This chapter first explains why it is likely that the alien civilizations we encounter will be forms of superintelligent artificial intelligence (SAI). Next, it turns to the question of whether superintelligent aliens can be conscious – whether it feels a certain way to be an alien, despite their non‐biological nature. The chapter draws from the literature in philosophy of AI, and urges that although we cannot be certain that superintelligent aliens can be conscious, it is likely that they would be. (...) It then turns to the difficult question of how such creatures might think, and attempts to identify some goals and cognitive capacities likely to be possessed by superintelligent beings. The chapter discusses Nick Bostrom's recent book on superintelligence, which focuses on the genesis of SAI on Earth. Finally, it isolates a specific type of superintelligence that is of particular import in the context of alien superintelligence, biologically‐inspired superintelligences (BISAs). (shrink)

This paper addresses the seemingly insurmountable challenges the problem of personal identity raises for the prospect of radical human enhancement and synthetic consciousness. It argues that conceptions of personal identity rooted in psychological continuity akin to those proposed by Parfit and the Buddha may not provide the sort of grounding that many transhumanists chasing the dream of life extension think that they do if they rest upon ontologies that assume an incompatibility between identity and change. It also suggests that process (...) ontologies that take change to be primary, such as those that align with contemporary systems biology, offer a better way out of the personal identity dilemma. But the solution in this case, which regards biological organisms as processes rather than things, may constrain the possibility of biologically inspired superintelligent aliens (BISAs), which Schneider (following Bostrom 2014) posits as possibly the most common form of (extraterrestrial) superintelligence in the universe. (shrink)

Eye movement desensitization and reprocessing therapy is a well-established therapeutic method to treat post-traumatic stress disorder. However, how EMDR exerts its therapeutic action has been studied in many types of research but still needs to be completely understood. This is in part due to limited knowledge of the neurobiological mechanisms underlying EMDR, and in part to our incomplete understanding of PTSD. In order to model PTSD, we used a biologically inspired computational model based on firing rate units, encompassing the cortex, (...) hippocampus, and amygdala. Through the modulation of its parameters, we fitted real data from patients treated with EMDR or classical exposure therapy. This allowed us to gain insights into PTSD mechanisms and to investigate how EMDR achieves trauma remission. (shrink)

A special area of human-machine interaction, the expression of emotions gains importance with the continuous development of artificial agents such as social robots or interactive mobile applications. We developed a prototype version of an abstract emotion visualization agent to express five basic emotions and a neutral state. In contrast to well-known symbolic characters (e.g., smileys) these displays follow general biological and ethological rules. We conducted a multiple questionnaire study on the assessment of the displays with Hungarian and Japanese subjects. In (...) most cases participants were successful in recognizing the displayed emotions. Fear and sadness were most easily confused with each other while both the Hungarian and Japanese participants recognized the anger display most correctly. We suggest that the implemented biological approach can be a viable complement to the emotion expressions of some artificial agents, for example mobile devices. (shrink)

Synthetic biology is often understood in terms of the pursuit for well-characterized biological parts to create synthetic wholes. Accordingly, it has typically been conceived of as an engineering dominated and application oriented field. We argue that the relationship of synthetic biology to engineering is far more nuanced than that and involves a sophisticated epistemic dimension, as shown by the recent practice of synthetic modeling. Synthetic models are engineered genetic networks that are implanted in a natural cell environment. Their construction is (...) typically combined with experiments on model organisms as well as mathematical modeling and simulation. What is especially interesting about this combinational modeling practice is that, apart from greater integration between these different epistemic activities, it has also led to the questioning of some central assumptions and notions on which synthetic biology is based. As a result synthetic biology is in the process of becoming more “biology inspired.”. (shrink)

