Linkpost to a Summary of "Imagining and building wise machines: The centrality of AI metacognition" by Johnson, Karimi, Bengio, et al.

post by Chris_Leong · 2025-04-10T11:54:37.484Z · LW · GW · 0 comments

Contents

  Authors of the Paper:
  Abstract of Summarised Paper:
  Why I wrote this summary: 
  Thinking of Commenting?
None
No comments

Isn't this post a duplicate?

I originally posted a link to the paper on Less Wrong.

When I decided to write a summary, the default option would have been to write a separate post. However, I wanted the comments to stay attached. So I decided to edit the summary into the linkpost and post a new linkpost that links to the summary.

If I'd gone the normal route, there would still have been both a summary and linkpost on Less Wrong. The only difference is that this makes the commenting situation slightly neater.

How does my summary compare to the original paper?

Obviously, the summary is shorter :-).

Additionally, I have:

• Streamlined the user journey with collapsable sections

• Communicated some ideas in an easier to digest format

• Added commentary and a glossary

Whilst I've tried as hard as possible to represent the views in the original (including making some updates in response to feedback from one of the authors), I can't guarantee perfect accuracy.


Link to summary [LW · GW]
Link to original paper

Authors of the Paper:

Samuel G. B. Johnson, Amir-Hossein Karimi, Yoshua Bengio, Nick Chater, Tobias Gerstenberg, Kate Larson, Sydney Levine, Melanie Mitchell, Iyad Rahwan, Bernhard Schölkopf, Igor Grossmann

Abstract of Summarised Paper:

Recent advances in artificial intelligence (AI) have produced systems capable of increasingly sophisticated performance on cognitive tasks. However, AI systems still struggle in critical ways: unpredictable and novel environments (robustness), lack of transparency in their reasoning (explainability), challenges in communication and commitment (cooperation), and risks due to potential harmful actions (safety). We argue that these shortcomings stem from one overarching failure: AI systems lack wisdom.

 Drawing from cognitive and social sciences, we define wisdom as the ability to navigate intractable problems - those that are ambiguous, radically uncertain, novel, chaotic, or computationally explosive - through effective task-level and metacognitive strategies. While AI research has focused on task-level strategies, metacognition - the ability to reflect on and regulate one's thought processes - is underdeveloped in AI systems. In humans, metacognitive strategies such as recognizing the limits of one's knowledge, considering diverse perspectives, and adapting to context are essential for wise decision-making. We propose that integrating metacognitive capabilities into AI systems is crucial for enhancing their robustness, explainability, cooperation, and safety.

By focusing on developing wise AI, we suggest an alternative to aligning AI with specific human values - a task fraught with conceptual and practical difficulties. Instead, wise AI systems can thoughtfully navigate complex situations, account for diverse human values, and avoid harmful actions. We discuss potential approaches to building wise AI, including benchmarking metacognitive abilities and training AI systems to employ wise reasoning. Prioritizing metacognition in AI research will lead to systems that act not only intelligently but also wisely in complex, real-world situations.

Why I wrote this summary: 

Firstly, I thought the framing of metacognition as a key component of wisdom missing from current AI systems was insightful and the resulting analysis fruitful.

Secondly, this paper contains some ideas similar to those I discussed in Some Preliminary Notes on the Promise of a Wisdom Explosion. In particular, the authors talk about a "virtuous cycle" in relation to wisdom in the final paragraphs:

By simultaneously promoting robust, explainable, cooperative, and safe AI, these qualities are likely to amplify one another:

  • Robustness will facilitate cooperation (by improving confidence from counterparties in its long-term commitments) and safety (by avoiding novel failure modes[1]).
  • Explainability will facilitate robustness (by making it easier to human users to intervene in transparent processes) and cooperation (by communicating its reasoning in a way that is checkable by counterparties).
  • Cooperation will facilitate explainability (by using accurate theory-of-mind about its users) and safety (by collaboratively implementing values shared within dyads, organizations, and societies).

Wise reasoning, therefore, can lead to a virtuous cycle in AI agents, just as it does in humans. We may not know precisely what form wisdom in AI will take but it must surely be preferable to folly. 

Thinking of Commenting?

I recommend following the link to the summary and commenting there so that the comments are all in the same place.

0 comments

Comments sorted by top scores.