Trent McConaghy: Ocean Protocol – AI & the Data NFT Marketplace (V4) #481

Transcript

welcome to epicenter the show which talks about the Technologies projects and people driving decentralization and the blockchain revolution I'm Frederica Ernst and I'm speaking with Trent McConaughey today who has been on many times before he's one of the co-founders of ocean protocol um which um deals with um data in an on-chain manner we will go into that in just a second um before we do let me briefly tell you about our sponsors today our sponsor today is Omni Omni is your new favorite Mighty chain Mobile Wallet Omni supports more than 25 protocols so you can manage all of your assets in one place but what's really special about

only is what you can do inside the wallet want to get yield Omni allows you to get the best apis with zero fees in three tabs need to swap Omni Aggregates are major bridges in Texas so you can bridge and swap across all supported networks in one transaction directly in your wallet 11 nfts Omni offers the broadest nft support of any wallet so you can collect and manage your favorite nfts across all chains in one place Omni truly is the easiest way to use web3 and it's fully self-custodial meaning you never have to trust anyone with your assets other than yourself and they support ledger so give it a try at omni.app hey Trent it's so good to have you on again

um you've been on epicenter on multiple occasions um nevertheless briefly tell us who you are sure hi everyone and thanks for having me on it's great to be here again um so a brief background I spent more than 20 years in the world of AI um focusing just on AI uh largely on AI for Designing computer chips driving Moore's law that sort of thing for use by Circuit Engineers as well as a lot on Creative AI um you know getting AI to do things that was previously considered things that only humans could do with Creative Design and things since 2013 I've been deeply focused on blockchain First with ascribe which was basically uh nfts on bitcoin

um ethereum didn't even exist then then that pivoted into big chaindb which was basically mongodb wrapped with tenement bft consensus and since 2017 the focus has been ocean protocol decentralized data exchange protocol so you have worked on Ocean for the better part of a decade so since 2016 or so that's like seven years now can you rehash what ocean was about in the beginning and kind of what this trajectory has looked like over the past seven years uh sure so um uh you know by background right I had spent a lot of time in Ai

and I was always sort of as a technology a general purpose technology it was sort of my first love I just always thought it was amazing and fascinating and couldn't believe my luck that I could find a way to work on it professionally for years and years um and you know in 2015 when uh I got also Denver well by 2010 I had to become excited by blockchain and Bitcoin a huge nerd and um you know that led eventually to um doing a scribe and when I started working on a scribe um and really full time at it um I was a little bit sad that I felt like I was pausing my work on AI just because AI is you know um has such great potential as a technology um it's a really big lever for um you know changing the world for

the better um and blockchain too so it's sort of like there's these two technologies that you know I felt that I could access that I could help that I could you know help make a difference with so I thought that I was putting Ai and pause um but there was this you know idea with the Scribe um basically buying and selling digital art and empowering creators that you know it was worth putting AI on pause and um you know worked on that worked on Victory DB which got a bit closer to the AI because of the Big Data thing but what led to Ocean was in um 2016 um I started thinking a lot more about AI again um in in depth and I started to learn you know what are the very specific USPS of of blockchain compared to traditional

databases and um you know big chaindb is basically a database with blockchain properties so we had to be very precise in understanding those and the main properties were decentralized that you know such that you can get immutability sorry decentralized such that you can have political uh coordination among humans all the sort of things you hear about with Dows now um the next benefit was immunability or property that leads to benefits which is immutability which allows for Providence Trails censorship resistance and more and finally the third one was assets the idea of um it's only your Bitcoin if you have the keys to your Bitcoin you know Yorkies your Bitcoin not your keys not your Bitcoin to paraphrase Andreas Antonopoulos and uh a little bit later I I realized there was one more the most

important one of all probably which is incentives um you know incentives are sort of like the superpower blockchains um if you uh you basically can get people to do stuff by incentivizing them with tokens and so those four properties that lead to very specific benefits decentralization immutability assets and incentives I thought about a lot and then I was playing around and basically hanging with friends as well as writing I wrote some blog posts to explore you know turn the crank how can these four things help for AI how can these four things help for Big Data and um and basically turning the crank um on that you know it turns out that it helps a lot right for example

um decentralization can help for AI and data things like collective bargaining around data you know we each as individuals we have a lot of data but we might consider selling it rather than having you know Facebook Etc essentially sell it on our behalf and then get all the money but it's a huge amount of effort to do on our own but if we can join some collective bargaining mechanism for example the adao then we could you know a hundred thousand of us a million of us 10 million of us then someone can bargain on our behalf and we can get the benefits so turn the crank on each of those things right decentralization immutability in that one it's Providence Trails of of the history of the data and the training assets you know not your keys not your data not your not your trained model Etc

and then incentives right how do you incentivize people to um you know share data in a way that is privacy preserving Etc so all of those things um you know I wrote about and got quite excited about and this was around late 2016. there was one other set of thinking I had around AI Dows this idea that um imagine you have a Dao where um it's humans sorry it's AIS rather than humans kind of controlling it right and this is right on the heels of the Dao coming out you know that was I think mid 2016. so um it was pretty exciting to think about what might happen there and it leads to the ideas of um self-owning AIS uh rights for AIS all of that you know basically an AI that you know has its own wallet and can accumulate wealth on its own over time

and there's quite a few different configurations there so um basically it's those things that led to Ocean the AI that I was thinking and then thinking on how can blockchain help the data and finally how can blockchain help Ai and on those latitude articles all the roads pointed the Crux of the problem was decentralized Data Marketplace the heart of it was Data you know one thing that um the AI World discovered in about 2005 or so was the unreasonable effectiveness of data you know you don't have to you know have a PhD worth of inventing new algorithms instead you can just throw 10x more data at a problem and you can drop your error down by 2X or more and you can keep doing it another 10x another 10x to chop down your error more and more and more and this is a bit embarrassing for AI people because you

know everyone wants to have a PhD to say they're cool and they're smart and instead you know you can just take you know pure engineering and throw more data at it and you can get a lot from that and that's what's been happening you know ever since 2005 or so you know we're 18 years into that and we're seeing the effects these days with you know gpt3 GPT 3.5 et cetera so yeah that's what led to Ocean we we realized okay um more data can help a lot um but if we're not careful it's gonna all that data is going to end up in the hands of a few powerful players like was already happening by 2017. the likes of Facebook Google um or you know well-funded AI companies these days the likes of open AI so how can you help to level the playing field

around that how do you level the playing field around data how do you level the playing field around Ai and that is the aim of yeah no that makes that it makes a total sense and also how he talked about having all these components that kind of make web3 um uh systems special come together absolutely so if you look at the last couple of years um of ocean protocol you guys have gone through a series of major upgrades can you speedrun us through these uh sure so um you know we in the spring of 2022 we released ocean V4 so a very brief Speed Run ocean V1 was about

Sovereign data so it was where uh if you have the keys to that data uh you own the data and in Ocean we we realized the heart of this was access control so it's not about storing the data um you know that is lower down in this deck whether it's centralized storage on your own machine on some centralized um storage uh Cloud such as Amazon S3 or decentralized storage such as file coin or our weave so that's lower in the stack one level up is you know basically when you go to share or something right so when you go to share something um you can think of that as granting access to someone else so ultimately it's who has the control to be able to share access and that's um the layer that ocean operates so it's you can think of it as decentralized access

control so our V1 was really about you know enabling decentralized Access Control where you can access it if you have the keys to it and a good way of thinking but these days you know sort of 2023 era framing would be token gated apis and token gated dapps although we only hit tokens a little later on I'll mention that so that's V1 with Sovereign data you know Yorkies your data not your keys not your data V2 people kept asking us okay um this is all great you know I've got data I can share it to others but what if someone else gets access um you know has um has access to that data via the access control what if they download it what will stop them from sharing it to others so in our V2 we said okay well

