sites for Western scientific exploration. Many paleontological specimens, including dinosaur bones, were discovered in China by foreign scientists and explorers and exported to museums in France, Sweden, and the United States. After the establishment of the Nationalist Government in Nanjing in 1927, anti-imperialist Chinese intellectuals attempted to prevent foreigners from exporting specimens unearthed on Chinese territory. In the summer of 1928, the fossils (...) discovered in the Gobi Desert by the Central Asiatic Expedition of the American Museum of Natural History were detained in China. A series of negotiations ensued between Chinese and American diplomats and scientists over the issues of the ownership of fossils and the format of international scientific collaborations. By studying the interests and stakes for players in the different social worlds involved in the dispute, this essay explores the relations among science, diplomacy, and the politics of deep time. (shrink)

This book explores human-nature connectedness through deep ecological philosophy and conservation social science. Emphasizing ecologically-inclusive identities, it argues that connection to nature is more important than many environmental advocates realize and that deep ecology contributes much to the increasingly pressing conversations about it.

Deep learning is currently the most prominent and widely successful method in artificial intelligence. Despite having played an active role in earlier artificial intelligence and neural network research, philosophers have been largely silent on this technology so far. This is remarkable, given that deep learning neural networks have blown past predicted upper limits on artificial intelligence performance—recognizing complex objects in natural photographs and defeating world champions in strategy games as complex as Go and chess—yet there remains no universally (...) accepted explanation as to why they work so well. This article provides an introduction to these networks as well as an opinionated guidebook on the philosophical significance of their structure and achievements. It argues that deep learning neural networks differ importantly in their structure and mathematical properties from the shallower neural networks that were the subject of so much philosophical reflection in the 1980s and 1990s. The article then explores several different explanations for their success and ends by proposing three areas of inquiry that would benefit from future engagement by philosophers of mind and science. (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)

Deep neural networks (DNNs) have had extraordinary successes in classifying photographic images of objects and are often described as the best models of biological vision. This conclusion is largely based on three sets of findings: (1) DNNs are more accurate than any other model in classifying images taken from various datasets, (2) DNNs do the best job in predicting the pattern of human errors in classifying objects taken from various behavioral datasets, and (3) DNNs do the best job in (...) predicting brain signals in response to images taken from various brain datasets (e.g., single cell responses or fMRI data). However, these behavioral and brain datasets do not test hypotheses regarding what features are contributing to good predictions and we show that the predictions may be mediated by DNNs that share little overlap with biological vision. More problematically, we show that DNNs account for almost no results from psychological research. This contradicts the common claim that DNNs are good, let alone the best, models of human object recognition. We argue that theorists interested in developing biologically plausible models of human vision need to direct their attention to explaining psychological findings. More generally, theorists need to build models that explain the results of experiments that manipulate independent variables designed to test hypotheses rather than compete on making the best predictions. We conclude by briefly summarizing various promising modeling approaches that focus on psychological data. (shrink)

Some disagreements concern our most fundamental beliefs, principles, values, or worldviews, such as those about the existence of God, society and politics, or the trustworthiness of science. These are ‘deep disagreements’. But what exactly are deep disagreements? This paper critically overviews theories of deep disagreement. It does three things. First, it explains the differences between deep and other kinds of disagreement, including peer, persistent, and widespread disagreement. Second, it critically overviews two mainstream theories of deep (...) disagreement, the Wittgensteinian account and the Fundamental Epistemic Principle account, before introducing a Hybrid account. Finally, it explores the notion that deep disagreements can be deeper than others. (shrink)

