The purpose of this article is to draw attention to an aspect of intelligence that has not yet received significant attention from the AI community, but that plays a crucial role in a technology’s effectiveness in the world, namely teaming intelligence. We propose that Al will reach its full potential only if, as part of its intelligence, it also has enough teaming intelligence to work well with people. Although seemingly counterintuitive, the more intelligent the technological system, the greater the need for collaborative skills. This paper will argue why teaming intelligence is important to AI, provide a general structure for AI researchers to use in developing intelligent systems that team well, assess the current state of the art and, in doing so, suggest a path forward for future AI systems. This is not a call to develop a new capability, but rather, an approach to what AI capabilities should be built, and how, so as to imbue intelligent systems with teaming competence.

Current semantic parsers either compute shallow representations over a wide range of input, or deeper representations in very limited domains. We describe a system that provides broad-coverage, deep semantic parsing designed to work in any domain using a core domain-general lexicon, ontology and grammar. This paper discusses how this core system can be customized for a particularly challenging domain, namely reading research papers in biology. We evaluate these customizations with some ablation experiments

Recent increase in interest in using rotorcraft (helicopters and tilt-rotor craft) for public transportation has spurred research in making rotorcraft less noisy, particularly as they land. The ground noise associated with landing trajectories followed by rotorcraft depends in part on the changes in altitude and velocity of the rotorcraft during flight. Acoustic models of ground noise taking altitude and velocity effects into account can be used in an optimization process to determine a set of potentially quieter pilot operations. However, optimizing solely for acoustic properties produces patterns that abstract away from the environment in which the trajectory is flown. A quiet procedure flown over a residential area can create considerable annoyance. To overcome this limitation of acoustic-based optimization we propose a hybrid cost model for optimization that combines acoustic criteria with a land use model that views noise-sensitive areas around landing facilities as weighted obstacles. The result is a 3D route planning problem with obstacles. We introduce a system, called NORA (Noise Optimization for Rotorcraft Approach) that allows for the computation of trajectories that simultaneously solve for acoustically quiet patterns that also avoid land sensitive areas.