AI x Crypto

24:29And that so obviously what I'm hearing you guys say is that there are tremendous applications at the intersection of general verifiable computing, which blockchains and crypto have definitely been significantly advancing and accelerating that whole area. We've been covering a ton of it ourselves if you look at our ZK Canon and zero knowledge category, you'll see so much of this covered there. But where are we exactly right now in terms of what they can do? Because you guys talked a lot about what they can't do yet and what the opportunity is, which is exciting. But where are we right now? Like, what can they actually do?

25:00Yeah, so right now they can basically do classification for medium -sized models. So not something as big as GPT -3 or 4, but medium -sized models. It is possible to prove that the classification was done correctly. Training is probably beyond what can be done right now, just because training is so compute -intensive that for proof systems were not there yet. But like Ali said, we have other ways to do it. For example, we can have multiple people do the training and then compare the results, yeah, so that now there are game theoretic incentives for people not to cheat. If somebody cheats, somebody else might be able to complain that they computed the training incorrectly, and then whoever cheated will not be paid for their effort.

25:40Right, right. So there's an incentive for people to actually run the training the way it was supposed to run. Right. And so basically that's sort of not like a hybrid phase, but it's basically like alt approaches until more of this comes to scale and performance is scaled to a point where we can get there. Yeah, I would say that for some models classification can be proved in zero knowledge today. For training right now, we have to rely on optimistic techniques. Yeah, great. So, Ali, you mentioned Compute is one of the three prongs, and you also mentioned that data and then the models for machine learning themselves.

26:12Do you want to tackle now data and sort of the opportunities and challenges there where it comes to the crypto slash AI intersection? Yeah, absolutely. So I think that there is an opportunity, even though the problems involved are very difficult to both both decentralize the process of sourcing data for training of large machine learning models from a broader community, again, instead of having a single centralized player, just collect all of the data and then train the models themselves. And this could work by creating a kind of marketplace that's similar to the one that we just described for compute, but instead incentivize people to contribute new data to some big data set that then gets used to train a model.

26:51The difficulty with this is similar in that there's a verification challenge. You have to somehow verify that the data that people are contributing is actually good data. And that it's not either duplicate data or garbage data that was just sort of randomly generated or not real in some way. And to also make sure that the data doesn't somehow subvert the model and some kind of poisoning attack where the model actually becomes either backdoored or just sort of less good or performance than it used to be. And so there's the question of how do you verify that that is the case and that's maybe an open hard problem for the community to solve.

27:28It may be impossible to solve completely and you might have to rely on a combination of technological solutions with social solutions where you also have some kind of reputation metric that members in the community are able to earn to build up credibility such that when they contribute data the data can then and be trusted a little bit more than it would be otherwise. But what this might allow you to do is that you can now truly cover the very, very long tail of the data distribution. And one of the things that's very challenging in the world of machine learning is that your model is really only as good as the coverage of the distribution that your training data set can achieve.

28:08And if there are inputs that are far, far out of the distribution of the training data, then your model might actually behave in a way that's completely unpredictable. And in order to actually get the model to perform well in the edge cases and the sort of Black Swan data points or data inputs that you might experience in the real world, you do want to have your data set be as comprehensive as possible. And so if you had this kind of open decentralized marketplace for the contribution of data to a data set, you could have anyone who has very, very unique data out in the world contribute that data to the network, which is a better way to do this because if you try to do this as a central company, you have no way of knowing who has that data.

28:49And so if you flip it around and create an incentive for those people to come forward and provide that data on their own accord, then I think you can actually get significantly better coverage of the long tail. And as we've seen, that performance of machine learning models continues to improve as the data set grows and as the diversity of the data points in the data set grows. And so this can actually supercharge the performance of our machine learning models to an even greater degree, we're able to get even more comprehensive data sets that cover the whole distribution. So let me turn this on its head.

