BITTENSOR是一個區塊鏈，它使挖掘其主要目的 - 並在任何數字服務都可以符合條件的程度上抽象。

因此，區塊鏈旨在成為機器智能的市場。現在，一家研究初創公司希望使用它來加速新藥的發現。

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是一個區塊鏈，它使挖掘其主要目的 - 並在任何數字服務都可以符合條件的程度上抽象。因此，區塊鏈旨在成為機器智能的市場。現在，一家研究初創公司希望使用它來加速新藥的發現。

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.“

通常這是熱空氣 - 即使對我們來說，很難確定某事是胡扯還是革命性。一個故事的一個例子可能很有趣，或者只是最近出現在《福布斯》中：這個分散的AI，雜誌的頭條新聞“可以徹底改變藥物開發”。

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.

這裡的意思是一個神經網絡，專為藥物研究而設計，並通過Bittensor（TAO）區塊鏈分散。這恢復了一個舊的想法：不是通過“無用”哈希而是科學計算來執行加密貨幣開採。大約八到十年前，像Gridcoin這樣的硬幣嘗試了這一點 - 不再成功地分散科學採礦。

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

現代樁機制和神經網絡的結合可能最終使這一努力可行嗎？

Simulating Chemical Reactions at the Atomic Level

模擬原子水平的化學反應

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

藥物開發通常是一個非常漫長而艱鉅的過程，涉及數百個步驟，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擬來簡化和改進此過程。在計算機中構建和模擬化合物，而不是在物理上開發和測試藥物。這使研究人員可以在更少的時間內測試更多的分子候選者，至少這就是目前滲透到製藥行業的希望。

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啟動了一個專門的神經網絡，用於此目的，稱為Egret-1。它的目標是模擬原子水平的化學反應。到目前為止，這是非常密集的，即使是科學的超級計算機，也需要大量時間才能實際模擬幾秒鐘的幾秒鐘。 Rowan旨在改善這一點，而不是像Chatgpt一樣在互聯網數據上訓練其神經網絡，而是通過使用量子機械方程。 AI學會重建這些方程式的結果。

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，對於像Egret-1這樣的模型，Rowan解釋說，它們需要“通過密度功能理論（DFT）生成的更多高質量數據。要生成此數據，Egret-1將利用Bittensor網絡的分散計算能力來執行這些模擬。”

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.

該子網是BITTENSOR區塊鏈的一部分（稍後更多）。目的是創建由初創企業啟動的Bittensors子網網。宏觀群島最近於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子網（SN25）旨在降低蛋白質折疊模擬的成本。它採用了gromacs標準（模擬蛋白質折疊），但將其集成到競爭設計中。這種結構激勵礦工開發機器學習模型，以盡可能有效地解決蛋白質折疊。系統中的驗證者使用特定的啟發式方法檢查礦工的產量。表現最佳的礦工從Bittensor（Tao）接收令牌，這是一個回報。

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.

這個競爭過程旨在降低成本並提高效率。目前，共有30個主動驗證器同時進行了3,000多個模擬。自2024年6月以來，已經完成了超過400,000個蛋白質折疊任務。這距離Alphafold的成就還很遠，但這是一個開始。

The Abstraction of Mining

採礦的抽象

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

為了了解宏觀山，沒有辦法深入研究Bittensor本身。 Bittensor背後的核心思想令人著迷：

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.

人們可以將比特幣視為數字商品的分散市場，該市場獎勵礦工產生哈希斯。對於比特幣而言，這個市場只是達到目的的一種手段：確保對數字分類帳（區塊鏈）的共識，以促進分散的交易系統。相比之下，Bittensor使數字商品市場本身結束。

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的核心創新是區塊鏈核心功能（轉移值等）與驗證系統的運行的分離，該系統定義瞭如何創建數字商品市場。這很重要：在經典共識機制（如工作證明和股份證明）中，共識算法包括何時與共識相關的輸入（A哈希或股份）是有效的。但是，Bittensor僅確定在哪些情況下共識本身變得有效。

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.”

共識任務本身可以用任何語言編寫，並被完全驗證的鏈接驗證，可以使用大量數據和計算能力。 “ Bittensor通過將智能合約引入比特幣的逆創新（數字市場），從而帶來了以太坊添加到比特幣中相同的抽象。”

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邏輯並實現了不同系統的構建一樣，Bittensor也使甚至可以使復雜且模糊的機製作為共識工作成為可能：例如，機器智能，蛋白質折疊，數據存儲，模型培訓等。

Bittensor does not define the consensus task itself, but rather

Bittensor並未定義共識任務本身，而是