DeSci Mini-Summit

[Music] there we go so let's start with funding everybody likes money let's talk about why it's broken so funding in science is broken for a variety of reasons some of which are included here it's centralized to get funding in science it's a very lengthy process it's very much elitist which was what we talked about at scos it's conservative in nature so it's often times risk averse in a discipline that is very risk on um it's susceptible to regulatory political and narrative capture which means that if you're not doing the sexy science of the time AI is a good example in this instance you're unlikely to get funded or your likelihood of getting fund it goes down drastically and these kind of all come together to give rise in the frame of applic application science in something known as the Valley of Death there are a number of other phenomenons but I think this is the one that people are most comfortable or recognize the most and what the valley of death is is essentially in the life cycle of a project of a scientific project the timeline there is a point where you have to transition from theoretical Sciences so that which is done in Academia to more practical Sciences like R&D and therapy development and things that get you closer to actual impact and that transition often takes a large influx of money to scale up a project in such a way as to make it meaningful and so this red line is what you would expect to see a real influx of money to push you over the hurdle but what you actually see is a dip here and this is called the valley of death and often times this is a product of the fact that funding and concentration of funding here tends to restrict itself towards those applications that are either return the highest return on investment or that we can see the foreseeable impact but there's a lot of science that gets pigeon held here and doesn't get to move forward and so this directly stifles the prospect of scientific innovation the next pillar is ownership and when I used to give this talk it was always in the framework of Ip so in science the major unit of ownership is IP but this actually extends beyond that but with the the con with the concept of Ip what you have is intellectual property is great for a scientist allows them to monetize and determine who gains access to their to their to their ideologies to whatever they're developing but it's also expensive and csome to generate it's susceptible to institutional capture which means that or in PR in practical sense if you're a scientist doing something at an institution the institution actually owns the majority of your IP so if you leave that institution your IP stays there you don't take that with you so scientists don't really get to govern what their IDs lead to as an asset class it's very much a liquid so it's hard to valuate IP part of that is because it's not free to market dynamics due to the nature of IP but the other reason for that also is that IP tends to get lost in institutions there's such a massive amount of Ip and there's so much bureaucracy around IP that you will have shelves metaphorical shelves of Ip that get lost in the in the just the normal proceedings of what happens in in an institution and so it's not openly obal you can't query it you can't find out about and therefore you can't evaluate it the other aspect is that there's limited security around IP there's very few Frameworks that IP actually covers when it comes to Scientific products or scientific value a lot of the things that come out of the scientific industry are not do not fall under the category that can become IP they're oftentimes intangible assets and that is Network effects presentations software different aspects of data and these do not get encapsulated by IP so we leave a lot off the table as scientists in terms of the value that we can leverage to either help further our research further someone else's research improve our quality of life as a science SST through monetization and the like and then in addition to that you have lack of data custody Frameworks so to the point of of IP IP is a is a physical is a legal structure so there are data Frameworks but what happens to things like genetics there are no legal Frameworks in terms of governing and owning your own genetics you cannot own your own genetics even though it is a part of yourself it is very difficult to own data because there is no legal framework around that data and so we need to come up with better Frameworks of ownership so we can govern the different types of assets that are coming out of the scientific industry publishing uh is just straight out broken um if you heard any conversations from any talks from Chris uh Tima from a first approval and many of the other dii projects that are working on publishing the reason why we have so many is that publishing is a huge issue the scientific publishing Market is projected to be $13 billion by 2032 practically none of that gets redistributed to the scientists that are actually pump or that are actually um propagating the science coming out of here right this is all monetized by second order and third order party users so whether that be journals data Brokers all the like it's not the scientists themselves that are monetizing in addition the scientific publishing industry is predatory and expensive we have examples of this whereby we see that editors have decided to walk away from example El because of their predatory practices you have suboptimal peer riew systems which allows low ethical unverified unreplicated science to move forward and we have examples of that with papers that are seminal in their industry in this instance Alzheimer's that have been retracted and then you have all of these giving rise to things like the replication crisis it also funnels into the funding issue but in the case of publishing replication is a huge issue the vast majority of scientific papers can't be replicated and that's not to say that is because scientists are doing bad science but often times we don't have the units of knowledge or the data units to really convey the information that needs to be conveyed so that you can replicate that science properly so that you can validate it uh value it and monetize it so there's a desperate need for us to reorganize reformulate and reinvigorate the publishing cycle data management is another huge issue um it's a very fundamental one in the data management cycle we are still using archaic tooling we often times talk about bringing science from Web Two to web 3 most of science operates in web 1 and web zero so we're talking about making huge leaps in innovation in an industry that is the Pinnacle of innovation but that's still leverages extremely archaic Frameworks I was speaking to somebody who was talking about how in the Netherlands they still fax your medical records between institutions right that's a huge issue alongside this archaic tooling there are also archaic procedures a lot of the man a lot of the processing data management and science is done manually so Excel sheets PDFs are all hand done the data itself is siloed so the databases don't actually speak to each other so if I'm looking to work with Carlos or Chris or Tony or anybody in this industry and my data is located in that database and yours is in that one we can't really talk to each other or we have to make these ad hoc adapters to make it composable we also suffer from something called link rot and Link rot is a product of the lack of Persistence of our data so over the course of a database's lifespan let's say 20 years it is estimated that about 80% of the data is lost due to link rot and that means that you often times if you give you know in the next 10 or 20 years when you click for papers you're going to start getting 404 issues where either the underlying data has been removed and is no longer where this indicator points or the highway to get there so the actual Road system that gets to that point has worn down we need to have better ways for Persistence of scientific data and this is not a this is a non-trivial fact if you think about it science data is what upholds everything that we do this conversation that we're having this live stream that we're having on YouTube is all dedicated on data banks and if those data banks are lost what are we building on how do I prove that this is the actual way to do it in addition to that the data that we have currently and the way that we convey that data especially is very static we're living in a world where we're pumping out so much data and things are constantly evolving it makes no sense that we're leveraging static Frameworks to convey this data we see it in the open source software process if you go to GitHub you can see Push Pull requests and push requests showing the updating of a git repository if I I publish a paper that paper is static and the only way for me to update it is to then publish another paper but I can't do that incrementally I have toass a large amount of data and then redo that and go through all the issues that exist in the publishing framework and so you see hopefully you see that all these pain points actually feed into one another and exacerbate each other and so from a technical perspective you have that and what this gives rise to is things like we have no standards a lot of science a lot of scientific processing and data protocols have no standards or have a lack of Standards which means that they can't operate with each other because they're operating two different languages science needs a standardization to allow for interoperability we also have things we also have a lack of machine readability in the world of now ai but prior to that precursors to AIS our data should be readily available in such a way as to allow machines to read them make it easier to distill out that data make it simpler for me as a scientist to understand what what someone else is doing we are lacking these things and then the third pillar and one that speaks specifically to my heart mostly is the issues with access so as I mentioned at the beginning all of these these four main pillars actually tend to culminate in this issue on access and this manifests in things like pay walls it's expensive to read scientific journals for us it's an annoyance if you live in a first world or first income country paying $35 to $50 to read scientific article that sucks I just had to get my badge again cuz I forgot at home it cost me $100 that sucks but it's not going to break my bank but if I live in a second income or a third income country and I'm trying to read a paper that's prohibitive and so we need better incentive Frameworks there are language barriers institutional elitism if you don't go to a high IV Le college or if you're not from if you don't do your acade your academic road trip or your academic trajectory in the United States North America or any recognized colleges universities and when you come here your degree doesn't mean anything even though you might be fully qualified to be able to participate in the record the scientific record that you're interested in you have socioeconomic and cultural isms that are prevailing in science this is not a product of this is not secluded in science this is everywhere but trickles into science you have G geographic isolation which goes back to institutional elitism but also the fact that different Industries do not exist in all places right my from Barbados from the Caribbean we don't have a burgeoning R&D industry but there's no reason for that not to be the case right there's just as many smart people across the world as there are in places where you have these industries and then finally you have concentration of funding which is another issue that basically exacerbates all of this so a perfect example and what this looks like if you look at it on a graph is the scientific paper trail so this is looking at Publications coming out of different regions around the world and what you'll notice is the United States Western Europe and CH Asia but mostly the Chinese region of Asia are the major producers of scientific innovation or scientific Publications and that leads out things like South America Africa and the rest of the APAC regions which are where the bulk of the humanity lives and so a lot of us most of the