Sosa versus Kornblith on Grades of Knowledge

In a series of works Ernest Sosa (see Sosa 1991, 2007, 2009, 2010, 2011, 2015, 2017) has defended the view that there are two kinds or ‘grades’ of knowledge, animal and reflective. One of the most persistent critics of Sosa’s attempts to bifurcate knowledge is Hilary Kornblith (see Kornblith 2004, 2009, 2012). Our aim in this paper is to outline and evaluate Kornblith’s criticisms. We will argue that, while they raise a range of difficult (exegetical and substantive) questions about Sosa’s ‘bi-level’ epistemology, Sosa has the resources to adequately respond to all of them. Thus, this paper is a (qualified) defence of Sosa’s bi-level epistemology

The Dreaming Variational Autoencoder for Reinforcement Learning Environments

Reinforcement learning has shown great potential in generalizing over raw sensory data using only a single neural network for value optimization. There are several challenges in the current state-of-the-art reinforcement learning algorithms that prevent them from converging towards the global optima. It is likely that the solution to these problems lies in short- and long-term planning, exploration and memory management for reinforcement learning algorithms. Games are often used to benchmark reinforcement learning algorithms as they provide a flexible, reproducible, and easy to control environment. Regardless, few games feature a state-space where results in exploration, memory, and planning are easily perceived. This paper presents The Dreaming Variational Autoencoder (DVAE), a neural network based generative modeling architecture for exploration in environments with sparse feedback. We further present Deep Maze, a novel and flexible maze engine that challenges DVAE in partial and fully-observable state-spaces, long-horizon tasks, and deterministic and stochastic problems. We show initial findings and encourage further work in reinforcement learning driven by generative exploration.Comment: Best Student Paper Award, Proceedings of the 38th SGAI International Conference on Artificial Intelligence, Cambridge, UK, 2018, Artificial Intelligence XXXV, 201

Lucid Data Dreaming for Video Object Segmentation

Convolutional networks reach top quality in pixel-level video object segmentation but require a large amount of training data (1k~100k) to deliver such results. We propose a new training strategy which achieves state-of-the-art results across three evaluation datasets while using 20x~1000x less annotated data than competing methods. Our approach is suitable for both single and multiple object segmentation. Instead of using large training sets hoping to generalize across domains, we generate in-domain training data using the provided annotation on the first frame of each video to synthesize ("lucid dream") plausible future video frames. In-domain per-video training data allows us to train high quality appearance- and motion-based models, as well as tune the post-processing stage. This approach allows to reach competitive results even when training from only a single annotated frame, without ImageNet pre-training. Our results indicate that using a larger training set is not automatically better, and that for the video object segmentation task a smaller training set that is closer to the target domain is more effective. This changes the mindset regarding how many training samples and general "objectness" knowledge are required for the video object segmentation task.Comment: Accepted in International Journal of Computer Vision (IJCV

Songlines and Navigation in Wardaman and other Australian Aboriginal Cultures

We discuss the songlines and navigation of the Wardaman people, and place them in context by comparing them with corresponding practices in other Australian Aboriginal language groups, using previously unpublished information and also information drawn from the literature. Songlines are effectively oral maps of the landscape, enabling the transmission of oral navigational skills in cultures that do not have a written language. In many cases, songlines on the earth are mirrored by songlines in the sky, enabling the sky to be used as a navigational tool, both by using it as a compass, and by using it as a mnemonicComment: accepted by JAH