29:22And if we're gonna incentivize people to contribute data, basically we're gonna incentivize people to create fake data so they can get paid. Yeah, so we have to have some sort of a mechanism to make sure that the data you're contributing is authentic. Exactly. And you can imagine a couple of ways of doing this, right? I mean, one way is actually by relying on trusted hardware, right? Maybe the sensors themselves are embedded in some trusted hardware that we would only trust data that's properly signed by the hardware. That's one way to do things. Otherwise, we would have to have some other mechanism by which we can tell whether the data is authentic or not.

29:56Completely agree. That'd be the biggest open problem to solve. And I think that as benchmarking for machine learning models gets better, I think there's two important trends in machine learning at the moment. there's improving the measurement of the performance of a machine learning model. And for LLM's, that's still very much in its early stages, and that it's actually quite hard to know how good an LLM is because it's not as if it were like a classifier where what the performance of a model is is very clearly defined. With an LLM, it's almost as if you're testing the intelligence of a human, right?

30:28And coming up with the right way of testing how intelligent and LLM like ChatGPT is, is an open area of research. But over time, I think that'll become better and better. And then the other trend is that we're getting better at being able to explain how it is at a model works. And so with both of those things, at some point, I might become feasible to understand the effect that a dataset has on a machine learning models performance. And if we can have a good understanding of whether or not a dataset that was contributed by a third party, helped the machine learning models performance, then we can reward that contribution and we can create the incentive for that marketplace to exist.

31:07So just to summarize so far, what I heard you guys say is that there's trusted hardware that can help check the accuracy of the data that's being contributed and the models that are being contributed. Ali, you mentioned briefly reputation metrics and that type of thing can help. You also mentioned that there might be a way not necessarily now, but sometime in the near future to check how the data is influencing the outcomes in a particular model so that you can actually, it's not quite explainable, but the idea is that you can actually attribute that this data set caused this particular effect.

31:37So there's a range of various techniques you guys have shared so far. Well, the final thing is that you could do the same thing for the third prong, which is models. I can imagine if you could create an open marketplace for people to contribute, a trained model that is able to solve the particular kind of problem. So imagine if on Ethereum you created a smart contract that embedded some kind of test, be it like a cognitive test that an LLM could solve or some classification test that a classifier machine learning model that is a classifier could solve. And if using ZKML, someone could provide a model alongside a proof that that model can solve that test, then again, you now have the tools that you need to create a marketplace that incentivizes as people to contribute machine learning models that can solve certain problems.

32:25So many of the problems that we've discussed, the open problems that we've discussed on how to do that are also present here. In particular, there's a ZKML piece where you have to be able to prove that the model is actually doing the work that it's claiming that it's doing. And then also we need to be able to have good tests of how good a model is, right? So being able to embed a test inside of a smart contract that then you can subject a machine learning model to evaluate how good it is. It's just another very nascent part of this whole technology trend. But in theory, it'd be great if we eventually get to a world where we do have these very open, bottom -up, transparent marketplaces that allow people to contribute and source compute data and machine learning models for machine learning that essentially act again as a counterweight to the very, very centralized and enormous tech companies that are driving all of the AI work today.

33:18I really love all of you. You mentioned that because there's been a long standing problem in AI in practice that it can solve a lot of things for like the bell curve, the middle of the norm, but not the tail ends. And that's a classic example of this is like self -driving cars, right? Like you can do everything with a certain amount of standard behaviors, but it's the edge cases where the real accidents and catastrophic things can happen. So that was super helpful. And I know you talked about some of the incentive alignment and incentives are providing accurate and quality data and even incentive for just even contributing anything in the long tail of data overall.

33:50But on the long tail side, a quick question that popped up for me when you were talking of like, it sort of begs the question of then who makes money where in this system? I couldn't not put wondered what is the business model kind of come in then in terms of making money for companies, because I always understood that in the long tail of AI, in a world of this kind of available data sets, that your proprietary data is actually your unique domain knowledge and kind of the thing that only you know, in that long tail so do you guys have any quick responses to that? So I think that the vision behind crypto intersecting with AI is that you could create a set of protocols that distributes the value that will eventually be captured by this new technology by AI amongst a much larger group of people.

