人工智能在认知任务上取得了显著进步，但仍面临鲁棒性、可解释性、合作性和安全性等挑战。文章认为这些问题源于人工智能缺乏智慧，并提出智慧的概念：在面对复杂问题时，通过有效的任务级和元认知策略进行导航。文章强调元认知（反思和调节思维过程）在人类智慧决策中的重要性，并建议将元认知能力融入人工智能系统，以增强其鲁棒性、可解释性、合作性和安全性。文章认为，构建智慧型人工智能是解决人工智能安全和伦理问题的关键，并探讨了构建智慧型人工智能的潜在途径，包括元认知能力基准测试和训练人工智能系统使用智慧推理。

🤔 **人工智能面临挑战：** 尽管人工智能在认知任务上表现出色，但仍存在鲁棒性差、缺乏可解释性、合作困难以及潜在安全风险等问题。

💡 **智慧是关键：** 文章认为，人工智能的这些不足源于缺乏“智慧”。智慧被定义为在面对复杂、不确定、新颖、混乱或计算量巨大的问题时，能够通过有效的任务级和元认知策略进行导航的能力。

🧠 **元认知是核心：** 人类智慧决策的关键在于元认知，即反思和调节自身思维过程的能力，例如认识自身知识的局限性、考虑不同观点以及适应情境等。

🤝 **构建智慧型AI：** 文章提出，将元认知能力整合到人工智能系统中，是增强其鲁棒性、可解释性、合作性和安全性的关键。

🚀 **智慧AI的未来：** 通过优先发展人工智能的元认知能力，可以构建出不仅智能而且能够在复杂现实世界中明智行动的系统。

Published on November 11, 2024 4:13 PM GMT

Authors: 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: 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.

Comment: I'm mainly sharing this because of the similarity of the ideas here to my ideas in Some Preliminary Notes on the Promise of a Wisdom Explosion.

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