human race is not able to participate readily in the scientific in contributing in the scientific record of knowledge and that's a huge problem and so when you tally all that up one of the examples one of the phenomenons that come out of all of this is something called disruptive science or the lack thereof disruptive science so there have been many papers that have highlighted the fact that over the course of the last 100 or so years there has been a precipitous drop off in Innovative science or disruptive science so science can either happen incrementally where we make small steps base off of previous building blocks or you can have these intellectual leaps forward where you make huge huge leaps in science now this graph takes into consideration the fact that as you do more science the easier science gets done and that leaves harder and harder science despite that fact we still have an precipitous and egregious drop off in Innovation and so we need to figure this out if we're looking to really revolutionize the world and live in a world where we have solar Punk lunar Punk and all these futuristic perspectives that drive The Innovation and bring us to the epitome of what we hope are Society is on this planet and for those of you in the dpace movement off this planet and so how do we fix this dii and this is what we're here to talk about is the new kid on the Block but I would be shagrin if I wasn't to talk about the predecessors that led us to where we are now D is new open science and Innovation and trying to solve this issue is not and so the open science movement is our precursor now personally the issue that I see with with the open science movement is that it was too early for its time it tried to innovate and revolutionize in a framework and a paradigm that did not want it I think what we have now in web 3 being in this venue being in this space we have the opportunity to now create this novel Paradigm that can allow open science and now dii to achieve the goals that it's set out to do so what is dii and this is the ethereum foundation organization and if anybody here has a better definition send it to me please D is decentralized science obviously which is a movement that aims to build public infrastructure for funding creating reviewing crediting storing owning and disseminating scientific knowledge fairly and equitably using decentralized technology now I've had the debate with many people saying well why don't you list this as web 3 and the reason that I personally don't list as web 3 is that dii is not web 3 web 3 is a component of dii dii can take tooling and Ethos from web 3 but dii and web3 are are not synonymous and I think that's a really important consideration when you go to talk to people that are not in this space and so this is the dite Tex stack most of you here are already in D so I don't need to really go over that um D is really or the Tex St is an amalgamation of distributed Ledger technology which means we're move from a centralized Point whether that be in terms of databases or data Frameworks or compute Frameworks to a distributed network of stakeholders that each have a say in the game in con in collaboration with cryptography which is actually not a new technology it's quite old and shotty will talk to you about this till the cows come home but essentially cryptography is just the ability to exclude somebody from being access so you have a network where you can decide who gets access to what and this together gives birth to a variety of different Tech blockchain compute to data zero knowledge computation smart contracts tokenization and it's interesting because over the last couple of months the entirety of the space has been converging around this novel technology of attestations it's not really new but it seems like everybody is moving towards that and that's a really interesting phenomenon so I'm not going to go into this because we have fine speakers that are much more technical than myself and have been working specifically with these but see these are some of the examples of how D are tackling issues in these five paradigms or under these five pillars and this though outdated at this point I think is our most up toate graph of the D ecosystem and there are many more people there are folks in the crowd here that are doing fantastic projects that aren't here and we're H to have you on here so if you haven't filled out the dii uh industry analysis report survey yet please go do so it's all over Twitter just fill it out and we'll have you up on an equivalent graph but we have examples of fantastic teams amazing communities all tackling things from collaboration via dials for a variety of different purposes funding Community chats open science data storage funding and institutions that a little bit more trai arts and nfts and specifically arts in the form of scientific communication I think we can pretty up science I think one of the things that we can take from the web three space is that we can have Buffy corns in our Publications we can make science communication fun again and we can make it so that this side next year this is completely packed and over there is less packed cuz we're cool and we're doing better stuff and so remember that when you move when you leave from here our goal is to pack this side next year or have our own venue because we have that much interest um and so before I finish off for those you who don't know there is a hacker house going on for dii and this is specifically looking and working on interoperability so there are a variety of tools as I just showed you in the ecosystem map we suffer from similar problems in the open science movement in that we're not composing with each other when we should especially since we're leveraging technology that is made to be composable and I think we're slowly going there but that could be done faster and so that doesn't just mean within the d space but also within the open science framework there are slew of open science tools speak to John star he'll tell you about it he'll show you a fantastic ecosystem graph there are thousands of tools we're a small blip in the in the in the movement of open science so it behoove us to integrate with these participants and help Propel us all forward so if you're interested in composing with d sign being interop and you're hacker come to the hacker house we also have a lot of fun time and so that being said this is me if you have questions for those of you who are online um pleas feel free to reach out to me on Twitter telegram uh sorry by email come to D World to learn more about projects go to nerd to learn more about composability big shout out to the DSi Summit for putting this all together and I think to everyone's point for those who don't know the DSi Summit is a community organization we did this all or mostly as a community so a lot can be done when we work together and that's it thank you okay I don't know Levy are you here oh there's Levy is Lisa here Lisa woken no all right Levy come up love you have to go around next side yeah go on so we're going to be having the vision talk and we'll see we'll see if I can communicate it I have some pictures that I'm still building are they just gonna I'm down just have a talk here you go bro here's the clicker if you want to walk around like I awesome thank you awesome thanks okay test one two three can you hear me yeah okay but louder what was that oh that's yeah it's better uh yeah hello everyone uh so uh my name is Levy I did not prepare slides this is just going to be a brief talk uh giving an overview of data management uh compute and AI uh in general for blockchains but for D in particular uh so these are these are mostly uh like a set of points that if if you're a developer uh uh building in the space if you're a researcher who wants to join in the space uh or if you're just in general uh have a general interest in decentralized science these are the types of things that you want to uh be keeping in mind uh when whenever you're doing what you're doing so um data ownership and management and uh location so the first thing when you're talking about uh uh we're trying to do computation over data the first question is where is the data located right um is the data located uh on a is it private data that's located on a person's phone is it private data that's located in a data server somewhere uh you know if we're talking about like a Healthcare company for example or a social media company uh you know uh those uh uh companies store user data locally on their own servers right uh alternatively you can imagine um uh something that you can store on your phone like uh uh you know uh your tra your travel data but even then for example uh you know Google keeps track of wherever you go but you can imagine uh text messages right uh DMS and so on uh so the first thing you want to ask when you're looking at data is where is it located and if you're doing computations over it what are the inputs and what are the outputs um and that's going to Define how you're going to be uh designing the protocols that you're going to be building um so one thing that you really really want to keep in mind especially because we're working in web 3 and blockchains is are you storing the data onchain or offchain right and so if you're storing the data onchain this has a lot of benefits in terms of uh Providence and in terms of immutability but if for example you're posting you know personally identifiable information um this is probably something you don't want to post onchain right and so you want to use an offchain solution like ipfs and for those of you who don't know ipfs is a Content addressed file storage system so um in your know traditional uh you know HTTP when you look up a URL when you look up something you look up where it is you look up its location uh in an ipfs you look up what it is so you basically have an identifier for a set of information uh and that's called a a hash uh and then um you have hash tables that can route requests for where to extract the data that you want to have uh and so for something like let's say Healthcare data um if you want to even and keep in mind ipfs is a public network although you can have private ipfs networks and that's and you know that that or they're being built um uh you uh want to you can have uh your own data stored offchain and then if somebody wants to access it you can um give them temporary access and then do something called Rec cryption which is uh effectively re-encrypting the data by adding something to it thereby uh not giving somebody access to the updated data um and another thing you want to definitely want to keep in mind when you're talking about the data is um uh uh when you want want to do computations over that data is where is that data located and how much of it is there right if you're looking at a one terab data set um and you want to run some kind of computations over it um if you try to download that one terab data set it's going to be expensive it's going to take a long time let alone 5 terabytes or 10 10 terabytes uh which are you know normal data set uh sizes in uh scientific Computing right if you're talking about like satellite data if you're talking about uh you know uh kind of let's say uh logs of of of some you know sensor data or something we're talking about really truly massive scale data and so you can't just be moving that around here you know here and there because the costs are just astronomical and the time costs are astronomical and so um you know it started emerging recently um the a paradigm called compute over data or compute two data um and what basically what compute over data is says the philosophy behind it is um rather than bringing the compute uh the data to the compute you bring the compute to the data right so you take whatever computation you want done you put it like let's say a Docker image and then you send that Docker image to where the data is located and you have the computation run locally uh and then you get the result back and this saves dramatically on costs uh and also has really massive benefits in terms of privacy right because if you let's say go back to the healthcare example uh the healthcare data example or the social media data example um you don't have to be moving that data that really sensitive