Part-1: Will Artificial Intelligence Help Maintain U.S. Naval Superiority over China’s Growing Naval Power?

Background

It is hard to pick up a magazine or newspaper today without seeing something about the amazing things artificial intelligence and/or machine learning (AI/ML) are doing to change our lives for the better. Most people enjoy the benefits of talking to our computers, cars, and home specialty devices like Google Home and Amazon Alexa but don’t think about or care that these technologies are enabled by natural language processing (NLP), one of the today’s most advanced forms of AI/ML. It is even possible today to get real time language translation earbuds to help us more easily explore visits to foreign countries, bringing the Star Trek universal translator that much closer to reality (https://www.startrek.com/database_article/universal-translator).

While these commercial uses for AI/ML are enriching our lives, the potential military use of AI/ML has ushered in an “unspoken” new cold war between China and the United States. Although AI rapid development and deployment initiatives are being supported and coordinated at the highest levels of Navy, the traditional Naval research & development (R&D) model is highly unlikely to pace the Chinese Navy’s rate of AI adoption, given China’s national drive for AI superiority. Something different must be done to bring Naval AI deployment timelines closer to the nuclear ballistic missile submarine deployment rates achieved in the post WWII years!

Artificial Intelligence (AI) and Machine Learning (ML) research has been funded at Defense Advanced Research Projects Agency (DARPA) for over thirty years, however, it wasn’t until the last few years that AI emerged to global commercial prominence in this big data era. To outpace the growth of AI in the U.S., in 2017 China’s central government announced ambitious AI plans to keep pace with the U.S. by 2020, achieve AI breakthroughs by 2025, and be the world leader in AI by 2030. Author Hemant Taneja, in his new book Unscaled, makes the case that, “Artificial Intelligence is this century’s electricity,” because as he states, “AI is powering all sorts of profound new technologies and companies that will change work and life.”

AI for Naval Warfare

Since the dawn of warfare, superior technology has provided a significant edge to military powers and consistently been used as strategic leverage for U.S. National Security. AI is now being considered one of DoD’s future advantages, similar to the advantage stealth technology provided the U.S. in the 1980’s. However, unlike stealth technology where the U.S. held a commanding lead, DoD may not be able to leverage AI technology faster than China. For DoD and particularly the Navy, the question becomes, what is the fastest way to deliver effective AI into Navy operations.

Current Navy AI development activities are following the traditional technology development model which includes early development work by the Office of Naval Research (ONR) with transition to Program of Record (POR) Program Management (PM) organizations for system integration, certification, deployment, and sustainment within the Navy’s Systems Commands. These AI efforts are focused on man-on-the-loop weapon coordination & control, man-in-the-loop command & control (C2) and Intelligence, Surveillance, Reconnaissance (ISR) planning and decision aids, and a Development Security Operations (DevSecOps) environment to support AI development and training infrastructure.

While these new AI initiatives are being worked to shorten the transition from ONR to POR deployment, they are still operating with traditional acquisition constraints. ONR constraints include: no or minimal use of licensed software; open source software if the licenses are DoD friendly; and, the research must be ONR-hard, meaning developing new AI capabilities not available in the commercial market. POR’s on the other side of the technology divide, prefer subscription or licensed open source products such as RedHat Linux to help maintain and leverage commercial software advances. In addition, new POR capabilities must be integrated, Information Assurance (IA) certified, and weapons certified where applicable.

Although these rapid AI development and deployment initiatives are being supported and coordinated at the highest levels of Navy, the traditional Naval development model is highly unlikely to pace the Chinese rate of AI adoption given their national drive for AI superiority. If the Navy is going to prevail, AI development and deployment timelines must become similar to the sub-decade nuclear ballistic missile submarine deployment timelines that were achieved in the post WWII years!

Related Commercial AI Activity

In his new best-selling book, AI Superpowers: China, Silicon Valley, and the New World Order, Kai-Fu Lee (former U.S. citizen, now a resident of Taiwan) provides a detailed overview of the AI relationship between the U.S. and China, based upon his background as an AI speech recognition expert, leader of Microsoft’s Chinese research division, and later head of Google’s China division. While Lee does not comment on the military implications of AI, he does break down his estimates on where China and the U.S. are with respect to the four major waves of AI. In his book AI Super-Powers, Kai-Fu Lee compares AI development between the two countries, but argues for cooperation to avoid the cold war winner-take-all mentality.