Endowing artificial systems with explanatory capacities about the reasons guiding their decisions, represents a crucial challenge and research objective in the current fields of Artificial Intelligence (AI) and Computational Cognitive Science [Langley et al., 2017]. Current mainstream AI systems, in fact, despite the enormous progresses reached in specific tasks, mostly fail to provide a transparent account of the reasons determining their behavior (both in cases of a successful or unsuccessful output). This is due to the fact that the classical problem (...) of opacity in artificial neural networks (ANNs) explodes with the adoption of current Deep Learning techniques [LeCun, Bengio, Hinton, 2015]. In this paper we argue that the explanatory deficit of such techniques represents an important problem, that limits their adoption in the cognitive modelling and computational cognitive science arena. In particular we will show how the current attempts of providing explanations of the deep nets behaviour (see e.g. [Ritter et al. 2017] are not satisfactory. As a possibile way out to this problem, we present two different research strategies. The first strategy aims at dealing with the opacity problem by providing a more abstract interpretation of neural mechanisms and representations. This approach is adopted, for example, by the biologically inspired SPAUN architecture [Eliasmith et al., 2012] and by other proposals suggesting, for example, the interpretation of neural networks in terms of the Conceptual Spaces framework [Gärdenfors 2000, Lieto, Chella and Frixione, 2017]. All such proposals presuppose that the neural level of representation can be considered somehow irrelevant for attacking the problem of explanation [Lieto, Lebiere and Oltramari, 2017]. In our opinion, pursuing this research direction can still preserve the use of deep learning techniques in artificial cognitive models provided that novel and additional results in terms of “transparency” are obtained. The second strategy is somehow at odds with respect to the previous one and tries to address the explanatory issue by avoiding to directly solve the “opacity” problem. In this case, the idea is that one of resorting to pre-compiled plausible explanatory models of the word used in combination with deep-nets (see e.g. [Augello et al. 2017]). We argue that this research agenda, even if does not directly fits the explanatory needs of Computational Cognitive Science, can still be useful to provide results in the area of applied AI aiming at shedding light on the models of interaction between low level and high level tasks (e.g. between perceptual categorization and explanantion) in artificial systems. (shrink)

Deep Neural Networks (DNNs) are becoming increasingly important as scientific tools, as they excel in various scientific applications beyond what was considered possible. Yet from a certain vantage point, they are nothing but parametrized functions fθ(x)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\varvec{f}_{\varvec{\theta }}(\varvec{x})$$\end{document} of some data vector x\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\varvec{x}$$\end{document}, and their ‘learning’ is nothing but an iterative, algorithmic fitting of the parameters to data. Hence, what could be special (...) about them as a scientific tool or model? I will here suggest an integrated perspective that mediates between extremes, by arguing that what makes DNNs in science special is their ability to develop functional concept proxies (FCPs): Substructures that occasionally provide them with abilities that correspond to those facilitated by concepts in human reasoning. Furthermore, I will argue that this introduces a problem that has so far barely been recognized by practitioners and philosophers alike: That DNNs may succeed on some vast and unwieldy data sets because they develop FCPs for features that are not transparent to human researchers. The resulting breach between scientific success and human understanding I call the ‘Actually Smart Hans Problem’. (shrink)

Deep brain stimulation (DBS) is optimistically portrayed in contemporary media. This already happened with psychosurgery during the first half of the twentieth century. The tendency of popular media to hype the benefits of DBS therapies, without equally highlighting risks, fosters public expectations also due to the lack of ethical analysis in the scientific literature. Media are not expected (and often not prepared) to raise the ethical issues which remain unaddressed by the scientific community. To obtain a more objective portrayal (...) of DBS in the media, a deeper collaboration between the science community and journalists, and particularly specialized ones, must be promoted. Access to databases and articles, directly or through science media centers, has also been proven effective in increasing the quality of reporting. This article has three main objectives. Firstly, to explore the past media coverage of leukotomy, and to examine its widespread acceptance and the neglect of ethical issues in its depiction. Secondly, to describe how current enthusiastic coverage of DBS causes excessive optimism and neglect of ethical issues in patients. Thirdly, to discuss communication models and strategies to enhance media and science responsibility. (shrink)

The application of deep learning methods to problems in natural language processing has generated significant progress across a wide range of natural language processing tasks. For some of these applications, deep learning models now approach or surpass human performance. While the success of this approach has transformed the engineering methods of machine learning in artificial intelligence, the significance of these achievements for the modelling of human learning and representation remains unclear. Deep Learning and Linguistic Representation looks at (...) the application of a variety of deep learning systems to several cognitively interesting NLP tasks. It also considers the extent to which this work illuminates our understanding of the way in which humans acquire and represent linguistic knowledge. Key Features: combines an introduction to deep learning in AI and NLP with current research on Deep Neural Networks in computational linguistics. is self-contained and suitable for teaching in computer science, AI, and cognitive science courses; it does not assume extensive technical training in these areas. provides a compact guide to work on state of the art systems that are producing a revolution across a range of difficult natural language tasks. (shrink)

