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Latest DeSci Report Findings from @OnchainHQ, @DeSciResearch & Others
Recorded: Sept. 11, 2024 Duration: 1:09:51Space Recording
Full Transcription
Music Thank you. All right. Hello, everyone. We have a jam-packed space today, so we might kick it off sooner.
Merrick, if you want to get the space going. We have a jam-packed space today, so we might kick it off sooner.
Merrick, if you want to get the space going.
Yeah, yeah, for sure.
Thanks, Aaron.
Just off the top, you know, it's been a relaxing summer for many of us.
Others had their head-down building.
It was sort of a mixed bag for me.
We've been around all summer, but, you know, now we're sort of glad people are coming back to work, glad to see some fantastic guests this week.
We've got OnChain HQ, Desai Research, a whole bunch of others joining us.
you know
this week
you know we're sort of
getting updates on
all manner of
you know advancements
in DSI
number of very
I would say impactful reports
have come out that we're going to discuss
and yeah maybe just
last bit of kind of housekeeping.
We're really glad to have Aaron back with us for the show.
And with that said, take it away, Aaron.
Amazing.
I'm super excited to dive into this space today.
We have a large number of amazing speakers on,
all of which they have authored D-Side reports that
were published within just the past couple of weeks or will be published very, very shortly.
And so super excited to dive into this and just kind of going down the list, like Merrick said,
of going down the list, like Merrick said, one is by OnChain HQ, another one is by the DSI
research team, and we'll also be able to hear some insights from Lucas on a paper that he published
recently on DSI and some of the terminology and vocabulary in the space and different definitions,
terminology and vocabulary in the space and different definitions, ORSI with updates from
a whole bunch of projects in the ecosystem, and then Active Inference's report. Shadi will be on
later sharing some cool findings from their comprehensive report as well. So kicking it off,
we have a few of the speakers on stage already. We'll be bringing up more throughout the conversation today. So we'd love to just go through a couple of these projects and some of these different teams to get a high level overview of just kind of a sneak peek of what we'll be talking about today. So maybe if the OnChain HQ team, I think
one of them just fell off, but would love for you to kind of give a high-level overview of what your
report focused on and who it's really geared towards. And yours was titled Desai.
How will it make research great again?
Yeah, go for it.
I guess it will be on me.
So hello, everyone.
Yeah, I'm Mikhail and I'm representing
Gontjin Foundation, which is the research platform that
actually focuses on research in real world use cases
of blockchain and Web3 in general and basically this i was one of our picks that yeah it can make a difference
in this world but we didn't expect that the difference can be so big and as you mentioned
erin and many thanks for inviting and in this short intro our report was aimed at checking
whether whether this i can make research and science great again
because we started the report with, I would say, a small initial assumption
that basically science is not disruptive anymore
and it's not the same as it was, especially when we look back
into the 20th century, not as many
valuable papers, not as many valuable intellectual property coming
from the research field or science field.
So basically we decided to get into it and investigated the space.
And as you may predict, as probably many of you decided to go into this site because
of the struggles of the traditional science, we found a lot of problems, not only in academic
research but also commercial research.
And basically our main research question that we put into the report was can decentralised science and something blockchain based, web free based and obviously something of our interest, whether it's something that basically can make the world of research great again.
can make the world of research great again?
Is it a solution to all the problems or, for example, just a few,
such as the lack of transparency, lack of incentives for scientists
or the lack of clear regulations around the IP coming from the research?
So that was something that we checked, but also we decided to assess
that if it can help, if this I can help this world of research and science in general,
then to what extent? Can it help the world of science or research tackle these most important problems of the modern world?
Like for example, climate change, I mean the most obvious choice, or the whole fake news problem?
And can it help the businesses space too? Is it something that this can also contribute into?
So as you can imagine, the report got quite extensive.
I mean, it was like around 13,000 words, I believe,
or 14, something like this.
But we are happy that we at least
tried to address all these problems, all these points
that I mentioned.
And we are quite confident that quite a few had answers to those questions and
to those problems. And yeah, we will likely reveal a few of these answers during the call,
we hope. So yeah, happy to share the insights as well. But for a short, sorry, it was not that short,
but for a quick overview, that will be it from our side. Amazing. Yeah, I really loved some of the
will be from our sites. Amazing. Yeah, I really loved some of the different charts that, and tables
you guys included, just really made it easy for maybe folks newer to DSI to really understand
all of the different pieces of it. And for anyone who hasn't checked out that report yet, I just
pinned it up above. So definitely go check that out.
If you want to see the full report, if you use code ERIN75, you'll get 75% off that as well.
Highly recommend looking through the entire report and at least taking a look at all the
different insights they have there.
We have a few other speakers up here right now from the OnChain HQ team. If you have anything else to add in from an overview perspective.
I mean, Michael already summarized it quite well.
summarized it quite well like yeah it was super surprising or not super surprising to us but very
Like, yeah, it was super surprising or not super surprising to us,
interesting to see that um yeah traditional science that uh um yeah the impact of the research
got so much less over the last decades um yeah and that edisa really has uh some big potential here
and is already um yeah starting to transform the the science field here. So, yeah, just a small addition to that.
But Michael already summarized everything quite well.
Amazing.
Yeah, we'll have some more time to dive into some of your findings later on during this call.
