# Introspective RSI vs Extrospective RSI

![On the left, a blue man solves a rubix cube with a thought bubble depicting himself thinking about the rubix cube. On the right, a blue man is being studied with medical equipment by other blue men.](https://39669.cdn.cke-cs.com/rQvD3VnunXZu34m86e5f/images/88ee0be05291f5668c03e6142b68cd5ea508ae4b32f9cff9.png)

| **Introspective-RSI** | **Extrospective-RSI** |
| --- | --- |
| The *meta-cognition* and *meso-cognition* occur within the same entity. | The *meta-cognition* and *meso-cognition* occur in different entities. |
| Much like a human, an AI will observe, analyze, and modify its own cognitive processes. And this capacity is privileged: the AI can make self-observations and self-modifications that can't be made from outside. | AIs will automate various R&D tasks that humans currently perform to improve AI, using similar workflows (studying prior literature, forming hypotheses, writing code, running experiments, analyzing data, drawing conclusions, publishing results). |

Here are some differences between them (I mark the most important with *):

1.  ***Monitoring opportunities**: During E-RSI, information flows between the AIs through external channels (e.g. API calls) which humans can monitor. But in I-RSI, there are fewer monitoring channels.
2.  ***Generalisation from non-AI R&D:** E-RSI involves AIs performing AI R&D in the same way they perform non-AI R&D, such as drug discovery and particle physics. So we can train the AIs in those non-AI R&D domains and hope that the capabilities and propensities generalise to AI R&D.
3.  **Latency**: I-RSI may be lower latency, because the metacognition is "closer" to the mesocognition in some sense, e.g. in the same chain-of-thought.
4.  **Parallelisation:** E-RSI may scale better through parallelization, because it operates in a distributed manner.
5.  **Diminishing returns:** Humans have already performed lots of AI R&D, which so there might be diminishing returns to E-RSI (c.f. ideas are harder to find). But I-RSI would be a novel manner of improving AI cogntion, so there may be low-hanging fruit leading to rapid progress.
6.  **Historical precedent**: Humans perfoming AI R&D provides a precedent for E-RSI. So we can apply techniques for improving the security of human AI-R&D to mitigating risks from E-RSI, such as research sabotage.
7.  **Verification standards**: To evaluate E-RSI, we can rely on established mechanisms for verifying human AI-R&D, e.g. scientific peer-review. However, we don't have good methods to verify I-RSI.
8.  ***Transition continuity**: I think the transition from humans leading AI-R&D to E-RSI will be gradual, as AIs take greater leadership in the workflows. But I-RSI might be more of sudden transition.
9.  **Representation compatibility**: Because of the transition continuity, I expect that E-RSI will involve internal representations which are compatible with human concepts.
10.  ***Transferability**: E-RSI will produce results which can more readily be shared between instances, models, and labs. But improvements from I-RSI might not transfer between models, perhaps not even between instances.

*See* [*here*](https://www.lesswrong.com/posts/XHd75cuHhWcBDd8to/recursive-self-improvement-is-three-different-things) *for a different taxonomy — it separates E-RSI into "scaffolding-level improvement" and other types of improvements.*