Does the universe embody beautiful ideas? Artists as well as scientists throughout human history have pondered this "beautiful question." With Nobel laureate Frank Wilczek as your guide, embark on a voyage of related discoveries, from Plato and Pythagoras up to the present. Wilczek's groundbreaking work in quantum physics was inspired by his intuition to look for a deeper order of beauty in nature. In fact, every major advance in his career came from this intuition: to assume that the universe embodies (...) beautiful forms, forms whose hallmarks are symmetry--harmony, balance, proportion--and economy. There are other meanings of "beauty," but this is the deep logic of the universe--and it is no accident that it is also at the heart of what we find aesthetically pleasing and inspiring. As he reveals here, this has been the heart of scientific pursuit from Pythagoras, the ancient Greek who was the first to argue that "all things are number," to Galileo, Newton, Maxwell, Einstein, and into the deep waters of twentieth-century physics. Gorgeously illustrated, A Beautiful Question is a mind-shifting book that braids the age-old quest for beauty and the age-old quest for truth into a thrilling synthesis. Yes: the world is a work of art, and its deepest truths are ones we already feel, as if they were somehow written in our souls.--From publisher description. (shrink)

Some disagreements concern our most fundamental beliefs, principles, values, or worldviews, such as those about the existence of God, society and politics, or the trustworthiness of science. These are ‘deep disagreements’. But what exactly are deep disagreements? This paper critically overviews theories of deep disagreement. It does three things. First, it explains the differences between deep and other kinds of disagreement, including peer, persistent, and widespread disagreement. Second, it critically overviews two mainstream theories of deep (...) disagreement, the Wittgensteinian account and the Fundamental Epistemic Principle account, before introducing a Hybrid account. Finally, it explores the notion that deep disagreements can be deeper than others. (shrink)

With the early success of the deep ecology movement in attracting adherents and with the increasing threat of a global ecological catastrophe, one would have expected this movement to have triumphed. We should be in the process of radically transforming society to create a harmonious relationship between humans and the rest of nature. Instead, deep ecology has been marginalized. What has triumphed instead is an alliance of managerialism, transnational corporations and neo-liberalism committed to replacing communities with markets and (...) transforming every facet of nature and society into means for increasing the profitability of corporations and expanding GDP. This outcome is here explained by the failure of deep ecologists to understand the ideological, political and economic forces driving ecological destruction, and thereby what is required to overcome these. To regain the initiative, it is argued, it will be necessary to recover the whole project of the Radical Enlightenment upholding the Renaissance quest for liberty that was subverted by the scientific materialism and possessive individualism of the Moderate Enlightenment. Not only will it be necessary to transform science to develop a comprehensive alternative conception of nature and humanity and its destiny, it will be necessary to revive and develop the institutions of the Radical Enlightenment to subordinate markets to democratically organized communities at multiple levels, as communities of communities committed to augmenting the life of these communities, including the life of ecological communities. Doing so will involve reformulating the grand narrative of emancipation as a dialogic, polyphonic grand narrative to orient individuals, organizations and nations to create a global ecological civilization. -/- . (shrink)

Longlisted for the PEN/E.O. Wilson Literary Science Writing Award A leading neuroscientist offers a history of the evolution of the brain from unicellular organisms to the complexity of animals and human beings today Renowned neuroscientist Joseph LeDoux digs into the natural history of life on earth to provide a new perspective on the similarities between us and our ancestors in deep time. This page-turning survey of the whole of terrestrial evolution sheds new light on how nervous systems evolved in (...) animals, how the brain developed, and what it means to be human. In The Deep History of Ourselves, LeDoux argues that the key to understanding human behavior lies in viewing evolution through the prism of the first living organisms. By tracking the chain of the evolutionary timeline he shows how even the earliest single-cell organisms had to solve the same problems we and our cells have to solve each day. Along the way, LeDoux explores our place in nature, how the evolution of nervous systems enhanced the ability of organisms to survive and thrive, and how the emergence of what we humans understand as consciousness made our greatest and most horrendous achievements as a species possible. (shrink)

