BITTENSOR是一个区块链，它使挖掘其主要目的 - 并在任何数字服务都可以符合条件的程度上抽象。

因此，区块链旨在成为机器智能的市场。现在，一家研究初创公司希望使用它来加速新药的发现。

Bittensor is a blockchain that makes mining its main purpose—and abstracts it to such an extent that any digital service can qualify. The blockchain is thereby intended to become a marketplace for machine intelligence. Now, a research startup wants to use it to accelerate the discovery of new drugs.

BITTENSOR是一个区块链，它使挖掘其主要目的 - 并在任何数字服务都可以符合条件的程度上抽象。因此，区块链旨在成为机器智能的市场。现在，一家研究初创公司希望使用它来加速新药的发现。

Often that’s hot air—even for us it’s hard to determine whether something is bullshit or revolutionary. An example of a story that could be either intriguing or just hot air recently appeared in Forbes: This decentralized AI, the magazine headlines, „could revolutionize drug development.“

通常这是热空气 - 即使对我们来说，很难确定某事是胡扯还是革命性。一个故事的一个例子可能很有趣，或者只是最近出现在《福布斯》中：这个分散的AI，杂志的头条新闻“可以彻底改变药物开发”。

What is meant here is a neural network, specifically designed for drug research and decentralized via the Bittensor (TAO) blockchain. This revives an old idea: performing cryptocurrency mining not through “useless” hashes, but through scientific computation. About eight to ten years ago, coins like Gridcoin tried this—without ever succeeding in truly decentralizing scientific mining.

这里的意思是一个神经网络，专为药物研究而设计，并通过Bittensor（TAO）区块链分散。这恢复了一个旧的想法：不是通过“无用”哈希而是科学计算来执行加密货币开采。大约八到十年前，像Gridcoin这样的硬币尝试了这一点 - 不再成功地分散科学采矿。

Could it be possible that the combination of modern staking mechanisms and neural networks will finally make this endeavor feasible?

现代桩机制和神经网络的结合可能最终使这一努力可行吗？

Simulating Chemical Reactions at the Atomic Level

模拟原子水平的化学反应

Drug development is typically a very long and arduous process, involving hundreds of steps and taking on average more than 13 years.

药物开发通常是一个非常漫长而艰巨的过程，涉及数百个步骤，平均要走了13年以上。

However, this process can be simplified and improved using artificial intelligence and molecular simulations. Instead of developing and testing drugs physically, compounds are constructed and simulated in computers. This allows researchers to test more molecular candidates in less time—at least, that is the hope currently permeating the pharmaceutical industry.

但是，可以使用人工智能和分子模拟来简化和改进此过程。在计算机中构建和模拟化合物，而不是在物理上开发和测试药物。这使研究人员可以在更少的时间内测试更多的分子候选者，至少这就是目前渗透到制药行业的希望。

In April, Rowan Labs launched a specialized neural network for this purpose called Egret-1. Its goal is to simulate chemical reactions at the atomic level. Until now, this has been incredibly resource-intensive—even scientific supercomputers require a lot of time to realistically simulate just a handful of atoms for a few seconds. Rowan aims to improve this, not by training its neural network on internet data, as ChatGPT does, but by using quantum mechanical equations. The AI learns to reconstruct the results of those equations.

4月，Rowan Labs启动了一个专门的神经网络，用于此目的，称为Egret-1。它的目标是模拟原子水平的化学反应。到目前为止，这是非常密集的，即使是科学的超级计算机，也需要大量时间才能实际模拟几秒钟的几秒钟。 Rowan旨在改善这一点，而不是像Chatgpt一样在互联网数据上训练其神经网络，而是通过使用量子机械方程。 AI学会重建这些方程式的结果。

For models like Egret-1 to be successful, however, Rowan explains they require “much more high-quality data generated through density functional theory (DFT). To generate this data, Egret-1 will leverage the decentralized computing power of the Bittensor network to perform these simulations.”

但是，对于像Egret-1这样的模型，Rowan解释说，它们需要“通过密度功能理论（DFT）生成的更多高质量数据。要生成此数据，Egret-1将利用Bittensor网络的分散计算能力来执行这些模拟。”

This subnet is part of the Bittensor blockchain (more on that in a moment). The goal is to create a mesh of Bittensors subnets, which are launched by startups. Macrocosmos was established recently, in 2024.

