Summary of On the Undecidability Of Artificial Intelligence Alignment: Machines That Halt, by Gabriel Adriano De Melo et al.
On the Undecidability of Artificial Intelligence Alignment: Machines that Halt
by Gabriel Adriano de Melo, Marcos Ricardo Omena De Albuquerque Maximo, Nei Yoshihiro Soma, Paulo Andre Lima de Castro
First submitted to arxiv on: 16 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The research paper rigorously proves that the inner alignment problem in artificial intelligence (AI) is undecidable. This finding is equivalent to Turing’s Halting Problem, which is notoriously difficult to solve. However, the authors propose a solution by constructing an enumerable set of provenly aligned AIs from a finite set of operations. The study emphasizes the importance of ensuring AI architectures are inherently aligned rather than relying on post-hoc adjustments. Additionally, the paper introduces the concept of a halting constraint that guarantees AI models always terminate in finite execution steps. Examples and models are presented to illustrate these ideas, making a compelling case for adopting an intrinsically hard-aligned approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Artificial intelligence (AI) is trying to make sure its decisions align with human values, but it’s very difficult to do this. In fact, some problems in AI can’t be solved at all! This paper shows that one of these problems, called the inner alignment problem, is impossible to solve. But don’t worry, because the authors also show a way to build AIs that are aligned from the start. They even suggest adding a special rule to make sure the AI stops working after a certain amount of time. |
Keywords
» Artificial intelligence » Alignment
