“You can lie all you please when you tell other folks about the Rangers,” advised Major Robert Rogers in 1759, “but don’t never lie to a Ranger or officer.”
That guidance is recalled in a newly updated Ranger Handbook published by the U.S. Army last week.
The Handbook is a compilation of doctrine, tactics, history and lore associated with the Army’s elite Ranger special operations force.
One learns, for example, that “Proficiency with knots and rope is vitally important for Rangers, especially in mountaineering situations. Familiarity with the terminology associated with knots and rope is critical.” Various knots are helpfully explained and illustrated, though the descriptions alone will hardly make the reader proficient.
The Army Ranger Regiment “is a lethal, agile and flexible force, capable of conducting many complex, joint special operations missions. . . . Their capabilities include conducting airborne and air assault operations, seizing key terrain such as airfields, destroying strategic facilities, and capturing or killing enemies of the nation.”
Two Army Rangers were killed in action in Afghanistan on April 27, the Department of Defense announced today.
Americans are paying too much for almost everything, because the United States has long treated its trucking industry as an artifact to be preserved rather than as an opportunity for innovation.
These ideas aim to advance the detailed policy solutions needed to foster public trust and implement fairness in the adoption of AI across diverse domains, from healthcare and government benefits to rural access, education, and worker protections.
The evidence is clear: algorithmic pay-setting is established in app-based work, and payroll/timekeeping failures show how software can produce systemic wage harm at scale
While a few states have taken steps to implement decision-making mechanisms for certain AI systems, too many leaders are simply accepting narratives about AI’s purported public benefit at face value – jumping to the “how” of AI implementation before thoroughly vetting potential systems and deciding whether they are appropriate to use at all.