34:33Essentially a community of people, all of them can contribute and all of them can take part of the upside of this new technology. And so then the people who would be making money would be the people who are contributing compute or the people who are contributing data or the people who are contributing new machine learning models to the network such that better machine learning models can be trained and bigger more important problems can be solved. The other people that would be making money at the same time are the people who on the other side are on the demand side of this network. people who are using this as infrastructure for training their own machine learning models.

35:08Maybe their model does something interesting in the world. Maybe it's the next generation of chat GPT and then that then goes on to make its way into a bunch of different applications. Like say enterprise applications or whatever it is that those models may be used for. And those models drive value capture in their own right because those companies will have a business model of their own. And then finally, the people who might also make money are the people who build this network. So for example, create a token for the network. That token will be distributed to the community. And all of those people will have collective ownership over this decentralized network for compute data and models that may also capture some value of all of the economic activity that goes through this network.

35:51So you can imagine any payment for compute or any payment for data or any payment for models could have some fee imparted on it. You might just go to some treasury that's controlled by this decentralized network that all token holders that are part of this network have collective ownership and access to as well as the creators and owners of the marketplace. And that fee might just go to the network. So you can imagine that every transaction that goes through this network, every form of payment that pays for compute or pays for data or pays for models might have some fee that's imparted on it that goes to some treasury that's controlled by the whole network and by the token holders that collectively own the network.

36:32And so that's essentially a business model for the network itself. Great. Okay. So so far we've been talking a lot about the way that crypto can help AI. I mean, to be clear, it's not like unidirectional. All these things are kind of reinforcing and bidirectional and more interactive than one way. But for the purpose of this discussion, we're really talking about it being like, Here's our crypto can help AI. Let's now kind of turn it on its head and talk a little bit more about ways that AI can help crypto. Yeah, so there are a couple of interesting touch points there. One that's actually worth bringing up is this idea of machine learning models that are used to generate code.

37:08Many of the listeners have probably heard of co -pilots, which is a tool that's used to generate code. What's interesting is you can try to use these code generation tools to write to solidity contracts or to write cryptography code. And I want to stress that this is actually a very dangerous thing to do. Oh, do not do this at home, OK? Yeah, do not try this at home. Because what happens is very often these systems actually will generate code that works when you try to run it. Encryption is the opposite of decryption and so on. So that code will actually work, but it will actually be insecure.

37:39We've actually written a paper about this recently that shows that if you try to get a co -pilot to just write something as simple as just an encryption function. It will give you something that does encryption correctly, but it uses an incorrect mode of operation, so that you'll end up with an insecure encryption mode. Similarly, if you try to get it to generate solidly code, you might end up with solidly code that works, but it will have vulnerabilities in it. So you might wonder why does that happen? One of the reasons is because these models are basically trained on code that's out there, right?

38:10They're trained on GitHub repositories. Well, you know, a lot of the GitHub repositories actually are vulnerable to all sorts of attacks. And so these models learn about code that works, but not code that is secure. It's almost like garbage in garbage outs. Yeah. And so I do want to make sure people are very careful when they use these generative models to generate code that they very, very carefully checked that the code actually does what it's supposed to do and that it does it securely. One idea on that front thing, when curious what you think about this is that you can use AI models like LLM, sort of like ChatGPT, to generate code in conjunction with other tools to try to make the process less error prone.

38:52And so one example, one idea would be to use an LLM to generate a spec for a formal verification system. So basically you describe your program in English and you ask the LLM to generate a spec for a formal verification tool. Then you ask the same instance of the LLM to generate the program that meets that spec. And then you use a formal verification tool to see whether the program actually meets the spec. And if there are errors, that tool will catch the errors. Those errors can then be used as feedback back to the LLM. And then ideally, hopefully, the LLM can then revise its work and then produce another version of the code that is correct.