data over a network right and so this have Computing over the data where it's located has massive benefits uh from a privacy perspective um and so uh we basically also one side note about the data storage which is um there's you know of course we have blockchains and we also have distributed Ledger Technologies and we also have ipfs and you know offchain storage one uh kind of intermediate step here are are sharded blockchains right so um there are blockchains that have uh um you know sharding capabilities that basically allow only a subset of nodes to actually access particular data uh within uh that Shard um and that is a really interesting uh use uh a um a really interesting tool if for example you want to have immutable data uh on a blockchain that has consensus over it but also limit the limit the nodes that actually uh can can access that data right um so and then we you know now we're coming to compute and uh one of the major questions uh when we're talking about compute is verifiability right uh and so how do we actually know that computations are done in a a verifiable manner how do we know the computations were that the result you're getting is the actually the correct one and there's three main classes of verifiable computing uh the first one is of course uh you know really hot topic which are know uh cryptographic methods things like zero knowledge proofs multiparity computation uh and so on and so forth um and those are kind of very popular and they're being built uh you know fully homomorphic encryption that's another one uh the second category is uh are secure enclaves so trusted execution environments um and those uh have a lot of benefits they have some attack vectors but there's been a lot of work uh centered around patching up those attack vectors and uh the third uh the third method of course is optimistic verification right you just redo the computation from scratch and you see if you got the same result um and verifiability of course is super duper important because if for example you have an application like let's say Federated learning where um you you want to um you want to have uh you want to aggregate let's say a bunch of um let's say the results of running uh some computation over localized Healthcare data you need to know that that computation was done correctly so verifiability is a really crucial uh part of of of computing and especially just in distributed computing networks um there's a few other notes about the distributed computing networks which you'll probably talk about in Compu in general um which are uh um basically there of course incentives in distributed computing networks this is a really crucial topic uh for you know uh balancing you know supply and demand and so on uh and there's also so especially for dii when we're talking about uh what kind of computations do we want done and specifically when we're talking about having a scientific record um what uh how do we make sure that we keep track of all of the inputs and outputs of jobs that are being computed on this network right so for example if you want to have um uh a project to um let's say I'm just going to throw it out an example find the cure for cancer right uh you you are trying to sub you trying to uh using computational methods you're basically running a bunch of computations um it could be simulations or whatever and you basically need to know you have to keep track of the inputs and outputs of all the jobs who did the jobs how long did it take how are they being rewarded so having Providence over over the results and having a a record of who contributed what allows you to now reward people for for their contributions right and now we usually talk about rewarding scientific contributions in the context of uh scientific Publications and so on and so forth but we can also reward scientific uh just scientific Computing in general General right uh and that's that's a really important use case especially for dii so uh there's um yeah there's another you know kind of really important topic which is privacy uh and privacy is a major major problem in web 3 because of course we're using blockchains uh uh mostly to as our mediating data structures um and this is this is really uh kind of a Noy problem because on on the one hand we definitely want Providence uh over over people's data we definitely want to know that the the right computations are being done but people want privacy people don't want their personally identifiable information done uh done you know uh in a in a kind of open manner where anybody can look at it so what do you do about this and so there are cryptographic tools like I mentioned earlier Zeal knowledge proofs fully homomorphic encryption and so on and so forth and so if you're a a developer or if you're a researcher uh trying to do something with let's say Healthcare data or social media data it is really really important to keep in mind um where uh where is this data again where is the data being located where is it being stored what are the inputs and outputs uh and how are you maintaining the privacy of the people uh who whose data you're Computing over uh so there's also um there's also you know we should probably um I'm probably going to end in five minutes so there's like of course the hottest topic of the year right now is AI uh and we should uh definitely be talking about um what can AI do for science in general what can AI do for design particular so um language models have a lot of applications uh in science it's kind of uh kind of naturally it's you know popping up all the time we saw for example um Google's gnome which you know found something what was like 2 million new compounds something like this and things like this have been going on for a while um you know uh uh there was a research that came out about um balancing uh plasma with magnets and know tokomak react uh nuclear fusion reactors and so on that was done using reinforcement learning that's not a large language model but um the point is AI is being used uh increasingly to make scientific advancements um purely just in terms of like actual like knowledge um but there it can also be used for a lot of other things so obviously of course we can use llms for literature review um and literature search right we can dramatically accelerate how long it takes to actually understand come to an understanding of a field uh by leveraging you know uh you know um uh semantic embed ings and so on and so forth um uh so there's also of course dat extraction and organizing Collective scientific contributions right so when we're talking about um coordinating scientists all around the world uh especially in who might be speaking different languages you can basically use large language models to help coordinate you know um scientists A to actually help coordinate science to help communicate results right you can imagine having uh a large language model uh rep in fact having an agent represent you specifically and in convers with another researcher specifically and having those two autonomous agents uh you know go back and forth on uh uh results uh in Prior work and then once all that boring work is out of the way uh you can now uh when you're actually meeting with that other human being in person you can you start doing far more you know interesting and uh Advanced things um and so there's a kind of a few kind of um other topics uh so uh of course creating knowledge graphs is another very very big topic this is kind of ongoing effort uh on the part of that's happening a lot which is you could imagine just converting scientific documents to like from PDF to other formats and then creating uh semantic embeddings and knowledge graphs out of it uh you could you know uh be like if you want to be uh answering questions like what's the history of this equation uh what what tools were made to make advances in this equation and the next step of course is autonomous science which is asking it uh asking the llm uh make the next step make the next advancement in this uh in this computation and sorry in this equation um and that's really kind of like the Holy Grail in a lot in a lot of senses that that is kind of really true AGI and that's um and we have to it's our job I think for of a lot of people here to make sure that that happens in a decentralized manner with the people who are contributing the data and contributing the knowledge to this network uh do um are rewarded uh and that we're not really like leaving people behind by having all of this knowledge creation uh centralized in a few uh entities um and so there's a few um kind of uh miscellaneous topics also wanted to cover as well which is a distributed signing and encryption uh I did touch on this briefly when I talked about like multi party computation but um uh do you want to make uh when you're talking about a scientific community that is advancing towards a particular scientific goal um when if it wants to make changes to the scientific object to that it's creating um you want to have distributed Key Management right you want to have many different Keys uh sorry many different parties that each have their own public keys um uh Converge on you know agree on a particular uh uh State uh that they want to advance uh uh to from the current state uh and you want to have you know uh agreement between them in that manner and if they don't agree well then maybe that data that that scientific product that's being created should be forked um and so there's also kind of a few other kind of you know miscellaneous topics which is um whether we should have like open or closed models um this is kind of a very very big topic especially in AGI safety should we be regulating models um because they can be too dangerous if they're out there uh or should we be uh having open source models uh where anybody can access anybody can download and anybody can modify uh and this is a really tricky tricky topic with a lot of very um uh important implications for for you know uh basically Our Lives going forward because uh these large language models are really going to be increasingly uh affecting every part of our life very very soon um and uh the last uh you know the last thing I wanted to uh talk about was basically um orchestration um and uh whether you have a centralized entity orchestrating or decentralized entity orchestrating uh computational results um either from llms or from other let's say scientific computations could be like molecular simulations or you know um uh mapping the Milky Way or whatever it might be um and so uh there's a big difference between um having a kind of a centralized entity like a particular scientific institution uh handling uh you know sending out tasks handling in inputs and outputs of jobs um and you know uh coordinating it themselves or as opposed to having a consensus uh Collective consensus over the types of jobs that should be created in this network um and distributed accordingly uh so for example you could imagine uh a if you're doing some kind of uh um kind of like's say like a collective a genetic training a genetic algorithm or something like this and you're doing it in a distributed manner um you could basically have uh kind of you um you basically want to have a you can have the nodes that are deciding what the next step is you can have that decided in a distributed decentralized manner right you can have all of the nodes that are participating in it say okay this is the next thing that we want to try uh and or as opposed to having a centralized entity um to be choosing that uh so those are kind of some some basic over uh overviews of like uh the space in general it's obviously you know massive and it's growing dramatically um yeah so uh if you you have any questions feel free to talk to me after this um you know uh I'm also by the way a founder of Co-op pive uh c o p hi i v uh please uh follow me on Twitter and uh come talk to me and uh learn about what I'm working on uh and I'm happy to answer any questions uh about um verifiable Computing is my expertise but I know a lot about Ai and data storage um and uh decentralized science as well so yeah thank you so [Applause] much hey Lisa Walkin are you are you here no all right cool uh okay so let's uh transition over to a talk by Chris Hill and then we'll wrap it up with uh I think a