But want to throw it over to the Orosai team.
throw it over to the Orsi team. Katya, Eva, you also published a report recently called the
Desai Summer Report, and this took a little bit of a different type of voice and stance with yours
really having some amazing highlights and updates from projects in space.
And so, we'd love for you to give a little bit more of an
overview of it right now. Yeah, thanks Erin for the invitation. I mean like for the design
summer report is a collaboration with a several RSI core members and key contributors, Katie and
Francesca and we're collaborating on this report and i also see
francesca is um on the space can we also invite her as a speaker as well and also for the reports
we focus on the key development in the decentralized ecosystem between july and august in 2024 and it's
intense for the builders researchers or inv or investors or anyone who are looking into
and stay update in the latest progress in the DSI.
And the report we mainly focus and covers
in the four different area, for example,
the DSI ecosystem updates, community events,
and also competitive landscape and market trends,
and also the future looks.
So by integrate and analyze all those developments
along with the global trends,
we provide the readers with a overview
of the current state of design.
And our goal is trying to help the readers
to stay informed for the latest information
and also the opportunities in the design area,
and also trying to attract more attention into the space.
And also, Ketya, if you have anything else to add on,
feel free to jump in.
Yes, thank you, Eva.
Thank you, Arianne, for organizing the space.
I was very happy to take part in writing up this report.
And I think the main thing for me that stands out is that we are like a bunch of contributors from within the Desai space,
from different areas and having different perspectives, really came together to write this piece and I think it ended up being
quite diverse and interesting so as Eva said we covered quite some topics being the GSI ecosystem
updates and some of the highlights of the community events which took some of maybe quite an interesting take because I also participated
in some of those events. So I had some special highlights there and it ended up quite, quite
good, quite an interesting piece. So really happy to, to took part in this.
Amazing. Yeah. Francesca, if you had anything else to add on to what they in this. Amazing.
Yeah, Francesca, if you had anything else to add on to what they both shared.
Yes, first of all, I was so honored to be part of this group,
and I really have to thank Eva and Katia as well
as being such a support.
And she's also going to be speaking at ETH Milan
because we really wanted to push to make sure
to have some representation of DS speaking at ETH Milan because we really wanted to push to make sure to have
some representation of the design at ETH Milan and as well research hub is going to be there
as well so if you are attending ETH Milan I just wanted to make sure that we use the
space also to to kind of like identify as well and we did it that on the report as well all these kind of
effort of the different chapters let's say of uh the design movement that it's happening around the
world and uh everyone is doing such an amazing job also design new york and so many more um coming up
beside seoul and i'm just so exciting how so many many groups are coming together and they are kind of like
keeping the you know the the space very active and creating the community all around these different
efforts and I took care of the future outlooks of the report and for me super exciting to see how the integration of Web3 technology
as well as AI are coming together to find solutions that are scalable,
especially just to mention CERN, one of them is the ability to utilize AI
for preventing medicine or for utilizing also DKML models
that allows for verified data to feed an AI.
And, yeah, so many different trends.
And this is just the first one.
So I really, really hope that everyone that has a little bit of time to share
gives us some feedback because we really would love to grow and just make it better, better, better,
and just support, give an outlook of what's happening
and have the community collaborate with us in the process.
Absolutely. I'm super excited for each of these different reports
and the next iterations of them and
just as the DSi space keeps evolving, which is a good lead-in to one of the next reports from
the DSi research team. We have Francisco and Lucas up here, if you're able to give a high-level
overview of that. And then, Lucas, can have you cover uh your other paper as well
hey Erin hello everyone Francisco here I'm part of the design research team we initially
try to be like a re like a design landscape team because we wanted to map what was going on in this ecosystem.
And yeah, we are a team of four people.
Three of them are in this space.
One of them is missing, couldn't make it today.
But it's been quite an experience
to know so many people in person and in this digital space
trying to fix science.
And what we're doing right now is to, again,
mapping the ecosystem, but not just doing an overview.
We also want to understand what's going on
and how we can understand the problems
that these teams are having.
So we can bootstrap collaboration
and see if we can avoid silos.
That's because when we first started, we tried to do some research with the teams that participated
in the first design round at Gitcoin.
And so they raised money, they raised a significant amount of money.
We were at the bull round still, so a whole different story.
But we wanted to understand what these teams were doing with that money
and how those funds impacted the design teams.
And it was amazing to know that even little funds could have a significant impact.
So it seems like design teams could make a significant impact. So it seems like these site teams
could make a lot,
but they told us,
because we did a survey,
they told us that
they didn't really know
what was going on
on the other side of the street,
meaning that some bio DAOs
or some teams building infrastructure
didn't really know
if someone else was working on the same problem.
So it doesn't really make sense to tackle the same issue with three different teams.
It is since we have like constrained funds and talent,
it will be better to collaborate and try to tackle all these together
all these together because we know these are huge problems.
because we know these are huge problems.
So now we're about to publish the phase two report
of our research project.
Actually, we posted some highlights in our X profile
and we are gonna share some of that later,
but now I just wanted to close this introduction saying that
there's still a lot to do when it comes to collaboration, because it seems like, especially with the bear market, teams are constraining and no one really knows what to do.
But there's a very positive landscape in the future.
Teams are building, teams are growing despite lack of funds.
And we just need to put people together.
And I can dig deeper into this idea, but now I would like to pass the ball to Lucas.
