Competing Revolutions in Military Affairs
Artificial Intelligence, Machine Learning, and Information Age Conflict
October 19, 2021
Virtual Symposium Broadcasting Live from the
National Press Club, Washington, D.C.
Competing Revolutions in Military Affairs
Artificial Intelligence, Machine Learning, and Information Age Conflict
October 19, 2021
Virtual Symposium Broadcasting Live from the
National Press Club, Washington, D.C.
The fourteenth symposium in the Asymmetric Threat Symposium Series addresses challenges America must meet, capabilities we must obtain, and vulnerabilities our nation must fix to assure its national security and the security of its allies.
At the heart of 21st century great power competition are rival efforts by the United States and its competitors to revolutionize military affairs in an era of dazzling technologies. Cutting across all these technologies is the need for artificial intelligence and machine learning (AI/ML). Without AI/ML, no nation will be able to successfully compete economically, politically, or militarily in an increasingly data-driven and data-defined world. Effective creation and adoption of AI/ML capabilities will ultimately prove decisive if the United States hopes to effectively use emerging technologies to protect and defend against the efforts of near peer adversaries.
America and its allies are in a race we cannot lose. If America is to adequately strengthen and integrate domains, systems, and capabilities, AI/ML will be the decisive capability in achieving this goal. Without effective AI/ML development, deployment, and advancement, the United States will be unable to sufficiently strengthen or protect the deployment, survivability, precision, speed, and lethality of its national security efforts. This includes effectively contesting efforts aimed at America’s citizenry and institutions, not just our military forces and government entities. Detecting and defeating these threats will require innovative new usage of “bits and bytes” to create and properly integrate systems and capabilities.
What changes – in policy, authority, process, technology, tactics, and strategy – are required to better safeguard America’s global interests and prevail in this new era of great power competition? What can America’s public-private partnerships, to include those with national security companies, do to develop critically needed AI and ML tools, technologies, and capabilities that effectively protect our country and its interests?
Time is not on America’s side – the race is already on.
The Asymmetric Threat Symposium series is a non-partisan, not-for-profit, pro-bono forum for furthering the national dialogue on asymmetric threats to national security. It was founded in 2008 by Dr. J.P. (Jack) London, CACI’s Executive Chairman and Chairman of the Board from 2007 to 2021, and Dr. Warren R. Phillips, Lead Director on CACI's Board of Directors.
This site is designed to advance the dialogue on national and global security and provide a wider understanding of the asymmetric threat and how we can help counter it. It is intended to serve as the go-to source for fact-based resources and original research and provide a forum for review and discussion of pertinent themes and events. At its most basic level, asymmetric threat or warfare refers to conflicts in which the relative military power of combatants differs considerably.
The symposia described on this site have attracted an array of participants from military, government, industry, and academia. They assembled with the common purpose of developing new thinking on achieving an overarching national strategy that will effectively counter the threats posed by our enemies. The experts attending the sessions have focused on concrete approaches to structural, procedural, and resource changes needed to realign the elements of national power against very savvy and adaptable adversaries.
Please email AsymmetricSymposium@caci.com to tell us how we can improve this website, suggest a feature or area of interest, or submit an article, paper, or other item for inclusion.