39:33And eventually, if you do this again and again, you end up with a piece of code that ideally fully meets this spec and is formally verified to meet this spec. And because this spec is maybe readable by a human, you can maybe go through this back and see, like, yes, this is the program that I intended to write. And that could be an actual pretty good way to use LLMs to write code that also isn't as prone to errors as it might be if you were to just ask chat GPT to generate a smart contract for you. Clever. Yeah, this is great. And actually, this leads into another topic that is worth discussing, which is basically using LLMs to find bugs, right?

40:06So suppose a programmer actually writes some solidity code and now you want to test this. Code cracked. This is secure. And like Ali said, you can actually try to use the LLM to find vulnerabilities in that code. And there's been actually quite a bit of work on trying to assess how good LLMs are at finding bugs in software and solidity smart contracts and C and C++. Plus, there's one paper that came out recently that's actually very relevant. It's a paper from the University of Manchester, which says that you would run a standard static analysis tool to find bugs in your code. And it would find all sorts of memory management bugs or potential bugs, just a standard static analysis tool, no machine learning whatsoever.

40:45But then you would use an LLM to try and fix the code. Yeah, so you would propose a fix to the bug automatically. And then you would run the static analyzer again And then on the fixed code and the static analyzer would say, oh, the bug is still there or the bug is not there. And you would keep iterating until the static analyzer says, yeah, now the bug has been fixed and there's no more issues there. So that was kind of an interesting paper. This paper literally came out like two weeks ago. So for both of these papers, you just referenced and the one from the University of Manchester and also the one that you guys recently wrote on LLM's not being trusted to write correct code.

41:18It could be working code, but not necessarily secure. I'll link to those papers in the show notes so that listeners can find them. Just one quick question before we move on from this. So this is about the current state. Is this a temporary situation or do you think there will be a time when LLMs can be trusted to write correct, not just working, but secure smart contract code? Is that possible or is that just like way far off? That's a difficult question to answer. These models are improving by leaps and bounds every week. So it's possible that by next year, where these issues will already be addressed and that they could be trusted to write more secure code.

41:56I guess we're saying that right now, the current model is that we have GPT -4, GPT -3 and so on. If you use them to generate code, you have to be very, very careful and verified that the code that they wrote actually does what it's supposed to do in this secure. Well, and by the way, will we get to a point where the code that LLM's generate is less likely to contain bugs than the code that a human generates? And maybe that's the more important question, right? And because in the same way that you can never say that a self driving car will never crash, the real question that actually matters is, is it less likely to crash than if it were a human driver?

42:31That's exactly right. Because the truth is that it's probably impossible to guarantee that there will never be a car crash that is caused by a self driving car or that there will never be a bug that's generated by an LLM that you've asked to write any code. And I think this will only, by the way, get more and more powerful than more you integrated into existing toolchains. So as we discussed, you can integrate this into formal verification toolchains. You can integrate this into other tools like the one that Dan described, where you have a tool that checks for memory management issues. You can also integrate it into unit testing and integration testing toolchains.

43:03So that the LLAM is not just acting in a vacuum. It is getting real time feedback from other tools that are connecting it to the ground truth. And I think that through the combination of machine learning models that are extremely big, to train with all of the data in the world, combined with these other tools might actually make for programmers that are quite a bit superior than human programmers. And even if they might still make mistakes, they might just be superhuman. And that'll be a big moment for the portal of software engineering generally. Yeah, that's a great framing, Ali. So where does some of the other trends that come in for AI can help crypto and vice versa?

43:38Yeah, one exciting possibility in the spaces that we may be able to build decentralized social networks that actually behave a lot like Twitter might, but where the social graph is actually fully on chain and it's almost like a public good that anyone can build on top of, and you as a user, you control your own identity on the social graph, you control your own data, you control who you follow and who can follow you. And then there's a whole ecosystem of companies that build portals into the social graph that provide users experiences that are maybe somewhat like Twitter or somewhat like Instagram or somewhat like TikTok or whatever else they may want to build.

