English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Artificial intelligence (AI) has enormous potential to improve Air Forcepilot training by providing actionable feedback to pilot trainees on thequality of their maneuvers and enabling instructor-less flying familiarizationfor early-stage trainees in low-cost simulators. Historically, AI challengesconsisting of data, problem descriptions, and example code have been criticalto fueling AI breakthroughs. The Department of the Air Force-MassachusettsInstitute of Technology AI Accelerator (DAF-MIT AI Accelerator) developed suchan AI challenge using real-world Air Force flight simulator data. The ManeuverID challenge assembled thousands of virtual reality simulator flight recordingscollected by actual Air Force student pilots at Pilot Training Next (PTN). Thisdataset has been publicly released at Maneuver-ID.mit.edu and represents thefirst of its kind public release of USAF flight training data. Using thisdataset, we have applied a variety of AI methods to separate "good" vs "bad"simulator data and categorize and characterize maneuvers. These data,algorithms, and software are being released as baselines of model performancefor others to build upon to enable the AI ecosystem for flight simulatortraining.

AI Enabled Maneuver Identification via the Maneuver Identification Challenge