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 field of Artificial Life studies the nature of the living state, by modeling and synthesizing living systems. Such systems, under certain conditions, may come to deserve moral consideration similar to that of non-human vertebrates or even human beings. The fact that these systems are non-human and evolve in a potentially radically different substrate should not be seen as an insurmountable obstacle to their potentially having rights equivalent to non-human vertebrates or even human beings, if they are sufficiently sophisticated in other respects. Nor should the fact that they owe their existence to us be seen as reducing their status as targets of moral concern. On the contrary, creators of artificial life may have special obligations to their creations, resembling those of an owner to their pet or a parent to their child. For a field that aims to create artificial lifeforms with increasing levels of sophistication, it is crucial to consider the possible implications of our activities under an ethical perspective, and assess the moral obligations for which we should be prepared. If artificial life is “larger than life”, then the ethics of artificial beings should be “larger than human ethics”.

Developments in cognitive science, AI, and artificial life force us to consider minds and intelligences that are different from human minds. The dominant contemporary metaphor for any kind of mind is based on an understanding of the human brain and human experience, both of which frequently presuppose a notion of self. In some disciplines, including Buddhism, contemporary philosophy of mind, and cognitive science, much debate has focused on the nature of the self, and one insight from all these domains is that while we are strongly attached to notions of stable selves, it is also possible to conceive of selves as dynamic, interconnected, and illusory. We suggest that an interdisciplinary approach, drawing on fields with well-developed models of self in relation to agency, can offer new insights. We suggest that the view of self as illusory, and awareness of this illusion, in both human and non-human minds, may augment and qualitatively change the agent's affordances, or range of possible actions.

Criticality is thought to be crucial for complex systems to adapt, at the boundary between regimes with different dynamics, where the system may transition from one phase to another. Numerous systems, from sandpiles to gene regulatory networks, to swarms and human brains, seem to work towards preserving a precarious balance right at their critical point. Understanding criticality therefore seems strongly related to a broad, fundamental theory for the physics of life as it could be, which still lacks a clear description of how it can arise and maintain itself in complex systems. In order to investigate this crucial question, we combine critical learning with evolutionary simulation for a population of Ising-embodied neural networks, striving to find resources distributed over a 2D environment. The results show compelling dynamics in the combination of critical learning with evolutionary computation, highlighting the exploratory nature of critical systems and the pragmatism of evolutionary algorithms. We also analyze the genotypic exploration strategy, exhibiting a tension between local and global scale adaptation.

The complete Proceedings of the The 2018 Conference on Artificial Life: A Hybrid of the European Conference on Artificial Life (ECAL) and the International Conference on the Synthesis and Simulation of Living Systems (ALIFE)