Intelligence in current AI research is measured according to designer-assigned tasks that lack any relevance for an agent itself. As such, tasks and their evaluation reveal a lot more about our intelligence than the possible intelligence of agents that we design and evaluate. As a possible first step in remedying this, this article introduces the notion of “self-concern,” a property of a complex system that describes its tendency to bring about states that are compatible with its continued self-maintenance. Self-concern, as (...) argued, is the foundation of the kind of basic intelligence found across all biological systems, because it reflects any such system's existential task of continued viability. This article aims to cautiously progress a few steps closer to a better understanding of some necessary organisational conditions that are central to self-concern in biological systems. By emulating these conditions in embodied AI, perhaps something like genuine self-concern can be implemented in machines, bringing AI one step closer to its original goal of emulating human-like intelligence. (shrink)

Recent years have seen a gradual convergence of seemingly distant research fields over a single goal: understanding and replicating biological intelligence in artifacts. This work presents a general overview on the origin, the state-of-the-art, scientific challenges and the future of Biologically Inspired Cognitive Architecture (BICA) research. Our perspective decomposes the field into the four principal semantic components associated with the BICA challenge that together call for an integration of efforts of researchers across disciplines. Areas and directions of study where new (...) integrated efforts will be primarily needed are summarized. (shrink)

Animals and robots perceiving and acting in a world require an ontology that accommodates entities, processes, states of affairs, etc., in their environment. If the perceived environment includes information - processing systems, the ontology should reflect that. Scientists studying such systems need an ontology that includes the first - order ontology characterising physical phenomena, the second - order ontology characterising perceivers of physical phenomena, and a third order ontology characterising perceivers of perceivers, including introspectors. We argue that second - and (...) third - order ontologies refer to contents of virtual machines and examine requirements for scientific investigation of combined virtual and physical machines, such as animals and robots. We show how the CogAff architecture schema, combining reactive, deliberative, and meta - management categories, provides a first draft schematic third - order ontology for describing a wide range of natural and artificial agents. Many previously proposed architectures use only a subset of CogAff, including subsumption architectures, contention - scheduling systems, architectures with Ôexecutive functionsÕ and a variety of types of ÔOmegaÕ architectures. Adding a multiply - connected, fastacting ÔalarmÕ mechanism within the CogAff framework accounts for several varieties of emotions. H - CogAff, a special case of CogAff, is postulated as a minimal architecture specification for a human - like system. We illustrate use of the CogAff schema in comparing H - CogAff with Clarion, a well known architecture. One implication is that reliance on concepts tied to observation and experiment can harmfully restrict explanatory theorising, since what an information processor is doing cannot, in general, be determined by using the standard observational techniques of the physical sciences or laboratory experiments. Like theoretical physics, cognitive science needs to be highly speculative to make progress. Ó 2004 Published by Elsevier B. V. (shrink)

The European Legacy, Volume 17, Issue 4, Page 560-561, July 2012.

The human brain has some capabilities that the brains of other animals lack. It is to these distinctive capabilities that our species owes its dominant position. Other animals have stronger muscles or sharper claws, but we have cleverer brains. If machine brains one day come to surpass human brains in general intelligence, then this new superintelligence could become very powerful. As the fate of the gorillas now depends more on us humans than on the gorillas themselves, so the fate of (...) our species then would come to depend on the actions of the machine superintelligence. But we have one advantage: we get to make the first move. Will it be possible to construct a seed AI or otherwise to engineer initial conditions so as to make an intelligence explosion survivable? How could one achieve a controlled detonation? To get closer to an answer to this question, we must make our way through a fascinating landscape of topics and considerations. Read the book and learn about oracles, genies, singletons; about boxing methods, tripwires, and mind crime; about humanity's cosmic endowment and differential technological development; indirect normativity, instrumental convergence, whole brain emulation and technology couplings; Malthusian economics and dystopian evolution; artificial intelligence, and biological cognitive enhancement, and collective intelligence. (shrink)

The theory of biological evolution has often provoked disagreement, which has frequently been divisive and counterproductive. At other times this scientific paradigm has been discussed with an apologetic intent, to explain why the science of biology and the theology of creation cannot be seen to be mutually exclusive. This paper urges Christians to move decisively to a third type of discourse. The new field of comparative genetics has provided conclusive evidence that biological evolution has given rise to the diversity of (...) living forms, including human beings. Consequently, Christians should, with confidence, gladly accept the evolutionary paradigm and look upon evolution as a divinely ordained historical process that develops through random (stochastic, free) process, but that leads to a divinely purposed consummation. As a result, biological history in its freedom but directedness to God’s final purposes should elicit wonder. People who have come to faith in the God revealed redemptively in Jesus should uninhibitedly offer adoration and praise for evolutionary fruitfulness. Worship should characterise the human response to biological history.[1] [1] In appreciation of Professor Tom McLeish FRS, scientist, teacher, theologian. (shrink)