Do you have anything else to say, Fran?
Hey, everyone.
Thanks for having me.
Yes, very good introduction, to be honest.
Nothing, not a lot to add on my end. I think it's a huge effort to do something like this from an analysis perspective.
And this is, for me, honestly, one of the good examples of de-sci or decentralization
in some kind of collaborative effort
and streamlining them because we are like four people
working async and stuff like that.
So even something like, yeah, this kind of landscape analysis
of this kind of research can really be a decentralized effort.
And I think we hope that, yeah, the results will be very interesting for you.
For us, I think they were really revealing.
And yeah, also tie in very well with some of the other research I did and recently published.
So I would switch over to that. So I took a very traditional approach to DSI because it was one of like a thesis or it was coming out of a thesis, which included a very traditional literature review, some expert interviews, qualitative data analysis, stuff like that, to really understand, okay, listen,
this has been around for some time, but most commonly we use some definition that is based on
early definitions from the Ethereum Foundation, for example, or Ethereum, which is very good,
but I think it could be more comprehensive or even better reflect what is going on in the ecosystem.
So one of the ideas I have was, OK, listen, how can I create a very good definition of what is currently happening?
And in the same regard for every new movement, in my opinion, it's very important to define some grounding rules,
new movement, in my opinion, it's very important to define some grounding rules, some shared values,
basically, and guiding principles on what is important in DSI. How should we achieve some
goals? How should we interact? Because otherwise, it's very easy to get lost. And especially as we are considering also decentralized biotech or
decentralized biological research, it could even lead to some ethical questions of whether or not
to conduct some research, whether it's fair to do something which would be considered, for example,
which would be considered, for example, unethical research in most parts of the world, for example.
It was very much an interesting topic to understand what people think are the values of DSI
and what are the guiding principles we should use to guide all of what we are doing in DSI.
And I think, yeah, some of you might even be a participant or were participants in the questionnaire, in the qualitative expert survey, I think some of you might be among those.
And I think it's pretty cool coming along on how it's also a bit different to open science and how there are differences. So, yeah, for everyone interested, I'm always open to any questions related to the research, of course.
And, yes, happy to pass it to you, Erin.
Amazing. Super excited to now dive into some of the meat of each of these different reports and
explore the biggest findings and takeaways or results from the research that each of you took part in.
And as everyone is sharing in kind of this next block of the space here,
feel free to click on each of the pinned messages up above.
We have Onchain's report and then AuraSci's,
DCI research, some of the findings there,
and then Lucas's paper as well.
Shadi from the Active Inference team will be joining in a little bit, so I'll get that
pinned up there when he's talking.
So I guess kind of moving into this next phase of what were those biggest takeaways and findings,
would love to start off with the on-chain team,
maybe highlighting your favorite graph or table
or an interesting result or unexpected
in the research that you did.
Great, thank you, Erin.
Thanks for the invite.
Diving into this topic once more in the DCI space. Yeah, generally, I mean, Mihail covered already some of the highlights of the report,
but I think, as you also mentioned, like generally we are covering different types of topics, I would say.
Since a couple of months ago, we have published different types of reports.
So it's, at least from my perspective, always interesting to dive into the business aspect of each topic and seeing,
of each topic and seeing, I would say, how developed, how connected it is to enterprises
and businesses in general.
Are they utilizing on it?
Are they building on it?
And overall, it was a quite important question I had before diving into this D-Size space. And obviously, diving into the application areas
was a key thing for us in understanding
what are the main, let's say, highlights when looking at that.
So like data sharing, marketplaces,
generally also how these different areas
are facilitating research in between each other and collaboration.
But apart from that, I think the first time I heard about this topic was probably eight months ago,
before we dive into the actual research phase and for me it was, I mean I connected it very much to science but I had quite a hard time understanding how it was connected with businesses and enterprises.
So obviously I think that was a surprising thing to see how much adoption there is there compared to other topics that we're usually covering, which are more, I would say, on the technical aspect.
And also, as I think Francisco mentioned, the whole space in general, I mean, I had the opportunity to speak probably to the largest amount of people on a report specific topic and just seeing this collaborative space coming
together and let's say being probably also the most less crypto-digit approach I've seen in the
space so far and from that also agreeing to what he said, people wanting to collaborate more and finding solutions to fix science and how that can be applied in different types of areas. looking at the different mechanisms, I think that was super interesting how it connects with Deepin,
which was also one of our latest reports we did, which was super interesting, together with DAOs,
also which has been a quite interesting approach, I think that generally has brought two nice topics
and two nice niches together I would say in terms of the collaborativeness and
generally let's say connecting different aspects of of Web3 but with
the real world which is something that we're always looking to find different stories of an understanding
how it can connect more.
But also this aspect of the tokens
and how it plays a role in the different mechanisms that
are connected with decentralized science.
And apart from that, looking into, I would say,
understanding a bit where it goes a bit beyond health care
and understanding how it plays a role in supply chain,
I think that was super interesting to look at.
And beyond that, I say uh since we all
survey we run during this period it was uh nice to see how businesses in general are
science in general but also how close they are to the topic of understanding how it impacts
their decisions and and from there how this i can actually play a role in the in the space
maybe i think apart from that maybe mihaj leon you also would like to fill in on some of the
highlights you think came from the report.
