// UNMANNED SYSTEMS AND NEXT-GEN WARFARE TERM

Tactical Edge AI

Tactical Edge AI means running artificial intelligence programs on devices directly in a combat zone, such as on a drone or a vehicle. This allows for rapid, on-the-spot decision-making without relying on a connection to a remote data center.

TECHNICAL DEFINITION

Tactical Edge AI involves deploying machine learning algorithms and AI models directly onto forward-operating military hardware (e.g., sensors, vehicles, UAS) at the point of data collection in a battlespace. This paradigm enables real-time data processing, autonomous functions, and decision support in disconnected, intermittent, and low-bandwidth (DIL) environments, reducing latency for critical command and control (C2) and ISR tasks.

BACKGROUND

The United States Department of Defense has been analyzing and employing military applications of artificial intelligence since at least 2014. The program initially focused on drones and other robots, but has also been using large language models for military research and analysis. The current US policy on lethal autonomous weapons is Department of Defense Directive 3000.09, updated in January 2023.

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SYNONYMS & ALIASES

  • AI at the Edge
  • Forward-Deployed AI
  • Battlefield AI
  • Embedded AI
  • On-Device AI
  • Tactical AI

USAGE NOTE

It is frequently discussed in the context of enabling autonomous systems and accelerating the sensor-to-shooter timeline in contested environments.

DEVELOPERS

Organizations developing technology related to Tactical Edge AI.

  • Anduril Industries

    Develops the Lattice OS, an AI-powered software platform that uses sensor fusion and machine learning to autonomously detect, classify, and track targets, providing a unified operating picture for military personnel at the tactical edge.

  • Palantir Technologies

    Provides the Gotham Platform, used by defense and intelligence agencies to integrate disparate data sources and deploy AI/ML models at the edge for missions like intelligence analysis, mission planning, and targeting.

  • Shield AI

    Focuses on developing Hivemind, an AI pilot that enables swarms of aircraft to operate autonomously in denied environments without GPS or communications, allowing for intelligent teaming at the tactical edge.

  • BAE Systems

    Integrates AI and machine learning into a wide range of defense platforms, from combat vehicles to aircraft, to enable autonomous functions, threat detection, and decision support systems that operate directly in the field.

  • RTX Corporation (formerly Raytheon)

    Develops advanced AI algorithms for its sensor, command and control (C2), and missile systems. This includes on-board processing for automated target recognition and threat assessment in real-time.

  • Lockheed Martin

    Invests heavily in AI-enabled systems for all domains (air, sea, land, space). They are developing AI for autonomous platforms, predictive maintenance, and integrating AI into C2 systems to accelerate decision-making at the tactical level.

  • L3Harris Technologies

    Embeds AI and machine learning into its ISR (Intelligence, Surveillance, and Reconnaissance) and communication systems to provide automated analysis, signal processing, and enhanced situational awareness for warfighters at the edge.

  • U.S. Army Artificial Intelligence Integration Center (AI2C)

    A U.S. Army organization focused on accelerating the development and integration of AI capabilities, including those for tactical applications, to empower soldiers with data-driven insights and autonomous systems on the battlefield.

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