The 17th and 18th centuries were the theatre of the fight between two main theories concerning the development of organisms: preformationism (or preformism) and epigeneticism (or epigenesis). According to the first, the formation of new features during organisms’ development can be seen as the result of a mere unfolding of features that were preformed in the sperm, the egg, or the zygote. According to epigeneticism, there is no pre-existing form, and development is a process where genuinely new characters emerge from (...) formless matter. The debate involved naturalists, anatomists, physiologists, microscopists, medical doctors, and philosophers as well. Current developmental biology is, according to some, still inspired (or haunted) by the age-old controversy. The aim of this contribution is twofold. First, to discuss in which guise, if any, the old controversy is still shaping the contemporary debate in biology and philosophy of biology; and, second, to sketch Schelling’s position on that debate, suggesting that it may contain some still valuable philosophical insight. (shrink)

Non-biological superintelligent artificial minds are scary things. Some theorists believe that if they came to exist, they might easily destroy human civilization, even if destroying human civilization was not a high priority for them. Consequently, philosophers are increasingly worried about the future of human beings and much of the rest of the biological world in the face of the potential development of superintelligent AI. This paper explores whether the increased attention philosophers have paid to the dangers of superintelligent AI is (...) justified. I argue that, even if such a thing is developed and even if it is able to gain enormous knowledge, there are several reasons to believe that the motivation of such an AI will be more complicated than what most theorists have supposed thus far. In particular, I explore the relationship between a superintelligent AI's intelligence and its moral reasoning, in an effort to show that there is a realistic possibility that the AI will be unable to act, due to conflicts between various goals that it might adopt. Although no firm conclusions can be drawn at present, I seek to show that further work is needed and to provide a framework for future discussion. (shrink)

The human brain has some capabilities that the brains of other animals lack. It is to these distinctive capabilities that our species owes its dominant position. Other animals have stronger muscles or sharper claws, but we have cleverer brains. If machine brains one day come to surpass human brains in general intelligence, then this new superintelligence could become very powerful. As the fate of the gorillas now depends more on us humans than on the gorillas themselves, so the fate of (...) our species then would come to depend on the actions of the machine superintelligence. But we have one advantage: we get to make the first move. Will it be possible to construct a seed AI or otherwise to engineer initial conditions so as to make an intelligence explosion survivable? How could one achieve a controlled detonation? To get closer to an answer to this question, we must make our way through a fascinating landscape of topics and considerations. Read the book and learn about oracles, genies, singletons; about boxing methods, tripwires, and mind crime; about humanity's cosmic endowment and differential technological development; indirect normativity, instrumental convergence, whole brain emulation and technology couplings; Malthusian economics and dystopian evolution; artificial intelligence, and biological cognitive enhancement, and collective intelligence. This profoundly ambitious and original book picks its way carefully through a vast tract of forbiddingly difficult intellectual terrain. Yet the writing is so lucid that it somehow makes it all seem easy. After an utterly engrossing journey that takes us to the frontiers of thinking about the human condition and the future of intelligent life, we find in Nick Bostrom's work nothing less than a reconceptualization of the essential task of our time. (shrink)

This selective review explores biologically inspired learning as a model for intelligent robot control and sensing technology on the basis of specific examples. Hebbian synaptic learning is discussed as a functionally relevant model for machine learning and intelligence, as explained on the basis of examples from the highly plastic biological neural networks of invertebrates and vertebrates. Its potential for adaptive learning and control without supervision, the generation of functional complexity, and control architectures based on self-organization is brought forward. Learning without (...) prior knowledge based on excitatory and inhibitory neural mechanisms accounts for the process through which survival-relevant or task-relevant representations are either reinforced or suppressed. The basic mechanisms of unsupervised biological learning drive synaptic plasticity and adaptation for behavioral success in living brains with different levels of complexity. The insights collected here point toward the Hebbian model as a choice solution for “intelligent” robotics and sensor systems. Keywords: Hebbian learning; synaptic plasticity; neural networks; self-organization; brain; reinforcement; sensory processing; robot control . (shrink)