you know let's have it also where you have the option to have compute to data so in that case what happens is um your data never leaves the premises and never leaves you know your local storage wherever you have it and instead someone has there's an algorithm on top whether it's Computing an average training an ml model whatever and um the the person who is buying that data they're only buying the results of whatever that algorithm does you know a chain Range model on average whatever so that was ocean compute to data and that was part of our V2 by the way for doing this we had looked at a bunch of other privacy preserving protocols such as a moment work encryption multi-party compute ZK stuff et cetera and none of it was quite mature enough at the time you know that

was several years ago now I think four years ago so um but at the same time compute data this was much more of a devops thing you know can you arrange your compute and data in just the right way so it was practical to do you don't hear about this in privacy circles because it's hard to get published in a you know privacy conference just for you know managing Docker containers right but um from a product perspective it makes tons of sense um so that was V2 releasing ocean computed data it's and basically it also you know is yes with managing Docker containers Etc um you can have it play well with all of these other privacy preserving protocols and we have a blog post from that that was a V2 so V1 Sovereign data V2 privacy V3 um we had seen in D1 and V2 this was

mostly custom contracts for Access Control Etc and we had this giant list of things we wanted to see in a data economy we wanted to see data exchanges we wanted to see really great data custody in the form of wallets right and then things like multi-sig wallets and Hardware wallets we wanted to see data management the way that you know sap does in a centralized way for sharing data with others in um you know Bank level security et cetera healing management we wanted to see Dows you know collective bargaining around data all this and we're like wow this is like a mountain of things that we want to build or see happen and you know ocean you know we've never made had a huge team in Ocean typically between 20 and 40 people on the core team so um we asked okay how can we be smart

about this and um we realized that what we can do is tokenize Access Control and so what the heart of that means is for any given data set when you publish the data set um you have erc20 tokens to access that data set whether it's download or compute data or otherwise and so if you Frederica have 1.0 data tokens to access say trans DNA data as CS ESC file then you can come to uh the ocean ecosystem come to the provider component it's a middleware component and say hey here's 1.0 data tokens for Trans DNA data I would like to access it please and then um then it will handshake with you and

give you access either in the form of URL or for downloading or otherwise okay so that's um what we that's the heart of the idea of tokenizing access control and that is data tokens right because uh why and it's erc20 why erc20 because this is a fungible concept right I might want to share with 10 people honor people a thousand people and then so that was kind of a big breakthrough for us because by going erc20 it made us mid-ocean it enabled all these things it enabled um data wallets metamask became a data wallet um trezor became a hardware data wallet no says safe became um a multi-sig data wallet um and same thing for changes balancer became a data exchange for unisoft became the data exchange right all you

have to do is go and start a unisa pool some centralized exchange should come along and start adding data tokens if we wanted as well um Aragon and other Dows moloch suddenly become um Dao data and then you can get fancier too right you can have um data backed stable coins Etc so that was all enabled by this one thing by saying let's make the access control itself tokenized which is erc20 therefore interoperable with the rest of the ethereum and evm ecosystem so we did that for ocean V3 I'm very happy with that so then ocean you can think of as an on-ramp two um publish your data assets to into erc20s and then off ramp to consume them and everything in between people can

apply whatever uh blockchain and D5 tools they want and we make some things easy such as you know exchanges Etc but anyone can do anything that was our V3 actually as part of it we also launched uh an exchange called Ocean Market and in it um we had free pricing which is basically you know just share for free um fixed price you know buy and sell at a fixed price um you can price an ocean or whatever other tokens you want um as well as uh automatic pricing which we had with balance approvals um then I've you know had worked with balancer for a long time create a relationship with them so that was very nice um and that was our V3 and uh so we had the underlying technology you know the smart contract deployed to ethereum

mainnet um all you know fully decentralized immutable permissions Etc the middleware components um and the ocean Market front end as well as sdks slash drivers for both JavaScript and python that was our V3 and we launched that had really great traction um in terms of people buying and selling and people really liked the aspect of the trading on um you know especially doing on the data assets that was actually a little bit too crazy they were focusing too much on the speculation side of the data assets rather than actually buy buying and consuming the assets so we said okay for V4 we're going to soften this and we had seen one specific issue on rug pulls basically when you publish a data asset

you are a data token layout you know you have you know tens of thousands millions that you can mint yourself as that publisher and so in an amm context that is quite detrimental to anyone trying to stake on that so for our V4 we we fixed that uh or we thought we fixed that with one side is taking and at the same time we did two more things for a V4 uh which came out this this past spring uh we introduced ocean data nfts um and I'll get into that in a second as well as um better community monetization so uh I'll just go through these three things so on the one side is taking it basically it looked like a nice clean elegant solution to one side it's taking but within a month of releasing ocean V4

someone found an exploit um which was kind of sad we had already gone through a lot of security audits but this is how it is it wasn't an exploit in the logic of the software um in the smart contract level it was actually in some of the incentives so uh we actually realized you know uh there's too much focus on the automatic pricing with the speculation so we just simply turned it off and said everyone we're going to focus on the um on the fixed price and we're going to have another approach to curation because the up till that point the amms were a really great way to curate assets you know how much liquidity is in a given data token pool and I'll get into the into a bit but the summary of how we have curation now is with uh ve ocean and some some data form

and stuff so I'll talk about that in a bit um so in V4 uh the while we brought in um you know a fix for the rug pulls of data tokens we soon removed all the amm stuff simply because um it was uh detrimental overall it's still there on this more contract someone else can go and use it if they want but we don't recommend it but the other two things are still there and going strong and we're really happy with one of them was um basically these other two features uh data entities and Community modernization this was all around um people you know using ocean looking for more flexibility so let's stop for a second and think about IP in general if you are say

um AC DC and you publish a new album say Back in Black right um you you've already got a record deal with with Universal Music and you record the master tapes and you you um you have copyright as ACDC the band you have the copyright so you can do whatever you want with those um and if anyone else copies that and sells it on their own um you have full legal recourse to come after them to stop them Etc right and that copyright lasts for decades in fact 70 years after the death right of the creators so which just crazy too crazy long but so be it that's how the laws work um so basically AC DC would record this and then they could just sell and distribute on their own but you know that's not really the Forte of a band so they instead said we're gonna give an

exclusive license of essentially all the rights that copyrights gives copyright gives to Universal Music and then Universal um you know and basically called that the master tapes right so Universal then says okay we're going to go and manufacture a million CDs you know for back and black and each CD as has a very specific set of Rights anyone who buys that CD can listen to that for personal use but they can't go and play it directly in a radio station that's the different rights they can't go and you know sell resell that CD sometimes it happens in a used fashion but that's not built into the right but per se that's a CD but also what about you know yeah licensing to radio stations or streaming on Spotify or selling as vinyl or as cassette tapes each of those so there's a five or six or seven different

distribution formats with different licensing terms reach against that same base IP we saw the same thing with data right so when people people publish a data asset their their claiming copyright they're claiming that they have that IP and then um they might want to license that IP for just one day or maybe for one month or maybe for um compute data context versus a download context so basically what we said was let's map this idea for music of Master tapes to the licenses in the form of CDs et cetera to the world of blockchain in this case it's nfts are the master tapes we call those data nfts and then data tokens are the CDs or the cassette tapes or the vinyl Etc so when I go to publish

a data asset I publish a data nft to claim that copyright Etc and then on top of that I can deploy one zero one or many erc20 smart contracts so one smart contract might be for you know downloading for a day with a one day license one might be for downloading for um for with a month a one month license one might be for computed data and that's what we built for V4 and it's you know much more flexible um and yeah so it's the you know data nfts are erc721 erc721 one of one erc721 and the data tokens are the erc20 fungible so it's a really nice mapping

of non-fungible fungible um therefore you know using these things as they are intended you know the non-fungible is truly non-fungible the fungible is truly fungible Etc and um so that's the one thing and by the way these can be used anywhere like someone could take Ocean Market Fork it it's all attach it to the front end and launch their own you know music um nft marketplace with Ocean or digital or nft Marketplace and that was actually fun for us because it kind of reconciles back to ascribe 2013 right with nfts then so you know and actually the terms of service of Ocean Market is actually a riff on the terms of service from ascribe days so you know we were able to leverage all the the legalese for that and of course you know anyone running their own Market can have whatever legals they want at the heart of it