44:15But it's all on top of this same social graph that nobody owns, that there's no billion dollar tech company in the middle that has complete control over it and that can decide what happens on it. And so in that world, like that's an exciting world because it means that it can be much more dynamic and there can be this whole ecosystem of people building things and there's much more control by each of the users over what they see and what they get to do on the platform. But there's also the need to filter the signal from the noise. And there's this, for example, the need to come up with sensible recommendation algorithms that filter all of the content and show you a newsfeed that you actually want to see.

44:54And this will open the door to a whole marketplace, a competitive environment participants who provide you maybe with algorithms, with AI -based algorithms that curate content for you. And you as a user might have a choice. You can decide whether to go with one particular algorithm, maybe the algorithm that was built by Twitter or you can also go with one that's built by someone completely different. And that kind of autonomy will be great. But again, you're going to need tools like machine learning and AI to help you sift through the noise and to help parse through all of the spam that inevitably will exist in a world where generative models can create all of the spam in the world.

45:33What's interesting about what you said to is like it's not even about choosing between, it goes back to this idea you mentioned earlier and you mentioned this briefly about just having giving users the options to pick from marketplaces of free ideas and approaches that they can decide. But it's also interesting because it's not even only at a company to company level, it's really just like what approach works for you. Like you might be a person who's maybe more interested in the collaborative filtering algorithm that was in the original form of original recommendation systems, which is like collaborative filtering across people.

46:02So your friends' recommendations are the things you follow. When in fact, I personally am very different and much more interested in an interest graph. And therefore, I might be much more interesting people who just have similar interest to me. And I might pick that approach versus say something else that sort of like, Hey, this is the hybrid approach. Your only thing it's going to do is x, y and z just even being able to pick and choose that is already tremendously empowering. Like that's just simply not possible right now. And it can only be possible at crypto and AI. So that's a great example.

46:30Oh, yeah. Was there anything else to say on how AI can help it trust and security? So I think that kind of the meta picture is that crypto is the wild west because it's completely permissionless because anyone can participate. You kind of have to assume that whomever is participating might be an adversary and maybe trying to gain the system more hack the system or do something malicious. And so there's much more of a need for tools that help you filter the honest participants from the dishonest ones and machine learning and AI as an intelligence tool can actually be very helpful on that front.

47:06So for example, there's a project called Stello, which uses machine learning to identify suspicious transactions that are submitted to a wallet, and that flags those transactions for the user before those transactions are submitted to the blockchain. And that could be a good way to prevent the user from accidentally sending all of their funds to an attacker or from doing something that they will regret later. And that company basically sells to wallets, the companies like Metamask, search that then MetaMask can use the Intel and then do whatever it wants with it, either block the transaction or one of the user or sort of reframe the transaction so that that's no longer dangerous.

47:42And so that's one example. There are other examples as well in the context of MEV, which stands for Minor Extractable Value or Maximum Extractable Value, depending on who you ask, which is the value that can be extracted by the people who have control over the ordering of transactions on a blockchain. And that's often the miners or the validators of a blockchain. And AI here, I mean, can cut both ways and that those participants, if you're a validator on a blockchain and you have control over ordering of transactions, you can do all sorts of clever things to order those transactions in such a way that you profit.

48:17You can, for example, front -run transactions, you can back -run transactions, you can send which orders on Uniswap is a lot of transactions that you could craft such that you can profit from visibility of ordering transactions. And machine learning and AI might supercharge that ability because it can search for opportunities to capture more and more MEV. But then on the other hand, machine learning may help in the other way, in that it may help as a defensive tool. you may be aware before you submit a transaction that there is MEV that might be extractable from that transaction. And so then maybe you will either split up your transaction into multiple transactions so that there isn't a single validator that can complete the control.

48:58Or do something as a way of protecting yourself from an MEV extractor at some point in the transaction pipeline. So this is a way, again, where crypto plays a big role when it comes to security, when it comes to trust, when it comes to making the space more trustworthy to the end user. That's an example of AI making things difficult in crypto, and then crypto coming back and making things better for it. I'm actually with another example like that. So just like ML models can be used to detect fake data or maybe malicious activity. There's the flip side where ML models can actually be used to generate fake data.

