#214 – Buck Shlegeris on controlling AI that wants to take over – so we can use it anyway
Most AI safety conversations centre on alignment: ensuring AI systems share our values and goals. But despite progress, we’re unlikely to know we’ve solved the problem before the arrival of human-level and superhuman systems in as little as three years.
So some are developing a backup plan to safely deploy models we fear are actively scheming to harm us — so-called “AI control.” While this may sound mad, given the reluctance of AI companies to delay deploying anything they train, not developing such techniques is probably even crazier.
Today’s guest — Buck Shlegeris, CEO of Redwood Research — has spent the last few years developing control mechanisms, and for human-level systems they’re more plausible than you might think. He argues that given companies’ unwillingness to incur large costs for security, accepting the possibility of misalignment and designing robust safeguards might be one of our best remaining options.
Buck asks us to picture a scenario where, in the relatively near future, AI companies are employing 100,000 AI systems running 16 times faster than humans to automate AI research itself. These systems would need dangerous permissions: the ability to run experiments, access model weights, and push code changes. As a result, a misaligned system could attempt to hack the data centre, exfiltrate weights, or sabotage research. In such a world, misalignment among these AIs could be very dangerous.
But in the absence of a method for reliably aligning frontier AIs, Buck argues for implementing practical safeguards to prevent catastrophic outcomes. His team has been developing and testing a range of straightforward, cheap techniques to detect and prevent risky behaviour by AIs — such as auditing AI actions with dumber but trusted models, replacing suspicious actions, and asking questions repeatedly to catch randomised attempts at deception.