though you know at the L1 level the blockchain level it's fully decentralized you know there's no sort of legals attached people can attach legal meaning for whatever jurisdiction they are and finally the monetize the simple thing there is basically you can have uh people can monetize in various places if you're a Marketplace selling this stuff um you can get a cut now if you're a provider helping to provide some of the compute or some of the middleware um you can get a cut um if you're the marketplace you're publishing or for consuming I should mention um you can get a cut so so we made all that possible and just to make it much easier for for people to monetize in various ways to help keep the community healthy and strong so yeah that's what we're up to V1 V2 V3 V4 maybe it was a bit longer but to

summarize once again V1 was self-sovereign Data with Access Control V2 privacy V3 erc20 data tokens for huge interoperability and leveraging all the awesome stuff built in the ethereum and evm ecosystem and then V4 was a refinement of V3 especially with data nfts and community monetization that was incredibly comprehensive and I actually obviously I I took notes before I I went through your docs and kind of um uh put down questions and things to talk about before this episode and you actually went through all of my points one by one so um I I think um this was a perfect explanation I I

particularly like um the transposing this to to CDs because obviously everyone understands that nevertheless I have kind of a couple of of kind of remaining questions regarding this kind of topic area um so you gave an example of uh you know Trends DNA and you may be okay with me using your DNA for specific things so for instance I could maybe I'm I don't know I'm an Alzheimer's researcher and kind of you want to contribute to that right but you might not want your DNA data

um to be used for something else elsewhere so you might not want um the DNA to a DNA data to be used for I don't know my making a trend mice chimeras right um like in a bio lab somewhere in China or so so once you kind of you you have you put your data out there and you kind of you mint data tokens for Access do you have any way of kind of restricting um access for those data tokens I mean in principle they're freely transferable so I could I could sell them to the bio lab in you know Shenzhen and they could use it for whatever so what kind of

recourse do you have yeah that is a great question actually and um we actually have an answer you know we've iterated with multiple uh users large and small over the years everyone from individuals to to cities to governments to Big Enterprises and um one a really great collaborator over the years has been Daimler which is you know Mercedes-Benz right and so um that goes back years and as we've been iterating with them they launched um finally in production of their Marketplace um based on Ocean Technology about six months ago and um as we were iterating with them um towards launching that Marketplace in production um it's called eccentric that they have a spin-off specifically just for this um as they were iterating they said you

know we've got um this Automotive data that we want to be selling and some of our partners that want to sell via a Centric Marketplace but they don't want to sell to just anyone just like you said what can we do and so we worked with them and iterated and we came up with the idea of fine-grained permissions what does that mean it means that On Any Given data asset at data nft you can optionally tackle on top um an allow list or a deny list of say so you can make it where you know the allow list is like a white list sort of um here's the parties that can access right and that can be specified as eth addresses but it's also flexible enough

to allow credentials in various forms so then it's a form of role-based Access Control R back right which makes it super flexible so um and so you can do this from a whitelist or a blacklist perspective basically you can say here's the people who are allowed or you can say I'm going to allow anyone except for these very specific entities right and you know that solves the problem very nicely and it's very useful in some other contexts too for example what if you have you just want to be sharing data freely among say a Consortium right then you can say Okay um maybe there's 20 members of that Consortium you publish a data asset a data into that Consortium saying if this if someone shows up with a credential a verifiable credential like w3c style

verifiable credential or maybe a sold down to Oakland or whatever saying that they have this particular credential then they can access it otherwise they can't so that's an example so to summarize uh yes we have a very explicit answer for that called fine-grained permissions that makes perfect sense um what about the data nft so I I assume they're like can you give us an idea of how many um different data nfts have been minted yeah so um you know you can Mint one by the way in like one line of code right in JavaScript or in python or if you go to Ocean Market you know you can publish your own in you know a couple minutes five you know five minutes if you've never done it before two minutes if you

have right um and by the way I should mention uh you can do this on ethereum mainnet and we've also deployed to four other production chains so far and that's a Binet smart chain uh energy web chain polygon and Moon River and we've got a short list of another five or ten that we want to do including some l2s and obviously noses chain is on that list we're big fans of this chain um so uh I just want to mention that because that means that the publishing cost can be you know just a few cents or less um and uh from Ocean Market you just choose which one whichever one is your wallet your wallet is connected to um but going back to your question then um how many have been minted so far their uh last I checked it's at least 500 maybe it's even 750 by now I last

checked a few weeks ago um you can just go to market.oceanfortical.com and check maybe I can do that right now and just give you a very precise answer for your audience um it's listed at the very bottom here so pulling it up and here we go oh wow much more than I thought we have 1562 data nfts that have been published and those are what those are ones that are valid for Ocean Market there's ones Beyond two um a lot of these are just testings and so on right it's um you know to me what really matters is um are these useful right are people buying and selling these is their actual consume volume against this Etc right so because I could go and write a script that published you know publishes ten thousand more and you know run that

script in 10 minutes and then give you this other stat that is kind of meaningless unless it's really end to end for Value creation right but yeah these ones you know as far as they know no one is writing scripts to publish a whole bunch of these at once in a crazy way so so yeah that's the number so how do I understand what's in these uh data nfts right so basically I I mean obviously the person who publishes them they can also they meant these um these data tokens to access them so basically kind of before I you know go all in and purchase you know a data token to kind of access this how do I understand what kind of data is for sale and whether it's um good data yeah that's a that ladder question is a

great question so the first one to understand what uh kind of data is in there um for if you go to Ocean Market which is you know market.ocean protocol.com or some third-party Market that also can see the same data um you know they're all basically accessing the data that is listed on chain right so on chain all you know the data nft has all the metadata about the assets right it's and that metadata includes a title um a Creator um a description which is you know including markup Etc you kind of HTML markup et cetera um and then the main thing is it has um it describes access how do you access this thing right and usually access is um well the downloadable version is it's

a URL right a URL that might be or a URI should be more General so it could be uh an HTTP style URL that points to a specific F3 bucket for example it could be an ipfs URI it could be an R weave URI Etc and we have native support for ipfs and RV by the way um so that's the heart of it is you know a few pieces of metadata and then this URL if it's not downloadable and said compute data then it has a bit more information about you know um uh what the uh basically compute script that needs to run is typically around wrapped in a Docker container and then um it can have a whitelist or Blacklist of who can access that too and um so

that's the heart of it but then um there's a bunch of extra fields of course too um and it all follows the the ERC 721 format at the base we've also extended it such that it follows the openc format therefore anything you publish in Ocean Market or one of the you know ocean.s oceanfy um you can render it in openc as well you know you can it just shows up there those links through motion Market too we've also extended it with ERC 725 which is what uh Fabian Volvo sheller invented uh erc20 inventor he did that he did that for identity for his work with Luxo and fashion Etc but he knew that it would be much more General so you know we we iterated with him um to put that into ocean and ERC 725 is an extension to erc721

um one part of it allows um basically arbitrary key value store so you can have any other key value Pairs and that basically turns it into sort of a nosql type database right so we have that as well and we have that very easy to access so that's basically what's inside of data nft um and you know we we've biased it towards data use cases but people can use it for anything right so actually the day that um Puja and Oliver and vitalik published their sold bound tokens piece by coincidence I was publishing a piece on profile nfts into ocean ply readme's so right after it published I DM preacher's like hey put you here you go Implement it's done and you know you can turn off transferable

yes or no right so so people are using ocean data nfts for still bone tokens it's just you know another approach to Identity and stuff et cetera we have read me zero on that too and actually we have a git coin hackathon happening right now around the cellphone token aspect of data entities so I hope that answers your first question uh maybe I'll pause there before I answer your second question yeah no this this answers my first question but my second question how do I know whether the data is good data in terms of how do I know it's actually what what it says on the phase of it is actually what's in there yeah exactly that is a great question and it's not the sort of thing you know you can answer definitively unless um so what we don't want to do is have some trusted Authority in between