panel with uh each of the speakers well we'll dive a little bit into funding ownership in communities how that relates to computes and bringing it back all into why we're here today so just a couple announcements while Chris gets set up uh we've been passing around a QR code and NFC chip to record basically who's who's been here today and the purpose behind that is to collect um metrics around the impact of the events who's attended and how we can close the Loop behind actually evaluating the real world impact of events like this um also please do note there is a social uh this is just one of the many events uh for the week There's scos that's happening in the morning through afternoon different sessions there you can check it out at scos that's scios um. D.C Community you can check out the the schedule there and we also have our Flagship event at the University of Colorado Jake Jabs Event Center on March 1st do catch us to hear from folks from seti and the goair foundation and lots of really exciting work and then there will be an open bar events that evening on the 1 so we've paid for 200 beers so do please come out and help us drink them so without further Ado Chris thank you shotti so great honor to be here at e Denver which is the birthplace of D two years ago back in 2022 I'm Chris um one of the co-founders of D labs and today I'll take you for a ride in meta sign space science is broken we've all heard it and it is certainly true but what is it that is broken exactly there's a coordination device out there one that lies Upstream of policy technology and human progress it is the protocol by which knowledge is produced and validated it is that coordination device which is broken at the center of this coordination device lies a point in space that grants access to the incentive layer of science a point in space from which all flows promotions tenure grants career your future as a researcher that coordination point is the paper it is the primary product of scientists it is the crystallization of new knowledge a narrative a story of Discovery it is an ancient piece of social technology with its own recognizable Norms when you see it you know what it is it is a way of communicating a way of exposing ourselves to the scrutiny of our peers and to pay homage to those that came before it is the peer review academic manuscript but what hides behind the paper a piece of technology called the Vore VOR stands for version of record it is an act of speech by Publishers behind it still lies a protocol formats PDFs and XML a unique key called the DUI with bibliographic metadata and metrics from this object of Technologies flows the inputs and the outputs to the incentive layer of science from the V flows the currency of citations and impact factor that come to reward every agent coordinated by that system scientists funders institutions R&D heavy businesses all receive rewards from this incentive layer this coordination device is what orchestrates the system of science it determines what we publish papers the rest doesn't really count data Cod software all of the things that make science verifiable and reproducible are treated as an afterthought as second class citizens it determines when we publish completed research projects that contain a completed narrative it determines where we published we select venues based on a metric the impact factor and to a lesser extent today the appeal of its Community it determines how we publish we submit word or latex documents into terrible manuscript submission systems that can take the better part of a day to navigate the manuscript is then processed by the journal's editor and invited referees these are the unpaid laborers of this coordination device we then pay to write on the scientific record ranging from ,000 to $10,000 for our work to be open to the public or we sell our rights and let Publishers erect a pay wall before it and it determines what we certify an ill defined process called peerreview where we opine and highlight the flaws of our colleagues work so that we can render judgment on its suitability for the chosen Community it determines how we evaluate research citations impact factor H index it is a complex game indeed and a and it coordinates multiple constituencies that each have a stake in the scientific ecosystem not just researchers but funding agencies policy makers University libraries tenure committees and research institutions all of them are signaling in one way or the other their value to the world based on the outputs of this coordination device what lies at the center of this coordination device we call it the VOR Singularity it begins with a protocol on its outer layer a protocol that enforces the Norms of science but also its technological substrate our papers have keys called dois a URL that redirects to the publisher site the publisher site will render an XML object on its web page with a link to download a PDF or often a pay wall gting access to its content interfacing with the VOR protocol is a complex expensive and timec consuming process it is extraordinarly hard today for a new journal to connect to it it can take years to be bestowed the journal impact factor metric the holy grail and to be accepted as a V validator and yet without connection to this protocol there is no access to the incentive layer this is what sets apart a Blog from a legitimate academic Journal those that make it through become certified V validators also known as Publishers they run validator Farms known as journals coordinating vast communities of editors and peer reviewers to carry out their services pro bono publishers ensure that the machine runs in good order so that money Flows In in exchange for their service of getting access to the incentive layer for meritorious work at the very center of this device lies the VOR oracles the VOR oracles are meta validator they certify they certify the processors they certify these validator Farms to ensure that they are legitimate in the system this is the web of Science and scopus while theor Singularity is slow to change it is often struck by shocks and must adapt every time a shock occurs the system consolidates further in the 2000 47 of the 47% of the market was controlled by the top 10 War validators today the top 10 validators control 73% of this Market it is an oligopoly that inevitably inevitably grows in power after each and every ecological stressors today there's there's a record high number of scientific societies and small Publishers simply throwing the towel and giving in to the levith ANS of divorce Singularity this process is accelerating in a decade there will be no sustainable scientific Society publisher left in this business economies of scale are such that small operations are completely unviable and while the Titans owing to the economies of scales data silos and vertical integration book record profits journals what are journals they used to be newsletters for scientific communities today there are certification pipelines to stack our CVS and boost University writings communities used to be everything no longer the rise of the pay to publish model has left in its wake alienating production lines it has turned the engine of progress into a volume game Journal editors worldwide are pressured to accept more and more papers and Publishers charged the price that the market can support a visual Circle where financial success is measured in production volume journals today are becoming alienating pressured to push out more and more PDFs production lines that are often flirting with predatory publishing caught in a race to the bottom to capture the market along with alienating production lines the rific of secured jobs and an ever increasing pressure to produce more papers grows the dark appeals of cheating fraud and turning to paper mills there's a professional cast of pirates out there that even the all powerful V oracles cannot keep out today the price to purchase authorship in a validator farm endorsed by one of the oracles is twice cheaper than the average pay to publish fee you can buy citation boosters for a few hundred bucks questionable research practices pecking and IDE fraud are calling entire Fields into question and with the AI Revolution the price of producing fakery is going down by the day soon we will no longer be able to tell unless the system changes fundamentally the paper industrial context is heading into a brick wall why is that are Publishers evil to the contrary they care greatly for quality in the scientific record unfortunately just as everyone else they're bound to the incentives of the system the truth is that Publishers are struck stuck in a bad metag game where a few overpowered strategies dominate consolidate vertical and hor horizontal Integrations are hugely profitable strategy tanks to the economies of scales from the sheer cost of efficiently interfacing with the VOR protocol capturing data scientists keeping cap scientist captures from A to Z hoarding data for the training of propriatary system and AI creating cascading models where one prestigious journals acts as a light bub to catch the Flies only to redirect you down a path of second and third tier offerings where the real money happens produce volume volume volume volume the subscription model and print had the advantage of making volume more expensive to produce with the digital and pay to publish model all guard rails are down underpowered strategies all the things that are good for science are bad for their bottom line expanding the unit of knowledge integrating code data and all other artifacts that make science reproducible as core to a publication expanding the scope of curation away from just simply certifying the soundness of a manuscript and the novelty of its results that is a losing strategy peer reviewers already work pro bono and adding more burden to them would not be viable expanding Community sovereignty the fact that peer review today is unpaid is a design Choice it needn't be this way but naturally that would chew into the 40% profit margins of these systems to pay peer reviewers and referees and editors so it can be done they're stuck in a bad metag game divorce Singularity it is a coordination device that lives Upstream of policy technology and Humanity's future despite being extremely profitable it is a minuscule Market considering its importance just 20 bips of the social media market for researchers everywhere it feels like a black hole that you cannot Escape but let us zoom out for a moment The vorus Singularity is just one point one way of coordinating humans and the creation of knowledge and it is certainly not the best way we just don't know because we haven't explored there are so many other possibilities out there in the vastness of metascience coordination space this vastness is Desi D is a call to explore the vastness of metascience coordination space it is a call to all of us to all of you divorce Singularity is not inevitable and it is not the only way it befalls us to explore the unknown our mission is to carry out this exploration and who knows what we will find out there what marvelous worlds await to be uncovered contemplate this space for a moment and imagine the possible Futures the antidote to divorce Singularity is the dii plurality but how can we escape divorce Singularity we need to unlock medicci space travel so what is is that so what do we do we need to expand the space of possible coordination mechanisms to create Rich protocols that lets communities find new optimums the first thing to do is to expand the unit of knowledge to empower communities with the means to create research objects not just PDFs and just XML research objects are rich integrated publication that one can build over time that can contain software code data and much more stored on an open Data Network that guarantees data integrity and accessibility research objects unlock plural modes of Publications we must expand the space of curation upgrade the nebulous notion of peer review to break it down to unbundle it and enrich it to expand Beyond soundness and Novelty the future of science isn't a paper published in it is a research object verified by where communities can issue attestations to verify granular attributes of published research open code reproducibility commercial