a16z: Explore four new business models based on the integration of AI and blockchain

Original video:Web3 with a16z, AI & Crypto

Original author: Dan Boneh (Professor at Stanford University, senior research consultant at a16z crypto), focusing on cryptography, computer security and machine learning; Ali Yahya (general partner at a16z crypto), formerly worked at Google Brain and is also the Google machine learning library TensorFlow One of the core contributors.

Arrangement Compilation: Qianwen, ChainCatcher

Stephen King once wrote a science fiction novel called The Diamond Age, in which there is an artificial intelligence device that acts as a mentor for people throughout their lives. When you are born, you are paired with an artificial intelligence that knows you very well—knows your preferences, follows you throughout your life, helps you make decisions, and guides you in the right direction. That sounds great, but you never want technology like this falling into the hands of giant middlemen. Because this will bring a lot of control to the company, as well as a series of privacy and sovereignty issues.

We wanted this technology to be truly mine, and a vision emerged that you could do that with blockchain.You can embed artificial intelligence in smart contracts. Keep your data private with the power of zero-knowledge proofs. In the coming decades, this technology will become increasingly intelligent. You can choose to do whatever you want, or change it in any way you wish.

So what is the relationship between blockchain and artificial intelligence? What kind of world will artificial intelligence lead us to? What is the current status and challenges of artificial intelligence? What role will blockchain play in this process?

AI and blockchain: competing against each other

The development of artificial intelligence, including the scene described in The Diamond Age, has always existed, but has recently experienced rapid development.

First, artificial intelligence is largely a top-down, centralized control technology.Encryption technology is a bottom-up, decentralized cooperation technology. In many ways, cryptocurrency is the study of how to build decentralized systems that enable large-scale human cooperation without a true central controller. In this regard, this is a natural way in which these two technologies can become one.

Artificial Intelligence is a sustainable innovation that can enhance the business models of existing technology companies and help them make top-down decisions. The best example of this is Google, which determines what content is presented to its users across billions of users and billions of page views. Cryptocurrency is essentially a disruptive innovation, and its business model is fundamentally contrary to the business model of large technology companies.As such, this is a movement spearheaded by fringe rebels rather than those in power.

Therefore, artificial intelligence may be closely related to all aspects of privacy protection, and the two will promote and interact with each other. Artificial Intelligence as a technology has created incentives that result in users having less and less privacy as companies want to capture all of our data. And artificial intelligence models trained on more and more data will become more effective. On the other hand, artificial intelligence is not perfect, and models may be biased, and biases may lead to unfair results. Therefore, there are many papers on algorithm fairness at this stage.

I think were headed down a path to AI where everyones data is aggregated into these massive model training to optimize the model. Cryptocurrencies are moving in the opposite direction, increasing personal privacy and empowering users to take control of their data.It can be said that encryption technology is a technology that competes with artificial intelligence because it can help us distinguish content created by humans or artificial intelligence from abundant content. In a world where content created by artificial intelligence is rampant, encryption technology will Become an important tool for maintaining and preserving human content.

Cryptocurrency is the Wild West because it is completely permissionless because anyone can participate. You have to assume that some of the parties involved are malicious.Therefore, there is a greater need for tools to help you filter out honest participants from dishonest participants, and machine learning and artificial intelligence, as intelligent tools, are actually very helpful in this regard.

For example, there are projects using machine learning to identify suspicious transactions submitted to wallets. In this way, these user transactions will be marked and submitted to the blockchain. This is a great way to prevent users from accidentally submitting all their funds to an attacker, or doing something they will regret later. Machine learning can also be used as a tool to help you determine in advance which transactions may also have mev.

Just as LLM models can be used to detect fake data or malicious activity, in turn, these models can be used to generate fake data.The most typical example is deepfakes. You can create a video where someone says something theyve never said before. But blockchain can actually help alleviate this problem.

For example, there are timestamps on the blockchain, showing that you said such and such a thing on this date. If someone fakes a video, then you can use the timestamp to deny it.All of this data, the truly authentic data, is recorded on the blockchain and can be used to prove that the deepfake video is really fake.So I think blockchain might help fight counterfeiting.
We can also rely on trusted hardware to do this. Devices like cameras and our phones sign the images and videos they capture as a standard. Its called C 2 PA and specifies how the camera signs data. In fact, one of Sonys cameras can now take photos and videos, and then generate a C 2 PA signature on the video. This is a complex topic and we will not dwell on it here.

Usually, when newspapers publish pictures, they dont publish pictures taken by cameras intact. They do cropping, do some licensing on the photo. Once you start editing the image, it means that the recipient, the end reader, the user on the browser is not seeing the original image, and C 2 PA signature verification cannot be performed.

