16,976 research outputs found
Nietzsche’s polychrome exemplarism
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
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
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
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
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
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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From traditional essay to 'Ready Steady Cook' presentation: Reasons for innovative changes in assignments
The prose essay, case study and laboratory report, composed by individual students in isolation from their peers, used to be the mainstay of undergraduate writing. However, in recent years an array of alternative assignment types such as blogs, letters and e-posters have widened the repertoire of texts expected. This article attempts to describe the reasoning behind changes in assignment types at undergraduate and master’s level at the beginning of the twenty-first century. Data from 58 semi-structured interviews with lecturers in three UK universities is used together with course handbooks and some clarifications with lecturers via email. Suggested reasons for new assignment types are grouped into three categories: external, lecturer-driven and student-driven. The article surmises that, because of these pressures, students are now expected to produce a wide variety of text types, and greater attention should be paid to guidance in new assignments for both native and non-native speaker students
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