potential there's so much that is ignored by the current model but that is vital for a sound and Rich scientific record we need to move towards pluralistic C PL pluralistic validation models today it is one paper one Journal a onetoone relationship this is an artifact of the copyright monetization model it doesn't need to be this way the future is the one to many relationship where we what we call pluralistic public uh pluralistic curation it is not one to one but one to many where different scientific societies can participate in the validation of a singular research object as Crossroads for communities to intersect finally we must expand the space of community sovereignty the strictures of the vo have robbed communities of their sovereignty we must return what has been lost to let scientific societies decide on their own terms how they publish what they publish what they certify and the flows of value for their contributors the unpaid nature of peer revie is not a fatality it is a designed choice we must let communities break free from the validated Farms of divorce Singularity ultimately the victory conditions for dii is to change the payoffs of the metag game to fix the broken strategies to align profit Maxi with metascience Maxis to make it worth worthy to carry out quality certification process that extends Beyond traditional peer review to expand the unit of knowledge the unit of value and to return Community sovereignty and to change the metag game we need web 3 Protocols are coordination device that set rules and by setting rules we can create new strategy space we must unbundle the data layer from the application layer and for this open State data networks are the next Frontiers for the scientific record from the printing press to the silos of web 2 to the freedom of web 3 D is a call to build it is a call to you the people of Biddle Hub mark my words by 2025 the D plurality will have become a credible alternative to divorce Singularity come and help us build the Escape vessels that will take us to the Uncharted confines of medicine space we call these Escape vessels arcs autonomous research communities we need arcs to escape the flood of volume the black hole of consolidation and the shackles of exploitation but there's so much yet to be built and this is why we need you the d space has been laying the groundwork for launching these vessels so get ready for liftoff medicine space await thei communities are launch joining the launch site ready to take off with their arcs on maintenance flight list a longevity research curation Community mow a decentralized spaced research program gitcoin a compute intensive science provider the dii foundation a meta science community and your Community Reach Out come to us and get ready but make no mistakes it will be incredibly hard to escape to reach Escape velocity from divorce Singularity it will take a lot to engineer viable vessels to travel into medicine space but the mission of dii is as urgent as it is vital if we can accelerate science we can accelerate Humanity so join us help us explore the d space help us unlock medicine space travel and reach escape velocity from divorce Singularity so that we may Usher together the dii plurality thank you thank you to my team thank you to the dii community as a whole and thank you to you e Denver dreamers of a decentralized future to be continued come and join us for Vis Vision talks tomorrow scos is an ongoing ditech conference this week and we'll have an incentive design seminar at CU Boulder as well Mark these dates thank you uh Chris stay on stage we're GNA close out with with a panel uh we're going to call back galani and Levi uh yeah can we get some more mics too down here and then Lisa this is your if you're here are you here Lisa I don't think she's here just bear with us while we get set up there's two wired mics here by the chair okay two wires got it cool all right so what we'll do first is I feel like uh we haven't had much chance for for questions at events like this and earlier today so we're going to pass a mic around um if anyone has any questions that they would like to follow up with any of the speakers with now now's your chance to ask about the vce singularity what do we mean by medicine space all right here we go we've got a we've got a question and uh please as you're passing around the QR code make sure if you haven't scanned it yet to to be sure to check in your proof of attendance for the dite Quest test do I ask should I should I ask my question now go ahead ask your question yes yeah so for somebody from the outside looking in what is the one thing that I can do to get involved or improve dii as an individual see you but oh never mind it works uh I mean contribute right what does contribute mean a lot of these Frameworks are open so whether it be joining D labs and contributing to a node as a scientist or building out your own node or investigating noes and seeing what you're interested in um providing GPU potential to networks so that you can then leverage Co-op Hive to monetize or to be a part of the the the marketplace uh join DSi World participate in events like this learn about the platforms contribute your knowledge develop your own engrams most of these platforms are open you you can go to their websites is usually a contribute tab or their communities like Dows if you're interested and I guess the answer to that question depends on what you're interested in doing figure out first what you want to contribute what you think you can contribute go and speak with the builders themselves in the community and then learn about new ways that you can contribute that you never thought of before yes um the space as a whole has an enormous amount of things to build um and it's an incredibly hard Challenge and so always on the lookout for open source contributors for engineers for game theorists for protocol Architects for people that are willing to um contribute to some of these many repositories and projects ultimately the goal of daa is to create an interoperable stack that is multi-layered and that will require uh an extraordinary high amount of effort to be honest to have a chance to compete against the B protocol so you know if you are a developer out there if you're someone with a an interest and a curiosity and contributing please come and talk to me come and talk to anyone uh around here on this panel and we'll find ways to really leverage your skills and your talent and to credit you so that you can really make a difference in the space yeah I I'd say follow your heart look in this space look at what bothers you about the scientific system about the world in general and dig into it deep and don't stop and keep going even if it sucks even if it's painful even if it takes a long time keep going and find people who are like-minded who who want to work with you and then find what where you think you would be most valuable where your contributions would be most valuable uh so my question is to the panel how would you make because we all care right we all nerds and care about all these protocols but how would you uh uh make the Greater Community Care about science and we're doing here to a level that they are participating without having it to be like too abstract for them to actually engage with it I think that's I think that's the question right like how do you if you can solve that then we're good um so to to Chris's point about the Vore singularity in the same way I think not I think the scientific industry especially as it relates to the Layman has been so far abstracted that people don't feel a part of the science despite science permeating all aspects of themselves so I think the the first step is making science understand or or sorry people understand in the same way that web 3 does is that you have your voice matters your vote matters your ability to contribute in a financial system even though you're not an accredited investor is important and so I think we can take similar ities from how web 3 has done it from the financial perspective and is still doing it and apply it to science you need not be an accredited scien a scientist you can be a nerd and we're all nerds at heart even the popular guys are nerds they're just really focused on women but you can create a research Collective on how to be a best sports player how to increase your stats how to increase your nutrition how to increase all this kind of stuff everybody has questions and everybody looks for answers everybody is essentially a scientist and I think if we it in those ways and just be very welcoming with how we do it and maybe tone down some of the excessive jargon until it's necessary then it becomes easy what are you interested in oh yeah bet cool we can help you build that or if we can't help you build that you can build it yourself you can find somebody else to build that a lot of this is community arcs are Community Co-op five is a community leveraging assets D is community so it's all community and I think there are enough communities out there that are hungering for change whether it be patient advocacy groups open science communities and collectives what have you there are people out there looking for Solutions I think we may not just be tapping into those and we're going to things like Financial Industries institutions and somebody had asked at scos like what is the how do you attack how do you get how do you speak to scientists and I think it was Ronin who mentioned that he goes from the bottom up we can't go to the institutions we have to go to the students ideally the professors but really the students and work our way up we have to become Insidious but I think the the value proposition is so important that it's Insidious by Nature right and I think if we can wrap it around again with the coolness and The Vibes and the community support and The Buffy corns and the good parties and weave all those things together it becomes a no-brainer we had a party last night and people were talking about science people who were not scientists we had acid out come and cook meat while still talking about science we already have the blueprint we just need to scale it I can say um so I think that we have to meet people where they are and find something that touches them personally because I think that's what will really bring people along uh like there's we we can talk you know a lot about kind of in this kind of um you know kind of high level philosophical you know Ambitions that we have you know we want everybody to be involved in how why what's in it for them right and the the the reality is that you know the world is a rough place and people don't have a lot of time and so the things that they're actually going to spend time on are the things that actually affect them and so we have to communicate when you're participating ating in this what are you affecting in the world um and how does it impact you how does it impact your friends your family your community uh and why should you spend time doing this and not something else that's a hard question to answer right it's a very hard question to answer why like why can why spend an hour you know if you're doing some really decentralized I don't know like let's say you're out collecting some soil sample locally from you and you know putting it through some like a local like U some some solvent or something that was sent to you in the mail to to test it out whatever um why is that important to you why like how how does that impact you maybe you care about the environment maybe you care about they being lead or cadmium or Mercury or arsenic in your in your soil or something you know you you have to you have to really touch people where they are because I feel like no very few people are go and there are to be clear especially in this community there are people who will Who will dive deep and deeply contribute for science in general but if we want to Le a very wide audience that most of whom have limited time uh we have to M make it clear why this is a more valuable use of their time and get them more excited to do to participate in De centralized science then whatever else they would might be doing which is could be could be scrolling Facebook or watching a TV show or whatever else so we really have to make the case for them and that really