saying this is good right so um if you look into the literature and the research on you know data marketplaces and buying and selling data there's hundreds maybe thousands of papers and lots of these papers are academic they have ideas about you know what means quality data and we take a very pragmatic view um you know call it evolutionary or call it a market-based view um the quality of the data is simply how much people have spent money on it to buy it and consume it that's it right um that is the most important signal are people buying and selling this thing um and you know otherwise people can make all their arguments about why they have pretty data why they have good data at the end of the day are are people actually using the data right so but you can view that as as a signal

um and overall you know why do you want to know whether data is good or et cetera you want it for Discovery typically for people to consume right so then it comes to the question of Discovery this is a you know a long researched question um in the world of interfaces um and web apps in particular over the last 25 years so if you think about Discovery there are three main pillars to Discovery browsing search and filtering and so if you want to have good Discovery you want to have at least basic support for all three and then make it better better and you want to have good signals around that so browsing means you know you go to marketoceanpritical.com or otherwise and you browse you scroll through seeing what's good what's bad Etc and um searching you know you type in one or a

few um uh text-based queries it gives you some results and then you can and then after that you can filter against it with other signals what sort of signals um can you have so signals you know this is complementary to the the discovery to the browse search filter uh aspect and so one key signal like I mentioned is what is the volume of a given data asset other signals include how much stake is there against it and I'll get into that in a bit uh probably when you ask I'll wait for that um and also you know who published it is it someone that I know they can choose to make themselves Anonymous or they can choose to attach their publishing profile to to ens that's you know supported directly or to other um identity things

um and uh you know in the future where we have you know in the backlog things like comments and readings so there's various signals um there's also though if someone has published something fraudulently let's say that I published a data set that's doing well you um you come along you decide to be you know uh evil Freddie which is probably probably never going to be the case um but let's say you publish this thing and start selling it as your own then the community can flag this and um if it gets flagged it comes to whoever is running that Marketplace you know so ocean core team is running Market to Ocean protocols.com we take a quick look uh and we basically uh then it gets put into purgatory which means the asset never shows up in Ocean Market um but there are rules for

um that coming out of purgatory and by the way if you're actually goes in purgatory you as the the publisher also going into a publisher Purgatory so we actually um took a riff from GitHub policies on this and it's around the use of um fraudulent publishing you know violation of copyright sensitive data impersonation all of those things so you know rather than saying it's blacklisted whitelisted it has Purgatory with very specific rules it's basically a state machine of you know a data enter a data asset entering that and ways that it can exit as well as for the actor itself so those are the basically overall to your question how do people discover if an asset is good or not this is really a question of Discovery where we have the main

Discovery pillars of browsing searching and filtering with really great signals behind including data consume volume stake um you know reading the description as well as for the really um flagrant violations we have purgatory totally agree that there are cases which are clear-cut so say I'm even if I take your data set every republish it as mine that is I mean that is pretty evident that you know uh it was your data set first and I just republished it um or you sell your DNA and I open it and there's nothing in there right so basically obviously that's pretty pretty obvious but say you sell something as

your DNA um sorry I I keep talking on about your DNA now um you said something as your DNA and I purchase it and try to clone you and instead of you I get I don't know just in Sun right so I mean it's not it's not evident you know from the get-go that this is not what it says and I assume uh the ocean Market team Marketplace team does not have you know capacity to kind of run inference on all of these things so how would that be handed yeah that's a great question so actually one thing I forgot to mention we also have it where the publisher can publish sample data so that um you know maybe five percent of the total data and

that can help to give people a feel for if the data is in the right place like if it looks okay you know uh data scientists can look at the distribution is this kind of saying Etc ultimately though in a lot of cases they're not going to know until it's useful and even if it's good data right like let's say I'm trying to predict the price of Ethan I buy data from Ocean Market to try to predict the price of beef better maybe some historical data for Tron right but you know turns out maybe the Tron has no bearing on the price of each right Tron price so you know don't know beforehand so it's actually really really tough to tell whether something is useful or not before and that's where you know ultimately it's people can speak against it people can give and you can see the

consume volume against it et cetera right but also if if it turns out that you know it was fraudulently published going back to this um just in some example um then after the fact you could come to me and say you know hey this was this is fraudulent here's why I believe so and the ocean uh core team makes a judgment call now this is just for Ocean Market itself like I mentioned earlier there is actually a GitHub repo you know github.com ocean protocol slash Market it's fully Apache too anyone can come along and copy and paste that and like Fork that make their own version stand it up within an hour we actually have a blog post that describes to do this in your hour so you can have your own data nft Marketplace or nft Marketplace in an hour you want to have your own CSS great

go change it half a day later you've got your own you know branded Marketplace um Etc and so if you really don't like what ocean core team is doing with its um curation or with what's in its Purgatory what's not then go have your own right and that's completely okay um so and you know there are a few third-party marketplaces out there Daimler is probably the top example but there's some other ones too and there's more people doing that all the time um and people are not only making small Forks but larger Forks you know the algovera team has leveraged the ocean Market code for their version of sort of like a web3 native hugging face if you will right um and there's a lot of stuff like that out there so um yeah overall you know all of this we're trying to be permissionless and

um follow this idea that you know at the L1 the on-chain stuff no one can change it's um immutable censorship resistant all of that um but um The Last Mile it really depends on the jurisdiction so you know um ocean core team is running Ocean Market out of the jurisdiction of Singapore um if you're using Ocean Market it actually has an arbitration Clause that points to Singapore but um yeah in the future it can be whatever you know as time goes on you know someone could have a fork of Ocean Market that has much that has fully decentralized arbitration using say Claris right that would be great we'd love to see that right you know know we've had that on our backlog for a long time but you know we have had other higher priority items I

the way of kind of generating data sets that are indistinguishable from from the real thing right so basically say I I have like data on weather patterns um right and basically I feed that into an AI and say generate me data patterns that you know are just like this one um and I generate a hundred different data patterns obviously there's nothing new to learn from them because basically the other other information that uh that went into it that went into making them was already in the first one um do you have any way of kind of making sure that where exactly the data comes came from is there some sort of

provenance that I can prove that I actually got it from I don't know I'm really Noah and I really got this from like uh satellites and uh ocean probes and whatever yeah that's a great question so yeah the ideal is kind of what you're hinting at where um the chain of provenance of data and you know it's basically data compute did a compute right data being transformed through some compute to another data set being transformed through another compute to another data set or data stream feed right um so the idea is that all of that is fully trustless on-chain trackable Etc right um You can loosen that slightly where um you have the hashes along the way or some other sort of tracking that's

partly there with the claims being made um and you know ultimately um the claims to be made you know you've got the economic incentives which we've kind of we've kind of been talking about so far right but ideally you can have it where it's even lower friction by fully on chain throughout the whole flow um there is some of that starting to show up in Ocean Market such as um The Sovereign nature initiative team Sni they are having sensor data that um it's coming straight from a sensor um being put on chain uh and recorded there and then being put into the ocean ecosystem right so that's a great example um and then you know ideally you have it where it goes from that going through uh compute that is on chain and then you know to some other new data set maybe of

hotspots for say temperature Etc rather than the raw temperature data or otherwise right um on-chain compute is still not super mature there has been you know uh decentralized compute teams but that's mostly been around uh decentralized compute marketplaces which isn't quite on chain compute right um but we have more and more happening over time where it's towards you know verifiable compute at least even if it's not on chain per se right and you know L2 L3 compute you can get away with it there too as we have you know that will be happening over time too so we're gonna get to you know ideally on chain data and compute for everything but the infrastructure is not quite there yet in the meantime we can have hashes and claims along the way and that can get

you most of the way right um so you know ocean basically we try to support you know the Best in Class Technologies um at the lower level um as it matures right so right now for example we're taking a very close look at the state of the art of um the decentralized compute Technologies whether it's the uh sort of decentralized compute marketplaces like the ixx and Golems of the world or it's more towards pure on-chain compute you know you could run wasm or sorry run um uh C or python on some wasm um L1 if you want right whether it's on Cosmos or otherwise um and uh as time go and then there's other approaches too of course so as time goes on uh we're gonna just make a