49:32The classic example of that is deepfakes. We can create a video of someone saying things they never said, than that video looks fairly realistic. And the interesting thing is that actually blockchains can help to alleviate the problem. And so let me just walk you through one possible approach where blockchains might be useful. Imagine it's a solution that might be only applicable to well -known figures like politicians or maybe movie stars and such. But imagine basically a politician would wear a camera on their chest and kind of record what they do all day long. Yeah. And then and create a Merkel tree out of that recording and push the Merkel tree commitments onto the blockchain.

50:10So now on the blockchain, there's a timestamp saying, on this and this date, you said such and such. On this and that date, you said such and such. And now if somebody creates a deep fake video of this politician saying things they never said, well, the politician can say, look, at this time where the video said I said this and that, I was actually somewhere completely different doing something unrelated. And the fact that all this data, the real data, the real authentic data is recorded on a blockchain, can be used to prove that the deep fake really is fake and not real data. Yeah, so this is not something that exists yet.

50:40It would be kind of fun for someone to build something like this. But I thought it's kind of an interesting example where blockchain might actually be helpful in combating deep fakes. Is there also a way to solve that problem and show other times, chance or provenance where you can do that sort of verification of what's true. It's not true without having to make a politician walk around with like a camera on their chest. Yes, absolutely. We can also rely on trusted hardware for this. So imagine, you know, our cameras, you know, the cameras on our phones and such, they would actually sign the images and video that they take.

51:12There's a standard. It's called the C2PA that specifies how cameras will sign data. In fact, there's a camera by Sony that now will actually take pictures and take videos and then produce C2PA signatures on those videos. So now you basically have authentic data. You can and actually kind of prove that the data really came from a C2PA camera. And now, so now if you maybe read a newspaper article and there's a picture in a newspaper article and it claims to be from one place, but in fact, it's taken from a different place, the signature could actually be verified by the fact that it's C2PA signed.

51:45There's a lot of nuances there. C2PA is a fairly complicated topic that there's a lot of nuances to discuss and maybe we won't get into here. Yeah, and I remember you talking about this work with me previously, I think you guys that are off site, But I also remember from that that it doesn't stand up to editing and as you know, editorial people like me and other content creators and honestly just about anyone who uses Instagram or any online posting, no one like uploads things purely Raleigh as they're originally created like everyone edits them. Yeah, typically when newspapers will publish pictures in a newspaper, they don't publish the picture from the camera as is.

52:20They will crop it. There's like a couple of authorized things they're allowed to do to the pictures. Maybe they grace scale it, definitely they down sample it so that they don't take a lot of bandwidth. The minute you start editing the picture, that means that the recipient, the end reader, the user on the browser, who's reading the article, can no longer verify the C2PA signature because they don't have the original image. Right. So the question is, how do you let the user verify that the image they're looking at really was properly signed by a C2PA camera? Well, as usual, this is exactly where zero knowledge techniques come in, where you can prove that the edited image actually is the result of applying just down sampling and gray scaling to a properly signed larger image.

53:04Yeah, and so instead of a C2P signature, we would have a ZK proof, a short ZK proof associated with each one of these images. And now the readers can still verify that they're looking at authentic images. So it's very interesting that ZK techniques can be used to fight disinformation. It's a bit of an unexpected application. That's fantastic. A very related problem, by the way, is proving that you're actually human in a world where all of the deep fakes creating the appearance of humanity will generally outnumber humans a thousand to one or a million to one. And most things on the internet might actually be generated by AI.

53:40And so one potential solution, which is related to what you're saying is to use biometrics to be able to establish that someone is actually human, but to then use zero knowledge proofs to protect the privacy of the people who are using those biometrics to prove their humanity. It's a one project in this category is called World Coin. Yeah. It's also a project in our portfolio and they use this orb. People may have seen this like shiny silver orb that uses red and all scans as biometrics geometric information to verify that you're actually a real human. And it also has all sorts of other sensors to make sure that you're actually alive and that it can't actually be a picture of an eye.

