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Recurrent (neural) networks have been deployed as models for learning musical processes, by computational scientists who study processes such as dynamic systems. Over time, more intricate music has been learned as the state of the art in recurrent networks improves. One particular recurrent network, the Long Short-Term Memory (LSTM) network shows promise for learning long songs, and generating new songs. We are experimenting with a module containing two inter-recurrent LSTM networks to cooperatively learn several human melodies, based on the songs' harmonic structures, and on the feedback inherent in the network. We show that these networks can learn to reproduce four human melodies. We then present as input new harmonizations, so as to generate new songs. We describe the reharmonizations, and show the new melodies that result. We also present a hierarchical structure for using reinforcement learning to choose LSTM modules during the course of melody generation.

Identifying shifts in the needs and behavior of social systems and markets depicts promising new market opportunities for creative organizations. Potential users might be adept in reflecting those shifts and irregularities in collaboration due to their outsider perspective. This paper is a systematic inquiry into the unique contribution of potential users and its embodiment in communication in co-creation activities. Following the principles of grounded theory, contributions and strategies of potential users are contrasted with those of current users within varying dyadic compositions. Tentative propositions pertaining to the merits and limitations of collaboration with potential users and with regard to emergences in varying dyadic compositions are derived. The resulting core category "enriched compensation and improvisation" shows that potential users' contributions unintentionally pervade the collaboration with social, cultural and emotional values, which can be regarded as problem finding as opposed to problem solving creativity. Their willingness to improvise leads to a series of ad-hoc changes to initial ideas which foster transformation when combined. Collaboration with potential users therefore depicts ways to leverage technological competences and shape promising new market opportunities.

This paper proposes a new interpretive stance for interpreting artistic works and performances that is relevant to artificial intelligence research but also has broader implications. Termed the expressive stance, this stance makes intelligible a critical distinction between present-day machine art and human art, but allows for the possibility that future machine art could find a place alongside our own. The expressive stance is elaborated as a response to Daniel Dennett's notion of the intentional stance, which is critically examined with respect to his specialized concept of rationality. The paper also shows that temporal scale implicitly serves to select between different modes of explanation in prominent theories of intentionality. It also considers the implications of the phenomenological background for systems that produce art.

Organizations stumble and fail for many reasons. One is that a dominant narrative, in the form of a story a company tells itself, one it shares with its audience/ market, or both, does not jibe with the lived experiences of a significant number of its employees and/or customers. This chapter is predicated on the idea that co-created stories are a way communities, organizations, and individuals can ease or eliminate the negative outcomes of dominant narratives. I propose that a basis for the structured co-creation of stories is what I call “game,” and that a definition of the game that can be applied to any co-created story is ERGO, an acronym for environment/roles/guidelines/objective. To explain the concept and its genesis, I take the reader through a community storytelling workshop I conducted in 2013, in which participants practiced applying the ERGO game structures to co-created community stories of drug abuse prevention.