point of supporting that better and better better as the technology comes ready this is this may be out of scope but in terms of AI um can you be sure that you can prove that some data is not made up is a way to actually uh mathematically prove that right yeah so going back actually I didn't answer your question before can you up Jake generate fake data there's actually a subfield of AI called synthetic data generation and this is actually done used lots and lots right the main general idea is you know you uh build a PDF a model that describes the distribution of the data and draw more random samples from that PDF right that's one way sometimes you need to go fancier than that but that's and that's

actually really useful for a lot of AI tools you know if you want to have a more biased sampling of um Zero versus one data true versus false data for building an AI classifier that's very useful so just I wanted to close that Loop um on your question of can you tell if it's been artificially generated or not um there are watermarking techniques out there for AI I just saw even this past week I think it was open AI or some other team was talking about you know watermarking inside some of our chat GPT generated responses um and of course though there's going to be an arms race back and forth without right you know someone with an their own AI technology will take a chat TPT generated AI try to figure out you know

what chat GPT is measuring against that and optimize against it right so this happens all the time you know back and forth optimizing internally in a loop and this is the idea you know one subfield of that is called generative adversarial networks Gans and this is really hot in the world of AI two or three years ago and it's used a lot right um in especially a lot of the world of game playing and all that that we see from deepmind alphago all that so um overall um to your question the answer is no there's no great perfect definitive way but there's a bunch of approximate stuff that helps you get along the way but um this is where we come to the economic side right if you you know are you going to train on data that you don't know where it comes from if you're going to

get monetization from it right and I know like a friend of mine had been running data at SoundCloud this was like eight years ago 10 years ago and they actually came up with a really awesome AI model to predict to recommend songs but uh the problem is they didn't know where a bunch of that data came from initially and they were really worried about copyright uh issues ETC and in the world of Music copyright is insane right like Universal Music and otherwise will come after you in a heartbeat so they actually never shipped that product because because of this worry and we have this problem right now too with a lot of the art being generated from stable diffusion Etc um a lot of that is almost certainly um you know copyright um infringing right um and a good example of you know in copyright basically if you have a work

that you have created and it Rifts even say 10 on someone else's work then you need to get um permission from that person right a great example of this is Vanilla Ice with his song Ice Ice Baby you know David Bowie came after him saying hey this is too much like one of my songs and in the and David Bowie one right who was right I don't know but the point is that um this is how copyright works right um so there's going to be a push in the world of AI for sort of Ip clean um you know not just for image generation but for data generation in general and then Providence is really going to matter so you know going on chain is going to help a lot and this is where ocean protocol can up a lot in my view you know like this is

it's helpful overall but you know the bigger picture of AI like you know we're not going to be worrying about copyright in half a year one year there's much bigger crazier things going to be happening in the world of AI soon so we can get into that in a bit here but uh um you know copyright is the least of our concerns so just to clarify say um I have something like stable diffusion and basically it's trained on say five million different pictures right and basically one of those pictures is my picture but basically there was I mean those pictures all came from somewhere right so basically um it's a it's a are there consequences for the AI That's being generated by the

model that's being generated by what it's been trained on so basically are you saying that kind of the copyright on that basically training something on copyrighted or copyrightable things is not necessarily allowed or allowable well you can do it but it's going to be hard to uh make money from that right let's say for example I I train an AI on a million different songs including uh David Bowie's song Under Pressure I believe it is right um and then um my AI spits out a song basically that looks like you know Ice Ice Baby from Vanilla Ice right so it went you know uh uh and that would have meant it was probably partly inspired by

David Bowie's um under pressure right so uh David Bowie will will say hey like it doesn't matter whether it went through the AI brain and computation or my brain in computation or Vanilla Ice's brain in computation at the end of the day it's someone trying to make money from it right um not the AI but someone right so you've been analyze tried making money from it uh and in the end it didn't pan out um so if it's some AI researcher trying to sell that song that was generated but it sounds an awful lot like under pressure then David Bowie has full right and at the end of the day who decides the courtroom decides right that that's the ultimate Arbiter it's very expensive but you know what will happen I'm sure is going to happen is we're going to have you know thousands of

letters sent out by people uh you know copyright lawyers and there's actually um letter factories that do this already on behalf of the music industry and the image industry from for Getty for Universal et cetera and they're going to be basically emailing uh all the people doing these AI images saying this is under copyright you know uh cease and desist right otherwise we're going to charge you you know a thousand dollars or ten thousand dollars for every image that you're selling uh without our licensing right so it's going to get shut down and that's going to happen within a year it's sad but this is how it is right this is simply the state of the art um of of legals and that's not going to change anytime soon because you know the lobbies are too powerful and ultimately it's kind of sad because copyright was

meant to protect the artist and in this case it actually is protecting David Bowie or Picasso or whatever um so you know if you're a Creator and you want your stuff to be remixed and you want to get a cut then go and publish it cc0 where you're saying I'm gonna give away you know I um give away all my rights to this and people can do whatever they want with this and if you want go and pay me one percent afterwards as a thank you right and I think that's a really great direction for people to go and so I'm going to deliver has written about this in blog posts about this direction for nfts ETC published the nft cc0 and that way the existing legal system can't interfere and instead it's all about do you have the private keys to that particular image do you have the

provenance and I think that's by far the healthiest thing you can do right um and um so we still have this Corpus of body of work of all the music of the past and images of the past it's going to be problematic but as much as people can go ccc0 the more they should data has the opportunity to go that sooner because we don't have as much data where people care as much about back catalogs of data so you know everyone out there thinking about data please put a cc0 protect it with um with blockchain and let technology protect you rather than the this archaic legal system that's my my summary super interesting so say I have an AI and it generates a song or picture um that is not sufficiently like any of

the pictures it has been trained on to kind of trigger those copyright infringements do I still somehow infringe on the copyright of the artist just by training on the on the picture because I mean clearly I mean humans do this all the time right say say I'm I'm an artist I can go to the National Gallery I can look at pictures and kind of yeah it kind of it it does something to my brain and may kind of have an effect on kind of what it outputs after but is this different for for AI so is this uh I mean do can can I can I um demand that an AI not be trained on a

picture of so basically it's just training and AI on a picture that I own um is that copyright infringement uh well it it if it's a picture that you own that you only you care about then it doesn't matter right but let's say I train an AI on a picture you own and I don't do anything with it um I don't publish it I don't try to get any financial gain from it or anything then no one cares right it doesn't matter right a good example of that is um about 15 years ago this musician came along and took um the The White Album I think from I forget you in the Black Album I think the White Album from Beatles and Black album from Jay-Z or

something and mixed them called it the gray album um games remotes yes um but um that was totally copyright infringing right but they just dumped it on to BitTorrent and just left it they didn't do anything else with it so it didn't matter what copyright lawyers tried to do in court because there was no monetary gain it doesn't matter like what are you gonna sue for right so so that's completely okay so ultimately it comes down to is someone trying to make money from it yes or no but if basically if I let someone use my AI right then I am making money from it because basically so so that would then not be okay so basically I need to own the data I need to own rights to the data that I

train my AI on if I want to use the AI commercially yeah yeah okay well yeah exactly yeah it's it's kind of too bad that you know well it's actually in many ways like copyright law can be used for good or for bad right and um you know part of the vision and inscribed was saying Okay um and ascribed once again was the nft marketplace on bitcoin basically right um part of the vision was seeing that artists didn't really understand copyright or any of that um they didn't know how to leverage it to protect themselves so when they registered their work on a scribe you know and you know uh had the proof of claim on bitcoin blockchain Etc they were also getting this copyright claim with pure legal and language et cetera

and then when they transferred ownership to someone else it was a license to the next person then they didn't have to think about the the legals right it was just there out of the box for them so um you know there's kind of two ways to protect yourself or overallness one is legal system copyright all that lawyers um courts the other way is technology right um and so traditionally it's all been legal system Etc and blockchain offers the possibility to have you know really great protection via technology and you can have both that was kind of a bit of the vision of ascribe but you know now we're getting to a realm where we can potentially get away with just a lot of just blockchain if we have the right technology and we're getting there right so that's why I was suggesting the cc0 thing Etc so overall you know copyright