requires finding uh finding it finding where it touches them personally and and and meeting them there yeah to to add to these uh two excellent answers um we just we have to build great products we have to build great products that add value to the scientist's workflow that showcases their work in an incredibly positive light and more important than that we have to build platforms and systems to support communities because at the end of the day um it's it's all about belonging it's all about belonging to a scientific Community it's all about finding like-minded peers with which you can interact with that will add value to your quest for knowledge so creating these excellent products that abstract away all the complexities of the technologies that we use in languages in ways that an average internet platform users understands and Vibes with in a way that provides affordances to create belonging with communities that's absolutely essential because communities are really at the center of scientific progress and finding ways to foster a sense of belonging a sense of admiration a sense of utility out of partaking in these communities work is really a fundamental part of creating uh the future of D and the future of Science in general I actually think we also have a I actually think we also have a cheat code um behind us is a variety of communities that have already been built and understands a lot of the philosophy that we're trying to build we can vampire attack that okay and I'll say that very loudly we can vampire attack web 3 because essentially over there are people devising economic Frameworks and economic policies that are don't really lead anywhere there's no terminal utility which is why you have a level of speculation and fluctuation and volatility we have the opportunity to provide terminal utility to functionality so not just transacting for transaction transacting Sate but to actually lead to a cause so tokens can be bound for functionality that is inherent to the system and therefore get locked so when you're devising tokenomics it makes it a lot easier when you have token syns versus token emissions we can easily take easily Fork a lot of the Frameworks and toonomic models that exist over over there bring it here and make them a lot more viable and functional and so I think the added part of having community and we talk about it is incentives we need to Define very accurate incentives that align with what we're trying to each do in the space and pay people and it's not that hard it's really not that hard we are building a knowledge economy but you can build economies on top of anything and it's just leverage what they have we have terminal utility integrate it and let's move forward that's an easy way way to vampire attack other Eon other communities and get them on board you heard that over there we're coming we're coming for you going to suck your blood [Applause] hackers so Chris you started to talk about what my question was regarding abstracting away all the complexity of the technology behind blockchain and cryptography and and things like that but my question for the panel is how do we talk to scientists and not scare them away from from the blockchain stuff because for them it's about can this help them solve problems I get that but when as soon as you start talking about blockchain unless they're part of a very small group of people who have already embraced it it becomes problematic in their minds often and so I wonder what you guys think about that yeah so so I mean it's it's it's fundamental to abstract away the complexity at the end it's all about creating useful products that add value to your workflow that make you feel a sense of belonging in community and you can abstract away a lot of that complexity um and so so so that's very that that's fundamental for example reusing a lot of the existing infrastructure such as Orchid such as the archive plat family of platforms and creating value for them we can create value for orchid by registering achievements Publications attestations on existing infrastructure we can create value for pre-print platforms by mirroring attestations and indicators of value we should not build in a vacuum that's never going to work we have to build in a way that we interact and integrate with the existing infrastructure such that it becomes a a no-brainer because at the end of the day that's what you want as a scientist you want to get recognition for your work you want to get your work out there you want to have peers that look at it that build on top of it and for pre-int PL forms all over the world they're struggling with trust and markers of reliability and I think there's a lot we can do there to cross fertilize between the existing infrastructure and the infrastructure that D is building while abstracting away a vast majority of the technological complexities which in my mind they are inevitable we have better systems for persistent identifiers we have better system for archival preservations all of these things are inevitable unless the scientist is going to go into the back end don't mention it right I don't go to a website and say well I'm running on JavaScript which is why you should use me no I go to Amazon because they can get my shipment to me tomorrow talk about your value proposition um I think a lot of us in science especially in the academic sphere don't pitch to we don't pitch like entrepreneurs right when you pitch to a VC they don't necessarily care upfront how you're going to do it they want to know what you're going to do and how they're going to make money same thing for scientists I'm going to x times your cost recovery right I'm going to make it so that it's x times faster to do whatever I can make sure that you can guarantee to not be scooped through this metric I don't it doesn't matter how you do it just the fact that you do do it and that's what their outcome is so I think we pride ourselves or at least at the beginning the space prided itself on leveraging this new technology Whoopi now I think we've reached a level of maturity or hopefully approaching a level of maturity where it's like okay we're using blockchain that's fantastic but what are we actually yielding in terms of outcomes and if you just focus on that like it's not hard just focus on that and people will come is a a sort of a combination of those other two answers which is I would take a two pronged uh approach the first prong is um really Hammer home how much the current system sucks don't talk about blockchain is like every cuz academics hate like Academia for to to a large extent they they're already sick of it and you just want to you really want to kind of like grind that in like you really have to emphasize how much it sucks and just constantly remind them and annoy them and like remind them of how miserable they are and how miserable their PHD years are and how poor they are because they're being exploited and really really remind them of how shitty things are and then simultaneously like Chris was saying lead by example make a product that works and solves their problems and Abstract away all of the complexity so they don't even know that their blockchain's in the background they might get interested at some point but they don't care they're stressed they're poor and they're being pushed to publishing 10 papers a year when in reality they should be publishing one paper every two years or something like that uh and so yeah it's really a two-pronged approach which is Hammer home why it sucks and then offer them an alternative path forward so um I have a question I'm a tech VC and the question is like how do you convince VCS to invest in this space because right I had a chat with a few colleagues and it's kind of difficult to convince them and I think the the market is not there and maybe you need to convince them that there's enough nerds that want to build stuff and there's a business model behind that you how do you approach that you just call it account abstraction and then they'll invest in it so I think there's multiple answers to that question um ultimately what you want to do is a product that people love I think if you're VC and you tell me you know okay how am I going to recoup my investment in N series what's your business model what's your CAC what's your you know what's your your marginal uh uh uh um uh um profit all of these things are kind of besides the point because at the end of the day if you make something people love where you have actual scientists coming on these systems and these platforms creating better research better knowledge there's always ways down the road in which you can monetize some of this right but I think we have to be very mindful about that um for example it's it's it's a space where hybrid structures that are both for profit and nonprofit are very important both for creating legitimacy and also for creating separations of concerns so yeah to answer your question if you're a VC and you're in it you know for a short-term token pump you're you know dii doesn't need you um if you're in it for you know changing humanity and advancing the future and making a a bet on a team that has a big dream um that's something that will stay with your firm forever I mean it was your company it was your VC firm that solved the replication crisis I mean what more more do you want going back to your LPS you know what do you think they're going to say they're going to be oh well they solved the replication crisis but they didn't do 100x I'm not investing in their next fund that's not how it's going to go that conversation they're going to be damn guys how did you make that beted that was incredible so you have to think also about the impact and I think it's hard because you know you're profit Maxis and that makes a lot of sense but you know zoom out a bit and understand just how fundamental for Humanity it is to solve some of these core problems in the production of knowledge I think that's a very good point there is definitely a perspective shift that needs to exist in VCS uh we had a I had a conversation with a woman who used to work at venture capital and made reference to the fact that there are different forms of VCS that are now starting to crop up that may be more vable to the kind of things that exist in D that being said we talk about d as this monolithic industry and it's not in defi you have lending protocols you have amms you have all these different permutations and they all require different investment thesis same thing in dii you have projects that are focused on IP which is maybe more amendable to traditional Ro ic's you have projects that are focused on publishing and that maybe have a longer time horizon or more or an indirect venue or indirect Pathway to to return on investment that maybe doesn't meet that type of thesis thesis but maybe amendable to other things like foundations or these hybrid VCS so I think to flip the question a little bit VCS need to understand what they want and understand maybe the space better so that they can see okay this is IP this is more traditional I can invest in this directly whereas something like a dside Labs or any of the other ones that we mentioned previously that are taking a more slower approach but are are going to change revolutionarily how science happens you need have a different investment thesis and maybe this is an opportunity for a VC to actually broaden what it's capable of doing to innovate on itself we talk a lot about how we need to change the scientific publication uh the sorry the scientific framework but within the scientific framework is the financial industry the publication and IP legislature and all these kind of things and it can be a DI bidirectional Innovation conversation that can exist here there's opportunities within the space and and outside the space that can start to crop up by interacting with these different stakeholders uh there's um give a those are amazing answers I'm going to take a totally different perspective which is um there's a lot of money to be made uh in fixing the problems of science a lot of money to be made the classic example in D Labs is tackling this very you know really amazing manner is uh solving the problem of uh Fair data so findable accessible interoperable reusable data the lack of this data