Amid a barrage of news stories warning about superintelligent machines rendering humanity extinct, a CNN story describing the opposite scenario – one in which the US military’s reliance on brittle chatbots almost brought the US into war with China – went mostly unnoticed by the public.
The biggest international AI news of the past three weeks was the Anthropic engineer Jacob Coxon’s resignation. According to him, OpenAI and Anthropic are “racing straight towards self-improving superintelligence and gambling with our lives”. Coxon’s description of a “terminator” scenario, a machine becoming much smarter than humanity and deciding to wipe us out, captured the public’s, journalists’ and politicians’ attention, stirring waves of fear-inducing, credulous reporting, and policy actions.
The California governor, Gavin Newsom, issued a mandate to create an “AI kill-switch”; New York’s governor, Kathy Hochul, is similarly exploring the possibility of having “AI kill-switches”; and Senator Bernie Sanders and Congressman Greg Casar introduced legislation to “ban artificial superintelligence”.
While so many of us were focused on fictional superintelligent machines that we were told could hypothetically cause human extinction, however, there was a more serious story being reported.
A series of events almost got us to armageddon, not caused by “rogue, superintelligent AI”, but by government reliance on error-prone systems that are marketed by their manufacturers as being close to superintelligent.
A CNN report published on 18 September, which hasn’t been verified by other major outlets, describes how the US “almost started a war” with China, based on an intelligence report that claimed a Chinese ship was transporting components of nuclear weapons. Military personnel made plans to intercept the Chinese vessel, complete with aircraft and soldiers ready to board the ship. A military skirmish of this kind between China and the US could quickly escalate and spiral into world war three.
It was only moments before executing on the plan that American officials further investigated the intelligence report and found that it had been generated with the use of a chatbot and contained erroneous information about the vessel’s contents.
Unlike the terminator, “rogue AI”, scenario, this incident didn’t galvanize lawmakers into announcing calls for regulation so as to avoid potential human extinction. Media outlet after media outlet didn’t warn the public that we almost triggered a war against a nuclear superpower because of our military’s reliance on error-prone chatbots.
We describe the large language models (LLMs) powering these chatbots as stochastic parrots, that is, models that are designed to regurgitate the patterns of the data they are trained on. News coverage describing them as “powerful” “rogue” models that could render humanity extinct all on their own, bolsters perceptions of tools based on these models being infallible enough to merit usage in stakes as high as warfare.
Even CNN’s more critical coverage describes the chatbot used by the US military as presenting “profound risks” because it is “powerful, new and relatively poorly understood technology”.
In fact, today’s LLMs and the chatbots they power are not poorly understood, nor are they powerful in the sense of being effective tools for this use case. They are systems for generating plausible-looking text, built out of enormous, haphazardly collected datasets and then “fine-tuned” (that is, further trained) to be especially appealing to their intended users.
CNN wasn’t able to learn which chatbot product was used in this case, but the LLM powering the product was probably fine-tuned to output text with the stylistic hallmarks of intelligence reports. Both the basic functionality and the risks of such a model have been well-understood and well-documented for years, including in our 2021 paper On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?
Claims that system behavior is mysterious arise from deliberate mystification on the part of those selling those systems, and a misapprehension of what their output is by those procuring and using it.