is your friend even right now um with you know Ocean Market with ocean in general um and other data nft other nft marketplaces too that just think about this well right like variable has awesome licensing they were very thoughtful about it full kudos to the wearable team um other marketplaces you know for nfts you know depends on the marketplace but some really not great so you know that uh status quo right now we're in pretty good shape for all nfts a very good shape for data nfts um and going forward though you know let's all transition to you know um evolve Beyond what's the legal system offers and have something pure and clean with just blockchain and that's the vision right so everything you know L1

decentralized immutable permissionless then you don't need this sort of archaic system of copyright Etc let's kind of change gears a little bit let's talk about your Marketplace so who are your users at the moment uh I mean both uh buy side and sell side and uh do you think um those users those user groups will change so um maybe to just go more General so Ocean Market is one of the many applications or uses on top of ocean uh stack so to refresh rate there's the smart contracts that are deployed to five chains right now in production there's the middleware uh including the JavaScript and python drivers slash uh sdks and then there's the front-end apps

so Ocean Market is a front-end app um like mentioned before there's more than 1 500 um assets for sale on that um and you know there are buyers and sellers if you look at the data for sale um some of the things with top volumes include um some video game uh data assets things like Clash of Clans um assets there um other uh assets around nfts and D5 there's some uh um virtual world assets that are quite popular um so that's sort of now but I see it as really such early days that it's not predictive of what things will look like you know one year from now five years from now whatever right it's just sort of what's getting going now besides

what's in Ocean Market um there is uh several there are many really great teams that have emerged in the ecosystem doing really great stuff that are kind of you know in many ways independent of what's going on in Ocean Market right so um there is I briefly mentioned before there's a team called algovera that is building a web 3 version of hugging face for your audience I'll give some background you know probably most of your audience knows GitHub right um it's a front end forget that is really great that you know most developers use um that uh use to uh publish their uh code to have uh Version Control on um to share to work collaboratively et cetera on and you know there's web three

versions of that coming along too of course um and imagine if you're an AI researcher a data scientist whatever is there an equivalent to GitHub for sharing your models to iterate on your models your scripts around that and um there have been many tools like that over the years such as openml but hugging face really emerged in the last year year and a half as it just blew up and took off in the best possible way for data scientists to share the models so it's got tens of thousands of models now I think 50 000 unless I checked um there's tens of thousands of data scientists on there using iterating now it is still a web too um but it's really great right you know one step at a time so uh hugging face has a really great Community um you know world-class Engineers

working at hugging face iterating of the community they're publishing you know the latest large language models within you know a week of them being published elsewhere et cetera et cetera so that is the state of the art for sort of community of data scientists out there in the world um but of course with hugging face it's really hard to you know monetize your model right what if you want if you're a data scientist and you want to make money as a living but you know buying and selling models what about having different access control um what about if you have a model that um you know hugging face itself is based in USA what if you have some models that um the US government doesn't like right um from say Russia or something right um should hugging face the company be stopping you from publishing that right

and so uh this team algovera um part of the ocean ecosystem they have been working on a web 3 version of hugging face that um basically you know has a lot of the features of talking face but then also you know the sort of social Community features um for buying it for for sharing models Etc but then also working towards the monetization ETC one of their Stepping Stones interestingly was integrating ocean itself into hugging face itself so that got some of the functionality but you know from that they realized okay this is you know getting there but then we need to have something more native yet so they're doing that another example is in the world of web 2 there's something called Kegel and this is data science competitions and um you know if you're a data scientist maybe you know a

PhD student and you know you're probably poor most BSG students are pretty poor and you want to get make an extra Buck you know to win um can you do it by using your AI chops right uh you know win a thousand dollars here five thousand dollars there whatever even 500 bucks makes a difference um so Kegel came along um I think in the mid 2000s or so out of Australia and pretty quickly it blew up in a good way um and became that de facto platform for competitions for among data scientists um and it Rift on this Netflix prize which is a million dollars for Netflix with their stuff but anyway Kegel um you know over the years has been the leading platform for data science competitions and anytime you go there to kegel.com k-a-g-g-l-e.com you'll see you know 10

20 50 competitions um for data scientists to participate in with prize money of you know 100 bucks thousand bucks ten thousand bucks more once again there's challenges because it's uh you know centralized company in fact Kegel was bought by Michael so after sorry Google Google a few years back so it's controlled by a centralized company and uh similar issues to what hugging face hats right so the censorship issues the control issues as well um you know privacy in this case too there's um large corporations that come to Kegel Google and say Hey you know we want to have this private competition and then Kegel will hook up maybe their top 100 data scientist participants and loop them in to this thing under kyc Etc

what if you can do that all trustless so there's a team now called descents d-e-s-e-n-s-e that is doing basically decentralized Kegel and overcoming the uh the issues of cable right or decentralized data science competitions in general and we've been running our own data science competitions in Ocean for about a year now um they're called ocean data bounties for things like predict the price of Ethan more and now we are starting to use this desense platform because it's got mature enough to use that so that's also really healthy right it's sort of a proven model from web2 but has issues in web2 that you know we can leverage for web3 besides that maybe I'll just run a quick through more there's this idea of Federated learning which Google

um has you know well pioneered but others have been driving it where you want to collect together data sets from say um 10 or 100 or a thousand different hospitals to say predict the price of to predict cancer better right but if you collect that together centralized it's a privacy nightmare of course Google and others push something called Federated learning where um they said let's collect it all together but it's still running on Google data centers Etc so it's sort of like privacy theater Google can still see it they just pretend that they can't but what if you could do a truly decentralized Federated learning right and that's where um you know ocean um stack can really help so there's a team called felt token that's doing that truly decentralized Federated learning leveraging ocean computed data and more to enable you know things like you know

Gathering Together data sets across a thousand or ten thousand hospitals so that you can build a model to predict cancer across say a billion people people which is amazing right and that makes a huge difference the sooner that you can detect cancer the better you know imagine you can detect um say lung cancer at stage one instead of stage two that might make all the difference for survivability so these things really matter uh keeping going there's data Dao there are one of it's basically doing data co-ops data Dows um and uh there's Delta Dao which is working closely with Gaia X guy X is a European wide data initiative um that's being driven by the German government French government and more around trying to help ensure that Europe itself has data sovereignty they really you know don't like the idea that you

know most um web 2 apps Etc are running on uh AWS or Microsoft infrastructure on data centers in Europe but controlled by American companies this is actually sort of very dangerous to sovereignty of Europe so they say we want to be able to not rely on this what do we do and so they kicked off this initiative called guy actually would four years ago and um the vision of it was actually pretty similar to Ocean and when we saw it we're like great that's pretty cool and we've been monitoring it and we were early members Etc and now there's this team called Delta doe that um is a just a phenomenal team and they're working with the Gaia X main core team the CTO CEO Etc as well as the very many various folks of guyax for various use cases for automotive for financial ability for

