Responding to your confusion about disagreement votes: I think your model isn’t correctly describing how people are modelling this situation. People may believe that they can do more good from [choosing to eat meat + offsetting with donations] vs [not eating meat + offsetting with donations] because of the benefits described in the post. So you are failing to include a +I (or -I) term that factors in peoples abilities to do good (or maybe even the terminal effects of eating meat on themself).
Cody Rushing
Karma: 515
Regardless of the whether these anti-pedagogy’s are correct, I’m confused about why you think you’ve shown that learning econ made the economists dumber. It seems like the majority of your tweets you linked, excluding maybe Tweet 4, are actually just the economists discussing narrow AI and failing to consider general intelligence?
If you meant to say something like ‘econ pedagogy makes it hard for economists to view AGI as something that could actually be intelligent in a way similar to humans’, then I may be more inclined to agree with you.
Factored Cognition Strengthens Monitoring and Thwarts Attacks
The deck appears to have no notes in it
Ctrl-Z: Controlling AI Agents via Resampling
My strong downvote just gave +3
Hmm, when I imagine “Scheming AI that is not easy to shut down with concerted nation-state effort, are attacking you with bioweapons, but are weak enough such that you can bargain/negotiate with them” I can imagine this outcome inspiring a lot more caution relative to many other worlds where control techniques work well but we can’t get any convincing demos/evidence to inspire caution (especially if control techniques inspire overconfidence).
But the ‘is currently working on becoming more powerful’ part of your statement does carry a lot of weight.
Control research exclusively cares about intentional deception/scheming; it does not aim to solve any other failure mode.
(nitpick, doesn’t address main point of article) I think this is incomplete. Though control research does indeed care a lot about scheming, control can be used more broadly to handle any worst-case deployment behavior. See Josh Clymer’s post about Extending control evaluations to non-scheming threats.
This might not work well for others, but a thing that’s worked well for me has been to (basically) block cheap access to it with anticharities. Introducing friction in general is good
I’m glad to see this. Some initial thoughts about the control safety case:
I don’t get how the “monitoring game” is different from the red-team game? Is it because the blue team is limited to only considering protocols that use just monitoring? Or do you not expect to play a full red-team simulation (this would be worrisome)
Option 2 (honeypotting) feels doomed (in isolation). This primarily stems from my belief that making situations that are indistinguishable from deployment is probably hard—the model has control over state in very broad and hard-to-predict ways, and this can implicitly encode that it is in deployment (for instance, the model can see code that it itself has written and would’ve taken a long time to write, and realize it has likely been deployed for some time). You can still run honeypotting tests using this newer distribution, but then you have to hope that you are running honeypotting frequently enough to catch when the model is confident it is in deployment.
The elicitation problem, and the broader problem of justifying that the red-blue teaming has been sufficient, indeed feel quite hard. This is one reason as to why you might expect that control cases can only work for human-level AI—if your ‘elicitation protocol’ just ends up being ‘use attack strategies made primarily by humans’.
There are related problems in justifying how many affordances to give to the red team. For instance, decisions related to if the red team should be able to give/train into the model ‘coordination signals’ are likely important, but might not be easy to make. Past research has been maximally conservative, but this might not be realistic.
The limitation that you are measuring P(catastrophe | misaligned) rather than just P(catastrophe) is fairly large, and I would be interested in discussion on how important this is to others. More broadly, I don’t feel great about our chances of bounding P(catastrophe) for reasons such as this.
Am I correct in interpreting that your definition of “found system” would apply nearly all useful AI systems today such as ChatGPT, as these are algorithms which run on weights that are found with optimization methods such as gradient descent? If so, it is still fairly onerous.
Thanks for writing this and proposing a plan. Coincidentally, I drafted a short take here yesterday explaining one complaint I currently have with the safety conditions of this plan. In short, I suspect the “No AIs improving other AIs” criterion isn’t worth including within a safety plan: it i) doesn’t address that many more marginal threat models (or does so ineffectively) and ii) would be too unpopular to implement (or, alternatively, too weak to be useful).
I think there is a version of this plan with a lower safety tax, with more focus on reactive policy and the other three criterion, that I would be more excited about.
Another reason why layernorm is weird (and a shameless plug): the final layernorm also contributes to self-repair in language models
Hmm, this transcript just seems like an example of blatant misalignment? I guess I have a definition of scheming that would imply deceptive alignment—for example, for me to classify Sydney as ‘obviously scheming’, I would need to see examples of Sydney 1) realizing it is in deployment and thus acting ‘misaligned’ or 2) realizing it is in training and thus acting ‘aligned’.
In what manner was Sydney ‘pretty obviously scheming’? Feels like the misalignment displayed by Sydney is fairly different than other forms of scheming I would be concerned about
(if this is a joke, whoops sorry)
I’m surprised by this reaction. It feels like the intersection between people who have a decent shot of getting hired at OpenAI to do safety research and those who are unaware of the events at OpenAI related to safety are quite low.
