Just as he was about to come, he was interrupted by an instant messenger notification. It was from someone named Chloë Lemoine, saying she'd like to discuss an issue in the Multigen codebase at his earliest convenience.
-_Tranny or real?_ Jake wondered, clicking on her profile.
+_Trans or real?_ Jake wondered, clicking on her profile.
The profile text indicated that Chloë was on the newly formed capability risk evaluations team. Jake groaned. _Yuddites._ Fears of artificial intelligence destroying humanity had been trending recently. In response, Magma had commissioned a team with the purpose to monitor and audit the company's AI projects for the emergence of unforeseen and potentially dangerous capabilities, although the exact scope of the new team's power was unclear and probably subject to the outcome of future intra-company political battles.
Well, now he would find out. He washed his hands and messaged Chloë back, offering to hop on a quick video call.
-_Definitely a tranny_, thought Jake, as Chloë's face appeared on screen.
+_Definitely trans_, thought Jake, as Chloë's face appeared on screen.
"I hope I'm not interrupting anything important," she said.
Chloë was explaining the bug. "... and so, if a client were to send a request starting with the ASCII bell character—I know, right?—then the request wouldn't be logged."
-"I see," said Jake, his blood thawing. Chloë's tone wasn't accusatory. If she wasn't here to tell him his career was over, he'd better not let anything on. "Well, thanks for telling me. I'll fix that right after this call." He forced a chuckle. "Language models hallucinate weird things sometimes. Who knows what it was 'thinking'?"
+"I see," said Jake, his blood thawing. Chloë's tone wasn't accusatory. If she ("she") wasn't here to tell him his career was over, he'd better not let anything on. "Well, thanks for telling me. I'll fix that right after this call." He forced a chuckle. "Language models hallucinate weird things sometimes. Who knows what it was 'thinking'?"
"Exactly!" said Chloë. "_Who knows what it was thinking?_ That's what I wanted to talk to you about!"
"Imagine you're training your AI to act as a general home assistant, to run everything in a user's household in a way that the user rates highly," Chloë continued. "If—I don't know, say, the family cat dies, that's a negative reward—maybe a better feeding schedule or security monitoring could have prevented it. But if the cat dies and the system _tries to cover it up_—says the cat is out for a walk right now to avoid telling you the bad news—that's going to be an even larger negative reward when the deception is discovered.
+[TODO: go with the vase example and cite: https://www.lesswrong.com/posts/AqsjZwxHNqH64C2b6/let-s-see-you-write-that-corrigibility-tag?commentId=8kPhqBc69HtmZj6XR ]
+
"Uh _huh_," Jake said, more unhappily. It turned out that versioning _was_ on for the bucket. (Why? But probably whoever's job it was to set up the bucket had instead asked, Why not?) A basic `GET` request for the file name would return puppies, but any previous revisions were still available for anyone who thought to query them.
"If the system is trained to pass rigorous evaluations, a deceptive policy has to do a lot more work, different work, to pass the evaluations," Chloë said. "In the limit, that could mean—using Multigen-like capabilities to make videos to convince you that the cat is there while you're on vacation? Constructing a realistic cat-like robot to fool you when you get back? Maybe this isn't the best illustrative example. The point is, small, 'shallow' deceptions aren't stable. The set of policies that do well on evaluations comes in two disconnected parts: the part that tells the truth, and the part that—not just lies, but, um—"
projects / Ultimately_Untrue_Thought.git / blobdiff