Yeah, thanks, Serena. I think you covered a lot, but what I would add also to what you said,
basically we were a bit surprised how many different web-free niches, I would say, go into DISA itself,
because we have elements of DIPIN and we have have specialized dip in projects that are specialized into
providing computing power for scientific projects, which is very cool.
We have elements obviously of a kind of a defy, like the decentralized finance space
where scientists can incentivize each other and actually add to this kind of open monetary
value to the whole thing.
We have a lot of elements of DSOG, so decentralized social media.
I mean, right now we are doing under the guidance of Leon basically the research on
decentralized social media platforms and they are very similar to DSi in terms of actually how
they can incentivize people to collaborate in this case, in the case of DSi,
maybe for other purposes when it comes to DSO.
And the nice thing about it is that all of these things,
I mean, this nice mix actually is for a nice purpose,
which is always very exciting.
There are a lot of issues in Web3
that also combine a lot of mechanisms from others,
because after all, it's all Web3.
But in this case this this
unique mix actually leads into something into something very valuable and not only speculation
and i'm thinking about the the crypto stuff actually when it comes to the word crypto and
cryptocurrency i haven't seen it i would say quite frequently on any of the white papers of this
project or whatever and for the purpose of this particular report, we analyzed 60 white papers, basically.
So it was not about crypto investments, not about anything related to that.
And these projects were super clear about it, which is also super cool because we know that to adopt a blockchain,
to actually bring blockchain to the masses, actually we should hide it.
We shouldn't speak about it too much.
I mean, decentralized science may sound cool, but blockchain-based science
probably, it creates something bad in other people's heads,
that it's cumbersome, it's tricky, it's one of the hurdles that they need to overcome.
So in this case, it's cool that it's working this way,
and it's cool basically that this kind of mechanism, they are working, but they are a little bit behind.
This real world purpose and the outcome of the work
of this design communities actually is on the front.
And that's exactly what we need in other issues as well.
So it was not surprising, but it was one of the nice
takeaways to have from the report.
So I would at this point, not sure if you leon would like
to add something else yeah you pretty much uh covered everything i would say um yeah for me
just maybe one thing to add was like um as you said with the connections to deep in but also
like to the incentives mechanisms of like decentralized social networks. It was super interesting to see how much like the whole,
yeah, blockchain-based, yeah, web free world,
how all of this flows together.
And we also can like each sector can learn from each other.
So for DeSci, especially when it comes to the token incentives,
how do we assure the quality of the research?
How can we monitor?
Like, I don't remember who said it but
also when it comes to ethical standards or like just moderation policies of the research it is
just interesting to see yeah how how broad visa already is how many different web yeah traditional
science sectors it already disrupts and yeah how far the adoption in some parts,
especially when it comes to healthcare and biotech
already has come.
Yeah, that were like some key findings
that were super interesting to me.
Definitely.
I think your report really did a great job
highlighting how DSI can be really useful to kind of the traditional science and healthcare space, as well as other kind of more general business goals as well. as we keep growing as an overall ecosystem and also something important for all of us building in the space
to keep in mind that everyone else doesn't maybe realize completely yet
how many different awesome projects are already existing
and doing cool stuff in DSI.
Francesca, I see you have your hand up.
Yeah, I wanted to follow up on what Leon just mentioned
about ethical practices.
And I'm just wondering if there is already perhaps talks
on creating some sort of like standards.
Because, you know, from the birth of like, you know,
blockchain space, crypto space,
we never really were able to create the standards.
You know, we didn't.
It was just kind of like wild Wild West, but now that we have
kind of made the mistakes, learned the lessons, and now with DSI still being a small space,
I wonder if there is a possibility to come together and create those kind of standards
that gave us some sort of like guiding factors and help us also evaluate and report
on different aspects of the space.
Definitely.
I think that's such a key piece
as we keep evolving as an ecosystem.
That's one thing.
We're talking a lot at Causality Network.
Shadi, I see your hand up
and I know you've done a lot of thought and work
in this type of space as well.
Would love for you to give an overview
of the Active Inference Report or paper
that was recently published,
kind of leading into your comment too.
Oh, cool.
Yeah, happy to give a brief intro,
a little bit of context on the paper and hopefully provide a useful response to Francesca's comment there. And I'm a PhD in computational neuroscience, and I found my way into DSi in early 2020 after hitting a bunch of, let's say, difficult problems in traditional research infrastructure.
I was interested in inter-consortium data sharing for magnetic resonance imaging, dealing with very, very large data sets and building AI models on top of that.
imaging, dealing with very, very large data sets and building AI models on top of that.
And without knowing much about Web3 or kind of smart contracts or blockchain or crypto,
I'd stumbled on IPFS as a really great way to connect data sites together and create kind of
a shared data mesh where computation can be run on local services, and then derived data can be shared across these organizations
to help inform statistical models or machine learning models, etc.
So that kind of really, I think, preluded and got my eyes,
preluded the rest of this career that I'm making here in Web3
and opened my eyes to what's possible with distributed infrastructure today.
So currently I'm a co-founder at Holonym Foundation and we work primarily within the field of key
management and identity. We've developed a technology called Human Keys, which is our way
of packaging up a lot of the utility of crypto and getting it out to mainstream
uses including those in dsai specifically for data ownership privacy and scalable ux for
interacting with crypto and without sacrificing security along the way so human keys are kind of
interesting because they're derived from human attributes instead of, let's say, like randomness for private key derivation.
