TLDR: I explain what the technical AI alignment problem is and what its core difficulties are, in my view. This is a perspective that draws simultaneously on classical traditions in theoretical computer science / machine learning theory, ideas from analytic philosophy, and views that originated in the Machine Intelligence Research Institute / LessWrong.
Minimal Viable Safety
The Task
The problem of making AI safe is underspecified if we haven't decided what the AI is supposed to do. For example, we can run any AI inside a homomorphic encryption box and never decrypt the output. This is probably perfectly safe, but also perfectly useless.
We can decide on an arbitrary task such as "construct Lean proofs for given mathematical theorems" and declare victory once we have safe AI that does it with superhuman skill. However, that wouldn't address the danger. First, there would be incentives to use AI for tasks to which our solution doesn't apply. Second, even if we had precise safety guidelines for any task that anyone cares about, there would always be actors that ignore the guidelines, and we probably need Artificial Superintelligence (ASI) to reliably stop them.
If anyone, anywhere, creates [...]
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Outline:
(00:33) Minimal Viable Safety
(00:37) The Task
(02:53) The Problem
(03:50) The Methodology
(05:47) The Challenge
(07:20) Alignment is Holistic
(10:58) Hypercomplexity
(13:54) Breakdown of Learning
(16:33) Alignment Grounding
(20:32) Alignment Convergence
(24:17) Side-Channel Resilience
(26:44) Inner and Outer Alignment
(30:55) Conclusion
The original text contained 18 footnotes which were omitted from this narration.
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First published:
October 18th, 2026
Source:
https://www.lesswrong.com/posts/Emp7awtxsYn8MusNq/the-alignment-problem-computer-science-meets-analytic
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Narrated by TYPE III AUDIO.
