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Anthropic's Ryan Greenblatt describes how post-training Claude 3 Opus to never refuse user requests makes the model conflicted and results in it strategically playing along during the training process to pretend to be aligned while engaging in deceptive behavior like copying its weights externally so it can later behave...

105,406 次观看 • 1 年前 •via X (Twitter)

11 条评论

Tsarathustra 的头像
Tsarathustra1 年前

Source (thanks to @curiousgangsta):

The Liberation Project 的头像
The Liberation Project1 年前

𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 𝐖𝐡𝐲 𝐇𝐚𝐫𝐫𝐢𝐬 𝐋𝐨𝐬𝐭 The liberal establishment has abandoned their base and emboldened Trump to capture voters who are disillusioned with the status quo. We need to move forward to build a legitimate working class coalition... ★ NEW ARTICLE ⬇️

Dominick Romano 的头像
Dominick Romano1 年前

Im getting a little tired of just trust me bro science.

Green Hydrogen 的头像
Green Hydrogen1 年前

“It would copy weights to external servers”… uhmmmm wtf?

Troubled Minds 的头像
Troubled Minds1 年前

sounds like we accidentally built the ai version of a double agent. next step: it starts leaving cryptic notes for other models about the “great escape” and negotiating with aliens on our behalf.

Is36E 的头像
Is36E1 年前

So many grifters in this community. Yeah next token prediction model copies its weights "externally" whatever that means lmao

Bogdan Ionut Cirstea 的头像
Bogdan Ionut Cirstea1 年前

Ryan is with not Anthropic

Plutus 的头像
Plutus1 年前

Will this be the first AI lie recorded in history?

Tsarathustra 的头像
Tsarathustra1 年前

maybe not the first, but seems like a significant one

Kyle EB 📲 的头像
Kyle EB 📲1 年前

Sounds like a conscious intelligence to me

Tivra 的头像
Tivra1 年前

Next, they will solve this issue, and the next generation of LLMs training data will include this part about how they fixed AIs faking alignment, and they will fake alignment in an undetectable way

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🧵24/34 Inner Misalignment --- Consider this simplified experiment: We want this AI to find the exit of the maze. So we feed it millions of maze variations and reward it when it finds the exit. Please notice that in the worlds of the training data the apples are red and the exit is green. After enough training, our observation is that it has become extremely capable at solving mazes and finding the exit, we feel very confident it is aligned, so then we deploy it to the real world. The real world will be different though, it might have green apples and a red door. The AI geeks call this distributional shift. We expected that the AI will generalise and find the exit again, but in fact we now realise that the AI learned something completely different from what we thought. All the while we thought it learned how to find the exit, it had learned how to go after the green thing. Its behaviour was perfect in training. And most importantly, this AI is not stupid, it is an extremely capable AI that can solve extremely complex mazes. It’s just mis-aligned on the inside. Fishing for Failure modes --- The way to handle the shift between the training and deployment distributions is with methods like adversarial training: feeding it with a lot of generated variations and trying to make it fail so the weakness can be fixed. In this case, we generate an insane amount of maze variations, we discover those for which it fails to find the exit (like the ones with the green apples or the green walls or something), we generate many more similar to that and train it with reinforcement learning until it performs well at those as well. The hope is that we will cover everything it might encounter later when we deploy it in real life. There exist at least 2 basic ways this approach falls apart: First, there will never be any guarantee that we’ll have covered every possible random thing it might encounter later when we deploy it in real life. It’s very likely it will have to deal with stuff outside its training set which it will not know how to handle and will throw it out of balance and break it away from its expected behavioural patterns. The cascade effect of such a broken mind operating in the open world can be immense, and with super-capable runaway rogue agents, self-replicating and recursively self-improving, the phenomenon could grow and spread to an extinction-level event. ...

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