前沿资讯：使AI透明化，建立信任与创新

AI is taking over our lives but exactly what goes on inside AI systems is unclear. Two researchers from EQTY Lab shine a light on how to make these mechanics more visible.
人工智能正在接管我们的生活，但人工智能系统内部究竟发生了什么却并不清楚。EQTY 实验室的两位研究人员为我们揭示了如何让这些机制更加清晰可见。

Part of the magic of Generative AI is that most people have no idea how it works. At a certain level, it’s even fair to say that no one is entirely sure how it works, as the inner-workings of ChatGPT can leave the brightest scientists stumped. It’s a black box. We’re not entirely sure how it’s trained, which data produces which outcomes, and what IP is being trampled in the process. This is both part of the magic and part of what’s terrifying.
生成式人工智能的神奇之处在于，大多数人都不知道它是如何工作的。从某种程度上说，甚至可以说没有人完全清楚它是如何工作的，因为 ChatGPT 的内部运作可能会让最聪明的科学家也束手无策。这是一个黑盒子。我们并不完全清楚它是如何训练的，哪些数据会产生哪些结果，以及在这个过程中践踏了哪些知识产权。这既是神奇的一部分，也是可怕的一部分。

What if there was a way to peer inside the black box, allowing a clear visualization of how AI is governed and trained and produced? This is the goal — or one of the goals — of EQTY Lab, which conducts research and creates tools to make AI models more transparent and collaborative. EQTY Lab’s Lineage Explorer, for example, gives a real-time view of how the model is built.
如果有一种方法可以窥探黑箱内部，让人们清楚地看到人工智能是如何管理、训练和生产的，那会怎样？这就是 EQTY 实验室的目标，或者说是目标之一，该实验室开展研究并开发工具，使人工智能模型更加透明、更具协作性。例如，EQTY 实验室的 "线性资源管理器"（Lineage Explorer）可以实时查看模型是如何建立的。

All of these tools are meant as a check against opacity and centralization. “If you don’t understand why an AI is making the decisions it's making or who's responsible, it's really hard to interrogate why harmful things are being spewed,” says Ariana Spring, Head of Research at EQTY Lab. “So I think centralization — and keeping those secrets in black boxes — is really dangerous.”
所有这些工具都是为了防止不透明和集中化。"EQTY实验室研究主管阿丽亚娜-斯普林（Ariana Spring）说："如果你不了解人工智能为什么会做出这样的决定，或者谁该对此负责，那么就很难审问为什么会出现有害的东西。"因此，我认为集中化--以及将这些秘密保存在黑盒子里--真的很危险。

Joined by her colleague Andrew Stanco (head of finance), Spring shares how crypto can create more transparent AI, how these tools are already being deployed in service of climate change science, and why these open-sourced models can be more inclusive and representative of humanity at large.
在她的同事安德鲁-斯坦科（Andrew Stanco，财务主管）的参与下，斯普林分享了加密货币如何创造更透明的人工智能，这些工具如何已经被部署到气候变化科学服务中，以及为什么这些开源模型可以更具包容性和代表全人类。

Ariana Spring: We're pioneering new solutions to build trust and innovation in AI. And generative AI is kind of the hot topic right now, and that's the most emergent property, so that's something that we're focused on.
阿丽亚娜-斯普林 我们正在开拓新的解决方案，以建立人工智能领域的信任和创新。生成式人工智能是当下的热门话题，也是最新兴的特性，所以这也是我们关注的重点。 

But also we look at all different kinds of AI and data management. And really trust and innovation are where we lean into. We do that by using advanced cryptography to make models more transparent, but also collaborative. We see transparency and collaboration as two sides of the same coin of creating smarter and safer AI.
此外，我们还关注各种不同的人工智能和数据管理。真正的信任和创新是我们的优势所在。我们通过使用先进的加密技术，使模型更加透明，同时也更具协作性。我们认为透明度和协作是创造更智能、更安全的人工智能的一体两面。

So, in a process as complex as AI training, having those tamper-proof and verifiable attestations — both during the training and afterwards — really helps. It creates trust and visibility.
因此，在像人工智能训练这样复杂的过程中，拥有这些防篡改和可验证的证明--无论是在训练期间还是训练之后--真的很有帮助。它能创造信任和可见性。