How The United States Army Is Leveraging AI

By Kathleen Walch

The modern warfighter needs to rely on various technologies and increasingly advanced systems to help provide advantages over capable adversaries and competitors. The US Department of Defense (DoD) understands this all too well and must therefore integrate Artificial Intelligence and Machine Learning more effectively across their operations to maintain advantages. 

US Army 's AI Project (Picture source: Cisin)

To remain competitive, the US Army has created the Army Talent Management Task Force to address the current and future needs of the war fighter. In particular, the Data and Artificial Intelligence (AI) Team shapes the creation and implementation of a holistic Officer/NCO/Civilian Talent Management System. This system has transformed the Army's efforts to acquire, develop, employ, and retain human capital through a hyper-enabled data-rich environment and enables the Army to dominate across the spectrum of conflict as a part of the Joint Force. Kristin Saling, Chief Analytics Officer & Acting Dir., Army People Analytics is an integral part of getting the Army AI ready and shared her insights with us for this article. She will also be presenting at an upcoming AI in Government event where she will discuss where the US Army currently stands on its data collection and AI efforts, some of the challenges they face, and a roadmap for where the DoD and Army is headed.

What are some innovative ways you're leveraging data and AI to benefit the Army Talent Management Task Force?

LTC Kristin Saling: We are leveraging AI in a number of different ways. But one of the things we're doing that most people don't think about is leveraging AI in order to leverage AI – and by that I mean we're using optical character recognition and natural language processing to read tons and tons of paper documents and process their contents into data we can use to fuel our algorithms. We're also reading in and batching tons of occupational survey information to develop robust job competency models we can use to make recommendations in our marketplace.

AI as a Hype Tool

By  Akshit Mishra 

Source: Skynet Today

To look beyond the hype, we must understand its genesis and propagation.

You are late for a work presentation due to rush-hour traffic, and you think to yourself, “if only the car could do this highly repetitive action autonomously and I could present at my meeting from the road”. But you have to feed the gas pedal every five seconds to move less than a few yards. If Silicon Valley’s stalwarts such as Waymo, Uber, and Lyft had delivered on their decade-old promises, this could have easily been a commonplace sight by now where cars would drive us to and from destinations without any human intervention.

In March 2021, Lyft relinquished its hope to create a fully-autonomous self-driving system and offloaded its self-driving division to Toyota’s Woven Planet Holdings subsidiary for $550 million. Rethink Robotics, a venture led by AI stalwart Rodney Brooks, also closed its doors in 2018 after dedicating a decade to ushering collaborative efforts between humans and intelligent robots for industrial automation. Additional fiascos in the robotics & AI space gave the impression that all wasn’t well in the much-hyped “AI kingdom”.

Although many quickly jumped the gun and declared that AI was doomed for another winter, people seemed to have missed the fact that these incidents were merely a surface-level effect of a much deep-rooted problem. For instance, Rethink Robotics’ predicament was a direct consequence of its obsession with creating the smartest robot, even if that was not the need of the hour. As a result, Rethink Robotics ended up creating overly complicated systems that were economically infeasible to scale, so they were futile to customers. Similarly, leading up to its divestment in Aurora, Uber ATG was plagued by a myriad of incidents ranging from the fatal pedestrian incident in Phoenix in 2019 to the infamous lawsuit against Anthony Levandowski.

If AI was not the reason for these grandiose failures, then why was it thrown under the bus? It’s mainly due to the fixation people have developed with the field because of the hype around it. To understand that, let’s look at how AI gained its status-quo.