With the model using countless data points from 2014 to the present moment, including Russia’s first invasion of Ukraine, there were plenty of patterns and outcomes to observe. When the machines sift through historical patterns, they do not care for human notions of what a “rational” Putin might do-only for the likelihood that an observed pattern has led to a certain outcome in the past. They were right about the state of Putin’s preparations but projected their own definition of rationality onto the Russian leader. Some experienced Russia policy hands didn’t want to believe that Putin would start a war with so few troops, such poorly prepared units, and such a high risk of economic disaster for Russia. Just as importantly, the machines are dispassionate, making it easier to circumvent human biases and wishful thinking. AI is The Big Short a million times over-looking not only at mortgages but at everything that could conceivably be interesting and doing it simultaneously, automatically, and virtually in real time. Think of The Big Short, the movie about curious bankers wading through masses of mortgage data, finding suspicious quirks, and sleuthing house-to-house to uncover the shenanigans that led to the 2007 subprime crisis. What AI can do-and humans cannot-is look at everything everywhere at once and very fast. But it would take thousands of open-source investigators or intelligence analysts to replicate just one small part of the machine model. Twitter users, after all, posted open-source satellite images showing Russian equipment collecting near the border before the war. Some of what AI does is not very different from traditional sleuthing. Add up enough of these signals, and our system can make aggression predictions in future hot spots around the globe with specific levels of confidence. Sometimes the correlation is weak, but other times the pattern is strong. They look for patterns: Whenever X has happened in the past, Y has often been the outcome. In fact, that is how AI works: Large language models learn by sifting through past data-in our case, about 10 years’ worth, going back to just before Russia’s 2014 invasion of Crimea. By late October 2021, our machines were telling us that war was coming.ĭid the machines tell us with 100 percent certainty that Russia would invade? No, but they told us that the pattern of Russian activities leading up the war made it extraordinarily likely that Putin would order the attack. Russian officers’ spending patterns at local businesses made it obvious they weren’t planning on returning to barracks, let alone home, anytime soon. All kinds of details caught our attention: Weapons systems moved to the border regions in 2021 for what the Kremlin claimed were military drills were still there, as if pre-positioned for future forward advances. Instead, we were watching what the Russians were actually doing by tracking often small but highly important pieces of data that, when aggregated effectively, became powerful predictors. We weren’t trying to divine Putin’s motivations, nor did we have to wrestle with our own biases and assumptions trying to interpret his words. We got it right because we weren’t bound by the limitations of traditional foreign-policy analysis. Relying on artificial intelligence to sift through almost inconceivable amounts of online and satellite data, our machines were aggregating actions on the ground, counting inputs that included movements at missile sites and local business transactions, and building heat maps of Russian activity virtually in real time. How? Our team at Rhombus Power, made up largely of scientists, engineers, national security experts, and former national security practitioners, was looking at a completely different picture than the traditional foreign-policy community. By the end of January, we had predicted the start of the war almost to the day. Read more from the issue.īut in Silicon Valley, we had already concluded that Putin would invade-four months before the Russian attack. This article appears in the Summer 2023 print issue of FP. Erik Carter illustration for Foreign Policy Paul Scharre, Stanley McChrystal, Alondra Nelson, and more thinkers on the dawn of a new age in geopolitics. The on-image text reads: The Scramble for AI. A Foreign Policy magazine cover illustration shows a glowing AI projection figure emerging from a pile of technological machinery and semiconductors.
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