数据所有权与AI的未来

I learned the world through the internet; I think for my daughter, they will learn the world through AI. 我通过互联网认识世界；我想对我的女儿来说，她们将通过人工智能（AI）认识世界。

A company called Scale AI (Data Labeling Company: specializing in data annotation and validation), which is known for being a data referring company, has got so much data from across the globe. 一家名为Scale AI（数据标注公司：专门从事数据注释和验证）的公司，以其数据引用业务而闻名，他们从全球各地收集了海量数据。

People sorting, labeling, and sifting reams of data to train and improve AI for companies like Meta (Facebook's parent company), OpenAI (AI research and deployment company), Microsoft (Technology corporation), and Google (Technology corporation).

人们对海量数据进行分类、标注和筛选，以训练和改进像Meta（Facebook的母公司）、OpenAI（人工智能研究与部署公司）、微软（科技公司）和谷歌（科技公司）这样的公司的人工智能。

Meta is paying nearly $15 billion for a Scale AI stake, I've confirmed with a source.

我已向消息人士证实，Meta正斥资近150亿美元收购Scale AI的股份。

This just shows you how much there is a need for data. We all contribute data to AI: your tweet, the posts that you do on Facebook, the videos that we upload on YouTube are the ones AI models are trained on, but us as users, we don't get paid for it. 这仅仅表明了对数据的巨大需求。我们都在为人工智能贡献数据：你的推文、你在Facebook上发布的帖子、我们在YouTube上上传的视频，都是人工智能模型赖以训练的素材，但作为用户，我们却没有因此获得报酬。

The data economy (Economic Sector: focused on the collection, processing, and exchange of data) today is valued at about $1 trillion. This is the data across the globe from enterprises, organizations, and individual people on the internet. 如今，数据经济（经济领域：专注于数据的收集、处理和交换）的价值约为1万亿美元。这些数据来自全球各地的企业、组织以及互联网上的个人。

Let's assume about 5% of that is contributed by individuals; that's close to about $500 billion. $500 billion worth of data that's taken away from you and you're not getting paid for it. 假设其中约5%是由个人贡献的，那将接近5000亿美元。价值5000亿美元的数据被从你手中拿走，而你却没有因此获得报酬。

We want to change that. What if we can flip the switch here and have people have the ownership to that? We want to build a system where you can contribute your data and you get paid for that. 我们想改变这一点。如果我们能在这里扭转局面，让人们拥有这些数据的所有权，那会怎样？我们希望建立一个系统，让你贡献数据并因此获得报酬。

It's not just for AI researchers or developers, the common man owns a lot of data. 这不仅仅是针对人工智能研究人员或开发者的，普通人也拥有大量数据。

We have close to about a million users who are contributing data sets for that. It's like how nations went ahead and fought for oil. Now larger organizations are going to fight for data. Data is the new oil, and people have to realize that they own that oil, they own that data. But it's time to fight back and earn a piece of that. 我们有近百万用户正在为此贡献数据集。这就像国家曾经为石油而战一样。现在，大型组织将为数据而战。数据是新的石油，人们必须意识到他们拥有这些石油，他们拥有这些数据。现在是反击并从中分一杯羹的时候了。

OpenLedger：构建公平的AI生态系统

I'm Ram, I'm one of the core contributors at OpenLedger (AI Blockchain: a blockchain platform designed for data ownership and monetization in AI). OpenLedger is an AI blockchain (Artificial Intelligence Blockchain: a blockchain platform specifically designed to support AI applications and data management) where people have the data sets. AI systems need these data sets. 我是Ram，我是OpenLedger（AI区块链：一个为人工智能中的数据所有权和货币化设计的区块链平台）的核心贡献者之一。OpenLedger是一个AI区块链（人工智能区块链：一个专门为支持人工智能应用和数据管理而设计的区块链平台），人们拥有数据集，而人工智能系统需要这些数据集。

So, as an application, you can use OpenLedger to go ahead and contribute a data set that you own. We have a lot of data contributors that initially came on board. We have close to about 10 ecosystem projects building AI models on us. We have close to about a million users who are contributing data sets for that. 因此，作为一个应用程序，你可以使用OpenLedger贡献你拥有的数据集。我们最初吸引了许多数据贡献者。我们有近10个生态系统项目正在我们的平台上构建人工智能模型。我们有近百万用户正在为此贡献数据集。