_This paper outlines the case for believing that we will have superhuman artificial intelligence_ _within the first third of the next century. It looks at different estimates of the processing power of_ _the human brain; how long it will take until computer hardware achieve a similar performance;_ _ways of creating the software through bottom-up approaches like the one used by biological_ _brains; how difficult it will be for neuroscience figure out enough about how brains work to_ _make this approach work; (...) and how fast we can expect superintelligence to be developed once_ _there is human-level artificial intelligence._. (shrink)

Limit cycles, because they are constituted of a periodic succession of states (discrete or continuous) constitute a good manner to store information. From any points of the state space reached after a perturbation or stimulation of the cognitive system storing this information, one can aim to join through a more or less long return trajectory a precise neighbourhood of the asymptotic trajectory at a specific moment (or a specific place) on the limit cycle, i.e. where the information of interest stands. (...) We propose that the isochronal fibration, initially imagined and described by A. T. Winfree may be an excellent way to connect directly those two locations. Each isochron is indeed the set of points in temporal phase with one single point of the attractor. The characterisation of the isochronal fibration of various dynamical systems is not easy and until now has principally only been done numerically but not analytically. By integrating the homogeneous solutions of the dynamical system we can solve this fibration in the case of the well known anharmonic pendulum. Other isochronal fibration on classical examples such as the van der Pol system and the non-symmetrical PFK limit cycle are obtained numerically and we also provide the first numerical study on 3-dimentional systems like the anharmonic pendulum with a linear relaxation on its third variable and the Lorenz attractor. The empirical approach seems us useful for dealing with the isochronal fibration which could constitute a powerful tool for understanding and controlling the dynamics of biological or biological-inspired systems. (shrink)

This Neurocomputing special issue is based on selected, expanded and significantly revised versions of papers presented at the Second International Conference on Brain Inspired Cognitive Systems (BICS 2006) held at Lesvos, Greece, from 10 to 14 October 2006. The aim of BICS 2006, which followed the very successful first BICS 2004 held at Stirling, Scotland, was to bring together leading scientists and engineers who use analytic, syntactic and computational methods both to understand the prodigious processing properties of biological systems and, (...) specifically, of the brain, and to exploit such knowledge to advance computational methods towards ever higher levels of cognitive competence. The biennial BICS Conference Series (with BICS 2008 recently held in Sao Luis, Brazil, 24–27 June, and BICS 2010 due to be held in Madrid, Spain) aims to become a major point of contact for research scientists, engineers and practitioners throughout the world in the fields of cognitive and computational systems inspired by the brain and biology. The first paper in this special issue is by Carnell who presents an analysis of the use of Hebbian and Anti-Hebbian spike timedependent plasticity (STDP) learning functions within the context of recurrent spiking neural networks. He shows that under specific conditions Hebbian and Anti-Hebbian learning can be considered approximately equivalent. Finally, the author demonstrates that such a network habituates to a given stimulus and is capable of detecting subtle variations in the structure of the stimuli itself. Hodge, O’Keefe and Austin present a binary neural shape matcher using Johnson counters and chain codes. They show that images may be matched as whole images or using shape matching. Finally, they demonstrate shape matching using a binary associative-memory neural network to index and match chain codes where the chain code elements are represented by Johnson codes. (shrink)

