Verify, Don't Trust: DeSci in Powering Trustless Innovation | Erin Magennis - Muse Matrix

[Music] and welcome today we are going to be talking about verifying don't trust DS role empowering trustless Innovation with blockchain I'm eron mcginness and I'm really excited to dive into what a lot of these different mechanisms that we might all be familiar with in web 3 and how that can be applied to the scientific sector I guess laying some initial groundwork across the board we're seeing how web 3 is disrupting these very trust-based systems maybe different Legacy systems across Finance governance media these are a few examples of different brands we might know and love next up in this evolution of disrupting these trust-based Legacy systems is science in my opinion and in the opinion of many others within the d space and this really is is rooted in the issue that trust in science is broken which I think a lot of people might not realize the full extent of this if they aren't deep within the scientific sector or exposed to it in different types of ways this is one really renowned example of how trust in science is broken so in 2006 as a society we found the cure to alzheimer's which was incredible billions of dollars went down this path of um based on some different research looking at some different proteins and 20 years of entire researchers careers and societal progress towards finding a cure and developing it further for Alzheimer's went down this pathway then just a couple years ago we found out that this data was manipulated this image which is of a western block was basically photoshopped that set back the progress that we had supposedly made by 20 years imagine where we could be at today imagine the different people's lives that could have impacted that's what I mean when trust in science is broken and this is just one example there are countless more other examples of how trust in society at large is broken are examples like this a photoshopped image of the Pope and this might do some harm within the religious sector but at a large scale this isn't costing people's lives this isn't completely transforming the quality of their life but science does which is why this matters and as we continue developing further into an era of AI we'll see more and more examples like this and examples like this within science as well and with those developments it will be harder to detect with even current mechanisms of of addressing fraud within science uh to realize those manipulations of different data and like we just discussed that can result in setting back different fields by 20 years or that can mean the difference between life and death for countless people another example of this is within the vaccine space so on the screen right now these this is a picture of covid vaccines that have passed all of the manufacturing checkpoints so this is up to quality standard right now but there aren't really checks in place to determine okay at this type of kind of more critical in-depth level should this really be injected into people's bodies and this might mean the difference between somebody getting life-altering side effects or the vaccine just not working for them or protecting them from different diseases and some of the different mechanisms that web 3 can help enable in building these trustless systems can dive in at this level and give you the transparency to make the decision of do you want to take this or not it gives you the information you need to fully trust the different science Health medicines you might be engaging with and all of this really comes back to an underlying principle of if it can be gamed it will be gamed especially if there are different economic incentives in place as well a few different examples of this within the scientific sector of gaming science is across the board starting at regular kind of consumer health type levels all the way up to large biotech pharmaceutical companies and also within the academic research space some examples are GSK they had a acquisition that cost them $720 million just one deal post acquisition they found out a bunch of the data was manipulated that's not us useful for their organization their different stakeholders and ultimately they were they lost that money due to that data manipulation and the incentive to not be fully transparent within the web 3 space there are a ton of different apps such as step in which was an example that gained a bit of popularity a couple of years ago but even within steppen using some of these Baseline web 3 mechanisms there is a lot of gamification because people were incentivized to take additional steps or fake steps or automate different portions of it to receive those different economic rewards so steppen really doesn't have that place within the health kind of web 3 space anymore unfortunately another example is within academic science where we're finding fraud Within science growing 10x over the past 10 years and that's only what's being detected so far there are a lot of really great examples of furthering this detection as well but paired with that is also furthering the ability to be sneakier about it and manipulate data more effectively ultimately doing uh more academic fraud and this is incentivized because researchers rely on in ing results flashy results kind of like the image of the Pope doesn't necessarily mean it's true and that's at the cost for all of us at a baseline this is really connected to the fact that there's this replication crisis going on right now and 70% of scientific experiments cannot be replicated and this is why we're still waiting for different discoveries and cures when we're thinking about decentralizing science across the board there's a fundamental challenge within this space that some of the other sectors that web 3 is able to really effectively address it's it's a little bit more complicated in science and that's due to the fact that a lot of Science is based on Hardware it's based on people interacting with different lab equipment different machines um to do these different experiments so when we're thinking about what does a trustless system within science look like it can't be purely digital like the financial sector was able to get away with or like media is able to get away with as well to build out those Trust Systems within it then science because it has such this core foundational layer in Hardware to build this trustless system it also needs to be happening at that Hardware level and through automation to be able to fully trust what's happening removing some of those different variables at play so what might that really look like it can look like starting at that Hardware integration level from there being able to cryptographically sign as that data is being created at that Source level having that proof be put on chain and then allowing access to view that verification instantly for consumers or whoever that end user might be that is relying on this data being true this is kind of highle flow of what this might look like and then from there that can extend into a lot of other mechanisms daps and other platforms it's kind of the foundational layer and in real life this can look like integrating with a really wide range of different lab equipment or Hardware devices from PCR machines to looking at covid vaccine Purity levels with Electro microscopes EEG devices and that can even extend into kind of the broader research domain with web 2 companies doing neuromarketing campaigns and then also with bioreactors that citizen scientists might use which is this entir new layer and ecosystem that is able to be unlocked if we're able to trust one another and Trust the data we each are producing doubling down on what this might enable or look like at scale is having an entirely verified bioreactor Mainframe and that can enable larger scales of data especially open data going into different data marketplaces incentivizing contribution building out more trustworthy AI or causal models um and having tokenization to be attributed to the people that are proving the work that they're doing as well as other prediction markets staps everything we're able to see in the broader web 3 landscape can then be built off of this First Trust layer within science this is really what enables true decentralized science this TR and this layers what can unlock what the future of science might be able to look like right now and that we're already trending towards this Creator economy of science the longtail of science where biohackers or maker spaces or citizen scientists communities or individuals are able to participate at the same level that different IV labs are able to participate with the same level of trust credibility and also the ability to build up reputation within this space which is really the currency within science right now paired with this is the fact that Hardware costs are coming down so different devices and equipment that used to be too expensive for the average person to be able to engage with or produce science with now they're able to just have it at their kitchen table the bioreactor EEG devices are great examples of this another Trend that's happening right now is historically R&D funding has been monopolized to a few different countries now a wider set of countries are heavily investing in R&D and kind of capturing those different markets however they're still not always able to play at the same level of of like financial success or economic Su success as certain regions by coming up to this standard of using real truth Tech and Trust Tech they're now able to play at this global scale unlocking more and more participation and that's really where discoveries of the future will likely come from which coming back to the beginning if more and more people were able to participate in exploring hey is this Alzheimer's research really true maybe instead of it taking about 20 years could have only taken two or a couple months to realize hey maybe this isn't the best path to spend billions of dollars going down and maybe other people's lives would be transformed in the meantime too if any of of this resonates with you we would love for you to connect uh the d space is a growing ecosystem and there's more and more opportunity to be able to participate at any level it doesn't require different credentials or background it just requires the ability or willingness to participate and the willingness to be transparent so trust can be exchange throughout this entire system my name is eron mcginness you can find me on telegram at just my name as well as causality Network where we're working on helping to enable the entire D landscape as well as a traditional science space under this umbrella there are a lot of other incredible projects systems within science can look like please reach out it's going to be a really exciting future of Discovery thank you [Applause]