Human-AI Teams in Aviation: Considerations from Human Factors and Team Science

Artificial Intelligence (AI) has transformed the way human-computer interaction (HCI) teams can collaborate and coordinate in various domains, including aviation and crew resource management (CRM). AI\u27s transformative capabilities enhance teamwork, efficiency, and safety, particularly in risk management. AI\u27s ability to process vast amounts of data and provide real-time insights enables informed decision-making and automation of repetitive tasks in aviation. By combining the strengths of AI and humans, outlined in our modified version of the ‘HABA-MABA’ framework, a dynamic teamwork relationship emerges, provided roles are successfully allocated. AI systems are able to act as intelligent assistants, offering timely recommendations, fostering effective communication, and facilitating coordination among crew members. Its adaptability and capacity for learning improve collaboration abilities, tailoring strategies to meet the team\u27s specific needs. This paper explores the theories, considerations, and implications of human-AI teams in aviation, highlighting potential benefits, training recommendations, and future research directions. While human-AI teams offer numerous benefits, addressing the risks, limitations, and ethical considerations is crucial to ensuring safe and efficient operations. Future research must prioritize transparency, explainability, adaptability, and real-world testing to unlock the full potential of human-AI teams and foster successful integration across diverse domains

Search for intermediate-mass black hole binaries in the third observing run of Advanced LIGO and Advanced Virgo

International audienceIntermediate-mass black holes (IMBHs) span the approximate mass range 100−105 M⊙, between black holes (BHs) that formed by stellar collapse and the supermassive BHs at the centers of galaxies. Mergers of IMBH binaries are the most energetic gravitational-wave sources accessible by the terrestrial detector network. Searches of the first two observing runs of Advanced LIGO and Advanced Virgo did not yield any significant IMBH binary signals. In the third observing run (O3), the increased network sensitivity enabled the detection of GW190521, a signal consistent with a binary merger of mass ∼150 M⊙ providing direct evidence of IMBH formation. Here, we report on a dedicated search of O3 data for further IMBH binary mergers, combining both modeled (matched filter) and model-independent search methods. We find some marginal candidates, but none are sufficiently significant to indicate detection of further IMBH mergers. We quantify the sensitivity of the individual search methods and of the combined search using a suite of IMBH binary signals obtained via numerical relativity, including the effects of spins misaligned with the binary orbital axis, and present the resulting upper limits on astrophysical merger rates. Our most stringent limit is for equal mass and aligned spin BH binary of total mass 200 M⊙ and effective aligned spin 0.8 at 0.056 Gpc−3 yr−1 (90% confidence), a factor of 3.5 more constraining than previous LIGO-Virgo limits. We also update the estimated rate of mergers similar to GW190521 to 0.08 Gpc−3 yr−1.Key words: gravitational waves / stars: black holes / black hole physicsCorresponding author: W. Del Pozzo, e-mail: [email protected]† Deceased, August 2020