Key points of “The Platonic Representation Hypothesis” paper:
Neural networks trained on different objectives, architectures, and modalities are converging to similar representations of the world as they scale up in size and capabilities.
This convergence is driven by the shared structure of the underlying reality generating the data, which acts as an attractor for the learned representations.
Scaling up model size, data quantity, and task diversity leads to representations that capture more information about the underlying reality, increasing convergence.
Contrastive learning objectives in particular lead to representations that capture the pointwise mutual information (PMI) of the joint distribution over observed events.
This convergence has implications for enhanced generalization, sample efficiency, and knowledge transfer as models scale, as well as reduced bias and hallucination.
Relevance to AI alignment:
Convergent representations shaped by the structure of reality could lead to more reliable and robust AI systems that are better anchored to the real world.
If AI systems are capturing the true structure of the world, it increases the chances that their objectives, world models, and behaviors are aligned with reality rather than being arbitrarily alien or uninterpretable.
Shared representations across AI systems could make it easier to understand, compare, and control their behavior, rather than dealing with arbitrary black boxes. This enhanced transparency is important for alignment.
The hypothesis implies that scale leads to more general, flexible and uni-modal systems. Generality is key for advanced AI systems we want to be aligned.
I am very very vaguely in the Natural Abstractions area of alignment approaches. I’ll give this paper a closer read tomorrow (because I promised myself I wouldn’t try to get work done today) but my quick quick take is—it’d be huge if true, but there’s not much more than that there yet, and it also has no argument that even if representations are converging for now, that it’ll never be true that (say) adding a whole bunch more effectively-usable compute means that the AI no longer has to chunk objectspace into subtypes rather than understanding every individual object directly.
For anyone interested in Natural Abstractions type research: https://arxiv.org/abs/2405.07987
Claude summary:
Key points of “The Platonic Representation Hypothesis” paper:
Neural networks trained on different objectives, architectures, and modalities are converging to similar representations of the world as they scale up in size and capabilities.
This convergence is driven by the shared structure of the underlying reality generating the data, which acts as an attractor for the learned representations.
Scaling up model size, data quantity, and task diversity leads to representations that capture more information about the underlying reality, increasing convergence.
Contrastive learning objectives in particular lead to representations that capture the pointwise mutual information (PMI) of the joint distribution over observed events.
This convergence has implications for enhanced generalization, sample efficiency, and knowledge transfer as models scale, as well as reduced bias and hallucination.
Relevance to AI alignment:
Convergent representations shaped by the structure of reality could lead to more reliable and robust AI systems that are better anchored to the real world.
If AI systems are capturing the true structure of the world, it increases the chances that their objectives, world models, and behaviors are aligned with reality rather than being arbitrarily alien or uninterpretable.
Shared representations across AI systems could make it easier to understand, compare, and control their behavior, rather than dealing with arbitrary black boxes. This enhanced transparency is important for alignment.
The hypothesis implies that scale leads to more general, flexible and uni-modal systems. Generality is key for advanced AI systems we want to be aligned.
I recommend making this into a full link-post. I agree about the relevance for AI alignment.
This sounds really intriguing. I would like someone who is familiar with natural abstraction research to comment on this paper.
I am very very vaguely in the Natural Abstractions area of alignment approaches. I’ll give this paper a closer read tomorrow (because I promised myself I wouldn’t try to get work done today) but my quick quick take is—it’d be huge if true, but there’s not much more than that there yet, and it also has no argument that even if representations are converging for now, that it’ll never be true that (say) adding a whole bunch more effectively-usable compute means that the AI no longer has to chunk objectspace into subtypes rather than understanding every individual object directly.