Lee characterizes AI development areas as:

  1. Internet AI – AI powered across the internet such as Google Maps.
  2. Business AI – AI used to improve business knowledge, processes, and competition.
  3. Perception AI – AI used to track people, patterns, and perceive the world around us.
  4. Autonomous AI – AI that enables fully autonomous self-driving cars, factory robots, and delivery drones.

As shown in this chart, Lee predicts that China is currently at parity or slightly ahead of the U.S. in Internet AI and Perception AI with the U.S. holding a significant lead over China in Business AI and Autonomous AI. His five-year out estimate, however, shows China leading or at parity with the U.S. in all but business AI. No matter how we look at this, with respect to the military adoption of AI, China is an AI competitor.

When reviewing the current U.S. commercial work with several big companies racing for AI dominance, one finds many commercial proprietary AI platforms where various industries are working to adopt the value of AI. These commercial AI platforms are focused in different directions for different functional areas, but all have a vibrant circle of developers and researchers working to improve their products and capabilities as shown in this Predictive Analytics Today article on the Top 15 Artificial Intelligence Platforms. Topping the list of AI platforms are offerings from well know companies like Amazon, Google, Microsoft, and IBM. In addition, several young AI platform companies are focused on different AI markets such as Infosys Nia, Wipro HOLMES, API.ai, Rainbird, Ayasdi, Mindmeld, KAI, Receptivity, and Meya. Open-source AI libraries are also available, some being used by ONR project managers to support their AI projects.

Adopting and Adapting

Because the Navy’s AI ONR development activities are assuming commercial licensed AI platforms are generally not useful starting points for these efforts, Navy is failing to fully explore the possible adoption and/or adaption of commercial AI R&D. Given the yearly multi-billion-dollar U.S. investment in AI, the comparatively small amount of Navy AI investment will be hard pressed to outlearn or outperform these commercial activities.

This leaves the unanswered question of whether evolving commercial AI platforms, and the associated startup developments, have anything to offer Navy’s AI efforts, or whether useful commercial AI could be more quickly adapted or adopted for Naval use. ONR’s traditional technology development model could put Navy significantly behind the Chinese Navy AI development given China’s strategic practice of cross connecting industry, finance, and military activities to gain greater advantage.

A viable alternative could be to structure a Navy AI program with features similar to the integrated DARPA/Navy/Air Force Long Range Anti-Ship Missile (LRASM) project. After demonstrating successful capability in the four years between 2009-2013, LRASM was authorized for limited Navy production in February 2014 as an urgent capability stop-gap. By integrating the DARPA and Navy NAVAIR team from the beginning this project eliminated all valley of death transition issues while taking advantage of Other Transaction Authority (OTA) for rapid contracting.

However, unlike LRASM, which is primarily military technology where little commercial technology can be leveraged, AI to support C2, ISR, and weapon control, can potentially be leveraged or repurposed from the many commercial AI platforms and startup activities that are competing with China’s commercial companies. Because AI is an urgent Navy need, it could take advantage of the Congressionally authorized Section 804, Middle-Tier Acquisition process and Other Transaction Agreements, to explore rapid AI capabilities in short six-month rapid turnaround prototype phases.

Such a Middle-Tier Acquisition could be structured with a small select government leadership team working together with industry led OTA teams to explore all possible unconstrained ways to adapt, adopt, repurpose, or develop effective AI warfare functionality. By establishing three or more teams with short phased Statement of Work (SOW) goals, teams could be continued, turned-off, or new teams added in six-month cycles. Where useful prototype functionality is demonstrated, follow-on agreements could be awarded to push the capabilities into POR production as appropriate.

Funding such an effort could be accomplished using Middle Tier Section 804 authorization and critical Fleet requirements. This effort would not require the disruption or elimination of the current ONR/POR project efforts, but rather, provide an alternate skunk-works activity applied to this emerging Naval warfare need.

Part 2 – will discuss the challenges of black box AI, explainable AI, AI spoofing, and brittle AI solutions. All are AI challenges that must also be addressed if AI is going to successfully augment Navy Command & Control!

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