At over 15k tokens, reading the full article requires significant time and effort. While it aims to provide comprehensive detail on QACI, much of this likely exceeds what is needed to convey the core ideas. The article could be streamlined to more concisely explain the motivation, give mathematical intuition, summarize the approach, and offer brief examples. Unnecessary elaborations could be removed or included as appendices. This would improve clarity and highlight the essence of QACI for interested readers.
My acquired understanding is that the article summarizes a new AI alignment approach called QACI (Question-Answer Counterfactual Interval). QACI involves generating a factual “blob” tied to human values, along with a counterfactual “blob” that could replace it. Mathematical concepts like realityfluid and Loc() are used to identify the factual blob among counterfactuals. The goal is to simulate long reflection by iteratively asking the AI questions and improving its answers. QACI claims to avoid issues like boxing and embedded agency through formal goal specification.
While the article provides useful high-level intuition, closer review reveals limitations in QACI’s theoretical grounding. Key concepts like realityfluid need more rigor, and details are lacking on how embedded agency is avoided. There are also potential issues around approximation and vulnerabilities to adversarial attacks that require further analysis. Overall, QACI seems promising but requires more comparison with existing alignment proposals and formalization to adequately evaluate. The article itself is reasonably well-written, but the length and inconsistent math notation create unnecessary barriers.
This seems like a rhetorical question. Both of our top-level comments seem to reach similar conclusions, but it seems you regret the time spent engaging with the OP to write your comment. This took 10 min, most spent writing this comment. What is your point?
My point is that your comment was extremely shallow, with a bunch of irrelevant information, and in general plagued with the annoying ultra-polite ChatGPT style—in total, not contributing anything to the conversation. You’re now defensive about it and skirting around answering the question in the other comment chain (“my endorsed review”), so you clearly intuitively see that this wasn’t a good contribution. Try to look inwards and understand why.
At over 15k tokens, reading the full article requires significant time and effort. While it aims to provide comprehensive detail on QACI, much of this likely exceeds what is needed to convey the core ideas. The article could be streamlined to more concisely explain the motivation, give mathematical intuition, summarize the approach, and offer brief examples. Unnecessary elaborations could be removed or included as appendices. This would improve clarity and highlight the essence of QACI for interested readers.
My acquired understanding is that the article summarizes a new AI alignment approach called QACI (Question-Answer Counterfactual Interval). QACI involves generating a factual “blob” tied to human values, along with a counterfactual “blob” that could replace it. Mathematical concepts like realityfluid and Loc() are used to identify the factual blob among counterfactuals. The goal is to simulate long reflection by iteratively asking the AI questions and improving its answers. QACI claims to avoid issues like boxing and embedded agency through formal goal specification.
While the article provides useful high-level intuition, closer review reveals limitations in QACI’s theoretical grounding. Key concepts like realityfluid need more rigor, and details are lacking on how embedded agency is avoided. There are also potential issues around approximation and vulnerabilities to adversarial attacks that require further analysis. Overall, QACI seems promising but requires more comparison with existing alignment proposals and formalization to adequately evaluate. The article itself is reasonably well-written, but the length and inconsistent math notation create unnecessary barriers.
Is it a thing now to post LLM-generated comments on LW?
This seems like a rhetorical question. Both of our top-level comments seem to reach similar conclusions, but it seems you regret the time spent engaging with the OP to write your comment. This took 10 min, most spent writing this comment. What is your point?
My point is that your comment was extremely shallow, with a bunch of irrelevant information, and in general plagued with the annoying ultra-polite ChatGPT style—in total, not contributing anything to the conversation. You’re now defensive about it and skirting around answering the question in the other comment chain (“my endorsed review”), so you clearly intuitively see that this wasn’t a good contribution. Try to look inwards and understand why.
Is this an AI summary (or your own writing)? If so, would you mind flagging it as such?
This is my endorsed review of the article.
It’s absolutely fine if you want to use AI to help summarize content, and then you check that content and endorse it.
I still ask if you could please flag it as such, so the reader can make an informed decision about how to read/respond to the content?