Can Computers Create Art?

This essay discusses whether computers, using Artificial Intelligence (AI), could create art. First, the history of technologies that automated aspects of art is surveyed, including photography and animation. In each case, there were initial fears and denial of the technology, followed by a blossoming of new creative and professional opportunities for artists. The current hype and reality of Artificial Intelligence (AI) tools for art making is then discussed, together with predictions about how AI tools will be used. It is then speculated about whether it could ever happen that AI systems could be credited with authorship of artwork. It is theorized that art is something created by social agents, and so computers cannot be credited with authorship of art in our current understanding. A few ways that this could change are also hypothesized.Comment: to appear in Arts, special issue on Machine as Artist (21st Century

NASA space station automation: AI-based technology review

Research and Development projects in automation for the Space Station are discussed. Artificial Intelligence (AI) based automation technologies are planned to enhance crew safety through reduced need for EVA, increase crew productivity through the reduction of routine operations, increase space station autonomy, and augment space station capability through the use of teleoperation and robotics. AI technology will also be developed for the servicing of satellites at the Space Station, system monitoring and diagnosis, space manufacturing, and the assembly of large space structures

Managing Diversity and Many Objectives in Evolutionary Design

This thesis proposes a new approach to evolving a diversity of high-quality solutions for problems having many objectives. Mouret and Clune's MAP-Elites algorithm has been proposed as a way to evolve an assortment of diverse solutions to a problem. We extend MAP-Elites in a number of ways. Firstly, we introduce a many-objective strategy called sum-of-ranks, which enables problems with many objectives (4 and more) to be considered in the MAP. Secondly, we enhance MAP-Elites by extending it with multiple solutions per "grid" cell (the original MAP-Elites saves only a single solution per cell). A few different ways of selecting cell members for reproduction are also considered. We test the new MAP-Elites strategies on the evolutionary art application of image generation. Using procedural textures, genetic programming is used with upwards of 15 lightweight image features to guide fitness. The goal is to evolve images that share image features with a given target image. Our experiments show that the new MAP-Elites algorithms produce a large number of diverse solutions of varying quality. The extended MAP-Elites algorithm is also statistically competitive compared to vanilla GP in this application domain

Ambient Adaptive Lighting

The concept of adaptive lighting suggests architectural lighting designs that adjust and react to the living practices of inhabitants and variations in the environmental conditions. Current developments in lighting technologies, such as LED light sources and IoT infrastructures, open for new opportunities with adaptive lighting, where the control of the lighting possibly operates as an IoT service rather than build into building management systems. The dynamic flux in lighting changes the experiential presence and brings focus on change and variation rather than states, levels and structures. The suggestion is to enable adaptive intertwinement through an expanded field of dynamic flux in the artificial lighting, and couple between the daylighting and the artificial lighting through an integration of ambient contexts. The project develops experiential prototypes, with which the dynamic design parameters of adaptive lighting can be investigated, analysed and scoped into architectural programming processes. The staging’s are full-scale architectural scenography’s, which situate investigations into how the experience parameters of fluctuating artificial lighting, integrated with daylight flux in an architectural space, are experienced to influence the experience of architectural space, social situations and everyday activities

From Model-Based to Data-Driven Simulation: Challenges and Trends in Autonomous Driving

Simulation is an integral part in the process of developing autonomous vehicles and advantageous for training, validation, and verification of driving functions. Even though simulations come with a series of benefits compared to real-world experiments, various challenges still prevent virtual testing from entirely replacing physical test-drives. Our work provides an overview of these challenges with regard to different aspects and types of simulation and subsumes current trends to overcome them. We cover aspects around perception-, behavior- and content-realism as well as general hurdles in the domain of simulation. Among others, we observe a trend of data-driven, generative approaches and high-fidelity data synthesis to increasingly replace model-based simulation.Comment: Ferdinand M\"utsch, Helen Gremmelmaier, and Nicolas Becker contributed equally. Accepted for publication at CVPR 2023 VCAD worksho