The question is, how do users confirm that the images they see are indeed properly signed by the C 2 PA camera? This is where ZK technology comes in, you can prove that the edited image is actually the result of downsampling and grayscale scaling of a correctly signed image. In this way, we can replace the C 2 PA signature with a simple zk proof and have a one-to-one correspondence with these images. Now, readers can still confirm that they are seeing real images. Therefore, zk technology can be used to counter these messages.

How does the blockchain break the game?

Artificial intelligence is essentially a centralized technology. It benefits in large part from economies of scale, as things are much more efficient running off of a single data center. In addition, data, machine learning models, machine learning talent, etc. are usually controlled by a small number of technology companies,
So how to break the situation? Cryptocurrencies can help us decentralize artificial intelligence through the use of technologies such as ZKML,It can be applied in data centers, databases, and machine learning models themselves. For example, in computing, using zero-knowledge proofs, users can prove that the actual process of inference or training a model is correct.

That way, you can outsource the process to a large community. Under this distributed process, anyone with a GPU can contribute computing power to the network and train models in this way, without having to rely on a large data center where all GPUs are concentrated.

From an economic point of view,Whether this makes sense is uncertain. But at least with the right incentives, the long tail can be achieved.You can take advantage of all the GPU power possible. Having all these people contribute computing power to model training or inference runs would replace the big tech companies that control everything. To achieve this, various important technical issues must be resolved. In fact, a company called Nvidia is building a decentralized GPU computing market, mainly used for training machine learning models. In this market, anyone can contribute their own GPU computing power. On the other hand, anyone can leverage any computation present in the network to train their large machine learning models. This will be an alternative to centralized big tech companies like openai, google, metadata, etc.

Consider a situation where Alice has a model that she wants to protect. She wants to send the model in encrypted form to Bob, who now receives the encrypted model and needs to run his own data on this encrypted model. How to do this? Then we need to use the so-called fully homomorphic encryption to calculate the encrypted data. If the user has an encrypted model and plaintext data, he or she can run the encrypted model on the plaintext data and receive and obtain the encrypted results. You send the encrypted result back to Alice, and she can decrypt it and see the plaintext result.

This is actually technology that already exists. The question is, the current technology works well for medium-sized models, can we scale it to larger models?This is quite a challenge and requires the efforts of many more companies.

Status, Challenges and Incentives

I think its about decentralization in computing. The first is the verification problem, which you can solve using ZK, but currently these techniques can only handle smaller models. The challenge we face is that the performance of these cryptographic primitives is far from sufficient for training or inference on very large models. There is therefore a lot of work being done to improve the performance of the proof process so that increasingly larger workloads can be proven efficiently.
At the same time, some companies are also using other technologies that go beyond encryption. Instead, using techniques of a game-theoretic nature, they allow more independent people to work. It’s a game-theoretic, optimistic approach that doesn’t rely on cryptography, but it’s still consistent with the larger goal of decentralizing AI or helping create an AI ecosystem. This is the goal proposed by companies like openai.

The second big problem is the distributed system problem. Like, how do you coordinate a large community to contribute gp to a network that feels like an integrated, unified computing underlay? There will be many challenges, such as how to break down the workload of machine learning in a reasonable way, and assign different workloads to different nodes of the network, and how to do all this work efficiently.

Current techniques can basically be applied to medium-sized models, but cannot be applied to models as large as gpt 3 or gpt 4. Of course, we have other methods. For example, we can have multiple people train and compare the results, so there is a game-theoretic incentive. Incentivize people not to cheat. If someone cheats, others may complain that their calculated training results are incorrect. This way, the cheater doesnt get paid.
We can also decentralize data sources in the community to train large machine learning models. Similarly, we can also collect all the data and train the model ourselves instead of a centralized institution. This can be achieved by creating a kind of marketplace. This is similar to the computing market we just described.

We can also look at it in terms of incentives, encouraging people to contribute new data to a large data set, which is then used to train models. The difficulty here is similar to the verification challenge. You have to somehow verify that the data that people contribute is really good data. The data is neither duplicates nor randomly generated junk nor is it somehow generated inauthentic.

Also, make sure the data doesnt subvert the model in some way, or the model performance will actually just get worse and worse. Perhaps we have to rely on a mix of technical solutions and social solutions, in which case you can also build credibility with some sort of site metrics that community members have access to so that when they contribute data, its more It turned out to be more believable.