there's like on the the lowend estimate is 10 billion EUR a year um in in in loss like value in productivity simply because we don't have the because data scientific data in particular but data in general is not findable accessible interoperable and reusable right that is a big problem that scientist that affects scientists every single day but also costs a lot of money it's a lot of money just totally wasted um and so right there that's at least 10 bli million EUR a year that just you know you can solve there's a I mean there's like tons coord like the publisher Parish crisis is is creating bad science it's creating worthless Publications it's wasting the the best years of a lot of care of of the the best years of a lot of researchers lives uh on just pointless nonsense it's literally just like you're glorified copywriter right um and you're just being put like it's it's just nothing it's just nothing is being made out of value I'm not saying of to be clear there's plenty of important amazing things that happen in in in phds and so on but the reality is we all know like there's there are pressures that waste the the time the money that's invested to begin with in the in the PhD students um and there's a lot of money wasted in in the time that they're uh like spending doing things that aren't productive and the list just goes on and on and on I mean decentralized clinical trials can rapidly Advance how how drugs are um how quickly drugs are brought to Market um uh you know we have we have there tons and tons of opportunities and so I'd say having a a clear delineation of where the the value proposition that the offers is is something that we can bring to BCS and be like hey listen this is the problem we're tackling this is how we're going to solve it this is going to be the value that it's going to bring this is you know going to be your Roi imagine if you could invest in the internet not a company but the internet imagine if you can invest in PDFs not Adobe but PDF what daa allows us to do is weave in economic incentives into tooling into protocols so when you invest in Labs you're investing in an entity you're investing in the part the the Chris and everybody else who's building out the framework but when they launch their if they launch an economic incentive built into the framework well you're investing in the future of what a PDF is and think about that if everybody adopts that of which everybody will adopt that you're investing in a fundamental unit of the Matrix of information and you couldn't do that in web 2 you can do that in web 3 so this is what I mean when I say you need to VCS or invest people who are interest in investing have to shift a little bit their mindset and understand what are the capabilities of web 3 distributed Ledger technology and what is the opportunity the real Economic Opportunity that is here to leave to Levy's Point science is so broken that it's almost it's just low hanging fruit it's hard but we're going to get there it's inevitable and are you going to be part of that or are you going to look back and be like damn I missed the pump now I got a fomo you don't want to be that person not in this space not with this caliber of people definitely not any other questions I think we got a question over here guy in the nerd sweater um we talk a lot about economic incentives we're talking about tech VCS uh money tokens all s all kinds of cool things but one of the things that I'm kind of concerned about is the reputation or the the sort of the appearance of economic incentives and Entrepreneurship combined with being a scientist and I think an Entre a scientist entrepreneur is an interesting new class of person but how do you feel like that's going to affect the the way that science is viewed and the way that scientists view themselves and and and how comfortable they feel to be a part of that that new paradigm where economic incentives the ability and the requirement to sort of have access to your own funding and raise your own money that sort of entrepreneurial Spirit becoming part of the scientist's uh Arsenal yeah great question um so it's no mystery that the industry and scientists in general have a strange relationship with money so on one hand um they just don't make enough I mean the the the the state of a PhD student or a post do is essentially close to poverty um there's no way for a pi today to increase the salary or the stipend of a PhD student this is wrong there should be ways in which your team can find Avenues to have additional income and we need to build this in the system and when this happens that can create tremendous welfare but I agree it's scary scientists have a strange relationship with money one of the question that pops up is okay well maybe I $400 on Research Hub do I have to pay overhead to my institution if it's Columbia it's 50% or not there's a lot of questions around this at the end of the day those scientists despite having a strange relationship with money they're constantly chasing it through indirect channels be it tenure be it grants all of these vehicles are extraordinarly important um and now the publishing industry has also a very strange relationship with money um the entire industry is incredibly Hush Hush about anything related to how much money they make how much an acquisition was how much a service can cost it's all price upon request because there's great unease about money it's considered something that's almost impure in a sense and yet everyone's chasing it so yeah I think we have to be mindful of the social norms in the space uh but I also think that you know for me if we could you know build products and systems that would increase the stiens of PhD students to livable wage we would be just incredibly proud of having accomplished such a feat and I can guarantee you that if this happens you know there will be a tremendous welfare benefits and scientists will line up to have a chance to participate in such systems that's a fantastic point because there is a lot of cultural taboo that exists in in in science and it's it's not hardcoded it's just this like balance between moral and ethics and something that exists in the gry line and you can see it a lot with different jurisdictions the United States is I think much more capitalistic than a lot of other places in the world and the biomedical space in the United States has different policies or Norms than let's say other places in Europe I've recently started looking working in decentralized biobanking and it's interesting when you talk about providing incentives to Patient populations for donating tissue now both Frameworks here and in the EUR and in Europe prohibit you from directly paying somebody to give their tissue but there's nothing that exists Beyond around use-based royalty distributions to a patient so if a third party end if a third party end user like Pharma licenses access to tissue or genetics there's no reason you can't redistribute some of that licensing uh fees to the patients in the United States when we speak to tissue Prov providers biobanks uh clinical sites they get it they see it I was on a call last week with a major biobanking Coalition in Europe I was a huge no no not because it's illegal because it's such a cultural shift in their mindset that they don't want to participate in it and so to Jud to to to Chris's point we do definitely need to tow the line in terms of understanding scientific cultures in a variety of different places and that was going to feed into how we onboard folks and what kind of opportunities are present but at the same time I'm actually not afraid of the idea of entrepreneurial even mercenary scientists because the goal is to drive Innovation and being a mercenary scientist does not necessarily mean that you're a bad person or that doesn't necessarily mean you're going to do unethical science it just means that you're motivated by the money and you're going to apply your skill set to it and I think that's a that's a fair trade-off within certain bounds of course and D works with Regulators we're working we want to work with the fdas just we want to work with the FD and other Regulators in the world because we do need to have guidelines and boundaries that being said there's no reason we can't have mercenary scientists and I think that's actually for the betterment of scientific innovation it's for the betterment of developing new novel tooling for inspiration for Innovation I think it's great um yeah those were yeah really good answers I I'm just going to say something quick about the kind of an archetype of a particular type of scientist which is the type of scientist that doesn't do it for the money uh like the classic example is um the person who solved the PO conjecture uh turned down the like a $1 million prize um and he said this he basically did it for the for the love of the science right and so when we are approaching scientists uh uh who really are in it for the for For the Love of what they're doing they have an allergy to money in a in a lot of cases um it's a very particular type of person I'm talking about by the way but these are some sometimes often the most brilliant people who drive the field forwards the most and the question is you know H how do we help them do what they do and to Chris's Point earlier it's about abstracting away a lot of the you know web 3 blockchain stuff in the background cryptocurrency stuff in the background and the entrepreneurial stuff in the background it's really about abstracting that away and clearly demonstrating that this alternative way of producing knowledge and disseminating knowledge is on a philosophical level better Paro dominates the current system and if we can convince them of that then I think their concerns about how they view themselves uh as entrepreneurs or as mercenaries or whatever it will fade away um you really need to you need to convince them of that yeah and to to add to this there's ways to make money palatable to scientists it's called prizes it's a very old mechanism that used to be the number one driver of scientific discoveries until we shifted to Grants but it used to be back in the days we had prizes you know kingdoms and kings and queens would set up prizes for great Innovations and whoever Sol it would get the prize and we should have more of that mechanisms we should bring prizes back and I think there's a lot of people working now on you know retroactive public good fundings which is another way of talking about prizes you know and and Hyper certificates and all sorts of new ways to create markets for public goods hey another word for prizes is bounties we you can have bounties right we have fantastic D Labs is sponsoring a bounty or has contributed to a bounty for hackathons we have so many members in the audience that have contributed to the D Summit by providing fun money for people to achieve real outcomes so Chris is absolutely right you can have prizes you can have bounties and people who take on bounties are mercenary scientists we already have these Frameworks it's time to reintroduce them to scientists and understand to Lev's point be amenable to be cognizant of what their internal culture is was very simple hey guys I got a question uh about Pharma and um biotech so on a 10-year time Horizon how could Pharma and biotech benefit from dii is there any mechanism that you could see any any value that we could create that they could benefit from yeah so we have a you know other other prominent d project called molecule that has been working on creating liquid vehicles for IPs called IP nfts um as a way to create funding for scientific research projects and to carry on uh the evaluation of commercialization potential and so for biotech VCS you could think about these vehicles as a way to create earlier opportunities for liquidity that can then be reinvested in the process of carrying on uh clinical trials and feasibility studies so there's a there's a vast space of potential for essentially creating financial instruments that can Bridge The Innovation Gap right that death trap that's between fundamental research and applied uh Innovation uh which is the valley of death and finding ways to to essentially create funding opportunities funding Vehicles is an incredibly worthy task because bridging that Gap could absolutely