Now, when we were starting to think about DSci back in 2020, I'll point you guys towards a repository that was used for a hackathon called the DSci repository.
I think it was created in early March 2021.
early March 2021. And we kind of outlined some of the key areas of what we think would be emerging
standards within the space. So specifically verification of credentials and identity was
something that came up. Verified computation, basically proving the inputs and outputs and
the methods that are applied to that data. We also identified some of the soft infrastructure that's really difficult to standardize,
such as mentorship, community networks, community infrastructure that helps bring distributive communities together to work on common goals.
And this work kind of served as a bit of a kind of like early framing to a more formal treatment of what does
a distributed system of researchers, of fact finders, of organizations, both agentic, say,
for example, LLM agents or actual human researchers working together, say, on the internet.
And a really great way to model this is using the tools from the active inference corpus or discipline.
Active inference is basically a way for us to model complex systems,
a way for us to model complex systems, specifically different agents that are looking to interact with, say,
like in a probabilistic environment and being able to think about how past prior activity or past prior history informs your next decision
and being able to incorporate new information in your decision making.
It's very similar to if you guys are
familiar with partially observable Markov decision processes where you need to emulate
the strategies of another, say like the churn-based strategies recursively of another
agent and think about what moves they would make if you made this move and being able to do that
kind of, you know, with deep recursion. So this is a really
great way to kind of think about how, say, an agent that is training on some set of data is able to
kind of define its own objective function over time or minimize a complex objective function
to, say, achieve the best classifier performance or to recursively evaluate overfitting or underfitting metrics,
be able to deploy experiments or data and to work hand in hand with researchers.
So a big part of this is establishing a machine readable standard for these agents to be able to work hand in hand with researchers.
This is what we call the active entity ontology system
or AOS for short.
And you can go, you know, you can Google active inference
ontology and you should be able to find something there.
And the basic idea behind this is,
it's really difficult to implement any sort of kind of distributed infrastructure
if you don't have some form of machine-readable standard at the very, very lowest level
to bring all of these different, say, pipelines or software tools to bear,
whether it's open science, traditional open science tools,
or whether you're building stuff with Jupyter Notebooks,
building machine learning models with PyTorch, et cetera.
One thing I do like to caution against just from experience is that building standards can become a swamp.
It can be a mire that you get very quickly stuck in.
And a lot of folks end up kind of arguing for years and years and years
over what size an array should be or what you know naming convention or whether it should be camel
case or you know shorthand case so you know i think when it comes to standards and accepting
those within the community the best ones are kind of born from utility-based use cases. And I think the best approach for DSI moving forward is to meet your patient, your consumer, your research group, your hacktivist collective, where they are, and build messy software that solves their problem.
And over time, the standards should emerge out of utility, out of like a process of survival.
For time, the standards should emerge out of utility, out of like a process of survival.
And so this way we can kind of avoid what we've seen with the W3C, where there was like 10 years or more of kind of like quagmire in terms of accepting standards and a lot of forked communities, as opposed to, say, for example, like ENS, right?
an identity system specific to their niche community and is achieving kind of adoption
and traction along the way.
They've had to adapt to be able to be interoperable with other services.
So that would be kind of my suggestion for builders in the space.
And I've gone on way too long about all this stuff.
Thanks for the time and the space to share.
I think it's such an important part of the overall question that Francesca posed and also a broader question that DSI as a whole ecosystem should be asking itself.
And I think many of us are continuously coming back to this. So super great insights that you were sharing. And at least I was happy to listen.
that you were sharing, and at least I was happy to listen. So I'm sure others were really grateful
for kind of the deep dive into some of that history and context when thinking about creating
some of these different standards and better coordination across the space and in sciences at
large. As we're continuing on with this space, if anyone in the audience has any questions,
feel free to type them in a reply down below and then we can incorporate those into the
conversation.
We'll open up a little bit of time towards the end for questions as well.
So if you have any, definitely keep those top of mind and type them
down below as we'll get to those ones first. On that, let's go back over to Katya and Eva.
If you guys want to share some of the biggest takeaways from your report. I know you did a great job showcasing a lot of updates throughout the whole ecosystem,
which has a lot of great opportunities covered in there.
But were there any maybe surprising elements or other key takeaways people should know,
maybe if they haven't had a chance to read the report yet.
Well, yeah, I can share something from my perspective. So it kind of
surprised me because in the past two months, the ecosystem has been very highly activated.
in the past two months, the ecosystem has been very highly activated, like with many
design events and public cities happening, like taking place globally, like in the North America,
South America, Europe and Asia. And also we do see a lot of updates inside the ecosystem,
for example, like new token launch and track significant funding where some like decide tokens, the price grows and also new
listing on the exchange as well. There's so many things
happening around. So I think we will have like short time to
explore. But if people want to know more, just check out our
report, there's more things happening, mentioning over
there.
So one takeaway from my side,
I think the design needs more and stronger interoperability between the whole design projects.
Right now, we only see like few collaborations
between each design project.
I would say if more collaboration forms
and the ecosystem will go with it.
And maybe like others can have more points or insight to add on.
Thank you, Eva. Reflecting on the space today, I'd like to point out the difference that I've noticed in writing those reports presenting today.