In the Qur’an, human distinctiveness was first questioned by angels. These established denizens of the cosmos could not understand why God would create a seemingly pernicious human when immaculate devotees of God such as themselves existed. In other words, the angels asked the age-old question: what makes humans so special and different? Fast forward to our present age and this question is made relevant again in light of the encroaching arrival of an artificial superintelligence (ASI). Up to this point in (...) history, humans have exceeded other creatures in various respects; now a possibility has arisen that another entity, namely ASI, will exceed humans at least on the level of intelligence and power. In relation to the age of angels, pre-modern Sunni exegesis construed human distinctiveness along the axes of reproductive knowledge and stewardship. Both brittle, distinguishing markers will disappear in the age of the ASI. Conversely, a more resilient and creative Islamic response can be derived from Ibn al-ʿArabī’s (d. 1240) views on God and the imago Dei. Inspired by the Akbarian perspective, this paper construes human distinctiveness in relation to a capacity to expansively respond to God’s love to be recognized, a response that relies on (a) imitating divine virtues that operate in counterintuitive and illogical ways, and (b) exhibiting fragility and lack rather than exceptionalism. ASI, while responding already in part to God’s love, needs to make strides towards these traits before it can answer divine love as commensurately as humans can. (shrink)

The idea of superintelligence is a source of mainly philosophical and ethical considerations. Those considerations are rooted in the idea that an entity which is more intelligent than humans, may evolve in some point in the future. For obvious reasons, the superintelligence is considered as a kind of existential threat for humanity. In this essay, we discuss two ideas. One of them is the putative nature of future superintelligence which does not necessary need to be harmful for humanity. Our key (...) idea states that the superintelligence does not need to assess its own survival as the highest value. As a kind of intelligence that is not biological, it is not clear what kind of attitude the superintelligent entity may evolve towards living organisms. Our second idea refers to the possible revelation of superintelligence. We assume that the self-revelation of such entity cannot be random. The metaphor of God as a superintelligence is introduced here as a helpful conceptual tool. (shrink)

I argue that contemporary philosophy of language in the analytic tradition rests on two fundamentally wrong assumptions: empiricism and externalism. After I show why these two assumptions are incorrect, I turn my attention to biological rationalism. Biological rationalism—a research program inspired by the work of Noam Chomsky—is committed to nativism and internalism. I believe biological rationalism provides the best framework to achieve a genuine understanding of language. I try to show this by considering the biological rationalist answers to major problems (...) in philosophy of language. (shrink)

Synthetic biology aims to synthesize novel biological systems or redesign existing ones. The field has raised numerous philosophical questions, but most especially what is novel to this field. In this article I argue for a novel take, since the dominant ways to understand synthetic biology’s specificity each face problems. Inspired by the examination of the work of a number of chemists, I argue that synthetic biology differentiates itself by a new regime of articulation, i.e. a new way of articulating the (...) questions and phenomena it wants to address. Instead of describing actual existing biological systems, the field aims to describe biological possibilities. In the second part I corroborate this hypothesis through a comparison between early research in the field of the origins of life and contemporary synthetic biologists, who are not so much interested in the historical origin of life on Earth, but rather in a universal biology of the possible origins of any life whatsoever. (shrink)

Imagine a world where machines can see and understand the world the way humans do. Rapid progress in artificial intelligence has led to smartphones that recognize faces, cars that detect pedestrians, and algorithms that suggest diagnoses from clinical images, among many other applications. The success of computer vision is founded on a deep understanding of the neural circuits in the brain responsible for visual processing. This book introduces the neuroscientific study of neuronal computations in visual cortex alongside of the psychological (...) understanding of visual cognition and the burgeoning field of biologically-inspired artificial intelligence. Topics include the neurophysiological investigation of visual cortex, visual illusions, visual disorders, deep convolutional neural networks, machine learning, and generative adversarial networks among others. It is an ideal resource for students and researchers looking to build bridges across different approaches to studying and developing visual systems. (shrink)

It is proposed here that Webb's ideas about robots as possible models of animals need some rethinking. In our view, even though widely used biorobotics strategies are fairly successful at reproducing the macroscopic behavior of biological systems, there are still several problems unresolved on the side of robotics as well as biology. Both mathematical and hardware-like robotics models should be feasible physiologically. Control principles elaborated in robotics are not necessarily applied to biological control systems. Although observations of flying birds inspired (...) aerodynamics and thus modern airplanes, little knowledge has been added to the neurophysiological principles underlying flight in birds. Chess playing computers might outperform most chess players, but they cannot be considered as physiologically feasible models of human thinking. (shrink)