Agricultural and so on and this is basically you know sort of and they're using ocean across the board though right so this is sort of ocean for you know not just individuals or um in a pure decentralized web3 fashion but really serving you know um Nations and um geopolitically brought more broadly and stuff and you know we're happy to serve that right you know ocean is meant for all it's like I've mentioned earlier in the call um it's around leveling the playing field for uh everyone um whether you're an individual a family a small startup a large startup an Enterprise a city a government a nation multiple Nations right we're not trying to say no to Nations we can't it's permissionless right and in fact you know we think it's really helpful if you know people everyone is leveraging this

this technology to manage their data assets so that's basically a sort of a run across the gamut of many excellent projects in the ocean ecosystem there's many more right um this is the tip of the iceberg um actually one recent one that's probably worth highlighting is something called Autobot Ocean and the reason it's worth highlighting is because because um it's a um the Builder he's got a website that shows different stats for ocean um and the traction and um one thing that's pretty cool is the ocean uh as of about a week and a half ago we crossed one million dollars in consume volume per week for ocean so that's a run rate of more than 50 million a year now that comes with a big caveat um it's a lot of that is driven by our data farming program and maybe I'll get

into that in a bit but you can ask with the caveat but I'll stop there because I otherwise I'm going yeah lots of stuff let's talk about the data farming let's get into it sure great so um so data farming is uh basically at the top of it uh it's inspired by this idea the superpower of blockchains um you know you can incentivize people to do stuff you can get people to do stuff by uh incentivizing them with tokens and Bitcoin is the OG on doing this right uh Bitcoin uh was set up such that um it incentivizes people to maximize its security it's sort of a subjective function called maximize security right and um how does it do it well it measures

security as the hash rate of the Bitcoin Network and so if you are contributing and right now every 10 minutes Bitcoin will pay out 6.25 Bitcoin um for rata to people who have um uh added to the security of the network and that's provided in an expected value sense so um if I add 10 to the hash rate of Bitcoin Network so 10 of the security of it I can expect every 10 minutes to get 0.625 Bitcoin now that's lumpy of course and it's you know on average one in ten times I'm gonna get point I'm gonna get 6.25 Bitcoin the other nine times I'm not you can smooth that out if you want if you join a pool but overall you're getting um expected um you know 10 of the

rewards every 10 minutes when it pays those out so that's the objective function that Bitcoin is going for right and uh other protocols do this too right like any other proof of work Network um at that level it is for um maximizing security Bitcoin until recently was like that um chain until recently was like that Etc um but you don't have to stop there you can use incentives to incentivize other things too and um so with ocean and and we've like you know I just I thought about this years ago and I realized wow this is really similar to optimization right if you're from the world of AI or optimization you're used to writing down an optimization problem which is a set of objectives and constraints you know maximize

um this and minimize that subject to these three constraints being met so in Ocean you know a lot of the early ideas of ocean was like okay hey let's leverage the idea of incentives to um maximize um the the traction of the ocean Network um towards around getting ocean ubiquitous for this Level Playing Field right and the way you get to ubiquity is sustained growth over long periods of time Facebook is ubiquitous because it kept growing at 10 30 a month for for years and years right so that's what you want you want ubiquity the way that the web is the way the internet is ETC um so you do this by growth and you can capitalize growth by leveraging incentives so and how do you leverage incentives it's all about this objective function

um or set of objectives and constraints so in Ocean we said okay what do we want to try to maximize and we realize you know what is the we could say let's try to maximize the amount of data that we published you do that great great but then you'll have you know people publishing tens of thousands of data sets and there's no consume right so that doesn't work right and it's basically watch publishing and um and people have seen this happen where um in an nft Marketplace they tried it and they had watched publishing and you know they fix it after a while but it took a while so you know you see this happening all the time you'll see this happening with exchanges for for wash trading right people are used to wash trading where exchanges say hey look at all this amazing volume we have but it's just

like crazy walk trading right and um so in Ocean we said okay well what is the best measure of like value creation that's what you really want in a data system right and like I mentioned before value creation um is really where the rubber hits the road for quality of data and you can measure this by um well I'll just talk about the data value creation link first of all so data value creation Loop someone spends money to buy data or create their own data then they use that to build an AI model then the AI model predicts something basically and guides them to an action they execute that action from that action they make money and then with that money they've made they use it to buy more data create more data they loop around so and you know it can loop around quickly in the sort of very fast

Snowball Effect or can be slowly you know maybe if it's found in five years ten years latency um you know a fast example would be D5 for the low latency and a slow example would be say you know Medical Health Data for a prediction of cancer because you need FDA approval et cetera so five years tenures okay so data value creation Loop that's the name of the game you really want everyone um in this data ecosystem to go through this data value creation Loop so when we're making incentives then we want we also want to incentivize people going through this data value creation Loop so a simple way to measure this is simply um at the point of them buying data and then at the point of them consuming data if they're doing both then it basically is a test for them going through this

creation Loop so that's what data farming measures we say we're going to reward you if you are driving data consumed volume once again data consumed volume is the amount of money um spent on buying data and consuming it in a given time interval in a week in a month whatever so that's what we reward people for now we also want to drive staking in the ocean ecosystem and we want to have some form of curation and I've hinted at this before so um you know we have duration in terms of data consume volume you know that's a great signal for curation but also you know we've got ocean token holders et cetera they want to stake they want to put their money where their month is and of course it's um when you stick you uh we we get people to point the stake

um towards um what they think the assets that will have high volumes that's the heart of data forming so high data consume volume to be precise and then they get rewarded based on how much take they have on those assets and the volume of those assets so it's those two things together so the reward is a function of the volume for given data asset and your stake in it and that couples the two things that's the heart of data forming that's the objective function and this then therefore incentivizes data consume volume but also really helps curation because people they want to point their ocean their stake their

their locked ocean to um where are they going to get reward and you know it's a zero sum on how much um state tuition they have right how much lactation they have so they want to point it towards the high data assets so they're incentivized to point to that and then they predict what that might be so that's what we have um one detail there is um I've talked about locked ocean a couple times what does that actually look like so we also want to you know within the world of of blockchain you know there's this challenge there's the the short termers the Degen the Apes and you know um they they buy they sell they go They're mercenaries whatever um and then there's the long-term risk the people who are really thinking about things for the long term you know one year four years ten years whatever right

I'm certainly a long-termer but at the same time a huge portion of the market overall is short-termers and um if you ignore that short term then um it's kind of at a disadvantage to help your own long-term growth so then the question is how do you reconcile this can you is it kind of crazy to think you can so it turns out the the folks at Curve track this problem with something called ve curve and they said we're going to have this um something called ve curve where you take your curve and you lock it your CRV tokens you lock it you get VEC RV and you you get rewards um as a function of how much VEC or CRV you hold critically VRC ve curve is not transferable so if you lock your ve your your curve if you lock your curve for

four years you get one 1V curve if you lock it for two years you get half of one you lock it for one year you get a quarter of a vehicle and less and less of course so if you want to maximize your yield from curve you lock it for four years and then you point it towards in in curves case its own amms and stuff right and that reconciles for Curves case the near-term degens the Apes Etc with the long term right in Ocean we do similarly we introduced ve ocean um uh in uh the fall a few months ago and it mechanics are just like V curve it's actually a copy and paste to the curve contracts right we think they're wonderful beautiful um and you know they've been battle hardened over years so um we we use those so people can take their ocean and

lock it up to four years and then if you lock for four years you get one ve ocean and then you point in our case you point your ve ocean to various data assets the data nfts and then um depending you know if you point your data your ve ocean to data nfts with high data consume volume you can earn a lot right at 10 20 or more uh API or if you point it to you know really bad assets that no one's consuming then you get much lower return right so that's the summary of of ocean and by doing this we get this really great new signal for curation and in fact it's such a good signal we found that in the browsing part when you go to Ocean Market it actually sorts the browsing by the amount of ocean

um stake at that the amount of ve ocean pointed to various State assets that's the default sort so that is data farming and ve ocean they go hand in hand um and yeah you know the cool thing about this it's staking but there's no risk of impermanent loss like you might take you know if you're adding liquidity to an AM or anything yet you still get really great yields and it has this really great mechanic version we get curation but at the same time it's sort of an optimized for the token itself right because we're reconciling near-term incentives with long-term incentives it's helping with the you know uh for for fixed price demand it's reducing Supply so it's sort of a win across the board we're quite happy with it so yeah I'll summarize there I'll stop there um maybe I'll summarize for everyone um ve ocean allows people to lock their

ocean for up to four years and then use that to gain rewards um in the data farming program data farming gives rewards based on how much ve ocean you have locked and allocated to assets and the volume of those assets and these things overall Drive traction for ocean and ultimately for us you know our idea of driving traction is driving data consumed volume and that's the ocean equivalent to bitcoin where in Bitcoin it's about driving security I I don't understand part of the curation part I think so um ideally when you pay for curation or when you pay people for you also want to um pay particularly for people who surface as as of yet undiscovered

content and so basically if I if I if I listen to your explanation economically the smartest thing would be to just uh point that whatever um most people are already pointed at because that's what's most discoverable and basically there's almost no one there's no incentive for kind of finding the Hidden Gem right yes well so truthful that is a great question so um if no one else has pointed to a given Hidden Gem yet um actually I'll explain the mechanics a bit more so with them the way the mechanics are are the each data asset gets rewards allocated to it each week Pro rata on how much volume it has right now right

so if one asset has 50 data consume volume another asset has 25 another 125 then um you know the 150 gets 50 of the rewards the rewards right now are 75 000 ocean a week which is uh on the order of twenty thousand dollars right um so that is um so that one asset with 50 we get ten thousand dollars a week allocated to that right so if you're the soul Staker to that asset then you'll get um uh that money right the rewards going to that asset if there's two stakers on that asset then it's uh and they're each taking equally you'll each get half of the reward going to that asset okay so that is how it is right now um which means that you right now you want to find assets that are the

