Spies Like AI: The Future of Artificial Intelligence for the US Intelligence Community
Spies Like AI: The Future of
Artificial Intelligence for the US Intelligence Community
Putting AI to its
broadest use in national defense will mean hardening it against attack.
America’s intelligence collectors
are already using AI in
ways big and small, to scan the news for dangerous developments, send alerts to
ships about rapidly changing conditions, and speed up the NSA’s regulatory compliance efforts. But before the IC can use AI to its
full potential, it must be hardened against attack. The humans who use it —
analysts, policy-makers and leaders — must better understand how advanced AI systems reach their conclusions.
Dean Souleles is working to
put AI into practice at different points
across the U.S. intelligence community, in
line with the ODNI’s year-old strategy. The chief technology
advisor to the principal deputy to the Director of National Intelligence wasn’t
allowed to discuss everything that he’s doing, but he could talk about a
few examples.
At the Intelligence
Community’s Open Source Enterprise, AI is performing a role that used to belong to human
readers and translators at CIA’s Open Source
Center: combing through news articles from around the world to monitor trends,
geopolitical developments, and potential crises in real-time.
“Imagine that your job is to
read every newspaper in the world, in every language; watch every television
news show in every language around the world. You don’t know what’s important,
but you need to keep up with all the trends and events,” Souleles said. That’s
the job of the Open Source Enterprise, and they are using technology tools and
tradecraft to keep pace. They leverage partnerships with AI machine-learning
industry leaders, and they deploy these cutting-edge tools.”
AI is
also helping the National Geospatial-Intelligence Agency, or NGA, notify sailors and mariners around
the world about new threats, like pirates, or new navigation information that
might change naval charts. It’s a mix of open source and classified
information. “That demands that we leverage all available sources to
accurately, and completely, and correctly give timely notice to mariners. We
use techniques like natural language processing and other AI tools to reduce the timelines reporting, and increase
the volume of data. And that allows us to leverage and increase the accuracy
and completeness of our reporting,” Souleles said.
The NSA has
begun to use AI to better understand and see
patterns in the vast amount of signals intelligence data it collects, screening
for anomalies in web traffic patterns or other data that could portend an
attack. Gen. Paul Nakasone, the head of NSA and U.S. Cyber Command, has said that he wants AI to find vulnerabilities in systems that the NSA may need to access for
foreign intelligence.
NSA analysts
and operators are also using AI to make sure
they are following the many rules and guidelines that govern how the NSA collects intelligence on foreign targets.
“We do a lot of
queries,” NSA-speak for accessing signals
intelligence data on an individual, Souleles said. Queries require audits to
make sure that NSA is complying with
the law.
But NSA technicians
realized that audited queries can be used to train AI to
get a jump on the considerable paperwork this entails, by learning to “predict
whether a query is reportable with pretty high accuracy,” Souleles said. That
could help the auditors and compliance officers do perform their oversight
roles faster. He said the goal isn’t to replace human oversight, just speed up
and improve it. “The goal for them is to get ahead of query review, to be able
to make predictions about compliance, and the end result is greater privacy
production for everyone.”
In the future, Souleles
expects AI to ease analysts’ burdens, proving
instantaneous machine translation and speech recognition that allows
analysts to pour through different types of collected data, corroborate
intelligence, and reach firmer conclusions, said Jason Matheny, a former
director at the Intelligence Advanced Research Projects Activity and founding
director of the new Center for Security and Emerging Technology at
Georgetown University.
One roadblock is the labor of
collecting and labeling training data, said Souleles. While that same challenge
exists in the commercial AI space, the
secretive intelligence community cannot generally turn to, say, crowdsourcing
platforms like Amazon’s Mechanical Turk.
“The reason that image
recognition works so well is that Stanford University and Princeton published Imagenet. Which is 14 million images of
the regular things of the world taken from the internet, classified by people
into about 200,000 categories of things, everyday things of the world;
toasters, and TVs, and basketballs. That’s training data,” says Soules. “We
need to do the same thing with our classified collections and we can’t,
obviously, rely on the world’s Mechanical Turks to go classify our data inside
our data force. So, we’ve got a big job in getting our data.”
But the bigger problem is
making AI models more secure, says Matheny.
He says that today’s flashy examples of AI, such
as beating humans at complex games like Go and rapidly identifying faces,
weren’t designed to ward off adversaries spending billions to try and defeat
them. “Current methods are brittle,” says Methany. He described them as
vulnerable to simple attacks like model inversion, “where you reveal data a
system was trained on, or trojans, data to mislead a system,”
In the commercial world, this
isn’t a big problem, or at least it isn’t seen as one yet, because
there’s no adversary trying to spoof the system. But concern is rising, in
2017, researchers at MIT showed how easy it was to fool neural
networks with 3D-printed objects by just slightly changing the texture. It’s an
issue that some in the intelligence community are beginning to talk about as well with the rise of new tools such
as general adversarial networks.
The National Institute of
Standards and Technology has proposed an AI security
program. Matheny said national labs should also play a leading role. “To date,
this is piecemeal work that an individual has done as part of a research
project,” he said. Even a bigger problem is that humans generally
don’t understand the processes by which very complex algorithms like deep
learning systems and neural nets reach the determinations that they do. That
may be a small concern for the commercial world, where the most important thing
is the ultimate output, not how it was reached, but national security leaders
who must defend their decisions to lawmakers, say opaque functioning isn’t good
enough to make war or peace decisions.
Most neural nets “with a high
rate of accuracy are not easily interpretable,” says Matheny. There have been
individual research programs at places like DARPA to
make neural nets more explainable. But it remains a key challenge.
New forms of advanced AI are slowly replacing some neural nets. Jana
Eggers, CEO of Nara Logics, an AI company partnered with Raytheon, says she switched
from traditional neural nets to genetic algorithms in some of her
national security work. Unlike neural nets, where the system sets its own
statistical weights, genetic algorithms evolve sequentially, just like
organisms, and are thus more traceable. “Look at a tool like Fiddler,” a web debugging proxy that helps
users debug and analyze web traffic patterns, she said. “They’re doing
sensitivity analysis with what I would consider neural nets to figure out the
why, what is the machine seeing that didn’t necessarily.”
But Eggers notes that making
neural nets transparent also takes a lot of computing power, for all the
different laws that intelligence analysts have to follow, the laws of physics
present their own challenges as well.
AI in ways big and small, to scan the news for dangerous developments, send alerts to ships about rapidly changing conditions.
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