The strategy-stealing assumption posits that for any strategy an unaligned AI could use to influence the long-term future, there is an analogous strategy that humans could use to capture similar influence. Paul Christiano explores why this assumption might be true, and eleven ways it could potentially fail.
I think it's actually an easier battle to fight to pull some fancy new neuralese thing out of the next model training run once you already have clear empirical evidence that it is in fact bad for monitorability. I think it's harder to try to get something pulled before you actually have concrete empirical evidence that it's bad.
GDM’s AGI Safety and Alignment Team is hiring for multiple roles, across all areas in this post on our recent work. This is the team at GDM, led by Rohin Shah, that aims to reduce existential risks from AI systems. You can listen to many of Rohin’s takes in his podcast on 80,000 hours.
There is no one ‘type’ that we are looking for—we want excellent people. We think of the role as ‘member of technical staff’ though different people will have more of a research engineer or scientist flavour. We are flexible on location though most people will be most productive in either San Francisco or London. You should apply here (for the US) or here (for the UK) after reading the guidance here.
Many of the basic...
Cross-posted from our new Substack
It’s been nearly two years since our last major update here in August 2024 and we wanted to share another recap of our recent work with the AGI safety community. Things have changed a lot since then. We are now fully in the midgame, and focus more on landing things in production.
We are the AGI Safety and Alignment Team (ASAT), the main group at Google DeepMind working directly on technical approaches to existential risk from AI systems. Last year we published An Approach to Technical AGI Safety and Security, which remains the best place to read our overarching vision.
Norms around chain of thought. Our impression is that our work meaningfully moved the field away from beliefs along the lines of “chain...
(Last revised: June 2026. See changelog at the bottom.)
Part of the “Intro to brain-like-AGI safety” post series.
In the previous post, I discussed the “short-term predictor”—a circuit which, thanks to a learning algorithm, emits an output that predicts a ground-truth supervisory signal arriving a short time (e.g. a fraction of a second) later.
In this post, I propose that we can take a short-term predictor, wrap it up into a closed loop involving a bit more circuitry, and we wind up with a new module that I call a “long-term predictor”. Just like it sounds, this circuit can make longer-term predictions, e.g. “I’m likely to eat in the next 10 minutes”. This circuit winds up being a kind of hybrid between Temporal Difference...
That’s very helpful, thanks. I think the key here is this part:
The short-term predictor is a learning algorithm. Given infinite time, we normally expect learning algorithms to settle into some steady-state configuration where they no longer update. We can think of this configuration as “what we are training it to do”. So, in this toy model, what are we training the short term predictor to do?
I’m trying to talk about what will happen given infinite time, in steady state, i.e. when it gets to a fixed-point / self-consistent solution. In steady-state / at a s...
Richard Ngo challenged me to set a time box and write down as many of the most important features of my formal epistemology as I can in one sitting. Here goes.
A second, different kind of answer to your challenge is a stochastic PDE. The PDG formalism is finitary (finite node set
As a concrete example, for the SPDE
Summary: One area we plan to explore at Resolution is personas and character training, operationalized as finding and controlling low-dimensional structure in models that emerges in pretraining and flows through post-training to superintelligence. The hope is to expand and systematize phenomena such as emergent misalignment, subliminal learning, and other empirical persona research, then intervene on this structure without accidentally hiding undesirable behavior elsewhere. If this approach resonates with you, consider working with us.
Our understanding of AI training and alignment as a field is very poor. If sufficient alignment of superintelligent AI agents requires pinning down the precise meaning of alignment and turning that meaning into high-accuracy training data and algorithms, we are likely to fail. Modern LLMs have trillions of parameters: our understanding...
This is an amazing review and IMO really compelling direction. So to speak.
One thing I constantly wonder, that possibly you could shed light on via this research program, is how well experiments on small models transfer to large ones. We know from pre training work that frequently ideas that work well at a small scale stop working once you scale up.
I think this might be relevant here in two ways. First, a bunch of the persona research is on small models, presumably due to accessibility reasons. But second, you might find a low dimension personality vector...