54:20And it's this system that has secure hardware and is very difficult to temper with such that the proof that emerges on the other end, which is a zero -knowledge proof that obscures your actual biometric information is very, very difficult to forge. And this way politicians could, for example, prove that their video stream or that signature of theirs or that participation of theirs on some online forum is actually their own and that they're actually human. What's really interesting about what you said, Oli, that's a great follow up to it. Dan was saying about ways to verify like authentic media versus like fake or deep fake media and this world of infinite media, as you would say, that we live in.

54:59But what are the other applications for proof of personhood type technologies like that? I think it's important because this is actually another example of how crypto can help AI more broadly too. Going back, we're kind of flipping back and forth there, but that's okay because we're just talking about really interesting applications period. So it's fine. Well, that's a really good question. One of the things that will become important in a world where anyone can participate online is to be able to prove that you are human for various different purposes. There's that famous saying from the 90s that on the internet nobody knows you're a dog.

55:30Oh, yeah. Yeah. I think maybe maybe a reshaped form of that saying is that on the internet nobody knows you're a bot. So then I guess this is exactly where proof of humanity projects become very important. Because it will become important to know whether you're interacting with a bot or with a human. For example, in the world of crypto, there's this whole question of governance. How do you govern systems that are decentralized that don't have any single point of control and that are bottom up and community owned? You would want some kind of governance system that that allows you to control the evolution of those systems.

56:03And the problem today is that if you don't have proof of humanity, then you can't tell whether one address belongs to a single human or whether it belongs to a bunch of humans or whether 10 ,000 addresses actually belong to a single human and are pretending to be 10 ,000 different people. And so today you actually have to just use a amount of money as a proxy for voting power, which leads to plutocratic governance systems. Exactly. But if every participant in a governance system could prove that they're actually human, and they could do so in a way that's unique, such that they can't actually pretend to be more than one human because they only have a single set of eyeballs, then the governance system could be much more fair and less plutocratic and can be based more on each individual's preferences rather than on the preference of the largest amount of money that's locked up in some smart contract.

56:55Actually, just to give an example of that, But today we're forced to use one token, one vote, because we don't have proof of humanity. Maybe we'd like to do one human one vote. But if you can pretend to be five humans, then of course that doesn't work. And so one example of what this comes up is something called quadratic voting. So in quadratic voting, basically if you want to vote five times for something, you have to kind of put 25 chips down to do that. But of course, you can do the same thing. You can just pretend to be five different people, each voting once. And that would kind of defeat the mechanism of quadratic voting.

57:28So the only way to prevent you from doing that is this exact proof of humanity, where in order to vote, you have to prove that you are a single entity, rather than a symbol of entities. And that's exactly where a proof of humanity would play an important role. Generally, identity on chain is actually becoming quite important for governance. Totally. That, by the way, reminded me of an episode that you and I did, Ali, years ago, with Phil Devin, member of Dark Dows. That was such an interesting discussion that totally relevant there. Totally. By the way, is it free proof of personhood or proof of humanity?

57:58What's the difference? Is it the same thing? Yeah, people use them interchangeably. Proof of human, proof of humanity, proof of personhood. Yeah, yeah. Okay, so keep going on this theme then of media and this kind of infinite abundance of media. Like, whether other examples, and we're, again, we're talking about crypto helping AI, AI helping crypto. Are there any other examples that we haven't covered here? Where the intersection of crypto and AI can bring about things that aren't possible by either one of them alone? Completely. I mean, another implication of these generative models is that we're going to live in a world of infinite abundance of media.

58:32And in such a world, things like community around any one particular piece of media or the narrative around a particular piece of media will become ever more important. Just to make this very concrete, there's two good examples here. Sound .xyz is building a decentralized music streaming platform, enabling artists, musicians essentially to upload music and to then connect directly with our communities by selling them NFTs that give people in those communities certain privileges. Like for example, the ability to post a comment on the sound .xplc website on the track, such that anyone else who plays the song can also see the comment.