accelerate Humanity's future and for biotech companies it's something that's very interesting I think there's lots of biod daas as well that are pooling resource pooling data pulling their the skills of their contributors towards uh a greater understanding of some fundamental diseases there's also uh uh data Dows in which patient populations with rare disease they band together to create incredibly valuable databases that uh can then uh have um redistributional proper properties back to the communities that have provided the data so this is a way for um for essentially incentivizing incentivizing data aggregation uniting communities creating vehicles that create welfare benefits back to these communities in an incentive aligned way which we don't just don't have today so yeah there's a lot of potential within the biospace uh which is incredibly Capital intensive incredibly risky uh it's a very very difficult space to operate in it's deep Tech it's deep biotech and and I think in the d space there's going to be a lot more emergent models about how we can bridge and bring liquidity in order to sponsor or essentially avoid or mitigate the effects of the Valley of Death so I I I personally classify this definition is fantastic in the frame of what kind of Novel Innovation D can bring to to to biotech and Pharma but in addition to that I think there's also operational efficiency which is very prominent and they can understand today we Pharma is a huge Market it doesn't quite operate in web zero but it operates in web one and a lot of the processes and operations that exist in Pharma are extremely inefficient and take time and time is money and so if you can streamline operations you can directly increase profits not by changing any other asset but just allowing things to move faster in addition in the biotech space if you look at things like crowdfunded cures and those entities that are looking to recycle therapies find second order applications of therapies essentially allowing you to increase the portfolio of return on investment of a particular asset web 3 or or ability to find new data around a particular topic find new IP Frameworks around those topics being able to converge and collaborate and consolidate different IPS for that that's another way and I think a third way and one of the ones that are most prominent and I think we need to start marketing more is cost recovery the value proposition I believe and I've spoken to a few Pharma investors in Pharma VCS that they have in things like vadal and these Dow communities is drisking dii grassroot dii as I like to call it is a lot more um risk on and so Pharma can take an investment put a million2 million doll investment in v and allow that Community to drisk assets or to do the at least the due diligence to determine what assets are more viable than not and then they can come in and they can broker deals with the Dows or the collectiv and whatever ideally paying those participants for their country contributions and then get those IPS and so they trunk it it still might take 10 years but they have to invest a lot less money there's a lot less risk and so I think there are few verticals that especially in the biotech space we can Market the value proposition of dii both Grassroots dii as I like to call us and those that are more Enterprise facing dii like that that attack directly the operations side and to show the value that what we have here I'll just add one point to that within my space um there's this prominent example of this company amen that tried you know engage in replicating uh uh 50 top scientific papers published in the top journals imun Immunology nature and so on and so forth for for early cancer treatment markers if I remember correctly they had a replication failure rate of 90% hundreds of millions of dollars wasted on the burner because the original research was just not robust now as a biotech company before I engage into listening or reading these academic papers and and drawing you know looking for new therapies based on these results I'm actually very interested to know if these results can be reproduced is the data available is the code available can I trust these results the answer today is no you really can't um and and imagine if you had a peerreview process quote unquote where you could have a much richer validation process where you could have the assurance that the results are reproducible that the data is available that you can build on it in safety imagine how much millions of dollars biotech as a whole could save if only science was more robust and only if validation process was more holistic and actually validated the things that make signs verifiable so for the Biotech Industry it would be a no-brainer to engage in a service economy that actually attest to the quality of published research beyond the traditional process of peer review and so that that makes me think of of another really interesting point right so we talk about the reusability of of of actual assets like drug Discovery right but when we're talking when we're thinking about things like Fair data principles and the r being reusable D nodes or D labs in the D no platform essentially creates you a place where you can upload negative data so your failures right and there's a lot of valuable information that exists in failures out of all of the profiles or the IP that a farmer is trying to develop the vast majority of them fail now that's a failure for the farma itself but that data around that failure may be extremely valuable to another farmer participant or another institutional participant and so the ability of farmer to recycle their negative data or leverage their negative data and create new marketplaces which can then be sold off so it becomes a new asset class for them or they can either profit from that or have cost recovery to then funnel more money or to divert s these these proceeds profits Revenue into other initiatives I think we have the opportunity here to create novel asset classes Define asset classes that are permissioned can be permissioned because we are talking about a highly competitive corporate industry they don't want to just leave it out to everybody but they can come to a note probably split one up internally kind of like a hyperledger of information and then Farm it out say Hey listen we this didn't come to term but this data may be extremely valuable to you amen or Vertex or Merc or whomever are you willing to buy it or do you want to come in together do you want to have a licensing agreement all these novel Frameworks are possible now with the tech that are being developed by all these people here now I think the The Innovation is just what we've scratched is just barely the surface a lot of cool things that are going to come out in the next five six seven years uh I don't have much to add uh only like a few minor points which is like lowering the barrier to entry uh for research that's a really big one uh more directly connecting the supply and the demand uh so um we have the researchers and scientists who have the supply who have the knowhow and are able to carry out EXP experiments and then the demand could is coming from regular people who say hey I have this problem I would be willing to pay a bit of money um or in in you know uh contribute a bit of money to a scientific project that may or may not result in IP or return to me directly but you know at least I'm connected to the science that's actually being done instead of paying taxes to the government which then gives the NIH what is it $50 billion dollar a year is their budget I think something like that a $50 billion doll a year budget with a massive U lack of very poor replication rate as Chris mentioned um and and then you know having no control over this absolutely monolith like this VOR Singularity like just like totally out of control you know uh and and this is by the way this is why a lot of people don't trust science where we have a real problem right now in this country around people not trusting science right uh it's not trusting scientists and they're not wrong to Natural scientists given how many scandals are constantly happening why are those scandals happening because incentive systems are broken they're they're they it encourages people to lie and cheat and steal uh and and and not to do actual proper science not everyone typically obviously it's the majority are honest but this is why people are getting sick and tired of the system and so um a lot of it has to do with Connecting People directly with the things that impact them and that's how you get people to care um and that's a source of Revenue that's a that that's you know improving how the the scientific processes go these two killed it explaining that um like there there's so much value to be created uh and and like it's low hanging fruit really I mean like we're we're all early here like we're all really really early here uh so this is a good time to be here another prominent example the ameloid hypothesis as the causative agent of Alzheimer's disease billions of dollars burned over a fraudulent paper if if we had open data open code and we could have verified the thing we could have passed it to like you know have a more holistic validation process we might have avoid it billions of dollars of wasted resources and we might have even saved lives of people that are affected today tragically but this terrible disease um think about it you know billions of dollars to produce a thousand or 2,000 PDFs I mean this is incredibly inefficient so we need better ways to to to attest to the quality of published research from billions to billion to Millions to million I think that's a good kpi that we should look for from the biomedical space in D can we bring down this this cost of doing any kind of innovation I think we can go ahead shot yeah I just wanted to give a little bit of space towards the end for one final sign off for for the audience um I think it's good to maybe we could go real quick uh one existential threat to science or Desi one massive opportunity that you think is worth taking time or effort into uh and then something perhaps that you're looking forward to either at this event or uh in in the next couple months I got 50 seconds um existential threats well we need the talent to flow in thei we need a couple of things right you need talented people to flow in we we need Capital to flow into the space that's how we're going to be building these Escape vessels uh and then we need to build great products that people love to use and so that is an incredibly difficult challenge but we will get there I'm confident in that space um and yeah existential threat narrative co-opting I think as we grow and as people become more aware of what we're happening and because a lot of these tools are open source there are going to be bad actors or less align actors that are going to try to make money grabs and I think we need to be as integral as standup as uh credible as possible to counteract that and I see everybody here in doing so so my vision is personally we're going to have a vision talk tomorrow or the day after um is a space where dii and all the barriers or dii allows for all the barriers to science to come down much much lower whereby you can be a chemist in the Amazon forest or an engineer teaching a child about quantum physics in the tundra Siberian Tundra and you are just as accredited or you just as meritocratic in doing this as anybody else so existential threats um I'm borrowing this directly from Jonathan Kung who I did see in the audience earlier which is um uh us all getting bought out in like a year or two for like 20 million bucks and just the same institutions that caused this problem in the first place just perpetuating the problem that can happen uh and uh how to prevent it um honestly I don't know I don't have a good answer to that question so that's something yeah maybe we can talk about during the vision talks it's a it's a really serious problem thank you to bid Hub thank you to the D Summit shout out to you John everybody else who has provided funding um this is one of many talks join the rest of our talks our events come to the houses go to all the people speak learn participate and let's have a good time thank you for an hour pan you guys are awesome