它从嘈杂的桌子开始

这是在2015年。研究人员是本·菲尔丁（Ben Fielding），他建造了一台装有早期GPU的大型机器来开发AI。

A noisy desk in a lab at Northumbria University, in northern England, is where a young AI researcher began his PhD track in 2015. He was working on some unorthodox ideas, exploring how “swarms” of AIs could talk to each other and learn from each other to improve the collective whole. But he was handcuffed by the realities of that noisy machine, which he had built to house early GPUs for his research.

在英格兰北部诺森比亚大学的一家实验室中的一间嘈杂的办公桌是一位年轻的AI研究员在2015年开始他的博士学位曲目的地方。他正在研究一些非正统的想法，探索AIS的“群体”如何可以互相交谈并互相学习并相互学习以改善集体整体。但是，他被那台嘈杂的机器的现实戴上了手铐，这是他为研究的早期GPU所建造的。

The machine was so loud it annoyed his lab-mates, and he had crammed it beneath his desk, leaving no room for his legs, which he had to awkwardly stick to the side of the desk.

这台机器很大，使他的实验室烦恼，他把它塞在桌子下面，没有腿部的空间，他不得不尴尬地贴在桌子的侧面。

At the time, Google was doing similar research with thousands of GPUs in a data center.

当时，Google正在数据中心对数千个GPU进行类似的研究。

“The things they were doing weren't crazy. I knew the methods … I had lots of proposals, but I couldn't run them,” says Ben Fielding, who is now CEO of Gensyn and a speaker at Consensus 2025.

“他们所做的事情并不疯狂。我知道这些方法……我有很多建议，但我无法运行它们。”现任Gensyn首席执行官，共识2025年的发言人Ben Fielding说。

Fielding and his co-founder, Harry Grieve, started Gensyn in 2020, with the goal of creating a decentralized network for machine intelligence.

Fielding和他的联合创始人Harry Grieve于2020年创立了Gensyn，目的是为机器智能创建分散的网络。

Gensyn is now rolling out the first stage of its Testnet, and the company's early tools are beginning to trickle out into the wild. Gensyn recently released its “RL Swarms” protocol, a descendant of Fielding's PhD work, and just launched Testnet, which brings blockchain into the fold.

Gensyn现在正在其测试网的第一阶段推出，该公司的早期工具开始滴入野外。 Gensyn最近发布了其“ RL Swarms”协议，该协议是Fielding博士学位作品的后代，并刚刚启动了TestNet，该协议将区块链带入了折叠。

Gensyn's Testnet is a beta version of the company's core infrastructure, offering a glimpse into the broader vision.

Gensyn的TestNet是该公司核心基础架构的Beta版本，可瞥见更广阔的视野。

In this interview leading up to the AI Summit, at Consensus in Toronto, Fielding gives a primer on AI Swarms, explains how blockchain snaps into the puzzle, and shares why all innovators — not just tech giants — “should have the right to build machine learning technologies.”

在这次接触到AI峰会之前，在多伦多共识上，Fielding对AI群进行了入门，解释了区块链如何进入难题，并分享了为什么所有创新者（不仅仅是技术巨头）都应该有权建立机器学习技术。”

This interview has been condensed and lightly edited for clarity.

这次采访已被凝结和轻微编辑，以澄清。

Jeff Wilser: Gensyn just launched its Testnet. What’s the gist of what it is?

杰夫·威尔瑟（Jeff Wilser）：Gensyn刚刚推出了Testnet。它是什么要点？

Ben Fielding: It's the addition of the first MVP features of blockchain integration with what we've launched so far. So a few weeks ago, we launched RL Swarm, which is reinforcement learning, post-training as a peer-to-peer network.

本·菲尔丁（Ben Fielding）：这是区块链集成的第一个MVP功能与我们迄今为止推出的功能。因此，几周前，我们启动了RL Swarm，这是加强学习，作为对等网络进行培训。

The easiest way to think about it is when a pre-trained model goes through reasoning training — like Deep-R1 — it learns to critique its own thinking and recursively improve against the task. It can then improve its own answer.

思考它的最简单方法是，当预先训练的模型通过推理培训（例如Deep-R1）学习时，它学会了批评自己的思维，并递归地反对任务。然后可以改善自己的答案。

We take that process one step further and say, "It's great for models to critique their own thinking and recursively improve. What if they can talk to other models and critique each other's thinking?" If you get many models together in a group that can all talk to each other, they can start learning how to send information to the other models … with the overall goal of improving the entire swarm itself.

我们将这一过程进一步迈出了一步，并说：“模型可以批评自己的思维并递归改进。如果他们可以与其他模型交谈并批评彼此的思想，该怎么办？”如果您在一个可以互相交谈的小组中将许多模型聚在一起，他们可以开始学习如何将信息发送给其他模型……总体目标是改善整个群体本身。

So that's the swarm training method, which allows many models to combine in parallel to improve the outcome of a final meta-model that you could create from those models. But at the same time, you have every single individual model just improving on its own. So if you were to come along with a model on a MacBook, join a swarm for an hour and then drop back out again, you would have an improved local model based on the knowledge in the swarm, and you would have also improved the other models in the swarm. It's this collaborative training process that any model can join and any model can do. So that's what RL Swarm is.

因此，这就是群训练方法，它允许许多模型并行组合，以改善您可以从这些模型中创建的最终元模型的结果。但是与此同时，您只有单独的单独模型只是自行改进。因此，如果您要在MacBook上配备一个模型，请加入一群小时，然后再次退出，您将根据群体的知识进行改进的本地模型，并且您还可以改善群中的其他模型。这是任何模型都可以加入的协作培训过程，任何模型都可以做到。这就是RL群。

Wilser: Okay, and then where does blockchain come in?

威尔瑟：好的，然后区块链进来？

Ben Fielding: So the blockchain is us moving forward some of the lower-level primitives into the system.

本·菲尔丁（Ben Fielding）：因此，区块链是我们向系统推进系统的一些低级原始图。

Let's just pretend that someone doesn't understand the phrase "lower-level primitives." What do you mean by that?

让我们假装某人不了解“低级原语”一词。那是什么意思？

If you think about the software stack, you've got a GPU stack in a data center. You've got drivers on top of the GPU. You've got operating systems, virtual machines. You've got all this stuff going up.

如果您考虑软件堆栈，则数据中心有GPU堆栈。您已经有驱动程序在GPU之上。您有操作系统，虚拟机。您已经上了所有这些东西。

A lower-level primitive is the closest to the bottom foundation in the tech stack.

一个低级原始的原始性是技术堆栈中最接近的基础。

Wilser: Yes, exactly. And the RL Swarm is a demonstration of what's possible, basically. It's just a somewhat hacky demo of doing really interesting large-scale, scalable machine learning. But what Gensyn's been doing for the past four-plus years, realistically, is building infrastructure. And so we're in this period now where the infrastructure is all at that v0.1 sort of beta level. It's all done. It's ready to go. We have to figure out how to show the world what's possible when it's quite a big shift to the way people think of machine learning.

威尔瑟：是的，正是。基本上，RL群是可能的。这只是做一个非常有趣的大规模，可扩展的机器学习的刺激性演示。但是，实际上，Gensyn在过去的四年中一直在建立基础设施。因此，我们现在正处于基础架构处于v0.1 beta级别的时期。一切都完成了。准备就绪了。我们必须弄清楚当人们对机器学习的看法发生很大的转变时，如何向世界展示可能会有什么可能。

It sounds like you guys are

听起来你们是