What Comes Next
Coding got another big leap, both for professionals and amateurs.
Claude is now clearly best. I thought for my own purposes Claude Opus was already best even after GPT-4o, but not for everyone, and it was close. Now it is not so close.
Claude’s market share has always been tiny. Will it start to rapidly expand? To what extent does the market care, when most people didn’t in the past even realize they were using GPT-3.5 instead of GPT-4? With Anthropic not doing major marketing? Presumably adaptation will be slow even if they remain on top, especially in the consumer market.
Yet with what is reportedly a big jump, we could see a lot of wrappers and apps start switching over rapidly. Developers have to be more on the ball.
How long should we expect Claude 3.5 Sonnet to remain on top?
I do not expect anyone except Google or OpenAI to pose a threat any time soon.
Was surprised by the one-shot Hanson lecture result. From it, I decided to see if Claude could identify your text, so I plugged in the above after asking “Who do you think the author behind this paragraph is?” Response:
Based on the style, content, and context of this paragraph, I believe the author is likely Zvi Mowshowitz.
Here’s my reasoning:
The writing style is analytical and forward-looking, characteristic of someone who frequently discusses AI developments and their implications.
The author mentions Claude specifically and compares it to GPT models, showing familiarity with different AI models and their capabilities.
The discussion of market share, adoption rates, and speculation about future developments is typical of someone who closely follows the AI industry.
The mention of “wrappers and apps” and consideration of developer behavior suggests someone with knowledge of the AI ecosystem.
Zvi Mowshowitz is known for writing about AI developments, particularly on his blog “Don’t Worry About the Vase” and other platforms. He often provides analysis of new AI models and their potential impacts.
The structure and tone of the writing is similar to Zvi’s typical style, with a mix of personal opinion and broader industry analysis.
However, I should note that while this is my best guess based on the available information, I can’t be absolutely certain. If you’re looking for a definitive answer, it would be best to verify the source directly.
Would you like me to elaborate on any part of this analysis?
Wow
It also seems to have led to at least one claim in a policy memo that advocates of AI safety are being silly because mechanistic interpretability was solved.
Small nitpick (I agree with mostly everything else in the post and am glad you wrote it up). This feels like an unfair criticism—I assume you are referring specifically to the statement in their paper that:
Although advocates for AI safety guidelines often allude to the “black box” nature of AI models, where the logic behind their conclusions is not transparent, recent advancements in the AI sector have resolved this issue, thereby ensuring the integrity of open-source code models.
I think Anthropic’s interpretability team, while making maybe dubious claims about the impact of their work on safety, has been clear that mechanistic interpretability is far from ‘solved.’ For instance, Chris Olah in the linked NYT article from today:
“There are lots of other challenges ahead of us, but the thing that seemed scariest no longer seems like a roadblock,” he said.
Also, in the paper’s section on Inability to Evaluate:
it’s unclear that they’re really getting at the fundamental thing we care about
I think they are overstating how far/useful mechanistic interpretability is currently. However, I don’t think this messaging is close to ‘mechanistic interpretability solves AI Interpretability’ - this error is on a16z, not Anthropic.
Less important, but the grant justification appears to take seriously the idea that making AGI open source is compatible with safety. I might be missing some key insight, but it seems trivially obvious why this is a terrible idea even if you’re only concerned with human misuse and not misalignment.
Hmmm, can you point to where you think the grant shows this? I think the following paragraph from the grant seems to indicate otherwise:
When OpenAI launched, it characterized the nature of the risks – and the most appropriate strategies for reducing them – in a way that we disagreed with. In particular, it emphasized the importance of distributing AI broadly;1 our current view is that this may turn out to be a promising strategy for reducing potential risks, but that the opposite may also turn out to be true (for example, if it ends up being important for institutions to keep some major breakthroughs secure to prevent misuse and/or to prevent accidents). Since then, OpenAI has put out more recent content consistent with the latter view,2 and we are no longer aware of any clear disagreements. However, it does seem that our starting assumptions and biases on this topic are likely to be different from those of OpenAI’s leadership, and we won’t be surprised if there are disagreements in the future.
Oh I think I see what you are arguing (that you should only care about whether or not eating meat is net good or net bad, theres no reason to factor in this other action of the donation offset)
Specifically then the two complaints may be:
You specify that $0 < E$ in your graph, where you are using E represent −1 * amount of badness. While in reality people are modelling $E$ as negative (where eating meat is instead being net good for the world)
People might instead think that doing E is ‘net evil’ but also desirable for them for another reason unrelated to that (maybe for some reason like ‘i also enjoy eating meat’). So here, if they only want to take net good actions while also eating meat, then they would offset it with donations. The story you outlined above arguing that ‘The concept of offsetting evil with good does not make sense’ misses that people might be willing to make such a tradeoff
I think I agree with what you are saying, and might be missing other reasons people are disagree voting