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加密貨幣新聞文章

早期的比特幣採用者現在深深地“牢固地”在分散的AI中

2025/05/27 00:04

現在,一些區塊鏈最早的採用者在分散的AI中深深地“牢固”自己,而Bittensor(Tao)等生態系統則是成長引擎。

Some of blockchain’s earliest adopters are now deeply “rentrenching” themselves in decentralized AI, with ecosystems like Bittensor (TAO) emerging as growth engines. These platforms are reshaping traditional venture capital models, enabling the best ideas to organically attract community support, staking and liquidity without the need for institutional gatekeepers.

一些區塊鏈最早的採用者現在在分散的AI中深深地“重新安排”自己,而生態系統如Bittensor(Tao)如生長引擎。這些平台正在重塑傳統的風險投資模型,從而使最佳想法有機地吸引社區支持,積分和流動性,而無需機構網守。

That was one of the key takeaways from Cointelegraph’s interview with Chris Miglino, the co-founder and CEO of DNA Fund, a digital asset investment firm he runs alongside fellow serial entrepreneurs Brock Peirce and Scott Walker.

這是Cointelegraph對DNA Fund的聯合創始人兼首席執行官Chris Miglino採訪的關鍵要點,DNA基金是數字資產投資公司,他與其他連續企業家Brock Peirce和Scott Walker一起經營。

DNA Fund manages, among other things, five distinct funds across a range of strategies, such as a high-yield fund, an algorithmic trading fund, an AI compute fund, a liquid token fund and a venture fund — serving both company and investor capital.

DNA基金管理,除其他外,在一系列策略中管理了五個不同的基金,例如高收益基金,算法交易基金,AI計算基金,液體令牌基金和風險投資基金,都為公司和投資者資本提供服務。

Miglino, who hosted Cointelegraph at a DNA House event during the Consensus conference in Toronto, Canada, was particularly excited about the firm’s AI compute fund.

米格利諾(Miglino)在加拿大多倫多共識會議上的DNA House活動中主持了Cointelegraph,對該公司的AI計算基金感到特別興奮。

“The biggest thing that we’re working on in the whole ecosystem is our AI compute fund, where we’ve been entrenched into the TAO ecosystem,” said Miglino, referring to Bittensor, a decentralized, open-source machine learning network.

“我們在整個生態系統中最大的事情是我們的AI計算基金,我們已經進入了TAO生態系統,” Miglino指的是Bittensor,這是一個分散的開源機器學習網絡Bittensor。

Bittensor’s backers say the network stands out for its subnets, which enable specialized, incentive-based marketplaces built around a specific AI or machine learning use case.

Bittensor的支持者說,該網絡在其子網中脫穎而出,該子網促進了圍繞特定的AI或機器學習用例構建的專業,基於激勵的市場。

DNA Fund is “actively mining on different subnets,” having committed roughly $50 million worth of compute to the TAO ecosystem, Miglino said.

Miglino說,DNA基金“正在積極地開採不同的子網”,對TAO生態系統承諾了價值約5000萬美元的計算。

“We’re willing and ready to talk to anybody that wants to launch inside that ecosystem,” he said.

他說:“我們願意並準備與想要在該生態系統內部發射的任何人進行交談。”

‘Decentralized AI is consuming everything we’re doing’

“分散的AI正在消耗我們正在做的一切”

“分散的AI正在消耗我們正在做的一切”

Decentralized AI — the framework for developing and deploying artificial intelligence systems across a distributed network rather than a centralized authority — is currently the main focus at DNA House, Miglino said.

Miglino說,分散的AI(用於開發和部署人工智能係統而不是集中權限的人工智能係統的框架 - 目前是DNA House的主要重點。

It’s “consuming everything we’re doing,” he said.

他說,這是“消耗我們正在做的一切”。

For Miglino, this paradigm has the opportunity to be “bigger than anything that’s ever existed [...] I think it has the opportunity to be bigger than Bitcoin.”

對於Miglino而言,這種範式有機會“比存在的任何事物都要大[...]我認為它有機會比比特幣更大。”

While that may appear as a herculean task, given Bitcoin’s (BTC) $2.1 trillion market cap and status as the first successful decentralized monetary system of the information age, technologists broadly agree that AI will profoundly reshape human society.

雖然這可能是一項艱鉅的任務,但鑑於比特幣(BTC)的2.21萬億美元的市值和地位是信息時代的第一個成功分散的貨幣體系,技術人員廣泛同意AI將深刻重塑人類社會。

DNA House is betting that ecosystems like Bittensor will drive that transformation in a decentralized way by offering developers the ability to launch businesses without having to raise outside capital:

DNA House敢打賭,像Bittensor這樣的生態系統將通過為開發人員提供開展業務而無需籌集外部資本的能力來推動這種轉變:

The idea that AI’s future will be decentralized is far from fringe. One of the earliest pioneers of artificial general intelligence, Ben Goertzel, told Cointelegraph that he realized the need for decentralization in AI as far back as the early 1990s, before even writing his first line of AI code.

AI的未來將被分散的想法遠非邊緣。人工通用情報的最早開拓者之一本·戈特策爾(Ben Goertzel)告訴Cointelegraph,他意識到在1990年代初期,在AI中需要權力下放,甚至在撰寫了他的第一行AI代碼之前。

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