The US Army wants enough birds in orbit to keep functioning in the face of anti-satellite attacks and still provide every combat brigade its own on-call satellite support. Credit: US Army.

Army leaders are making the case for three key technologies they want to combine into the foundation for future multi-domain warfare against Russia or China — satellites will spot the targets, cloud computing will share the targeting data, and AI will make sense of it. At least, that’s how it’s supposed to go.

According to a report in Breaking Defense online, none of these three capabilities discussed at an artificial intelligence conference here and at a think tank in DC fall in the Army’s traditional core competencies. None of them is on the service’s list of its modernization priorities for future war. All will require close cooperation — or painful turf wars — with the other services.

Cloud and AI are hot topics across the Pentagon – and the private sector for that matter — but even Army Secretary Ryan McCarthy acknowledges it’s unusual for the ground force to be so focused on objects in space, as opposed to ground stations to receive data from other services’ satellites.

“Normally, most Army leadership would not talk about satellites,” McCarthy said yesterday at the AEI think tank in DC. “But the Army is heavily investing in Low Earth Orbit satellite [capability]. You’re going to need a satellite architecture that’s going to find stuff and move information quickly. We’ve got to have this,” he said.

Why? You need to understand the Army’s modernization priorities and the rapidly evolving concept of future warfare which they serve, known as Multi-Domain Operations.

With modern Russian and Chinese air defenses designed to impede traditional airstrikes, the Army has made its No. 1 modernization priority what it calls Long-Range Precision Fires: a portfolio of precision-guided artillery from enhanced howitzers to hypersonic missiles that can reach more than a thousand miles.

These weapons will help blast a path through Russian and Chinese layered defenses to help enable the other services’ airstrikes, the Army’s own Next Generation Ground Vehicles — the service’s No. 2 priority — and new kinds of long-range, high-speed Future Vertical Lift aircraft — priority No. 3.

But how will the Army find targets at such distances? How will it pull together the targeting data from disparate sources? And how will it make sense of all this information fast enough to act on it?

That’s where space, cloud and AI come in, in the context of multi-domain, which seeks to combine the strength of US forces in land, air, sea, cyberspace, and outer space.

New satellites, mostly in Low Earth Orbit, will spot distant targets, relay long-distance communications and provide precision guidance and navigation even when GPS is jammed.

Whether by piggybacking on commercial constellations or launching its own low-cost LEO satellites, the Army wants enough birds in orbit to keep functioning in the face of anti-satellite attacks and still provide every combat brigade its own on-call satellite support.

While the whole military is increasingly interested in smaller and commercial satellites, it’s not clear how the Army’s effort will complement or compete with the LEO small-satellite efforts already well underway in both the Air Force and the new Space Development Agency.

Cloud computing will pool data from all those Army’s sensors — from the new satellites down to the IVAS smart goggles now in testing with foot troops. The Army is asking for $700 million over the next five years to fund cloud and big data projects. What’s less clear is whether the Army targeting cloud will build on top of the Defense Department’s embattled JEDI “general purpose” cloud or be a tailor-made Army cloud for some functions.

Finally, AI will rapidly sort through the massive influx of data to help human analysts and commander pick out targets and the best weapons to strike them. That is the idea, anyway.

Here, the Army’s Artificial Intelligence Task Force Director Brig. Gen. Matt Easley says it is working closely with the all-service Joint Artificial Center. This includes common interests such as the National Mission Initiatives.

The Army task force doesn’t want to duplicate work already being done elsewhere, Easley told the Detroit conference. So he is not focusing on the Joint All-Domain Command & Control required for multi-domain, multi-service operations. Instead, he’s honing in on the unique challenges that face AI in the Army.

“We have thousands of soldiers distributed across the battlefield, thousands of vehicles,” he said. That requires a very different scale of networking and AI than connecting smaller numbers of more expensive platforms, from Navy warships to Air Force jets to even Special Operations teams.

Industry has pitched lots of innovative technologies to the task force, Easley said, but if a product doesn’t work over the Army network, if it doesn’t scale to the Army’s sprawling force, then he doesn’t want it.

The Army is making some big demands of industry – and it’s not just for AI itself. To develop an AI system, you need a lot of data, much of which is scattered across different organizations in incompatible formats. To bring that all together in accessible form, Pentagon leaders believe, you need to put it on the cloud.

“Cloud has to happen to maximize AI,” McCarthy said. So, he said, at least in the near term, “you’ll probably see Army leaders talking about cloud and less so about AI because you’ve got to put the horse in front of the cart in order to pull it.”

Of the US$700 million the Army plans to spend over 2021-2025 — a plan currently in limbo with the rest of the federal budget — “a lot of this is infrastructure [we’ll] build over the next 24 months,” he said. “We’re pushing hard on this.”

While cloud comes first, however, McCarthy is optimistic about AI. “We’re very excited,” he said. “The Chinese and the Russians are investing against this, [but] they’ve got to send all their kids here because we’ve got all the brainpower, all the innovation is in America.”

That said, Russia is famous for mathematical and scientific talent, while China is investing heavily in AI education, research, and development.

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