The marketplace for definitions and theories of creativity is crowded indeed. No hard consensus exists on the elements of an all-embracing theory, or on what specific sub-processes and representations are required to support creativity, either in humans or in machines. Yet commonalities do exist across theories: a search for novelty and utility is implied by most theories, as is the notion that an innovation can be considered creative only if it is not too novel, and can be adequately grounded in the familiar and the understandable. Computational creativity (CC) is the pursuit of creative behavior in machines, and seeks inspiration from both AI and from human psychology. As a practical engineering endeavor, CC can afford to adopt a cafeteria approach to theories of creativity, taking what it needs from different theories and frameworks. In this paper we present a vision for CC research in the age of the Web, in which CC is provided on tap, via a suite of Web services, to any third-party application that needs it. We argue that this notion of Creativity as a Service – which is already a popular business model for human organizations – will allow CC researchers and developers to build ad-hoc mash-ups of whatever processes and representations are most suited to a given application. By offering CC as a centralized service, we can collect statistics on the most useful mash-ups, and therein obtain a new empirical basis for theorizing about creativity in humans and in machines.
The taxonomic composition of microbial communities varies substantially between environments, but the ecological causes of this variation remain largely unknown. We analyzed taxonomic and functional community profiles to determine the factors that shape marine bacterial and archaeal communities across the global ocean. By classifying 30,000 marine microorganisms into metabolic functional groups, we were able to disentangle functional from taxonomic community variation. We find that environmental conditions strongly influence the distribution of functional groups in marine microbial communities by shaping metabolic niches, but only weakly influence taxonomic composition within individual functional groups. Hence, functional structure and composition within functional groups constitute complementary and roughly independent "axes of variation" shaped by markedly different processes.
Some 70 years ago, computer scientist Alan Turing famously set the bar for artificial intelligence: a computer that could convince a human conversation partner that it was a person. On a recent spring afternoon in the Flow Machines laboratory, located on a quiet street in the Fifth Arrondissement of Paris, senior researcher Pierre Roy was more concerned with his music-making AI software's ability to create a convincingly catchy song. "So far, from the technical standpoint, no one knows how to do a proper song, to tell a story," he said. Flow Machines, a project of Sony Computer Science Laboratories in Paris that receives funding from the European Research Council, is developing an AI program that can compose compelling, professional-quality music -- an aim shared by similar ventures such as Jukedeck in the UK and Google's Magenta project. Ever since Turing defined his test, popular culture has fixated on the idea of sentient AI, both benign and catastrophically malign.
We present a corpus-based hybrid approach to music analysis and composition, which incorporates statistical, connectionist, and evolutionary components. Our framework employs artificial music critics, whichmaybe trained on large music corpora, and then pass aesthetic judgment on music artifacts. Music artifacts are generated by an evolutionary music composer, which utilizes music critics as fitness functions. To evaluate this approach we conducted three experiments. First, using music features based on Zipf's law, we trained artificial neural networks to predict the popularity of 992 musical pieces with 87.85% accuracy. Then, assuming that popularity correlates with aesthetics, we incorporated such neural networks into a genetic-programming system, called NEvMuse. NEvMuse autonomously "composed" novel variations of J.S. Bach's Invention #13 in A minor (BWV 784), variations which many listeners found to be aesthetically pleasing. Finally, we compared aesthetic judgments from an artificial music critic with emotional responses from 23 human subjects.
Algorithmic composition is the partial or total automation of the process of music composition by using computers. Since the 1950s, different computational techniques related to Artificial Intelligence have been used for algorithmic composition, including grammatical representations, probabilistic methods, neural networks, symbolic rule-based systems, constraint programming and evolutionary algorithms. This survey aims to be a comprehensive account of research on algorithmic composition, presenting a thorough view of the field for researchers in Artificial Intelligence.