In one example of the IC’s successful use of AI, after exhausting all other avenues—from human spies to signals intelligence—the US was able to find an unidentified WMD research and development facility in a large Asian country by locating a bus that traveled between it and other known facilities. To do that, analysts employed algorithms to search and evaluate images of nearly every square inch of the country, according to a senior US intelligence official who spoke on background with the understanding of not being named.
While AI can calculate, retrieve, and employ programming that performs limited rational analyses, it lacks the calculus to properly dissect more emotional or unconscious components of human intelligence that are described by psychologists as system 1 thinking.
AI, for example, can draft intelligence reports that are akin to newspaper articles about baseball, which contain structured non-logical flow and repetitive content elements. However, when briefs require complexity of reasoning or logical arguments that justify or demonstrate conclusions, AI has been found lacking. When the intelligence community tested the capability, the intelligence official says, the product looked like an intelligence brief but was otherwise nonsensical.
Such algorithmic processes can be made to overlap, adding layers of complexity to computational reasoning, but even then those algorithms can’t interpret context as well as humans, especially when it comes to language, like hate speech.
AI’s comprehension might be more analogous to the comprehension of a human toddler, says Eric Curwin, chief technology officer at Pyrra Technologies, which identifies virtual threats to clients from violence to disinformation. “For example, AI can understand the basics of human language, but foundational models don’t have the latent or contextual knowledge to accomplish specific tasks,” Curwin says.
“From an analytic perspective, AI has a difficult time interpreting intent,” Curwin adds. “Computer science is a valuable and important field, but it is social computational scientists that are taking the big leaps in enabling machines to interpret, understand, and predict behavior.”
In order to “build models that can begin to replace human intuition or cognition,” Curwin explains, “researchers must first understand how to interpret behavior and translate that behavior into something AI can learn.”
Although machine learning and big data analytics provide predictive analysis about what might or will likely happen, it can’t explain to analysts how or why it arrived at those conclusions. The opaqueness in AI reasoning and the difficulty vetting sources, which consist of extremely large data sets, can impact the actual or perceived soundness and transparency of those conclusions.
Transparency in reasoning and sourcing are requirements for the analytical tradecraft standards of products produced by and for the intelligence community. Analytic objectivity is also statuatorically required, sparking calls within the US government to update such standards and laws in light of AI’s increasing prevalence.
Machine learning and algorithms when employed for predictive judgments are also considered by some intelligence practitioners as more art than science. That is, they are prone to biases, noise, and can be accompanied by methodologies that are not sound and lead to errors similar to those found in the criminal forensic sciences and arts.
How much do we need humans in space? How much do we want them there? Astronauts embody the triumph of human imagination and engineering. Their efforts shed light on the possibilities and problems posed by travel beyond our nurturing Earth. Their presence on the moon or on other solar-system objects can imply that the countries or entities that sent them there possess ownership rights. Astronauts promote an understanding of the cosmos, and inspire young people toward careers in science.
When it comes to exploration, however, our robots can outperform astronauts at a far lower cost and without risk to human life. This assertion, once a prediction for the future, has become reality today, and robot explorers will continue to become ever more capable, while human bodies will not.
Fifty years ago, when the first geologist to reach the moon suddenly recognized strange orange soil (the likely remnant of previously unsuspected volcanic activity), no one claimed that an automated explorer could have accomplished this feat. Today, we have placed a semi-autonomous rover on Mars, one of a continuing suite of orbiters and landers, with cameras and other instruments that probe the Martian soil, capable of finding paths around obstacles as no previous rover could.
Since Apollo 17 left the moon in 1972, the astronauts have journeyed no farther than low Earth orbit. In this realm, astronauts’ greatest achievement by far came with their five repair missions to the Hubble Space Telescope, which first saved the giant instrument from uselessness and then extended its life by decades by providing upgraded cameras and other systems. (Astronauts could reach the Hubble only because the Space Shuttle, which launched it, could go no farther from Earth, which produces all sorts of interfering radiation and light.) Each of these missions cost about a billion dollars in today’s money. The cost of a telescope to replace the Hubble would likewise have been about a billion dollars; one estimate has set the cost of the five repair missions equal to that for constructing seven replacement telescopes.
Today, astrophysicists have managed to send all of their new spaceborne observatories to distances four times farther than the moon, where the James Webb Space Telescope now prepares to study a host of cosmic objects. Our robot explorers have visited all the sun’s planets (including that former planet Pluto), as well as two comets and an asteroid, securing immense amounts of data about them and their moons, most notably Jupiter’s Europa and Saturn’s Enceladus, where oceans that lie beneath an icy crust may harbor strange forms of life. Future missions from the United States, the European Space Agency, China, Japan, India, and Russia will only increase our robot emissaries’ abilities and the scientific importance of their discoveries. Each of these missions has cost far less than a single voyage that would send humans—which in any case remains an impossibility for the next few decades, for any destination save the moon and Mars.
