An overview of the German philosophy of science community is given for the years 1992–2012, based on a survey in which 159 philosophers of science in Germany participated. To this end, the institutional background of the German philosophy of science community is examined in terms of journals, centers, and associations. Furthermore, a qualitative description and a quantitative analysis of our survey results are presented. Quantitative estimates are given for: (a) academic positions, (b) research foci, (c) philosophers’ of science most important (...) publications, and (d) externally funded projects, where for (c) all survey participants had indicated their five most important publications in philosophy of science. In addition, the survey results for (a)–(c) are also qualitatively described, as they are interesting in their own right. With respect to (a), we estimated the gender distribution among academic positions. Concerning (c), we quantified philosophers’ of science preference for (i) journals and publishers, (ii) publication format, (iii) language, and (iv) coauthorship for their most important publications. With regard to research projects, we determined their (i) prevalence, (ii) length, and (iii) trend (an increase in number?) as well as their most frequent (iv) research foci and (v) funding organizations. We also distinguished between German-based and non-German-based journals, publishers, and funding institutions, making it thereby possible to evaluate the involvement of the German philosophy of science community in the international research landscape. Finally, we discuss some implications of our findings. (shrink)

The emerging contemporary natural philosophy provides a common ground for the integrative view of the natural, the artificial, and the human-social knowledge and practices. Learning process is central for acquiring, maintaining, and managing knowledge, both theoretical and practical. This paper explores the relationships between the present advances in understanding of learning in the sciences of the artificial (deep learning, robotics), natural sciences (neuroscience, cognitive science, biology), and philosophy (philosophy of computing, philosophy of mind, natural philosophy). The question is, what at (...) this stage of the development the inspiration from nature, specifically its computational models such as info-computation through morphological computing, can contribute to machine learning and artificial intelligence, and how much on the other hand models and experiments in machine learning and robotics can motivate, justify, and inform research in computational cognitive science, neurosciences, and computing nature. We propose that one contribution can be understanding of the mechanisms of ‘learning to learn’, as a step towards deep learning with symbolic layer of computation/information processing in a framework linking connectionism with symbolism. As all natural systems possessing intelligence are cognitive systems, we describe the evolutionary arguments for the necessity of learning to learn for a system to reach human-level intelligence through evolution and development. The paper thus presents a contribution to the epistemology of the contemporary philosophy of nature. (shrink)

: Creativity, mostly ignored in Western philosophy due to its supposed mysteriousness, has recently become a respected research topic in psychology, neuroscience, and artificial intelligence. We discuss how in science the approach has mainly been to describe creativity as an information-based process, coherently with a computational view of the human mind started with the cognitive revolution. This view has produced progressively convincing models of creativity, up to current artificial neural network systems, vaguely inspired by biological neural processing, but already competing (...) with human creativity in several fields. These successes suggest that creativity might not be an exclusively human function, but actually a way of functioning of any natural or artificial system implementing the creative process. We conclude by acknowledging that the information-based view of creativity has tremendous explanatory and generative power, but we propose a thought experiment to start discussing how it actually leaves out the experiential side of being creative. Keywords: Creative Cognition; Cognitive Neuroscience; Computational Creativity; Generative Algorithms; Cognitive Science La creatività come processo basato sull’informazione Riassunto: La creatività, spesso ignorata dalla filosofia occidentale per la sua presunta oscurità, in tempi recenti è diventata un rispettabile oggetto di ricerca per la psicologia, la neuroscienza e l’intelligenza artificiale. Vogliamo illustrare il modo in cui lo sguardo scientifico sia rivolto prevalentemente a considerare la creatività come processo information-based, coerentemente con la prospettiva computazionale sulla mente umana aperta dalla rivoluzione cognitiva. Questa prospettiva ha prodotto modelli della creatività sempre più convincenti, fino agli attuali sistemi di reti neurali artificiali, vagamente inspirati al processamento biologico neurale, ma già competitivi rispetto alla creatività umana in molti ambiti. Questi successi suggeriscono che la creatività possa non essere una funzione esclusivamente umana ma in effetti un modo di funzionare di un sistema naturale o artificiale capace di implementare il processo creativo. In conclusione, pur riconoscendo come il considerare la creatività come processo information-based possieda grande potere esplicativo e generativo, proporremo un esperimento mentale per aprire una discussione sul come questa prospettiva non copra in effetti il lato esperienziale dell’essere creativo. Parole chiave: Cognizione creativa; Neuroscienza cognitiva; Creatività computazionale; Algoritmi generativi; Scienza cognitiva. (shrink)

In the past 150 years there have been many attempts to draw parallels between cultural and biological evolution. Most of these attempts were flawed due to lack of knowledge and false ideas about evolution. In recent decades these shortcomings have been cleared away, thus triggering a renewed interest in the subject. This paper offers a critical survey of the main issues and arguments in that discussion. The paper starts with an explication of the Darwinian algorithm of evolution. It is argued (...) that this ‘formula’ is substrate-neutral, which means that biological evolution might not be the only Darwinian process. Other dynamic systems could evolve as well provided that certain conditions are met. In the case of human culture this seems to be the case. The paper then focuses on the notion of niche construction. It is argued that niche construction plays a crucial role in human evolution because it has altered the sources of natural selection and thus the path of evolution. Next two approaches to cultural evolution are discussed: sociobiology and memetics. I will argue that both approaches have flaws because they either underestimate the influence of culture or they stretch analogies too far. Finally two common objections against the idea of cultural evolution are addressed: Lamarckian inheritance and the issue of guided variation. I will argue that although cultural evolution differs from biological evolution in several respects, these discrepancies do not jeopardize the claim that cultural evolution is essentially Darwinian. (shrink)

This entry provides a substantive overview of research and debates concerning creativity in philosophy and related fields. Topics covered include definitions of creativity, whether creativity can be learned, whether it can be explained, attempts to explain creativity in cognitive science, and whether computer programs or AI systems can be creative.

Numerous studies in microbiology, eukaryotic cell biology, plant biology, biomimetics, synthetic biology, and philosophy of science appear to support the principles of the epistemological theory inspired by evolution, also known as “Evolutionary Epistemology”, or EE. However, that none of the studies acknowledged EE suggests that its principles have not been formulated with sufficient clarity and depth to resonate with the interests of the empirical research community. In this paper I review evidence in favor of EE, and also reformulate EE principles (...) to better inform future research. The revamped programme may be tentatively called Research Programme for Distributed Biological Intelligence. Intelligence I define as the capacity of organisms to gain information about their environment, process that information internally, and translate it into phenotypic forms. This multistage progression may be expressed through the acronym IGPT (information-gain-process-translate). The key principles of the programme may be summarized as follows. (i) Intelligence, a universal biological phenomenon promoting individual fitness, is required for effective organism-environment interactions. Given that animals represent less than 0.01% of the planetary biomass, neural intelligence is not the evolutionary norm. (ii) The basic unit of intelligence is a single cell prokaryote. All other forms of intelligence are derived. (iii) Intelligence is hierarchical. It ranges from bacteria to the biosphere or Gaia. (iv) The concept of “information” acquires a new meaning because information processing is at the heart of biological intelligence. All biological systems, from bacteria to Gaia, are intelligent, open thermodynamic systems that exchange information, matter and energy with the environment. (v) The organism-environment interaction is cybernetic. As much as the organism changes due to the influence of the environment, the organism’s responses to induced changes affect the environment and subsequent organism-environment interactions. Based on the above principles a new research agenda can be formulated to explore different forms of biological intelligence. (shrink)