I've been in this industry for close to a decade. The idea was to build an R&D company (Research and Development Company: focused on innovation and creating new technologies or products) around blockchain (Distributed Ledger Technology: a decentralized, distributed, and often public, digital ledger that is used to record transactions across many computers) and machine learning (AI Subset: a field of artificial intelligence that enables systems to learn from data without explicit programming). We saw there is a need for enterprises to bring in fairness and transparency within their ecosystem, within their organization. 我从事这个行业近十年了。最初的想法是围绕区块链（分布式账本技术：一种去中心化、分布式且通常公开的数字账本，用于在多台计算机上记录交易）和机器学习（人工智能子集：人工智能的一个领域，使系统能够从数据中学习而无需明确编程）建立一家研发公司（研究与开发公司：专注于创新和创造新技术或产品）。我们看到企业需要在其生态系统，在其组织内部引入公平性和透明度。

We had an opportunity to work with enterprises like Walmart, Sony, GSK, and many more. And what we realized is that especially a technology like blockchain brings equality among every user that uses that. OpenLedger is a contribution from that. 我们有机会与沃尔玛、索尼、葛兰素史克等众多企业合作。我们意识到，特别是像区块链这样的技术，能为所有使用它的用户带来平等。OpenLedger正是基于这一理念的贡献。

The idea was to not just service enterprises, but build a product that can be used by anyone across the globe and figure out how AI is impacting everyone's lives. We all have this epiphany at one point in time where you have conversations with your friend about a product that you want to buy, and you see that ad on Instagram, you know that your data is being used. 我们的想法不仅仅是服务企业，而是构建一个全球任何人都能使用的产品，并弄清楚人工智能如何影响每个人的生活。我们都在某个时刻有过这样的顿悟：你和朋友谈论想买的产品，然后你在Instagram上看到了那个广告，你就知道你的数据正在被使用。

I've had multiple epiphanies as that, and we've worked with firms where that is visible: people's information was used to make their product better. Sure, it gave convenience, but it also took privacy. 我曾多次经历这样的顿悟，我们也曾与一些公司合作，亲眼看到：人们的信息被用来改进他们的产品。当然，这带来了便利，但也牺牲了隐私。

That's going to happen with AI as well. AI is going to make money, all these large organizations are going to make money out of it, but you're not going to be part of that. As we evolve, as models evolve, models will become specialized where they need data sets from people, and in that case, we need to make sure that we can own our data and we get paid for it. And that's what OpenLedger is trying to solve. 人工智能也会发生同样的事情。人工智能会赚钱，所有这些大型组织都会从中赚钱，但你却无法参与其中。随着我们发展，随着模型演进，模型将变得更加专业化，它们需要来自人们的数据集。在这种情况下，我们需要确保我们能够拥有自己的数据并获得报酬。这正是OpenLedger试图解决的问题。

It's a platform where users can come and contribute data sets which could be, let's say, a knowledge that they have about trading or a knowledge about a particular subject. Let's say I know how to cook well; I can go ahead and contribute that. And then models can use this data, and if they use that data and they build an AI out of that, and this AI makes revenue or creates an impact, you should be a part of that, you should get a piece of that revenue. 这是一个平台，用户可以来贡献数据集，例如他们关于交易的知识，或关于某个特定主题的知识。比如，我知道如何烹饪得很好；我可以去贡献这些知识。然后模型可以使用这些数据，如果它们使用这些数据并从中构建一个人工智能，并且这个人工智能产生了收入或影响力，你就应该成为其中的一部分，你应该获得一部分收入。

We want to bring in fairness to this ecosystem. The people who contribute data, or a model developer, or a compute provider, or any kind of resource provider gets paid as part of the process, and that's what OpenLedger is all about. 我们希望为这个生态系统带来公平。贡献数据的人、模型开发者、计算资源提供者或任何类型的资源提供者，都能作为过程的一部分获得报酬，这就是OpenLedger的全部意义。

数据：新的石油与就业机遇

A lot of people say that AI is going to make people lose jobs. I don't think so. It might be a temporary thing, but it's going to create a lot of jobs. Data contribution itself could be a great gig economy (Labor Market: a free market system in which temporary positions are common and organizations contract with independent workers for short-term engagements). 很多人说人工智能会导致人们失业。我不这么认为。这可能是一个暂时现象，但它将创造大量就业机会。数据贡献本身就能成为一个很棒的零工经济（劳动力市场：一种自由市场系统，其中临时职位很常见，组织与独立工人签订短期合同）。