In 2020, NASA revealed of accomplishments titled “20 Breakthroughs From 20 Years of Science Aboard the International Space Station.” Seventeen of those dealt with processes that robots could have performed, such as launching small satellites, the detection of cosmic particles, employing microgravity conditions for drug development and the study of flames, and 3-D printing in space. The remaining three dealt with muscle atrophy and bone loss, growing food, or identifying microbes in space—things that are important for humans in that environment, but hardly a rationale for sending them there.
A Russian “suicide drone” that boasts the ability to identify targets using artificial intelligence has been spotted in images of the ongoing invasion of Ukraine.
Photographs showing what appears to be the KUB-BLA, a type of lethal drone known as a “loitering munition” sold by ZALA Aero, a subsidiary of the Russian arms company Kalashnikov, have appeared on Telegram and Twitter in recent days. The pictures show damaged drones that appear to have either crashed or been shot down.
With a wingspan of 1.2 meters, the sleek white drone resembles a small pilotless fighter jet. It is fired from a portable launch, can travel up to 130 kilometers per hour for 30 minutes, and deliberately crashes into a target, detonating a 3-kilo explosive.
ZALA Aero, which first demoed the KUB-BLA at a Russian air show in 2019, claims in promotional material that it features “intelligent detection and recognition of objects by class and type in real time.”
The drone itself may do little to alter the course of the war in Ukraine, as there is no evidence that Russia is using them widely so far. But its appearance has sparked concern about the potential for AI to take a greater role in making lethal decisions.
“The notion of a killer robot—where you have artificial intelligence fused with weapons—that technology is here, and it’s being used,” says Zachary Kallenborn, a research affiliate with the National Consortium for the Study of Terrorism and Responses to Terrorism (START).
Advances in AI have made it easier to incorporate autonomy into weapons systems, and have raised the prospect that more capable systems could eventually decide for themselves who to kill. A UN report published last year concluded that a lethal drone with this capability may have been used in the Libyan civil war.
It is unclear if the drone may have been operated in this way in Ukraine. One of the challenges with autonomous weapons may prove to be the difficulty of determining when full autonomy is used in a lethal context, Kallenborn says.
The KUB-BLA images have yet to be verified by official sources, but the drone is known to be a relatively new part of Russia’s military arsenal. Its use would also be consistent with Russia’s shifting strategy in the face of the unexpectedly strong Ukrainian resistance, says Samuel Bendett, an expert on Russia’s military with the defense think tank CNA.
Bendett says Russia has built up its drone capabilities in recent years, using them in Syria and acquiring more after Azerbaijani forces demonstrated their effectiveness against Armenian ground military in the 2020 Nagorno-Karabakh war. “They are an extraordinarily cheap alternative to flying manned missions,” he says. “They are very effective both militarily and of course psychologically.”
The fact that Russia seems to have used few drones in Ukraine early on may be due to misjudging the resistance or because of effective Ukrainian countermeasures.
But drones have also highlighted a key vulnerability in Russia’s invasion, which is now entering its third week. Ukrainian forces have used a remotely operated Turkish-made drone called the TB2 to great effect against Russian forces, shooting guided missiles at Russian missile launchers and vehicles. The paraglider-sized drone, which relies on a small crew on the ground, is slow and cannot defend itself, but it has proven effective against a surprisingly weak Russian air campaign.
The character of conflict between nations has fundamentally changed. Governments and militaries now fight on our behalf in the “gray zone,” where the boundaries between peace and war are blurred. They must navigate a complex web of ambiguous and deeply interconnected challenges, ranging from political destabilization and disinformation campaigns to cyberattacks, assassinations, proxy operations, election meddling, or perhaps even human-made pandemics. Add to this list the existential threat of climate change (and its geopolitical ramifications) and it is clear that the description of what now constitutes a national security issue has broadened, each crisis straining or degrading the fabric of national resilience.
Traditional analysis tools are poorly equipped to predict and respond to these blurred and intertwined threats. Instead, in 2022 governments and militaries will use sophisticated and credible real-life simulations, putting software at the heart of their decision-making and operating processes. The UK Ministry of Defence, for example, is developing what it calls a military Digital Backbone. This will incorporate cloud computing, modern networks, and a new transformative capability called a Single Synthetic Environment, or SSE.
This SSE will combine artificial intelligence, machine learning, computational modeling, and modern distributed systems with trusted data sets from multiple sources to support detailed, credible simulations of the real world. This data will be owned by critical institutions, but will also be sourced via an ecosystem of trusted partners, such as the Alan Turing Institute.
