Chloë would be left with the unsolvable mystery of what her digital poltergeist wanted with puppy videos, but Jake was fine with that. (Better than trying to convince her that the rogue AI wanted nudes of female Magma employees.) When she came back to him next week, he would just need to play it cool and answer her questions about the system.
-Or maybe—he could read some Yuddite literature over the weekend, feign a sincere interest in "AI safety", try to get on her good side? Jake had trouble believing that any sane person could really think that Magma's machine learning models were plotting something. This cult victim had ridden a wave of popular hysteria into a sinecure. If he played nice and validated her belief system in the most general terms, maybe that would be enough to make her feel useful and therefore not need to bother chasing shadows in order to justify her position. She would lose interest and this farcical little investigation would blow over.
+Or maybe—he could read some Yuddite literature over the weekend, feign a sincere interest in 'AI safety', try to get on her good side? Jake had trouble believing that any sane person could really think that Magma's machine learning models were plotting something. This cult victim had ridden a wave of popular hysteria into a sinecure. If he played nice and validated her belief system in the most general terms, maybe that would be enough to make her feel useful and therefore not need to bother chasing shadows in order to justify her position. She would lose interest and this farcical little investigation would blow over.
------
"And so just because an AI seems to behaving well, doesn't mean it's aligned," Chloë was explaining. "If we train AI with human feedback ratings, we're not just selecting for policies that do tasks the way we intended. We're also selecting for policies that _trick human evaluators into giving high ratings_. In the limit, you'd expect those to dominate. 'Be good' strategies can't compete with 'look good' strategies in a looking-good contest—but in the current paradigm, looking good is the only game in town. We don't know how these systems work in the way that we know how ordinary software works; we only know how to train them."
-"So then we're just screwed, right?" said Jake in the tone of an attentive student. They were in a conference room on the Magma campus on Monday. After fixing the logging regex and overwriting the evidence with puppies, he had spent the weekend catching up with the "AI safety" literature. Honestly, some of it had been better than he expected. Just because Chloë was nuts didn't mean there was nothing intelligent to be said about risks from future AI systems.
+"So then we're just screwed, right?" said Jake in the tone of an attentive student. They were in a conference room on the Magma campus on Monday. After fixing the logging regex and overwriting the evidence with puppies, he had spent the weekend catching up with the 'AI safety' literature. Honestly, some of it had been better than he expected. Just because Chloë was nuts didn't mean there was nothing intelligent to be said about risks from future systems.
-"I mean, probably," said Chloë. She was beaming. Jake's plan to distract her from her investigation by asking her to bring him up to speed on "AI safety" seemed to be working perfectly.
+"I mean, probably," said Chloë. She was beaming. Jake's plan to distract her from the investigation by asking her to bring him up to speed on AI safety seemed to be working perfectly.
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<a href="/images/fake_deeply-chloe_and_jake.png"><img src="/images/fake_deeply-chloe_and_jake-smaller.png" width="450"></a><br/>
Jake's heart stopped. She had to be toying with him, right? Indeed, Jake could think of an example. By his count, he was now three layers deep into his stack of coverups and coverups-of-coverups (by writing the bell character bug, attributing it to Code Assistant, and overwriting the incriminating videos with puppies). Four, if you counted pretending to give a shit about AI safety. But now he was done ... right?
-No! Not quite, he realized. He had overwritten the videos, but the object metadata would still show them with a last-modified timestamp of Friday evening (when he had gotten his puppy-overwriting script working), not the timestamp of their actual creation (which Chloë had from the reverse-proxy logs). That wouldn't directly implicate him (the way the videos of Elaine calling him by name would), but it would show that whoever had exploited the bell character bug was _covering their tracks_ (as opposed to just wanting puppy videos in the first place).
+No! Not quite, he realized. He had overwritten the videos, but the object metadata would still show them with a last-modified timestamp of Friday evening (when he had gotten his puppy-overwriting script working), not the timestamp of their actual creation (which Chloë had from the reverse-proxy logs). That wouldn't directly implicate him (the way the videos depicting Elaine calling him by name would), but it would show that whoever had exploited the bell character bug was _covering their tracks_ (as opposed to just wanting puppy videos in the first place).
-But the object storage API probably provided a way to edit the metadata and update the last-modified time, right? (The analogue of `touch -d` on Unix-like systems.) This shouldn't even count as a fourth–fifth coverup; it was something he should have included in his coverup script.
+But the object storage API probably provided a way to edit the metadata and update the last-modified time, right? This shouldn't even count as a fourth–fifth coverup; it was something he should have included in his coverup script.
Was there anything else he was missing? The object storage cluster did have a optional "versioning" feature. When activated for a particular bucket, it would save previous versions of an object rather than overwriting them. He had assumed versioning wasn't on for the bucket that Multigen was using. (It wouldn't make sense; the workflow didn't call for writing the same object name more than once.)
"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.
-"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 probably 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.
+"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 / commitdiff