I really appreciate this work!
I wonder if the reason MLPs are more polysemantic isn't because there are fewer MLPs than heads but because the MLP matrices are larger--
Suppose the model is storing information as sparse [rays or directions]. Then SVD on large matrices like the token embeddings can misunderstand the model in different ways:
- Many of the sparse rays/directions won't be picked up by SVD. If there are 10,000 rays/directions used by the model and the model dimension is 768, SVD can only pick 768 directions.
- If the model natively stores informati...
I think at least some GPT2 models have a really high-magnitude direction in their residual stream that might be used to preserve some scale information after LayerNorm. [I think Adam Scherlis originally mentioned or showed the direction to me, but maybe someone else?]. It's maybe akin to the water-droplet artifacts in StyleGAN touched on here: https://arxiv.org/pdf/1912.04958.pdf
...We begin by observing that most images generated by StyleGAN exhibit characteristic blob-shaped artifacts that resemble water droplets. As shown in Figure 1, even when the droplet
No, I'm pretty confident every expert is a neural network policy trained on the task. See "F. Data Collection Details" and the second paragraph of "3.3. Robotics - RGB Stacking Benchmark (real and sim)"
How much influence and ability you expect to have as an individual in that timeline.
For example, I don't expect to have much influence/ability in extremely short timelines, so I should focus on timelines longer than 4 years, with more weight to longer timelines and some tapering off starting around when I expect to die.
How relevant thoughts and planning now will be.
If timelines are late in my life or after my death, thoughts, research, and planning now will be much less relevant to the trajectory of AI going well, so at this moment in time I should weight timelines in the 4-25 year range more.
Value-symmetry: "Will AI systems in the critical period be equally useful for different values?"
This could fail if, for example, we can build AI systems that are very good at optimizing for easy-to-measure values but significantly worse at optimizing for hard to measure values. It might be easy to build a sovereign AI to maximize the profit of a company, but hard to create one that cares about humans and what they want.
Evan Hubinger has some operationalizations of things like this here and here.
Open / Closed: "Will transformative AI systems in the critical period be publicly available?"
A world where everyone has access to transformative AI systems, for example by being able to rent them (like GPT-3's API once it's publicly available), might be very different from one where they are kept private by one or more private organizations.
For example, if strategy stealing doesn't hold, this could dramatically change the distribution of power, because the systems might be more helpful for some tasks and values than others.
This variable could also affect t...
Deceptive alignment: “In the critical period, will AIs be deceptive?”
Within the framework of Risks from Learned Optimization, this is when a mesa-optimizer has a different objective than the base objective, but instrumentally optimizes the base objective to deceive humans. It can refer more generally to any scenario where an AI system behaves instrumentally one way to deceive humans.
Alignment tax: “How much more difficult will it be to create an aligned AI vs an unaligned AI when it becomes possible to create powerful AI?”
If the alignment tax is low, people have less incentive to build an unaligned AI as they'd prefer to build a system that's trying to do what they want. Then, to increase the probability that our AI trajectory goes well, one could focus on how to reduce the alignment tax.
I think this post is great and I'm really happy that it's published.