A Game-Theoretic Analysis of the Off-Switch Game

The off-switch game is a game theoretic model of a highly intelligent robot interacting with a human. In the original paper by Hadfield-Menell et al. (2016), the analysis is not fully game-theoretic as the human is modelled as an irrational player, and the robot's best action is only calculated under unrealistic normality and soft-max assumptions. In this paper, we make the analysis fully game theoretic, by modelling the human as a rational player with a random utility function. As a consequence, we are able to easily calculate the robot's best action for arbitrary belief and irrationality assumptions

Reimagining the River: An Outdoor Vision of the Anthropocene and the White River through the Lens of Place

poster abstractIn 2016, the International Union of Geological Sciences will decide whether or not human impact on the Earth constitutes a new geologic epoch – the Anthropocene. If agreed upon, this epoch will acknowledge the effects human agency has upon the stratigraphic record, and the implications of a human-driven world. Reimagining the River takes the global Anthropocene to the City of Indianapolis by creating an outdoor museum of the White River. This museum exhibit will display the past, present, and future of the White River, showcasing the historical narrative of the human-river relationship. Exploring the Anthropocene through the story of the White River will engage the citizens of Indianapolis to develop a sense of ownership for the intertwined state of the River and Indianapolis. The intention of this engagement is to build a community that reimagines what the river was, is, and can become. Reimagining the River will be located on the White River State Park Bridge, and will feature audiovisual elements that relate current scenes surrounding the River to the past. Historical photographs complemented with a brief historical narrative will be juxtaposed with the areas surrounding the installation, framing Indianapolis’ urban environment as the exhibit. The installation will be accessible to all demographics, including children and individuals with disability. The exhibit will also include resources to encourage further audience participation, including podcasts, geocaching, and a website. Ongoing research pathways will be created to encourage the tracking and measurement of audience engagement and understanding of how human agency has affected the White River, its tributaries, and the City of Indianapolis

Language Modeling Is Compression

It has long been established that predictive models can be transformed into lossless compressors and vice versa. Incidentally, in recent years, the machine learning community has focused on training increasingly large and powerful self-supervised (language) models. Since these large language models exhibit impressive predictive capabilities, they are well-positioned to be strong compressors. In this work, we advocate for viewing the prediction problem through the lens of compression and evaluate the compression capabilities of large (foundation) models. We show that large language models are powerful general-purpose predictors and that the compression viewpoint provides novel insights into scaling laws, tokenization, and in-context learning. For example, Chinchilla 70B, while trained primarily on text, compresses ImageNet patches to 43.4% and LibriSpeech samples to 16.4% of their raw size, beating domain-specific compressors like PNG (58.5%) or FLAC (30.3%), respectively. Finally, we show that the prediction-compression equivalence allows us to use any compressor (like gzip) to build a conditional generative model

A Game-Theoretic Analysis of the Off-Switch Game

The off-switch game is a game theoretic model of a highly intelligent robot interacting with a human. In the original paper by Hadfield-Menell et al. (2016b), the analysis is not fully game-theoretic as the human is modelled as an irrational player, and the robot’s best action is only calculated under unrealistic normality and soft-max assumptions. In this paper, we make the analysis fully game theoretic, by modelling the human as a rational player with a random utility function. As a consequence, we are able to easily calculate the robot’s best action for arbitrary belief and irrationality assumptions.This work was in part supported by ARC grant DP15010459