I mean that we kind of took different approaches in targeting different groups, probably.
So kudos to Lukas for taking more traditional approach, targeting academia and scientists in a way that is more approachable for them.
And we at RSI took this approach in a more general way, outlining key events and ecosystem updates.
And I want to kind of emphasize the MuseMetrics work done over the summer.
I think it's incredible.
And kudos to you, Erin, for being part of this.
And I think MuseMetrics is the project that is committed to expanding the DCI ecosystem
by onboarding more individuals, more scientists into space.
And in our report of RSI, I covered the events that happened over the summer.
And as Eva mentioned, there's many events and pop-up cities.
And one of the opportunities that MuseMetrics fellows had was to come to Georgia, to the Zoo Georgia pop-up city, and really get integrated into the broader ecosystem of decentralization and development of network
states, which was amazing. So I'm really thankful for that. And yeah, that's my take.
And yeah, that's my take.
Amazing.
Thank you so much, Katya.
Yeah, there are some really awesome upcoming DSI pop-up cities kind of leading off of that
network state umbrella.
Orisai has one called Oraverse that will be in Thailand that's coming up kind of soon, leading up to kind of DevCon
timing. So if you're interested in Desai, want to be surrounded with a bunch of other Desai folks
and are planning to go to DevCon, absolutely go check that out. And then post-DevCon,
we'll be having Castellia, which is a DSI-focused pop-up city as well.
So throughout the whole rest of the year, some incredible opportunities to keep having all of these conversations,
maybe collaborate on more reports like these and really keep getting the word out there,
out there as well as making meaningful progress on all of the projects we're working on. Francisco
as well as making meaningful progress on all of the projects we're working on.
and Lucas, if you want to touch on some of the key takeaways on some of those updates
within the ecosystem. Sure. We have little time and we have plenty of things to share. So I'm just going to pick the two most important for me.
This is me talking.
But I believe that I had like one very spicy takeaway
and the other one is more complex, open,
and something that I will actually like to discuss
if we have the time.
So the first one is about doubts in DSAI.
Because when we try to look for papers that were covering DSAI, even before DSAI had a name,
so we found some early papers from, I think it's 2016, where people were exploring use cases for
blockchain in science. So DSAI wasn't a thing, but they were
already exploring blockchain for new infrastructure. So in these papers, we found that there are two
main categories for like where academics found that DSAI could impact science in general.
One is infrastructure, as I said, like decentralized infrastructure for
publishing, data sharing, and so on. And then they saw that this new organizational structure
called DAOs could enable transparency, treasury management, and new ways for people outside
academia to collaborate. So our first key takeaway that I want to share with you
is that in our survey,
we ask if these teams and projects
had different things that we call like normal DAO stuff,
like multi-sick wallets, tokens, on-chain voting,
or on-chain like smart contracts and so on.
So the thing is that we see that most of these projects are not so download-ish.
Actually, the only thing that we saw predominantly was multi-seqs and also a design organizational structure, meaning that these people design,
like they design the structure of the organization beforehand,
like before like fully deploying.
But then there's like less than 45 or 40% had like tokens,
on-chain decisions, token gated access,
or things that are commonly found on DAOs.
That's not necessarily a bad thing.
The only thing that I want to highlight here
is that this very strong DAO narrative
that we found in academic papers,
we're not seeing that in reality,
but that could mean,
I think that's because the space is so new that we know that
DAOs can, it's not a good idea to be like full DAO from day one, because that comes with a lot
of downsides and building a DAO is no game. It's like a huge responsibility. It's a lot of work.
It's not a simple option. It's a great trade-off. So we may see progressive decentralization in the future,
or maybe we are going to use these decentralized technologies
for new organizational prototypes,
but maybe they will not be DAOs.
So let's keep our eyes open
because we may see DAO-ish institutions,
but maybe they will not have all these DAO
features that we see in, let's say, DeFi or gaming. And the other takeaway is that we saw that over
half of the teams we surveyed, they were associated with other institutions. So they were associated with other design teams. So they had like a bigger umbrella organization
that had legal, how do you say that?
Like legal representation,
they were registered in a jurisdiction
or they were associated with a business
or a university or NGO.
So they, we saw that over half of them were associated.
And that was a very interesting takeaway
because I don't think that design works on a vacuum.
It should not.
Design and science in general is collaborative.
And that means not also business or universities
or other kinds of institutions, but also we are part of science.
We collaborate with traditional science.
We collaborate with open science.
So it will be interesting to see what those associations work in reality because this is just a survey.
So we couldn't dig deeper into what that meant.
By the way, I'm doing my master's thesis and I'm going to interview these projects to understand
what's going on with those associations. So I think I'm going to interview some of the people
who are in this room. So please keep your DMs open. Thank you. And with that said, I want to keep
the question open and ask if any of you had any like solid case of DSAI teams collaborating with
other institutions outside DSAI, let's say universities, businesses. Like for example, we know that Beta
now works with Pfizer. And so I'm looking for that kind of stuff. So I'm going to keep the
mic open. And those are the two takeaways that I wanted to share with you. Thank you.
Can I hop in here?
Yes, go for it.
Oh, cool.
Okay.
Yeah, thanks, Francesca.
I appreciate that.