59:14This is similar to the old soundcloud feature that people might remember where you could have like this whole social experience on the music itself as it played on the website, its disability to allow people to engage with the media and to engage with each other often in a way that's economic because they're essentially buying this NFT from the artist as a way of being able to do this. And they are, as I've said, a fact, they're supporting the artist and helping the artist be sustainable and be able to create more music. But the beauty of all of this is that actually gives an artist a forum to really interact with their community.

59:47And the artist is a human artist And as a result of crypto being in the loop here, you can create a community around a piece of music that wouldn't Automatically exist around a piece of music that was just created by a machine learning model That's devoid of any human element right that doesn't have a community around it And so I think again in a world where a lot of the music that we're gonna be exposed to will be fully generated by AI the tools to build community and to tell a story around art around music around other kinds of media will be very important as a way of sort of distinguishing media that we really care about and really want to invest in and spend time with from media that may also be very good but it's just a different kind of media.

1:00:32It's media that was just generated by AI with less of a human element. And it may be by the way that there's some synergy between the two like it could be that a lot of the music will be AI enhanced or AI generated but if there's also a human element like say for example a creator leveraged AI tools to create a new piece of music, but they also have a personality on sound. They have an artist page, they have built a community for themselves, and they have a following. Then now you have like this kind of synergy between the two worlds where you both have the best music because it's augmented by the superpowers that AI gives you.

1:01:03But you also have a human element and a story that was coordinated and made real by the script to aspect, which lets you bring all of those people together into one platform. It's really quite amazing that even in the world of music, just like we talked about in the world of coding, where you have a human coder that's being enhanced by tools like co -pilot that generate code, we are seeing things like this where an artist is being enhanced by ML systems that help write, or at least parts of the music are being written and generated by an ML system. So it's definitely a new world that we're kind of moving into in terms of content generation.

1:01:42Basically, there's going to be a lot of spam that's generated by machine -generated arts, which people might not value as much as they value art is generated by an actual human. Maybe another way to say it is one of the points of NFTs was to support the artists. Yes. But if the artists themselves are now machine learning models, then who exactly are we supporting? Yeah. And so, yes, it's a question of how do we distinguish how to differentiate human -generated arts that need support versus machine -generated arts. Well, this is a philosophical discussion for over drinks maybe, but I would maybe go so far as to say that the promter is also an artist of sorts.

1:02:20And in fact, I would make the case that that person is an artist and the same thing has come about with, as this is a discussion and debate as old as time, and it just simply new technologies, old behaviors. It's the same thing as when playing out for eons and the same things playing out the writing, etc. Totally. Very true. Well, that actually opens up the door for collective art. For art that's generated through the creative process of a whole community as opposed to a single artist. There are actually already projects that are doing this where you have a process by which a community influences through some voting process on chain what the prompt for a machine -learning model like Dalee will be.

1:02:58Then you have Dalee use that prompt to generate a work of art. maybe you generate not one work of art, but like 10 ,000. And then you use another machine learning model that's also trained from feedback by the community to pick from those 10 ,000 the best one. Right, and so then now you have a work of art that was generated from the input of the community that was also sort of pruned and selected from a set of 10 ,000 variants of that work of art. Also through a machine learning model that's trained by the community to then generate one work of art that is kind of the product of this collective collaboration.

1:03:33That's incredible. I love it. Well, you guys, that's a great note to end on. Thank you both for sharing all that with the listeners of Web 3 with A6 and Z. Thanks, Sonal. Thank you so much.

1:03:46Thank you for listening to Web 3 with A6 and Z. You can find show notes with links to resources, books, or papers discussed, transcripts, and more at A6 and Zcrypto .com. This episode was produced and edited by Sonal Chauxy. That's me. The episode was technically edited by our audio editor, Justin Golden. Credit also to Moonshot Design for the art and all thanks to support from A6NZ Crypto. To follow more of our work and get updates, resources from us, and from others, be sure to subscribe to our Web 3 Weekly Newsletter. You can find it on our website at A6NZcrypto .com. Thank you for listening and for subscribing.

1:04:26Let's f*** go!