Storytelling as Inverse Inverse Planning.

Great storytelling takes us on a journey the way ordinary reality rarely does. But what exactly do we mean by this "journey?" Recently, literary theorist Karin Kukkonen proposed that storytelling is "probability design:" the art of giving an audience pieces of information bit by bit, to craft the journey of their changing beliefs about the fictional world. A good "probability design" choreographs a delicate dance of certainty and surprise in the reader's mind as the story unfolds from beginning to end. In this paper, we computationally model this conception of storytelling. Building on the classic Bayesian inverse planning model of human social cognition, we treat storytelling as inverse inverse planning: the task of choosing actions to manipulate an inverse planner's inferences, and therefore a human audience's beliefs. First, we use an inverse inverse planner to depict social and physical situations, and present behavioral studies indicating that inverse inverse planning produces more expressive behavior than ordinary "naïve planning." Then, through a series of examples, we demonstrate how inverse inverse planning captures many storytelling elements from first principles: character, narrative arcs, plot twists, irony, flashbacks, and deus ex machina are all naturally encoded in the flexible language of probability design.

Building machines that learn and think with people.

What do we want from machine intelligence? We envision machines that are not just tools for thought but partners in thought: reasonable, insightful, knowledgeable, reliable and trustworthy systems that think with us. Current artificial intelligence systems satisfy some of these criteria, some of the time. In this Perspective, we show how the science of collaborative cognition can be put to work to engineer systems that really can be called 'thought partners', systems built to meet our expectations and complement our limitations. We lay out several modes of collaborative thought in which humans and artificial intelligence thought partners can engage, and we propose desiderata for human-compatible thought partnerships. Drawing on motifs from computational cognitive science, we motivate an alternative scaling path for the design of thought partners and ecosystems around their use through a Bayesian lens, whereby the partners we construct actively build and reason over models of the human and world.

Concomitant extradural, subdural, and intraparenchymal abscesses of the brain in a patient with cerebral melioidosis - A case report.

Background: Extra axial abscess of the brain is a rare entity, moreover, extra-axial abscess concomitant with intraparenchymal purulent collections are scarcely reported in the literature. Etiology includes penetrating trauma, paranasal sinusitis, mastoiditis, craniospinal surgeries, and the rare spread of infectious agents through the hematogenous route. Case description: We present a case of a young male with Burkholderia pseudomallei Central Nervous System (CNS) melioidosis, forming abscesses in extra-axial and intraparenchymal planes without contiguity. Conclusion: This is to emphasize the importance of MR spectroscopy and other convenient methods in differentiating the etiology in cranial infections.

Reflectance recovery for airborne sensor images of 3D scenes.

An airborne sensor measures the radiance spectrum, which is dependent on the spectral reflectance of the ground material, the orientation of the material surface, and the atmospheric and illumination conditions. We present an algorithm to estimate the surface spectral reflectance, given the sensor radiance spectrum corresponding to a single pixel. The algorithm uses a nonlinear physics-based image formation model. A low-dimensional linear subspace model is used for the reflectance spectra. The solar radiance, sky radiance, and path-scattered radiance are dependent on the environmental conditions and viewing geometry, and this interdependence is considered by using a coupled-subspace model for these spectra. The algorithm uses the Levenberg-Marquardt method to estimate the subspace model parameters. We have applied the algorithm to a large set of synthetic and real data.

Estimating visible through near-infrared spectral reflectance from a sensor radiance spectrum.

We show that surface spectral reflectance can be separated from illumination effects in visible through near-infrared (350 nm-1740 nm) hyperspectral data by using only the information in a single radiance spectrum. The separation method exploits the fact that reflectance and illumination spectra typically lie in distinct subspaces. We present a comparison of a linear and a nonlinear algorithm for the separation. These algorithms compute an estimate of the spectral reflectance up to a scaling factor. In addition, we present an iterative method that is used to determine the starting point for the nonlinear algorithm. We also develop a method for selecting the dimension of the reflectance and illumination subspaces that is appropriate for material identification applications. The accuracy of the separation methods is quantified by application to noisy visible through near-infrared spectral data with a database of 107 materials and 3000 illumination spectra. The utility of the separation method for material identification is demonstrated with the same database. The results show that accurate reflectance recovery and material identification is possible by use of visible through near-infrared spectral data over the outdoor environmental conditions represented in this data set.