Despite its successes, to date Artificial Intelligence (AI) is still characterized by a number of shortcomings with regards to different application domains and goals. These limitations are arguably both conceptual (e.g., related to the underlying theoretical models, such as symbolic vs.connectionist), and operational (e.g., related to robustness and ability to generalize). Biologically inspired AI, and more specifically brain-inspired AI, promises to provide further biological aspects beyond those that are already traditionally included in AI, making it possible to assess and possibly (...) overcome some of its present shortcomings. This article examines some conceptual, technical, and ethical issues raised by the development and use of brain-inspired AI. Against this background, the paper asks whether there is anything ethically unique about brain-inspired AI. The aim of the paper is to introduce a method that has a heuristic nature and that can be applied to identify and address the ethical issues arising from brain-inspired AI (and from AI more generally). The conclusion resulting from the application of this method is that, compared to traditional AI, brain-inspired AI raises new foundational ethical issues and some new practical ethical issues, and exacerbates some of the issues raised by traditional AI. (shrink)

The central paradigm of arti?cial intelligence is rapidly shifting toward biological models for both robotic devices and systems performing such critical tasks as network management, vehicle navigation, and process control. Here we use a recent mathematical analysis of the necessary conditions for consciousness in humans to explore likely failure modes inherent to a broad class of biologically inspired computing machines. Analogs to developmental psychopathology, in which regulatory mechanisms for consciousness fail progressively and subtly understress, and toinattentional blindness, where a narrow (...) 'syntactic band pass' de?ned by the rate distortion manifold of conscious attention results in pathological ?xation, seem inevitable. Similar problems are likely to confront other possible architectures, although their mathematical description may be far less straightforward. Computing devices constructed on biological paradigms will inevitably lack the elaborate, but poorly understood, system of control mechanisms which has evolved over the last few hundred million years to stabilize consciousness in higher animals. This will make such machines prone to insidious degradation, and, ultimately, catastrophic failure. (shrink)

This volume reviews examples and notions of robustness at several levels of biological organization. It tackles many philosophical and conceptual issues and casts an outlook on the future challenges of robustness studies in the context of a practice-oriented philosophy of science. The focus of discussion is on concrete case studies. These highlight the necessity of a level-dependent description of robust biological behaviors.Experts from the neurosciences, biochemistry, ecology, biology, and the history and the philosophy of life sciences provide a multiplex perspective (...) on the topic. Contributions span from protein folding, to cell-level robustness, to organismal and developmental robustness, to sensorimotor systems, up to the robustness of ecological systems.Several chapters detail neurobiological case-studies. The brain, the poster child of plasticity in biology, offers multiple examples of robustness. Neurobiology explores the importance of temporal organization and multiscalarity in making this robustness-with-plasticity possible. The discussion also includes structures well beyond the brain, such as muscles and the complex feedback loops involved in the peculiar robustness of music perception. Overall, the volume grounds general reflections upon concrete case studies, opening to all the life sciences but also to non-biological and bio-inspired fields such as post-modern engineering. It will appeal to researchers, students, as well as non-expert readers. (shrink)

It is argued, that theory sf signs, especially in the tradition of the great philosopher Charles Sanders Peirce (1839–1914) can inspire the study of central problems in the philosophy of biology. Three such problems are considered: (1) The nature of biology as a science, where a semiotically informed pluralistic approach to the theory of science is introduced. (2) The peculiarity of the general object of biology, where a realistic interpretation of sign- and information-concepts is required to see sign-processes as immanent (...) in nature. (3) The possibility of an artificial construction of life, hereby discussed as a conceptual problem in the present form of the artificial life project and its implied definition of life. (shrink)

