Artificial Life (ALife) as an interdisciplinary field draws inspiration and influence from a variety of perspectives. Scientific progress crucially depends, then, on concerted efforts to invite cross-disciplinary dialogue. The goal of this paper is to revitalize discussions of potential connections between the fields of Computational Creativity (CC) and ALife, focusing specifically on the concept of Open-Endedness (OE); the primary goal of CC is to endow artificial systems with creativity, and ALife has dedicated much research effort into studying and synthesizing OE and artificial innovation. However, despite the close proximity of these concepts, their use so far remains confined to their respective communities, and their relationship is largely unclear. We provide historical context for research in both domains, and review the limited work connecting research on creativity and OE explicitly. We then highlight specific questions to be investigated in future work, with the eventual goals of (i) decreasing conceptual ambiguity by highlighting similarities and differences between the concepts of OE and creativity, (ii) identifying synergy effects of a research agenda that encompasses both concepts, and (iii) establishing a dialogue between ALife and CC research.

Visit this link to see a video version of this abstract. At this moment in technological history, it seems that AI-powered technology has the potential to evolve into almost anything within the next 20 years. While we expect machines to don various forms of intelligence, we also expect to integrate them into our daily lives in ways we haven’t yet imagined. How will their presence and capabilities affect our everyday human experience? While we’re often (rightfully) thinking about how our day-to-day lives will change, we rarely pause to consider the experience of the machines themselves. But there’s a good reason for this. What a machine “experiences” is difficult to define, much less measure. We also have difficulty understanding the concept of experience in general. We don’t fully understand the experiences of the many other living creatures who’ve shared our world for millennia. So while we cannot yet measure how models like ChatGPT[l] or Stable Diffusion[2] experience a written conversation, we may be able to experiment with different ways of translating a machine “experience” to a human one. How do current algorithms translate their inputs into an output, and what happens along the way? In this art installation, we introduce wearable technology meant to translate aspects of what a trained model allocates attention to into something a human can experience.

The exact dynamics of emergence remains one of the most prominent outstanding questions for the field of complexity science. I first discuss various perspectives on emergence, then offer a perspective on understanding emergence in a graph-theoretic representation. To test this, I analyze the dynamics of all possible spatial state spaces near the critical temperature in a 2-D Ising model. The size of different state spaces constrains a system's ability to explore various states within a finite time frame. In addition, the distribution of topological “determinism” for these state spaces remains constant for any particular temperature. At the critical temperature, this distribution is nearly linear, which is distinct from other temperatures. This approach may provide a path forward in building a mathematical framework that captures the dynamics of emergent phenomena. This is key to understanding emergence in biological systems, which are layered with various state spaces and observational perspectives.