Knowledge advantage can save lives, win wars and avert disaster. At the Central Intelligence Agency, basic artificial intelligence 鈥 machine learning and algorithms 鈥 has long served that mission. Now, generative AI is joining the effort.
CIA Director William Burns says AI tech will augment humans, not replace them. The agency's first chief technology officer, Nand Mulchandani, is marshaling the tools. There's considerable urgency: Adversaries are already spreading AI-generated deepfakes aimed at undermining U.S. interests.
A former Silicon Valley CEO who helmed successful startups, Mulchandani was named to the job in 2022 after a stint at the Pentagon鈥檚 Joint Artificial Intelligence Center.
Among agency projects: A ChatGPT-like generative AI application that draws on open-source data (meaning unclassified, public or commercially available). Thousands of analysts across the 18-agency U.S. intelligence community use it. Other CIA projects that use large-language models are, unsurprisingly, secret.
This Associated Press interview with Mulchandani has been edited for length and clarity.
Q: You recently said generative AI should be treated like a 鈥渃razy, drunk friend.鈥 Can you elaborate?
A: When these generative AI systems 鈥渉allucinate,鈥 they can sometimes behave like your drunk friend at a bar who can say something that pushes you outside your normal conceptual boundary and sparks out-of-the box thinking. Remember that these AI-based systems are probabilistic in nature, so they are not precise (They are prone to fabrication). So for creative tasks like art, poetry, and painting these systems are excellent. But I wouldn鈥檛 yet use these systems for doing precise math or designing an airplane or skyscraper - in those activities 鈥渃lose enough鈥 doesn鈥檛 work. They can also be biased and narrowly focused, which I call the 鈥渞abbit hole鈥 problem.
Q: The only current use of a large-language model at enterprise scale I鈥檓 aware of at CIA is the open-source AI, called Osiris, that it created for the entire intelligence community. Is that correct?
A: That鈥檚 the only one we have disclosed publicly. It鈥檚 been an absolute home run for us. We should broaden the discussion beyond just LLMs though -- as an example, we process huge amounts of foreign language content in multiple media types including video, and use other AI algorithms and tools to process that.
Q: The Special Competitive Studies Project, a high-powered advisory group focused on AI in national security, is out with a report saying U.S. intelligence services must rapidly integrate generative AI -- given its disruptive potential. It sets a two-year timeline for getting beyond experimentation and limited pilot projects and 鈥渄eploying Gen AI tools at scale.鈥 Do you agree?
A: CIA is all in 100% on utilizing these technologies and scaling them. We are taking this as seriously as we鈥檙e taking probably any technology issue. We think we've beaten the initial timeline by a big margin, as we鈥檙e already using Gen AI tools in production. The deeper answer is that we鈥檙e on the early side of a huge number of additional changes, and a large part of the work is to integrate the technology more widely into our applications and systems. These are early days.
Q: Can you name your large-language model partners?
A: I'm not sure naming the vendors is interesting right now. There is an explosion of LLMs available on the market now. As a smart customer, we are not tying our boat to a specific set of LLMs or a specific set of vendors. We are evaluating and using practically all the high-runner LLMs out there, both commercial-grade and open source. We are not viewing the LLM market as a singular one where a single lab is better than the others. As you鈥檙e noting in the market, models are leapfrogging one another with each new release.
Q: What are the most important use cases at CIA for large-language models?
A: Primary is summarization. It鈥檚 impossible for an open-source analyst at CIA to digest the firehouse of media and other information we collect every day. So this has been a game-changer for insights into sentiment and global trends. Analysts then dig into specifics. They must be able 鈥 with full certainty 鈥 to annotate and explain data they cite and how they reach conclusions. Our tradecraft has not changed. The additional pieces give analysts much broader perspective 鈥 both the classified and open-source pieces we gather.
Q: What are the biggest challenges of adapting generative AI at the agency?
A: There isn鈥檛 a lot of cultural resistance internally. Our employees deal with AI on a daily basis competitively. Obviously, the whole world is on fire with these new technologies and the amazing productivity gains. The trick is grappling with constraints we have on information compartmentalization and how systems are built. In many cases, the separation of data is not for security but legal reasons. How do we efficiently connect systems to get the benefits of AI while keeping all that intact? Some really interesting technologies are emerging to help us think this through 鈥 and combine data in ways that maintain encryption and privacy controls.
Q: Generative AI is currently about as sophisticated as an elementary school student. Intelligence work, by contrast, is for grown-ups. It鈥檚 all about trying to pierce an adversary鈥檚 deception. How does Gen AI fit into that work?
A: First, let鈥檚 emphasize that the human analyst has primacy. We have the world鈥檚 leading experts in their domains. And in many cases of incoming information, a huge amount of human judgment is involved to weigh its importance and significance 鈥 including of the individuals who may be providing it. We don鈥檛 have machines replicate any of that. And we鈥檙e not looking for computers to do the jobs of domain experts.
What we are looking at is the co-pilot model. We think Gen AI can have a huge impact in brainstorming, coming up with new ideas. And in boosting productivity 鈥 and insight. We have to be very deterministic about how we do it because, wielded properly, these algorithms are a force for good. But wielded incorrectly, they can really hurt you.