How should biological behaviour be modelled? A relatively new approach is to investigate problems in neuroethology by building physical robot models of biological sensorimotor systems. The explication and justification of this approach are here placed within a framework for describing and comparing models in the behavioural and biological sciences. First, simulation models – the representation of a hypothesis about a target system – are distinguished from several other relationships also termed “modelling” in discussions of scientific explanation. Seven dimensions on which (...) simulation models can differ are defined and distinctions between them discussed: 1. Relevance: whether the model tests and generates hypotheses applicable to biology. 2. Level: the elemental units of the model in the hierarchy from atoms to societies. 3. Generality: the range of biological systems the model can represent. 4. Abstraction: the complexity, relative to the target, or amount of detail included in the model. 5. Structural accuracy: how well the model represents the actual mechanisms underlying the behaviour. 6. Performance match: to what extent the model behaviour matches the target behaviour. 7. Medium: the physical basis by which the model is implemented. No specific position in the space of models thus defined is the only correct one, but a good modelling methodology should be explicit about its position and the justification for that position. It is argued that in building robot models biological relevance is more effective than loose biological inspiration; multiple levels can be integrated; that generality cannot be assumed but might emerge from studying specific instances; abstraction is better done by simplification than idealisation; accuracy can be approached through iterations of complete systems; that the model should be able to match and predict target behaviour; and that a physical medium can have significant advantages. These arguments reflect the view that biological behaviour needs to be studied and modelled in context, that is, in terms of the real problems faced by real animals in real environments. Key Words: animal behaviour; levels; models; neuroethology; realism; robotics; simulation. (shrink)

This article proposes an abstract mathematical frame for describing some features of cognitive and biological time. We focus here on the so called “extended present” as a result of protentional and retentional activities (memory and anticipation). Memory, as retention, is treated in some physical theories (relaxation phenomena, which will inspire our approach), while protention (or anticipation) seems outside the scope of physics. We then suggest a simple functional representation of biological protention. This allows us to introduce the abstract notion of (...) “biological inertia”. (shrink)

Synthetic biology is described as a new field of biotechnology that models itself on engineering sciences. However, this view of synthetic biology as an engineering field has received criticism, and both biologists and philosophers have argued for a more nuanced and heterogeneous understanding of the field. This paper elaborates the heterogeneity of synthetic biology by clarifying the role of design and the variability of design methodologies in synthetic biology. I focus on two prominent design methodologies: rational design and directed evolution. (...) Rational design resembles the design methodology of traditional engineering sciences. However, it is often replaced and complemented by the more biologically-inspired method of directed evolution, which models itself on natural evolution. These two approaches take philosophically different stances to the design of biological systems. Rational design aims to make biological systems more machine-like, whereas directed evolution utilizes variation and emergent features of living systems. I provide an analysis of the methodological basis of these design approaches, and highlight important methodological differences between them. By analyzing the respective benefits and limitations of these approaches, I argue against the engineering-dominated conception of synthetic biology and its “methodological monism”, where the rational design approach is taken as the default design methodology. Alternative design methodologies, like directed evolution, should be considered as complementary, not competitive, to rational design. (shrink)

This paper analyses the actual meaning of a transcendental philosophy of biology, and does so by exploring and actualising the epistemological and metaphysical value of Kant's viewpoint on living systems. It finds inspiration in the Kantian idea of living systems intrinsically resisting objectification, but critically departs from Kant's philosophical solution in as far as it is based in a subjectivist dogmatism. It attempts to overcome this dogmatism, on the one hand by explicitly taking into account the conditions of possibility at (...) the side of the subject, and on the other hand by embedding both the living and the knowing system into an ontology of complexly organized dynamical systems. This paper fits into the transcendental perspective in acknowledging the need to analyse the conditions of knowability, prior to the contents of what is known. But it also contributes to an expansion and an actualisation of the issue of transcendentality itself by considering the conditions of possibility at the side of the object as intrinsically linked to the conditions of possibility at the side of the subject. (shrink)