An SSE offers a multilayered simulation of a city, region, or country, including high-quality mapping and information about critical national infrastructure, such as power, water, transport networks, and telecommunications. This can then be overlaid with other information, such as smart-city data, information about military deployment, or data gleaned from social listening. From this, models can be constructed that give a rich, detailed picture of how a region or city might react to a given event: a disaster, epidemic, or cyberattack or a combination of such events organized by state enemies.
Defense synthetics are not a new concept. However, previous solutions have been built in a standalone way that limits reuse, longevity, choice, and—crucially—the speed of insight needed to effectively counteract gray-zone threats.
National security officials will be able to use SSEs to identify threats early, understand them better, explore their response options, and analyze the likely consequences of different actions. They will even be able to use them to train, rehearse, and implement their plans. By running thousands of simulated futures, senior leaders will be able to grapple with complex questions, refining policies and complex plans in a virtual world before implementing them in the real one.
One key question that will only grow in importance in 2022 is how countries can best secure their populations and supply chains against dramatic weather events coming from climate change. SSEs will be able to help answer this by pulling together regional infrastructure, networks, roads, and population data, with meteorological models to see how and when events might unfold.
Two decades after 9/11, many simple acts that were once taken for granted now seem unfathomable: strolling with loved ones to the gate of their flight, meandering through a corporate plaza, using streets near government buildings. Our metropolises’ commons are now enclosed with steel and surveillance. Amid the perpetual pandemic of the past year and a half, cities have become even more walled off. With each new barrier erected, more of the city’s defining feature erodes: the freedom to move, wander, and even, as Walter Benjamin said, to “lose one’s way … as one loses one’s way in a forest.”
It’s harder to get lost amid constant tracking. It’s also harder to freely gather when the public spaces between home and work are stripped away. Known as third places, they are the connective tissue that stitches together the fabric of modern communities: the public park where teens can skateboard next to grandparents playing chess, the library where children can learn to read and unhoused individuals can find a digital lifeline. When third places vanish, as they have since the attacks, communities can falter.
Without these spaces holding us together, citizens live more like several separate societies operating in parallel. Just as social-media echo chambers have undermined our capacity for conversations online, the loss of third places can create physical echo chambers.
America has never been particularly adept at protecting our third places. For enslaved and indigenous people, entering the town square alone could be a death sentence. Later, the racial terrorism of Jim Crow in the South denied Black Americans not only suffrage, but also access to lunch counters, public transit, and even the literal water cooler. In northern cities like New York, Black Americans still faced arrest and violence for transgressing rigid, but unseen, segregation codes.
Throughout the 20th century, New York built an infrastructure of exclusion to keep our unhoused neighbors from sharing the city institutions that are, by law, every bit as much theirs to occupy. In 1999, then mayor Rudy Giuliani warned unhoused New Yorkers that “streets do not exist in civilized societies for the purpose of people sleeping there.” His threats prompted thousands of NYPD officers to systematically target and push the unhoused out of sight, thus semi-privatizing the quintessential public place.
Despite these limitations, before 9/11 millions of New Yorkers could walk and wander through vast networks of modern commons—public parks, private plazas, paths, sidewalks, open lots, and community gardens, crossing paths with those whom they would never have otherwise met. These random encounters electrify our city and give us a unifying sense of self. That shared space began to slip away from us 20 years ago, and if we’re not careful, it’ll be lost forever.
In the aftermath of the attacks, we heard patriotic platitudes from those who promised to “defend democracy.” But in the ensuing years, their defense became democracy’s greatest threat, reconstructing cities as security spaces. The billions we spent to “defend our way of life” have proved to be its undoing, and it’s unclear if we’ll be able to turn back the trend.
In a country where the term “papers, please” was once synonymous with foreign authoritarianism, photo ID has become an ever present requirement. Before 9/11, a New Yorker could spend their entire day traversing the city without any need for ID. Now it’s required to enter nearly any large building or institution.
While the ID check has become muscle memory for millions of privileged New Yorkers, it’s a source of uncertainty and fear for others. Millions of Americans lack a photo ID, and for millions more, using ID is a risk, a source of data for Immigration and Customs Enforcement.
According to Mizue Aizeki, interim executive director of the New York–based Immigrant Defense Project, “ID systems are particularly vulnerable to becoming tools of surveillance.” Aizeki added, “data collection and analysis has become increasingly central to ICE’s ability to identify and track immigrants,” noting that the Department of Homeland Security dramatically increased its support for surveillance systems since its post-9/11 founding.
ICE has spent millions partnering with firms like Palantir, the controversial data aggregator that sells information services to governments at home and abroad. Vendors can collect digital sign-in lists from buildings where we show our IDs, facial recognition in plazas, and countless other surveillance tools that track the areas around office buildings with an almost military level of surveillance. According to Aizeki, “as mass policing of immigrants has escalated, advocates have been confronted by a rapidly expanding surveillance state.”