This paper outlines an approach for comparing Edmund Husserl’s late historical-teleological reflections in the Crisis of the European Sciences with Michel Foucault’s archaeology of discursive formations in his Archaeology of Knowledge, with a particular emphasis on the notion of an “historical apriori.” The argument is that each conception of historical reflection complements the other by opening up a depth dimension that moves beyond the traditional limits of the philosophy of history. In Husserl, the concept of the lifeworld fixes the (...) parameters of a “deep history” as the horizon of anonymous subjective comportment, while in Foucault the concept of the archive delimits an alternative deep history of the anonymous production of discursive formations. Together, Husserl and Foucault represent two important moments of the radicalization of the theme of history in twentieth century philosophy, one seeking to extend the limits of transcendental philosophy, the other contesting the domain of the transcendental altogether. (shrink)

This article presents in broad outline the theological concept of deep incarnation and brings it into dialogue with correlative ideas of deep history and deep sociality. It will be argued that neither Christology, nor evolution, can be properly understood from a chronocentric perspective. Evolution is not only about development but also about the exploration of ecospace. Likewise, a contemporary Christology should explicate incarnation as a divine assumption of the full ecospace of the material world of creation. It (...) will then be argued that an interactionist view of deep history is preferable to the evolutionary cognitive theory of religion. Against this background, the paper will explore Jesus of Nazareth's role in the context of post-axial mentalities. (shrink)

In the 1960s and 1970s, scuba technology, underwater cameras, and documentarians revealed a long-hidden underwater world to the public. At this time oceanographic science was growing exponentially. Historians of the marine sciences have focused their studies of the period on institutional and military partnerships, and on the scientist-administrators who shaped oceanographic research institutions (such as the Scripps Institution of Oceanography, the Woods Hole Oceanographic Institution, and the British National Institute of Oceanography). Underwater habitat development during the 1960s and 1970s, (...) however, deserves greater attention than it has yet received, as it highlights a peculiar confluence of military, scientific, and popular interest, characteristic of the period, in the colonization of the seafloor. Existing accounts have focused on American habitats, notably Sealab and Tektite, since these were the largest and best funded. But this approach overemphasizes a Cold War narrative in which the sole protagonists of the habitat programs were the United States and the Soviet Union. At least 65 habitats were built between 1962 and 1991. Some were state-sponsored, with significant programs run by French, German, Japanese, and Canadian teams. This essay takes as a case study the Canadian Sublimnos habitat as well as the underwater exploration programs it helped launch in Newfoundland (Lora-I) and in the Arctic (Sub-Igloo). The Canadian case demonstrates that technological expertise and public enthusiasm for underwater exploration should not be solely understood with reference to Cold War interests of the two superpowers. Rather, the international range of habitat programs of the 1960s and 1970s reveals an expanding interest in the vertical underwater dimension that was fueled by numerous national scientific aims and territorial claims. (shrink)

The representations of deep convolutional neural networks (CNNs) are formed from generalizing similarities and abstracting from differences in the manner of the empiricist theory of abstraction (Buckner, Synthese 195:5339–5372, 2018). The empiricist theory of abstraction is well understood to entail infinite regress and circularity in content constitution (Husserl, Logical Investigations. Routledge, 2001). This paper argues these entailments hold a fortiori for deep CNNs. Two theses result: deep CNNs require supplementation by Quine’s “apparatus of identity and quantification” in (...) order to (1) achieve concepts, and (2) represent objects, as opposed to “half-entities” corresponding to similarity amalgams (Quine, Quintessence, Cambridge, 2004, p. 107). Similarity amalgams are also called “approximate meaning[s]” (Marcus & Davis, Rebooting AI, Pantheon, 2019, p. 132). Although Husserl inferred the “complete abandonment of the empiricist theory of abstraction” (a fortiori deep CNNs) due to the infinite regress and circularity arguments examined in this paper, I argue that the statistical learning of deep CNNs may be incorporated into a Fodorian hybrid account that supports Quine’s “sortal predicates, negation, plurals, identity, pronouns, and quantifiers” which are representationally necessary to overcome the regress/circularity in content constitution and achieve objective (as opposed to similarity-subjective) representation (Burge, Origins of Objectivity. Oxford, 2010, p. 238). I base myself initially on Yoshimi’s (New Frontiers in Psychology, 2011) attempt to explain Husserlian phenomenology with neural networks but depart from him due to the arguments and consequently propose a two-system view which converges with Weiskopf’s proposal (“Observational Concepts.” The Conceptual Mind. MIT, 2015. 223–248). (shrink)