Data is also very relevant with enterprises. Data you find on the internet is very generalized, but the data that you would find in a firm, in an enterprise, is very specialized. And all of this knowledge did not come on the internet, people don't write blogs about it. A surgeon doesn't write about what his experience is in actually doing the surgery, an artist doesn't write about how he actually painted a picture. 数据与企业也非常相关。你在互联网上找到的数据非常通用，但在公司、在企业中找到的数据则非常专业化。所有这些知识都没有出现在互联网上，人们不会为此写博客。外科医生不会写他实际进行手术的经验，艺术家也不会写他如何绘画。

So it all comes down to the individual person's knowledge that they own. This knowledge would be needed for AI to truly get into all parts of our lives, more than just a chatbot. It needs to know knowledge about the entire world, it needs to know about very intricate details. In that case, people would reach out to individual users to get their knowledge. 所以这一切都归结为个人所拥有的知识。人工智能若要真正融入我们生活的方方面面，而不仅仅是一个聊天机器人，就需要这些知识。它需要了解整个世界的知识，需要了解非常复杂的细节。在这种情况下，人们会联系个体用户以获取他们的知识。

So we knew that the ChatGPT moment was not going to be just a spark; it's going to become much bigger. We're going to build AI systems that are very specialized in various use cases. But there is not enough data out there on the internet. 所以我们知道ChatGPT的出现不会只是一道火花；它会变得更加宏大。我们将构建在各种用例中都非常专业化的人工智能系统。但互联网上没有足够的数据。

We've seen a lot of independent developers who have a lot of innovative ideas who are actually building interesting AI models in the Asian region. They contribute data sets by using our nodes, which is basically a node they can download and have it as a plug-in. They can contribute data sets for that. 我们看到许多独立开发者，他们拥有许多创新想法，正在亚洲地区构建有趣的人工智能模型。他们通过使用我们的节点来贡献数据集，这基本上是一个他们可以下载并作为插件使用的节点。他们可以为此贡献数据集。

去中心化与社区力量

If you take a look at Web3 (Decentralized Internet: a new iteration of the World Wide Web based on blockchain technology), the internet was supposed to be decentralized, but because of convenience, we let larger organizations take that. So making sure that it does not go back to a bunch of centralized larger firms is very important. 如果你看看Web3（去中心化互联网：基于区块链技术的万维网新迭代），互联网本应是去中心化的，但由于便利性，我们让大型组织掌控了它。因此，确保它不会回到少数中心化的大公司手中，这一点非常重要。

We don't have money to fight for it. All we have is our own power, people coming together and building something against the larger organizations. In order for that to happen, you need to make people come together, and community building is very important as part of that. Building a very strong culture, building a very strong community, having the same goal, building systems that are open, verifiable, and rewarding is what makes people come together. 我们没有钱去争取。我们所拥有的只是我们自己的力量，人们齐心协力，共同对抗那些大型组织。为了实现这一点，你需要让人们团结起来，而社区建设是其中非常重要的一部分。建立一种非常强大的文化，建立一个非常强大的社区，拥有共同的目标，构建开放、可验证和有回报的系统，这正是让人们团结在一起的原因。

Let's take an example of Ethereum itself. Ethereum is a very community-driven blockchain, and that is why it's so strong today. Even though it has its ups and downs, Ethereum as an ecosystem is very strong. That's why I think community is very important in what we're building. 以以太坊为例。以太坊是一个非常社区驱动的区块链，这就是它今天如此强大的原因。尽管它有起有落，但以太坊作为一个生态系统非常强大。这就是为什么我认为在我们正在做的事情中，社区非常重要。

So to explain Proof of Attribution (Blockchain Tracking Mechanism: a system on a blockchain that tracks and verifies data ownership, usage, and contributor payments) in a very simple manner: what if there's a tracking mechanism where you can see who actually contributed for all of this, and you can also see it on chain that everyone who contributed is getting paid for it? 因此，要非常简单地解释归因证明（区块链追踪机制：一种在区块链上追踪和验证数据所有权、使用情况和贡献者报酬的系统）：如果有一个追踪机制，你可以看到谁实际为此贡献了所有内容，并且你也可以在链上看到所有贡献者都因此获得了报酬，那会怎样？

