In the field of visual art, metaphor is a way to communicate meaning to the viewer. We present a computational system for communicating visual metaphor that can identify adjectives for describing an image based on a low-level visual feature representation of the image. We show that the system can use this visual-linguistic association to render source images that convey the meaning of adjectives in a way consistent with human understanding. Our conclusions are based on a detailed analysis of how the system's artifacts cluster, how these clusters correspond to the semantic relationships of adjectives as documented in WordNet, and how these clusters correspond to human opinion.

In conjunction with Brigham Young University's Visual Arts program, we conducted a study centered around a system designed to be an artificial artist, in order to synthesize the ideas of visual artists and computer scientists. Participants from both disciplines designed activities that imposed the limitations of the artificial system on their fellow participants. These activities sparked discussion and insight into the nature of the creative process and how it can be better emulated in artificial systems. We present our system and several of the activities designed around it and discuss the synergistic results.

The process of creating art is an optimization problem for which the objective function is probably unknown and possibly undefinable. That objective function is imposed on the artist by an environment which may be composed of any of a number of sources: peers, a jury, society, the self. This does not imply that the function is arbitrary nor that the optimization is impossible; however, it does suggest an interesting interpretation of the artist at work.

Caesars Entertainment, the largest provider of branded casino entertainment, captures a wealth of data for 40 million+ customers through its Total Rewards program. In-depth data analysis has helped Caesars weather the economic downturn by prioritizing marketing spend, expense savings targets and identifying new revenue opportunities. This talk will describe how closed-loop marketing, state-of-the-art user segmentation, and ongoing experimentation via test and control groups have enabled Caesars Entertainment to achieve all-time high customer satisfaction scores and outperform the competition in a challenging economic climate. The lessons learned are generic and apply across multiple industries. Insights will also be provided on the next wave of challenges to be answered analytically.

Creativity is a nebulous, albeit vital part of every society. We outline an original computer system designed to produce artefacts that are perceived as creative, to do so through arguably creative processes, and to convey intention to an audience. We show how the computer system, called DARCI (Digital ARtist Communicating Intention), can augment the study of human creativity while advancing the field of artificial intelligence. Author Keywords Artificial creativity, neuroevolution, intention, visual arts

We use a type of reservoir computing called the liquid state machine (LSM) to explore learning transfer. The liquid state machine (LSM) is a neural network model that uses a reservoir of recurrent spiking neurons as a filter for a readout function. We develop a method of training the reservoir, or liquid, that is not driven by residual error. Instead, the liquid is evaluated based on its ability to separate different classes of input into different spatial patterns of neural activity. Using this method, we train liquids on two qualitatively different types of artificial problems. Resulting liquids are shown to substantially improve performance on either problem regardless of which problem was used to train the liquid, thus demonstrating a significant level of learning transfer.

In liquid state machines, separation is a critical attribute of the liquid - which is traditionally not trained. The effects of using Hebbian learning in the liquid to improve separation are investigated in this paper. When presented with random input, Hebbian learning does not dramatically change separation. However, Hebbian learning does improve separation when presented with real-world speech data.