激励网络：人工智能与现实世界之间缺失的联系

去中心化网络让一切变得更加复杂，但去中心化网络可以处理复杂的事情。当谈到解决人工智能（AI）对计算能力的贪婪需求时，问题可能足够复杂，以至于需要去中心化。

Decentralized networks complicate everything, but they can also handle complicated things. When it comes to artificial intelligence’s (AI) insatiable demand for computing power, the problem might just be knotty enough for decentralization.

去中心化网络使一切变得复杂，但它们也可以处理复杂的事情。当谈到人工智能（AI）对计算能力的永不满足的需求时，问题可能已经足够棘手，以至于需要去中心化。

Incentive networks are a type of decentralized network that reward individual behavior that benefits the network as a whole, fostering an “ecosystem” mentality. What distinguishes a simple ecosystem from an incentive network is its intentionality and mechanisms. An ecosystem is often a fortunate accident, the sum of competing forces deciding they are better off working within certain parameters than outside the group. An incentive network is designed for shared success from the get-go.

激励网络是一种去中心化网络，它奖励有利于整个网络的个人行为，培养“生态系统”心态。简单生态系统与激励网络的区别在于其意图和机制。生态系统往往是一个幸运的意外，竞争力量的总和决定了它们在某些参数内工作比在群体之外工作更好。激励网络从一开始就旨在实现共同成功。

But how does this relate to AI? Think of scalable AI applications as mechanical entities that churn out simple answers from ludicrously large data sets using computational power, like gas in a car. The more data you haul and the faster you want the answers, the more gas you burn, and the biggest and most complex AI models burn several times what smaller ones do: OpenAI’s GPT-4 cost $78 million in compute to train, while Google’s Gemini Ultra cost $191 million. With numbers that big, a system that can reduce hardware investments and dynamically allocate resources to reduce overall costs while remaining impartial to the participants is crucial — and that’s what incentive networks do.

但这与人工智能有什么关系呢？将可扩展的人工智能应用程序视为机械实体，它们使用计算能力从极其庞大的数据集中生成简单的答案，就像汽车中的汽油一样。您传输的数据越多，想要得到答案的速度越快，消耗的 Gas 就越多，而最大、最复杂的 AI 模型的消耗量是较小模型的几倍：OpenAI 的 GPT-4 的训练计算成本为 7800 万美元，而 Google 的 Gemini Ultra耗资1.91亿美元。由于数量如此之大，一个能够减少硬件投资、动态分配资源以降低总体成本、同时对参与者保持公正的系统至关重要——这就是激励网络的作用。

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