16,976 research outputs found

    Nietzsche’s polychrome exemplarism

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    In this paper, I develop an account of Nietzschean exemplarism. Drawing on my previous work, I argue that an agent’s instincts and other drives constitute her psychological type. In this framework, a drive counts as a virtue to the extent that it is well-calibrated with the rest of the agent’s psychic economy and meets with sentiments of approbation from the agent’s community. Different virtues are fitting for different types, and different types elicit different discrete emotions in people with fine-tuned affective sensitivity, making Nietzsche’s exemplarism doubly pluralistic. Exemplars show us how a type is expressed in different social and cultural contexts. Some live up to the full potential of their type, while others are stymied and demonstrate how pernicious influences can wreck a person’s psychology. While some exemplars inspire admiration that leads to emulation, others elicit a range of other emotions, such as envy, contempt, and disgust. If this is right, then Nietzschean exemplarism offers a richer, more evaluatively and motivationally nuanced moral psychology than the monochrome admire-and-emulate model currently popular

    Building Machines That Learn and Think Like People

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    Recent progress in artificial intelligence (AI) has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn, and how they learn it. Specifically, we argue that these machines should (a) build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; (b) ground learning in intuitive theories of physics and psychology, to support and enrich the knowledge that is learned; and (c) harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes towards these goals that can combine the strengths of recent neural network advances with more structured cognitive models.Comment: In press at Behavioral and Brain Sciences. Open call for commentary proposals (until Nov. 22, 2016). https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/information/calls-for-commentary/open-calls-for-commentar

    A Survey of Brain Inspired Technologies for Engineering

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    Cognitive engineering is a multi-disciplinary field and hence it is difficult to find a review article consolidating the leading developments in the field. The in-credible pace at which technology is advancing pushes the boundaries of what is achievable in cognitive engineering. There are also differing approaches to cognitive engineering brought about from the multi-disciplinary nature of the field and the vastness of possible applications. Thus research communities require more frequent reviews to keep up to date with the latest trends. In this paper we shall dis-cuss some of the approaches to cognitive engineering holistically to clarify the reasoning behind the different approaches and to highlight their strengths and weaknesses. We shall then show how developments from seemingly disjointed views could be integrated to achieve the same goal of creating cognitive machines. By reviewing the major contributions in the different fields and showing the potential for a combined approach, this work intends to assist the research community in devising more unified methods and techniques for developing cognitive machines

    Clustered marginalization of minorities during social transitions induced by co-evolution of behaviour and network structure

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    Large-scale transitions in societies are associated with both individual behavioural change and restructuring of the social network. These two factors have often been considered independently, yet recent advances in social network research challenge this view. Here we show that common features of societal marginalization and clustering emerge naturally during transitions in a co-evolutionary adaptive network model. This is achieved by explicitly considering the interplay between individual interaction and a dynamic network structure in behavioural selection. We exemplify this mechanism by simulating how smoking behaviour and the network structure get reconfigured by changing social norms. Our results are consistent with empirical findings: The prevalence of smoking was reduced, remaining smokers were preferentially connected among each other and formed increasingly marginalised clusters. We propose that self-amplifying feedbacks between individual behaviour and dynamic restructuring of the network are main drivers of the transition. This generative mechanism for co-evolution of individual behaviour and social network structure may apply to a wide range of examples beyond smoking.Comment: 16 pages, 5 figure

    Logical Form, the First Person, and Naturalism about Psychology: The Case Against Physicalist Imperialism

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    Physicalistic theories of psychology are a classic case of scientific imperialism: the explanatory capacity of physics, both with respect to its methods and to its domain, is taken to extend beyond the traditional realm of physics, and into that of psychology. I argue in this paper that this particular imperialistic venture has failed. Contemporary psychology uses methods not modelled on those of physics, embracing first-personal methodology where physics is strictly impersonal. I make the case that whether or not scientific imperialism is in general harmful, in this instance naturalists who reject first philosophy should give up physicalist imperialism. Using only general principles from the philosophy of logic plus accepted physicalist criteria of identity, I show that first-personal psychology embodies a minor but fruitful increase in expressive strength compared to impersonal psychology: the ability to distinguish descriptively indiscriminable posits

    Medicine is not science

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    ABSTRACT: Abstract Most modern knowledge is not science. The physical sciences have successfully validated theories to infer they can be used universally to predict in previously unexperienced circumstances. According to the conventional conception of science such inferences are falsified by a single irregular outcome. And verification is by the scientific method which requires strict regularity of outcome and establishes cause and effect. Medicine, medical research and many “soft” sciences are concerned with individual people in complex heterogeneous populations. These populations cannot be tested to demonstrate strict regularity of outcome in every individual. Neither randomised controlled trials nor observational studies in medicine are science in the conventional conception. Establishing and using medical and other “soft science” theories cannot be scientific. It requires conceptually different means: requiring expert judgement applying all available evidence in the relevant available factual matrix. The practice of medicine is observational. Prediction of outcomes for the individual requires professional expertise applying available medical knowledge and evidence. Expertise in any profession can only be acquired through experience. Prior cases are the fundament of knowledge and expertise in medicine. Case histories, studies and series can provide knowledge of extremely high reliability applicable to establishing reliable general theories and falsifying others. Their collation, study and analysis should be a priority in medicine. Their devaluation as evidence, the failure to apply their lessons, the devaluation of expert professional judgement and the attempt to emulate the scientific method are all historic errors in the theory and practice of modern medicine
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