To naturalize the concept of teleological causality in biology it is not enough to avoid assuming backward causation or positing the existence of an inscrutable te- leological essence like the élan vital. We must also specify how the causality of or- ganisms is distinct from the causality of designed artifacts like thermostats or asym- metrically oriented processes like the ubiquitous increase of entropy. Historically, the concept of teleological causality in biology has been based on an analogy to the familiar experience (...) of purposeful action. This is experienced by us as a disposition to achieve a general type of end that is represented in advance, and which regulates the selection of efficient means to achieve it. Inspired by this analogy, to bridge the gap between biology and human agency we describe a simple molecular pro- cess called autogenesis that shows how two linked complementary self-organizing processes can give rise to higher-order relations that resemble purposeful disposi- tions, though expressed in terms of constraints on molecular processes. Because the autogenic model is described in sufficient detail to be empirically realizable, it provides a proof of principle demonstrating a simple form of teleological causality. (shrink)

This aricle discusses the similarity between the propagation of pathogens (viruses and worms) on computer networks and the proliferation of pathogens in cellular organisms (organisms with genetic material contained within a membrane-encased nucleus). It introduces several biological mechanisms which are used in these organisms to protect against such pathogens and presents security models for networked computers inspired by several biological paradigms, including genomics (RNA interference), proteomics (pathway mapping), and physiology (immune system). In addition, the study of epidemiological models for disease (...) control can inspire methods for controlling the spread of pathogens across multiple nodes of a network. It also presents results based on the authors’ research in immune system modeling. (shrink)

One Health medicine aims to improve health by focusing on the relations between the health of humans, animals, and the environment. However, One Health does not provide a clear idea of these relations, which are still represented as conceptually separated and not as one health, as the name implies. Inspired by holobiont research, I suggest a new model and conceptual framework for One Health that expands the notion of the biological patient by providing a gradational and dynamic understanding of environments, (...) patients, and their relations. This new model conceptualizes humans and non-humans, individual organisms, and collectives, as belonging to one system that allows for more or less inclusive understandings of patients. As such, it resolves the conceptual tensions of different One Health approaches and supports the implementation of One Health as an interdisciplinary research field. (shrink)

In the late 1800s, Charles Darwin and other naturalists supported a blending view of inheritance whereby offspring possess a middling mix of their parents' traits. Many of these naturalists also argued that individuals pass at least some of their acquired characteristics to their descendants. Darwin proposed that acquired characteristics and other environmentally induced changes in a parent's hereditary material account in large part for the inheritable variations that drove evolution. Inspired by the evolutionary theories of his first cousin, Darwin, Francis (...) Galton developed hereditarian notions that helped to lay the foundation for both genetics and eugenics. Eugenics was endorsed by evolutionary geneticists such as August Weismann, Karl Pearson, W. F. R. Weldon, William Bateson, and Hugo de Vries, which, as a result, gave it enormous scientific credibility in America and Europe. This chapter explores the role of biology in the emergence of the eugenics movement in the Anglo-Saxon world. (shrink)

Kant famously identified 'What is man?' as the fundamental question that encompasses the whole of philosophy. Yet surprisingly, there has been no concerted effort amongst Kant scholars to examine Kant's actual philosophy of man. This book, which is inspired by, and part of, the recent movement that focuses on the empirical dimension of Kant's works, is the first sustained attempt to extract from his writings on biology, anthropology and history an account of the human sciences, their underlying unity, their presuppositions (...) as well as their methodology. In exploring his philosophical and epistemological foundation of the human sciences, it reveals an unexpected picture of Kant, a picture of a thinker who is profoundly attentive to the diversity, detail and complexity of the human world. (shrink)

Pluralistic approaches in the philosophy of biological individuality suggest that reality is divisible into multiple types of biological individuals (evolutionary, genetic, physiological, etc.). In this research I will argue in favor of this pluralistic ontology. Inspired mainly by John Dupré’s processual metaphysics, Ronald Giere’s perspectivism, and Hasok Chang’s active realism, I will suggest that: (i) biological individuals are temporarily stable nexuses in a flow of causal processes, (ii) individuations in biology represent real individuals only under the scientific perspectives that support (...) them, (iii) biology’s ability to recognize diverse types of individuals comes from epistemically successful individuation practices. These practices not only contact us with multiple sites of reality, but also invite us to embrace pluralism as an effective way to increase our scientific knowledge of nature. (shrink)