“天下没有免费的午餐”定理指出，所有学习算法在所有可能的学习任务上的平均表现相同。这意味着在某些序列上表现好的算法，在其他序列上必然表现较差。有人认为这暗示了通用人工智能的不可能性。然而，该定理仅在所有理论可能的序列集合上成立。如果算法操作的环境具有特定结构，此定理则不再是设计具有卓越预测或优化能力的算法的障碍。现实中，我们不关心预测完全随机的序列。AGI只需在我们的宇宙中表现良好，而我们的宇宙并非完全随机，其规律提供了大量结构，使我们能够做出好的预测。因此，该定理并未阻止人类利用宇宙结构进行发展，也不会阻止AI更有效地利用。

🌍 “天下没有免费的午餐”定理表明，所有学习算法在所有可能的学习任务上的平均表现相同，即在某些任务上表现出色的算法，在其他任务上必然表现不佳。

💡 该定理只在所有理论上可能的序列集合上成立。如果算法操作的环境具有特定结构，那么该定理对设计具有卓越预测或优化能力的算法没有阻碍作用。

🚀 现实中，我们无需关注预测完全随机的序列。AGI仅需在我们的宇宙中表现良好，而宇宙的规律提供了结构，使我们能够做出有效的预测，而无需在所有可能的宇宙中都表现优秀。

🧠 “天下没有免费的午餐”定理并未阻止人类利用宇宙的结构进行研究和开发，同样也不会阻止人工智能更有效地利用这些结构。

Published on February 4, 2025 2:02 AM GMT

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“No free lunch” theorems assert that, on average, every learning algorithm does equally well over all possible learning tasks. An algorithm that does better than chance at predicting some sequences must "pay for lunch" by doing worse at some other sequences.

Some have argued that these theorems imply that fully general intelligence is impossible, and therefore worries about AGI are overblown.

However, "no free lunch" holds only on the entire set of all theoretically possible sequences. The ones our algorithm does worse at may just be fully random, or designed to trick it. But if we start out knowing that the environment that our algorithm operates in has a certain structure, then the “no free lunch” results are not an impediment to designing algorithms with superior predictive or optimizing abilities.

Therefore, for practical AI design purposes, these theorems are often irrelevant. We aren't interested in "predicting" completely random sequences, and we don't mind if another algorithm outperforms us on that "task". No system can be so general as to perform well in every possible universe, but AGI is only required to perform well in one universe - ours. Our universe is far from maximally random, and its laws provide a lot of structure that lets us make good predictions while "paying for lunch" in other possible universes without that structure.

"No free lunch" hasn't prevented humans from exploiting the universe's structure for research and development, and won't prevent artificial systems from doing so much more effectively. The generality needed for AGI to exceed human abilities across the board is not the same kind of generality forbidden by these theorems.

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