So it's a tamper-proof record of your data's ownership. It's a record of how your data was used, and it's also a record of how much you're going to get paid if your data was used as well. So the reason why we have the Proof of Attribution is because we need to have a trustless system where they don't have to believe Ram; they can believe the system, they can believe the code. 所以它是一个防篡改的数据所有权记录。它记录了你的数据是如何被使用的，也记录了如果你的数据被使用，你将获得多少报酬。我们之所以有归因证明，是因为我们需要一个无需信任的系统，他们不必相信Ram；他们可以相信系统，相信代码。

I think that's very important. As a data contributor, you can go ahead and choose the model that needs the data, and you can start contributing data sets to that. It's that cyclic ecosystem we want to build. 我认为这非常重要。作为数据贡献者，你可以选择需要数据的模型，然后开始向其贡献数据集。这就是我们想要构建的循环生态系统。

All the data that is contributed is recorded on the blockchain so that we can track this and we can pay this. As a user, I know that I can prove my ownership by having it on chain. 所有贡献的数据都记录在区块链上，这样我们就可以追踪并支付报酬。作为用户，我知道可以通过链上记录来证明我的所有权。

垂直化AI模型与负责任的AI

Another interesting side that we have started to see: smaller model developers and innovative people who want to build something interesting have started using our product. A good example that's being built on OpenLedger is a bunch of doctors are building a sleep model. 我们开始看到另一个有趣的方面：小型模型开发者和那些想要构建有趣事物的创新者已经开始使用我们的产品。一个在OpenLedger上构建的很好例子是一群医生正在构建一个睡眠模型。

The model is trained on high-quality sleep data sets across the globe. They want to get access to the sleep data sets from various parts of the world so they can cover various races. This particular data set is very unique because they're going to correlate that with their health vitals. And then once the model is ready, you can just upload your sleep data and then it's just going to tell you what your body vital looks like. 该模型在全球范围内使用高质量的睡眠数据集进行训练。他们希望获取来自世界各地不同种族的睡眠数据集，以便覆盖更广泛的人群。这个特定的数据集非常独特，因为他们会将其与健康生命体征相关联。然后一旦模型准备就绪，你只需上传你的睡眠数据，它就会告诉你你的身体生命体征状况。

So like this, we have very interesting players who are building models on us, very niche, innovative ideas where you need data which is quite unique. We are quite excited about them. 像这样，我们有许多非常有趣的参与者正在我们平台上构建模型，他们拥有非常小众、创新的想法，需要相当独特的数据。我们对他们感到非常兴奋。

A lot of people ask me why this has to use blockchain; it could be a traditional AI company, but I don't think so. If you take a look at generalized models, the era of that will slowly fade away. Agents will become much more specialized. There'll be an agent for healthcare, there'll be an agent for legal, there'll be agents for every other sector out there. 很多人问我为什么这必须使用区块链；它也可以是一家传统的人工智能公司，但我不这么认为。如果你看看通用模型，它的时代将慢慢消逝。代理将变得更加专业化。将会有用于医疗保健的代理，用于法律的代理，以及用于其他所有行业的代理。

And you can have a general model power that, you need to have a specialized model that powers it. And the data set is among actually people. And these data sets can eventually become models, specialized AI models which then can be consumed by apps and agents that are going to be built on top of that. 你不能仅凭一个通用模型来驱动它，你需要一个专业化的模型来驱动它。而数据集实际上存在于人们之中。这些数据集最终可以成为模型，成为专业化的人工智能模型，然后可以被在其之上构建的应用程序和代理所消费。

So I think every aspect of our life will change. How we take a ride home, our doctor visits, and what we learn from all of that will change. I learned the world through the internet. I think for my daughter, AI will create a huge impact. They will learn the world through AI. 所以我认为我们生活的方方面面都将改变。我们如何搭车回家，我们看医生，以及我们从所有这些中学到的东西都将改变。我通过互联网认识世界。我想对我的女儿来说，人工智能将产生巨大的影响。她们将通过人工智能认识世界。

So that AI has to be responsible. So building a responsible AI system is upon us. If we encourage larger ecosystems to go ahead and consume our data and not reward us, then that's what is going to happen. I think now it's time to change it. If we can realize how important our data is, probably that's the best output that we can see out of AI. 所以人工智能必须是负责任的。因此，构建一个负责任的人工智能系统是我们的责任。如果我们鼓励更大的生态系统去消费我们的数据却不给予回报，那么就会发生这种情况。我认为现在是时候改变它了。如果我们能意识到我们数据的重要性，那可能就是我们能从人工智能中看到的最好的结果。