Susan Wolf objects to the Real Self View (RSV) of moral responsibility that it is insufficient, that even if one’s actions are expressions of one’s deepest or “real” self, one might still not be morally responsible for one’s actions. As a counterexample to the RSV, Wolf offers the case of JoJo, the son of a dictator, who endorses his father’s (evil) values, but who is insane and is thus not responsible for his actions. Wolf’s data for this conclusion derives from (...) what she takes to be our “pretheoretic intuitions” about JoJo. As it turns out, though, experimental data on actual pretheoretic intuitions does not seem to support Wolf’s claim. In this paper, we present such data and argue that, at least with respect to this particular objection, the RSV can survive Wolf’s attack intact. (shrink)

Historical sciences like paleontology and archaeology have uncovered unimagined, remarkable and mysterious worlds in the deep past. How should we understand the success of these sciences? What is the relationship between knowledge and history? In Scientific Knowledge and the Deep Past: History Matters, Adrian Currie examines recent paleontological work on the great changes that occurred during the Cretaceous period - the emergence of flowering plants, the splitting of the mega-continent Gondwana, and the eventual fall of the (...) dinosaurs - to analyse the knowledge of historical scientists, and to reflect upon the nature of history. He argues that distinctively historical processes are 'peculiar': they have the capacity to generate their own highly specific dynamics and rules. This peculiarity, Currie argues, also explains the historian's interest in narratives and stories: the contingency, complexity and peculiarity of the past demands a narrative treatment. Overall, Currie argues that history matters for knowledge. (shrink)

‘Shallow’ and ‘deep’ versions of scientific realism may be distinguished as follows: the shallow realist is satisfied with belief in the existence of the posits of our best scientific theories; by contrast, deep realists claim that realism can be legitimate only if such entities are described in metaphysical terms. We argue that this methodological discussion can be fruitfully applied in Everettian quantum mechanics, specifically on the debate concerning the existence of worlds and the recent dispute between Everettian actualism (...) and quantum modal realism. After presenting what is involved in such dispute, we point to a dilemma for realists: either we don’t have the available metaphysical tools to answer the deep realist’s demands, and realism is not justified in this case, or such demands of metaphysical dressing are not mandatory for scientific realism, and deep versions of realism are not really required. (shrink)

This paper argues that a notion of statistical explanation, based on Salmon’s statistical relevance model, can help us better understand deep neural networks. It is proved that homogeneous partitions, the core notion of Salmon’s model, are equivalent to minimal sufficient statistics, an important notion from statistical inference. This establishes a link to deep neural networks via the so-called Information Bottleneck method, an information-theoretic framework, according to which deep neural networks implicitly solve an optimization problem that generalizes minimal (...) sufficient statistics. The resulting notion of statistical explanation is general, mathematical, and subcausal. (shrink)

Environmental theory and practice can benefit greatly from Emmanuel Levinas’ non-ontological philosophy of the Other in order to address the current global environmental crisis. From this viewpoint, this article focuses on 2 major positions within deep ecology. We discuss the significance of transitioning from one of them, which represents biocentric responsibility, to the other, which seeks to achieve environmental justice by challenging the hegemony of institutionalised environmentalism. In Levinasian terms, this is represented by moving from the anarchic realm of (...) ethics (face-to-face) to the totalising realm of politics (humanity), where the naked face of the Other becomes visible in the presence of a Third, and decision-making and concrete disputes resolution becomes necessary. Within this framework, historical and current inequalities compel the global North to degrow in order to allow the Others to weather the consequences of resource overconsumption and inequality. (shrink)