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.

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.

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.

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.

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.