Otherwise, it will take a very long time to actually achieve coverage data distribution. One of the challenges of machine learning is that the model can only really cover the distribution that the training dataset can reach. If there are some inputs that are far outside the distribution of the training data, your model may actually behave completely unpredictable. In order for a model to perform well in edge cases, black swan data points, or data inputs that may be encountered in the real world, we need a data set that is as comprehensive as possible.

So if you have this kind of open, decentralized marketplace that provides data for datasets, you can have anyone in the world who has unique data contribute that data to the network, which is a much better way. Because if you try to do it as a central company, you have no way of knowing who owns the data. So if you can create an incentive for these people to come forward and provide that data, then I think you can actually get significantly better coverage of the long tail data.

So we have to have some mechanism to make sure the data you provide is real. One way is to rely on trusted hardware, let the sensor itself embed some trusted hardware, and we only trust data that is correctly signed by the hardware. Otherwise, we must have other mechanisms to distinguish the authenticity of the data.
There are currently two important trends in machine learning. First, performance measurement methods for machine learning models are constantly improving, but are still in their early stages, and it is practically difficult to know how well another model performs. Another trend is that we are getting better at explaining how models work.

So based on these two points, at some point, I might be able to understand the impact of the dataset on the performance of the machine learning model. If we can understand whether datasets contributed by third parties contribute to the performance of machine learning models, then we can reward this contribution and create momentum for the existence of this market.
Just imagine if you could create an open market where people contribute trained models that solve specific types of problems, or if you create a smart contract that embeds some kind of testing in it, if someone can provide a model using zkml, And proving that the model can solve the test is an endgame. You now have the tools you need to create a marketplace that is incentivized when people contribute machine learning models that solve certain problems.

How do AI and encryption form business models?

I think the vision behind the intersection of cryptocurrency and artificial intelligence is that you can create a set of protocols that distribute the value gained by this new technology of artificial intelligence to more people, and everyone can contribute and everyone can share The benefits of this new technology.

So the people who will be able to profit will be people who contribute computing power, people who contribute data, or people who contribute new machine learning models to the network, so that better machine learning models can be trained to solve more important problems .

The demand side of the network can also profit. They use this network as the infrastructure for training their own machine learning models. Maybe their model can contribute to something interesting, like a next-generation chat tool. In these models, since these companies will have their own business models, they themselves will be able to drive value capture.

Those who build the network also stand to gain. For example, create a token for the network and the token will be distributed to the community. All of these people will have collective ownership of this decentralized network for computing data and models, and will also be able to capture some of the value of all economic activity that occurs through this network.

As you can imagine, every transaction that goes through this network, every payment method that pays for computation, data, or models, is likely to be charged a fee that goes into a vault controlled by the entire network. Token holders collectively own the network. This is essentially the business model of the network itself.

Artificial Intelligence Promotes Code Security

Many listeners may have heard of copilot, which is a tool for generating code. You can try using these collaborative generation tools to write solidity contracts or cryptography code. What I want to emphasize is that this is actually very dangerous. Because a lot of times, when you try to run it, these systems actually generate code that works but is not safe.

In fact, we recently wrote a paper on this problem, which states that if you try to get a co-pilot to write a simple encryption function, it provides the correct encryption function. But it uses an incorrect mode of operation, so you end up with an insecure encryption mode.

You may ask, why does this happen? One reason is that these models are basically trained from existing code, and they are trained in github repositories. Many github repositories are actually vulnerable to various attacks. Therefore, the codes learned by these models work fine, but are not safe. Its like poor quality garbage producing garbage. So I hope people will be careful when generating code using these generative models, and double-check that the code actually does what its supposed to do, and does it safely.

You can use artificial intelligence models, combined with other tools to generate code, to ensure that the entire process is error-free. For example, one idea is to use the llm model to generate a specification for a formal verification system and ask llm to generate a specification for a formal verification tool. Then, ask the same llm instance to generate a program that conforms to the specification, and then use formal verification tools to see if the program actually conforms to the specification. If there is a vulnerability, the tool will catch it. These errors can be fed back to llm as feedback, and then ideally, hopefully, llm can modify its work and produce another correct version of the code.

Finally, if you repeat this, you end up with a piece of code that, ideally, satisfies this return value exactly, and is formally verified to satisfy this return value. And, since humans can read this traceback, you can look at this traceback and see that this is the program I wanted to write. In fact, there have been many attempts to evaluate LLMs ability to find software vulnerabilities, such as Unity smart contracts, C and C plus.

So, will we reach a point where LLM-generated code is less likely to contain bugs than human-generated code? For example, when we talk about autonomous driving, do we care about it being less likely to crash than a human driver? I think this trend will only get stronger and more integrated into existing tool chains.

