CLIPDraw is an algorithm that synthesizes novel drawings from natural language input. It does not require any additional training; rather, a pre-trained CLIP language-image encoder is used as a metric for maximizing similarity between the given description and a generated drawing. Crucially, CLIPDraw operates over vector strokes rather than pixel images, which biases drawings towards simpler human-recognizable shapes. Results compare CLIPDraw with other synthesisthrough-optimization methods, as well as highlight various interesting behaviors of CLIPDraw, such as satisfying ambiguous text in multiple ways, reliably producing drawings in diverse styles, and scaling from simple to complex visual representations as stroke count increases.

Video games often populate their in-game world with numerous ambient non-playable characters. Manually crafting interesting behaviors for such characters can be prohibitively expensive. As scripted AI gets re-used across multiple characters, they can appear overly similar, shallow and generally uninteresting for the player to interact with. In this paper we propose to evolve interesting behaviors in a simulated evolutionary environment. Since only some evolution runs may give rise to such behaviors, we propose to train deep neural networks to detect such behaviors. The paper presents work in progress in this direction.

Emergent processes in complex systems such as cellular automata can perform computations of increasing complexity, and could possibly lead to artificial evolution. Such a feat would require scaling up current simulation sizes to allow for enough computational capacity. Understanding complex computations happening in cellular automata and other systems capable of emergence poses many challenges, especially in large-scale systems. We propose methods for coarse-graining cellular automata based on frequency analysis of cell states, clustering and autoencoders. These innovative techniques facilitate the discovery of large-scale structure formation and complexity analysis in those systems. They emphasize interesting behaviors in elementary cellular automata while filtering out background patterns. Moreover, our methods reduce large 2D automata to smaller sizes and enable identifying systems that behave interestingly at multiple scales.