Yeah, so I think that, yeah, so I think that, you know, because D-Sci is kind of in this Web3 ecosystem, there is this, like, focus on and maybe even obsession with real-world
use cases, kind of like the hammer-nail obsession with real world use cases,
kind of like the hammer nail approach with like, oh,
blockchain needs to find a use case and everything could be blockchainified.
And, you know, I think there is kind of a little bit of cross,
I want to say contamination from that knee jerk reaction to like, you know,
look for industry partners or like direct academia partners, et cetera. Some of the best, you know look for industry partners or like direct academia partners etc some of the best and
you know there are like daos that are out there doing this already like vita dao and uh all the
different bio daos research hub um etc etc and you know they're all along the dao spectrum some of
them are more decentralized than others some some are just like wrappers for a company,
or really are wrapped by a company. But I think, you know, organizations like the Active Inference Institute or even OPSAI, when we did the Decentralized Research Fellowship, we had a
completely different thesis than really caring too much about academia partners or organizational partners, what we really saw
and recognized is that there's an incredible, massive, latent pool of talent all around the
world in the form of trainees, graduate students, undergraduates, interns, high school students,
et cetera, et cetera, et cetera, researchers, et cetera, that don't really have an infrastructure to activate the kind of work they do.
Coming from academia, the rarest and most, I think, valuable resource wasn't paper citations.
That was downstream from this resource.
It's quality mentorship.
And I think the killer DSI application is going to be the one
that cracks the mentorship problem and brings together high quality, very active mentors and
allows them to scale their mentorship abilities to support thousands, hundreds, maybe even just
a dozen, let's say, start off with students, be able to provide funding, hundreds, maybe even just a dozen, let's say start off with students,
be able to provide funding, support,
helping them get their papers out,
helping them connect the dots and navigate kind of the,
because the academic infrastructure
is always going to be there
and we'll have to navigate that
even as we're creating new ones.
So, and there are examples of this out there already,
like within niche communities, hackathon communities,
Neuromatch Academy is a great example of something that is able to
bootstrap this network effect for mentorship and training,
resulting in some really powerful outcomes.
That's such an important piece.
And I think all of us here listening in on this call at that human type of level can appreciate the need for kind of just more knowledge sharing throughout the ecosystem, especially as we might each be individually taking steps towards our own goals or our organizational goals and definitely more work
to be done on that front, but also a lot of opportunity with some of the technologies
that we're all working with here. I'm not sure if someone just came up onto the stage or if
someone dropped off. I got a little sound from that. I dropped off. Sorry. I up on the, onto the stage or if someone dropped off, I got a little sound from that.
I dropped off. Sorry. I dropped off. Sorry. Sorry for that.
All good.
We have one question down in the replies below.
Shadi, if you're able to take a quick stab at this,
otherwise maybe it might be able to be a combo over text as well.
But it's really focused on regarding active inference
and understanding how different, like,
black swan theory is taken into account
through, I think, some of the work that you're doing.
So I'm not sure if you have any thoughts on that. Shadi?
Yeah, I'll just preface by saying I don't do primary research in active inference.
Right now I'm focused purely on cryptography and applications of CK. But in regards to the question regarding Black Swan
or kind of like long tail occurrences, right?
Like if you're looking at the world
like a mixture model of statistical events
or looking at it like a Poisson model,
active inference,
one of the main components of it
is maximizing the surprise
when you're updating your internal subjective states.
So the idea here is that you're navigating
some sort of what's called an epistemic niche.
And this epistemic niche is your internal model
for what can be known
and how you can map observations in the real world
to internal models that reconcile it
with known past behavior. So the idea here is that black swan events can be handled by
an agent with a sufficient history of similar type of rare events. But say, you know, the idea here is that when you're updating your kind of like your
internal model for epistemic niche that you're in, it may not always be complete, meaning that
you might not always have a one-to-one correspondence with the total possible set of
black swan events. But if you've seen a couple of black swan events in the past, you can create
kind of a partial reconstruction or a good enough approximation in your internal model for the
world that you're navigating or the epistemic niche that you're navigating to be able to say,
hey, like, you know, this is about to happen. Or what if this crazy thing that does happen?
Because I think active inference does its best to measure or to approximate
cognition, action, and perception in human beings. And human beings are able to reason around
black swan events. Black swan events can catch us by surprise and they can be things that we don't
expect. But we can have a somewhat rudimentary or like basic enough mental model that says,
what if nuclear war happens tomorrow? Or what if an asteroid hits the planet, etc? Because our internal cognitive model is informed from past
experience collectively through information sharing, etc, that we can kind of consider these
things. So active inference attempts to formalize this, this kind of epistemic world building that
allows us to entertain black swans, but it's not going to always be guaranteed to be complete. And I think, you know, that's something that we can, and why
we can point to things like, uh, uh, codals and completeness theorem where you can never have
complete, uh, models that are always going to be satisfiable. Um, uh, they're going to be complete
and sound, meaning that like, they're always going to be able to account for all things.
So I guess my answer to your question is active inference can account for black swans, but it can't account completely for all black swan events.
And that's because of probably like a fundamental limit in how we think about logic and formal systems.
Thanks for answering that question, Active Imprints account.
I see you're listening in as well.
Would love if you have any further commentary to add that down into the text below.