该子网是BITTENSOR区块链的一部分（稍后更多）。目的是创建由初创企业启动的Bittensors子网网。宏观群岛最近于2024年成立。

A GROMACS subnet (SN25) is designed to lower the cost of protein folding simulations. It employs the GROMACS standard—to simulate protein folding—but integrates this into a competitive design. This structure incentivizes miners to develop machine-learning models that solve protein folding as efficiently as possible. Validators in the system check miners’ outputs using specific heuristics. The miners who perform best receive tokens from Bittensor—TAO—as a reward.

GROMACS子网（SN25）旨在降低蛋白质折叠模拟的成本。它采用了gromacs标准（模拟蛋白质折叠），但将其集成到竞争设计中。这种结构激励矿工开发机器学习模型，以尽可能有效地解决蛋白质折叠。系统中的验证者使用特定的启发式方法检查矿工的产量。表现最佳的矿工从Bittensor（Tao）接收令牌，这是一个回报。

This competitive process is intended to reduce costs and boost efficiency. Currently, there are 30 active validators simultaneously conducting more than 3,000 simulations; since June 2024, over 400,000 protein folding tasks have already been completed. That’s still far from what AlphaFold accomplishes—but it’s a beginning.

这个竞争过程旨在降低成本并提高效率。目前，共有30个主动验证器同时进行了3,000多个模拟。自2024年6月以来，已经完成了超过400,000个蛋白质折叠任务。这距离Alphafold的成就还很远，但这是一个开始。

The Abstraction of Mining

采矿的抽象

To understand Macrocosmos, there’s no way around delving into Bittensor itself. The core idea behind Bittensor is quite fascinating:

为了了解宏观山，没有办法深入研究Bittensor本身。 Bittensor背后的核心思想令人着迷：

One can think of Bitcoin as a decentralized marketplace for digital goods—a market that rewards miners for generating hashes. For Bitcoin, this market is simply a means to an end: securing consensus over a digital ledger (the blockchain) to facilitate a decentralized transaction system. Bittensor, by contrast, makes the digital goods marketplaces an end in themselves.

人们可以将比特币视为数字商品的分散市场，该市场奖励矿工产生哈希斯。对于比特币而言，这个市场只是达到目的的一种手段：确保对数字分类帐（区块链）的共识，以促进分散的交易系统。相比之下，Bittensor使数字商品市场本身结束。

Bittensor’s core innovation is the separation of the blockchain’s core function (transferring value, etc.) from the operation of the validation system, which defines how the digital goods marketplaces are created. This is important: in classical consensus mechanisms like Proof of Work and Proof of Stake, the consensus algorithm includes the rules for when a consensus-relevant input—a hash or a stake—is valid. Bittensor, however, determines only under which circumstances the consensus itself becomes effective.

Bittensor的核心创新是区块链核心功能（转移值等）与验证系统的运行的分离，该系统定义了如何创建数字商品市场。这很重要：在经典共识机制（如工作证明和股份证明）中，共识算法包括何时与共识相关的输入（A哈希或股份）是有效的。但是，Bittensor仅确定在哪些情况下共识本身变得有效。

The consensus tasks themselves can be written in any language and are validated entirely off-chain—allowing large volumes of data and computing power to be employed. “Bittensor brings the same sort of abstraction that Ethereum added to Bitcoin by introducing smart contracts to Bitcoin’s inverse innovation—the digital marketplaces.”

共识任务本身可以用任何语言编写，并被完全验证的链接验证，可以使用大量数据和计算能力。 “ Bittensor通过将智能合约引入比特币的逆创新（数字市场），从而带来了以太坊添加到比特币中相同的抽象。”

Just as Ethereum abstracts transaction logic and enables the construction of diverse systems, Bittensor makes it possible to allow even complex and fuzzy mechanisms as consensus work: for example, machine intelligence, protein folding, data storage, model training, and more.

就像以太坊摘要交易逻辑并实现了不同系统的构建一样，Bittensor也使甚至可以使复杂且模糊的机制作为共识工作成为可能：例如，机器智能，蛋白质折叠，数据存储，模型培训等。

Bittensor does not define the consensus task itself, but rather

Bittensor并未定义共识任务本身，而是