Capitalizing on AI and open source will enable the IC to utilize other finite collection capabilities, like human spies and signals intelligence collection, more efficiently. Other collection disciplines can be used to obtain the secrets that are hidden from not just humans but AI, too. In this context, AI may supply better global coverage
Meanwhile, at the National Geospatial-Intelligence Agency, AI and machine learning extract data from images that are taken daily from nearly every corner of the world by commercial and government satellites. And the Defense Intelligence Agency trains algorithms to recognize nuclear, radar, environmental, material, chemical, and biological measurements and to evaluate these signatures, increasing the productivity of its analysts.
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.