Undiscovered gems that are just about to hit High consume volume that no one else has taked on right um that's sort of the incentive right now um and but as soon as you know you find it other people might do a fast follow and get rewards for the rest of the week too so you know we measure how much you've staked on an asset Day by like 50 times throughout a week right now um so that's how the mechanics are but also you know what about someone even publishing an asset right what if there's uh you know should we incentivized there too so this is the second part and we're actually just about to inject extra rewards for people to publish themselves too now with all of this um there is one sort of caveat with all this and that's wash consume I can get

into that but hopefully that I'll pause there for that hopefully that answers your first question of um uh you know people basically lazily following everyone else um there's a bit of that right now um and for for right now we're happy with that but we keep evolving the objective function just like you know if you are someone who is designing um trying to solve an optimization problem you might run your Optimizer see how it works and then change the objectives and constraints and this is exactly what we're doing week by week month by month we keep tuning tuning tuning the objectives and constraints based on what we've learned and this is similar to liquidity mining programs such as a balancer where they came up with a very simple objective function at first for driving their own liquidity and balancer the balancer liquidity money program but then as time went on

they tuned tuned one example was at first they give gave equal reward for people um adding liquidity to a stable cooling pool versus adding liquidity to say each balancer pool but of course the risk of improvement and loss is much higher on eth balancer pool right so they after a few weeks they changed it such that you only get 10 it's a 10 multiplier um on the stable coin tools because you know that sort of of balances of the risk so we're doing a similar thing and by the way yeah so balancer kept evolving over time and then once it got hardened enough they handed it off to Dao like um um governance of that objective function and there's a similar thing happening with ocean so balancer was a big inspiration here and kudos to the

balancer team great team uh similar thing happening here and yeah so in the near term the the Pervert the the small perverse incentive you described where there's not enough incentive for people to publish their own or uncover their own um it's not fully solved yet but that's actually changing in coming weeks and months cool unfortunately we're kind of running out of time a little bit I would have been really interested interested to also hear about AI daus but maybe we can leave that for another time um tell us what's on your roadmap for 2023 and um how people can kind of get in touch with um the ocean Community you also have a really cool grants uh program and so how does one get involved and what's what's

on the docket for this year right so I'm going to wrap up the data farming thing because it's also part of a roadmap so right now um there is still an incentive to reward if someone publishes an asset and then fig consumes they can make money from it still so we call that watch consume but we actually did that on purpose to help to drive engagement and then we are squeezing it to be um such that um in about eight weeks or so it will be unprofitable because what you can do is you can make it where um yeah basically profitability goes down if uh with this over time with the right fees in the right place and stuff so so that's changing um and uh so in yeah about eight weeks Washington will no longer be profitable

and then after that the only thing that will be profitable is assets with truly legitimate consume and so therefore you know going back to this one million per week consumer volume it could be it could be where 90 even 99 of that is fake consumed so even though I stated that as a number it's not something to be 100 proud of yet um I'll be proud of the number once we have watch consume where it's no longer profitable to watch consume and so that's really the number to look for that said though you know ocean is getting pretty awesome traction across the board like I mentioned some of these other projects too and one other great project too is H2O the stable asset that's backed by by ocean and they're going for they're doing some other great stuff too so I'd mentioned that in terms

of road map so um you know for the last few years since we really launched as a project in 2017 we went through V1 V2 V3 V4 as mentioned as well as then launched the the data farming and the ve ocean uh if you think about building a version of something that's really about building and we've decided you know now we're at the maturity we you know we built everything we said we'd built in the white paper um and then some and so now the name of the game is really doubling down and focusing on traction according to data consumed volume so data farming is a key part of that and um we actually have we have we've had two grants program we filtered down to one we had a grants program that was we called it ocean Dow grants where it was um you know the community would curate

on giving grants to teams uh to you know build various things right anywhere from you know 1K 5K 10K 20K uh per team um even as often as monthly um and we realized you know there's a bit more gaming happening than that over time and we saw that there was two things we wanted to tune into words we wanted to make it retroactive kind of the way that um Bitcoin works you know great project and we also wanted to make it where it's really much more objectively measured Based on data consumed volume and I realized then if we actually built that built that into oceandale grants we end up with data farming so end game of oceandale Grants once you add retroactive and fully objective on data consume volume you literally end up at data forming so we

actually wound down ocean down grants simply because we already had shipped data forming so you know why build something redundant and it allowed us to you know make data farming that much more awesome we still have another grants program called Shipyard which is curated by the ocean core team you know the core team has a lot of context about what's important et cetera and there's been several teams going through that I think maybe 10 so far have gone through it with you know some really nice successes there so that's continuing to happen as a grants program um if you're interested in Shipyard just go to Ocean protocol ocean protocol.com and click on on the top right there's a link to either funding or Shipyard I forget um other other ways to kind of get involved um the main one is uh well actually maybe yeah other ways so you can um

go to Ocean Market buy assets and consume them as a data scientist you can publish assets as a data scientist or otherwise um you can you know if you hold Ocean or if you want if you buy ocean buy it and stake it you'll get passive rewards for um so basically half of the data forming rewards are straight for Passive holding of the ocean the other half are for the active so you know you can be a passive holder and still get some yields um there's the grants and um then finally there's the building side and this sort of two approaches to the building side there's the Python and the JavaScript approaches so the python is really tuned towards data scientists and the the JavaScript is really tuned towards um app developers dap developers so on

the data scientists you know probably if you're a data scientist and you want to um you know build some really cool um ml model have some flow with uh data science going on where you're publishing your assets you're selling them maybe doing the watch consume whatever then your Main Place To Go would be ocean Pi ocean dot pi and so that's a GitHub repo you know github.com ocean protocol slash ocean.pi and it's got readme It's a quick start and to be honest I think it's actually one of the best ways anyone in Python to get into web3 in general you can just go there there's quick starts you know within an hour you're going to be doing all this stuff with publishing assets all this and it's quite fun um on the JavaScript side ocean JS similar repo name you can go to that

repo go through its quick starts as well as um and then also that's for building apps of course right and that's one path to doing the ocean GS side um also you know we've got really great docs at docs to Ocean protocol.com to help overall conceptually but also a bit more fleshing out the JavaScript side so we've got one blog post that talks about how to Fork Ocean Market to have your own or just use some of the components of Ocean Market to build your own dapps whether it be for you know um your own uh you know data wallet you know there's a project called real data whale that has a data token focused wallet uh you know really first pass uh app and I'm well I'm really proud of that team um so or other things right data unions Etc et cetera so those are the various

ways you can get involved um you know from a pure building side there's the python side with ocean Pi or the JavaScript side with ocean.js there's the higher level with Ocean Market um there's you know with the ocean token um pure staking in the ocean or you know maxing out your yield with um with the data forming and then finally you know getting grants around any of these cool thank you Trent um it's been a pleasure having you on again always super good conversations and uh I look forward to the next time cool for sure yeah there's so much on just the AI side too so that's where by the way that's where ocean is headed right much more deeply on the AI et cetera right now basically think of to wrap up right

um up till now it was sort of phase zero of ocean building the stack doing all the hard technology leaps from here on a lot of it is basically doubling down on getting ocean used in all these really cool ways you know more and more deeply for AI so I'll conclude with that you know leveling the playing field for data and Ai and once again thank you for having me and yeah thank you for coming on