You can integrate it into a formal verification tool chain, and you can also integrate it into other tools, such as the previously mentioned tool for checking memory management issues. You can also integrate it into your unit and integration testing toolchains so that llm doesnt just act in a vacuum. It can get real-time feedback from other tools that connect it to ground truth.

I think that by combining very large machine learning models trained using all the data in the world, coupled with these other tools, it may be possible to make computational programs better than human programmers. Even if they still make mistakes, they might just be superhuman. This will be a big moment in software engineering.

Artificial Intelligence and the Social Graph

Another possibility is that we might be able to build a decentralized social network that actually behaves a lot like Weibo, but the social graph is actually entirely on-chain. Its almost like a public good that anyone can build on. As a user, you control who you are on the social graph. You control your data, who you follow and who can follow you. In addition, there are a large number of companies building portals in the social graph to provide users with experiences similar to Twitter, Instagram, tick tock, or whatever else they want to build.

But it’s all built on the same social graph, and no one owns it, and there isn’t a multibillion-dollar tech company in the middle that completely controls it.

Its an exciting world because it means it can be more vibrant and have an ecosystem built by people. Each user will have more control over what they see and do on the platform.

But at the same time, users also need to filter the signal from the noise. For example, a reasonable recommendation algorithm needs to be developed to filter all content and show you the news sources you really want to see. This will open a door to the entire market, a competitive environment composed of players providing services. You can use algorithms, use AI-based algorithms to curate content for you. As a user, you can decide whether to use a particular algorithm, maybe the one established by twitter, or something else. But equally, you also need tools like machine learning to help you sift through the noise, to help you parse all the junk information in a world where generative models can create all the junk information in the world.

Why is human proof important?

A very relevant question is, in a world where AI-fabricated content is rampant, how do you prove that you are indeed human?

Biometric technology is a possible direction. One of the projects is called World Coin, which uses retinal scans as biometric information to verify whether you are a real person to ensure that you are indeed a living person and not just an eye. photo. This system has secure hardware that is difficult to tamper with, so the proof that comes out on the other end, which is a zero-knowledge proof that masks your actual biometric information, is very difficult to fake in this way.

On the Internet, no one knows youre a robot. So I think this is where the proof of humanity project becomes very important, because knowing whether youre interacting with a robot or a human is going to be very important. If you dont have human evidence, then you cant tell whether an address belongs to one person or a group of people, or whether ten thousand addresses really belong to one person or are just pretending to be ten thousand different people.

This is critical in governance. If every participant in the governance system could prove that they are in fact human, and they could prove that they are human in a unique way because they only have one set of eyeballs, then the governance system would be fairer and less Then chaebolization (based on the preference for the maximum amount of money locked in a certain smart contract).

AI and Art

AI models mean that we will live in a world of infinite media abundance, a world in which the community around any given medium or the narrative around a given medium will become increasingly important.

For example, Sound.xyz is building a decentralized music streaming platform that allows artists and musicians to upload music and then directly connect with our community by selling them NFTs. For example, you can comment on a track on the sound dot xyz website so that other people who play the song can also see the comment. This is similar to the previous Sound Cloud feature. The act of purchasing NFT is also supporting artists, helping them achieve sustainable development and create more music works. But the beauty of it all is that it actually gives artists a platform to really engage with the community. Artists are everyones artists.

Because of the role of cryptocurrency here, you can create a community around a piece of music that wouldnt exist if the piece of music was just created by a machine learning model without any human element.

A lot of the music that were going to be exposed to is going to be completely generated by artificial intelligence, and the tools to build communities, to tell stories around art, around music, around other types of media are going to be really important and bring us together that we actually care about, really want to invest in, and Take the time to engage with media that differentiates it from other media in general.

There may be some synergies between the two, as much music will be enhanced or generated by artificial intelligence. But if there are also human elements involved, for example, the creator uses artificial intelligence tools to create a new piece of music, they have their own sound characteristics, they have their own artist page, their own community, and their own followers.

Now, there is a synergy between the two worlds, and you have the best music because AI empowers you with superpowers. But at the same time, you also have human elements and stories that are coordinated and enabled by cryptography that allows you to bring all these people together on one platform.

It’s definitely a new world when it comes to content generation. So how do we differentiate between human-generated art and machine-generated art that need support?

This actually opens the door for collective art, art that emerges through the creative process of an entire community rather than a single artist. There are already some projects doing this, where the community influences the chain through some voting process, generating artwork based on prompts from machine learning models. Maybe you generate not one piece of art, but ten thousand pieces. Then you use another machine learning model, which is also trained based on community feedback, and it can select the best one from these 10,000 works.