The potential for advances in information-age technologies to undermine nuclear deterrence and influence the potential for nuclear escalation represents a critical question for international politics. One challenge is that uncertainty about the trajectory of technologies such as autonomous systems and artificial intelligence (AI) makes assessments difficult. This paper evaluates the relative impact of autonomous systems and artificial intelligence in three areas: nuclear command and control, nuclear delivery platforms and vehicles, and conventional applications of autonomous systems with consequences for nuclear stability. We argue that countries may be more likely to use risky forms of autonomy when they fear that their second-strike capabilities will be undermined. Additionally, the potential deployment of uninhabited, autonomous nuclear delivery platforms and vehicles could raise the prospect for accidents and miscalculation. Conventional military applications of autonomous systems could simultaneously influence nuclear force postures and first-strike stability in previously unanticipated ways. In particular, the need to fight at machine speed and the cognitive risk introduced by automation bias could increase the risk of unintended escalation. Finally, used properly, there should be many applications of more autonomous systems in nuclear operations that can increase reliability, reduce the risk of accidents, and buy more time for decision-makers in a crisis.
The history of battle knows no bounds, with weapons of destruction evolving from prehistoric clubs, axes, and spears to bombs, drones, missiles, landmines, and systems used in biological and nuclear warfare. More recently, lethal autonomous weapon systems (LAWS) powered by artificial intelligence (AI) have begun to surface, raising ethical issues about the use of AI and causing disagreement on whether such weapons should be banned in line with international humanitarian laws under the Geneva Convention. Much of the disagreement around LAWS is based on where the line should be drawn between weapons with limited human control and autonomous weapons, and differences of opinion on whether more or less people will lose their lives as a result of the implementation of LAWS. There are also contrary views on whether autonomous weapons are already in play on the battlefield. Ronald Arkin, Regents' Professor and Director of the Mobile Robot Laboratory in the College of Computing at Georgia Institute of Technology, says limited autonomy is already present in weapon systems such as the U.S. Navy's Phalanx Close-In Weapons System, which is designed to identify and fire at incoming missiles or threatening aircraft, and Israel's Harpy system, a fire-and-forget weapon designed to detect, attack, and destroy radar emitters.
Although it tends look to the sky, Israel Aerospace Industries (IAI) came back down to Earth to develop RoBattle, an unmanned ground vehicle (UGV) that may soon be tasked with the type of risky missions typically assigned to foot soldiers. IAI's UGV is built to be maneuverable, dynamic, and tough. Six wheels with independent suspension enable RoBattle to scale obstacles, such as rubble and small walls, to access areas that would typically be out of reach for other robots. A modular robotic kit allows the machine to be modified and adapted with remote vehicle control, navigation, and real time mapping abilities, depending on its operational needs. RoBattle can operate independently or as support unit for convoy protection, decoy, ambush, attack, intelligence, surveillance, or armed reconnaissance, according to IAI.
Z Advanced Computing, Inc. (ZAC) of Potomac, MD announced on August 27 that it is funded by the US Air Force, to use ZAC's detailed 3D image recognition technology, based on Explainable-AI, for drones (unmanned aerial vehicle or UAV) for aerial image/object recognition. ZAC is the first to demonstrate Explainable-AI, where various attributes and details of 3D (three dimensional) objects can be recognized from any view or angle. "With our superior approach, complex 3D objects can be recognized from any direction, using only a small number of training samples," said Dr. Saied Tadayon, CTO of ZAC. "For complex tasks, such as drone vision, you need ZAC's superior technology to handle detailed 3D image recognition." "You cannot do this with the other techniques, such as Deep Convolutional Neural Networks, even with an extremely large number of training samples. That's basically hitting the limits of the CNNs," continued Dr. Bijan Tadayon, CEO of ZAC.