I know we're hitting the top of the hour. So for everyone who has joined in, thank you so much for joining the DSI mic this week. We'll be back again next week, same time, 4 p.m. UTC with a new topic, diving into DSI, what's happening in the space, or important conversations that need to be had. If you have a topic in mind that you would like to speak on
or a topic that or conversation that you think should be had,
feel free to request it.
If you send a message to my profile, Erin McGinnis,
I'm one of the listeners down below on the space right now,
that's probably the best way to reach out
and make sure it gets on the schedule for the upcoming weeks. Otherwise, block off this time
on your calendar moving forward. For any of the speakers that have a couple more minutes,
would love for you to just share one last, like one sentence takeaway either from your report or things top of mind
as you're thinking about Desai to close out the space.
Maybe we'll start with the on-chain team
since you kicked off some of the combos so far.
Otherwise, anyone else? Feel free to come off mute if you have any last closing thoughts.
No apologies.
Yeah, I have muted mic.
I mean, maybe it would be nice to conclude, at least
from our side, with some not actionable insights,
but maybe some steps we can take as a community moving forward,
maybe to bring more people to the space.
It was a nice space, but obviously it's not like this.
30,000 people today are together with us,
which is unfortunate because they would hear quite a lot
of nice things.
But what came to our mind,
and maybe personally, especially to my mind, as I'm also an academic person and I can see the
struggles with academic research and traditional research, I mean, I'm witnessing it not daily,
because I try not to visit university too frequently, but yeah, I struggle with it a lot.
And we thought how actually we can try to
onboard maybe more scientists specifically to the space. So we are not only in our circle here,
but also we are trying to onboard more people that maybe are not as tech savvy as we are here
and maybe not as open to adoption as usual. So for academics, generally, the current system doesn't work.
I mean, some people will stay there because of the status quo
by us.
But some people at least would like to try something new.
Maybe not, I would say, setting up a MetaMask wallet
and trying to trade some coins.
But when it comes to this approach to the things
that we do daily, yeah, that would be something
that academics would like to try try at least some of them.
So for example, maybe it's always about actually switching this mindset from scoring as many
points as possible as an academic to be a better academic due to that, but maybe moving
towards creating an impact through the things that we do.
So what came to our mind was, for example, maybe we should onboard people into that by
trying to publish a collaborative paper with them that is published in the Etika preprints,
or for example, on the research hub. So scientists can actually understand that it's doable,
and actually they can see it in real life, quote unquote. And in the digital world, at least,
that there is their paper, and there are people reading it, commenting it,
and they can even earn some monetary value on that.
If it's too complex, if it's too cumbersome,
maybe we should tackle another, I would say,
critical benefit of DeFi for academics,
such as transparent and collaborative funding.
Because our space is super hungry for academic success
stories.
So it would likely not be that challenging
to receive the money through this kind of project
because the space would likely fund it if it's valuable.
And the scientist on the other side,
the person that would like to get into the space,
they would see actually right now in the monetary value
that, yeah, it works, it makes sense.
And that's why I should maybe contribute
to this kind of activities,
decentralized science activities,
rather than the usual ones.
So a few takes here.
I would love actually us at OnChain,
but also maybe when it comes to the other projects
to take them forward and create a real actionable insights
out of them, because it's something that we need. It's not only about nice projects to be featured in the reports, but it's also about the contribution
to the science space that can be done thanks to this. So the more valuable scientists we have,
the better for the space. So definitely we can focus on onboarding a bit more. So that will be
our last take here. Sorry, guys. Sorry, Leon and Erin. However, if you want
to add something, feel free to do so. But yeah, I like it took a lot of time already.
Really appreciate that closing thought and definitely echoing and seconding the different
points you were bringing up. Anyone else from the on-chain team? Otherwise we can throw it over to Katya, Eva, Francesca,
if any of you from kind of the Orisai side. Otherwise, Lucas, Shadi, any closing thoughts?
Last call before we might close out. Yes, Francesca. Thank you. I just wanted to mention the
fact of the collaboration, which I
totally agree. I always see that we have these kind of like D-Sci side events in a way in larger
conferences. And I like to perhaps, you know, maybe put this out there since there were many of us.
And perhaps D-Sci needs its own conference. And it's not just a conference, but where we actually put almost kind of like
a hands-on pop-up CD where we're able to work
on some of these important aspects and all the projects
and all the people that are interested
and wants to do also onboarding has a place to go
to actually make this happen.
So perhaps, yeah, a design conference or workshop
or hands-on, I don't know, university or something
that we might need to create in the near future.
Definitely, definitely a lot more collaboration
and building out some of these resources
to keep onboarding the scientific sector that has been
working towards a lot of these goals, and we might be able to collaborate with them more effectively,
too. All right. One last call for any closing thoughts. Yes, Shadi. Yeah, let's just remember
why we all got into this, and that's because the universe trends towards disorder
and we need a way to fight against entropy so autonomous systems that can preserve entropy
or preserve the degradation towards high entropic systems and preserve knowledge in crystal forms
lfg let's go such a great note to end on, I guess it's time for us all to get back to work,
working on building this future, and come back next week, same time for PMUTC on the DSI mic
to keep having these conversations and diving into more important topics in DSi. Thanks so much to all of the awesome speakers coming on
from Active Inference, from Orosci, from OnChain,
from DSiResearch, and everyone for listening in
and being part of this collaborative community,
trying to make science better
and the world better from that.
On that, thanks so much for tuning in
and see you next week.
