Verifiable Compute for AI, ML and More: Web3's New Frontier | Alison Haire at SmartCon 2023
Summary
TLDRAlie, CEO and co-founder of Lilypad, discusses the project's focus on verifiable, trustless, decentralized compute as the new frontier for Web 3.0. Lilypad, incubated at Protocol Labs, aims to provide a scalable, trustless compute network with a global marketplace for GPUs, enabling mainstream adoption of Web 3 applications. Alie highlights the practical and ethical challenges of centralized AI and ML, emphasizing the importance of decentralized compute for innovation, accessibility, and data ownership. The project also envisions incentivizing node operators with LP tokens and plans for multichain compatibility.
Takeaways
- 🌐 Alie, the co-founder and CEO of Lilypad, discusses the project's incubation at Protocol Labs, highlighting the company's involvement in the decentralized web 3 space.
- 📈 Lilypad aims to address the current gap in verifiable, decentralized compute, which Alie sees as a critical component for the modern internet and a frontier for web 3 development.
- 🤖 The project focuses on AI and ML compute, aiming to provide a trustless and decentralized platform that can be utilized at an internet scale, facilitating mainstream web 3 applications.
- 🔐 Verifiable compute is presented as a solution to ensure the integrity and security of computations, which is particularly important as AI and ML become more integrated into web services.
- 💡 Lilypad's vision includes creating a new data economy by leveraging idle processing power and providing an accessible, permissionless compute network with a global marketplace for GPUs.
- 🌱 The project is designed to be highly native to running nodes, incentivizing users to contribute their spare GPU resources and be rewarded for their participation.
- 🚀 Lilypad has recently released version 2, with plans to expand to multi-chain compatibility and an incentivized testnet, indicating active development and a roadmap for future growth.
- 💼 Alie emphasizes the practical and ethical challenges of centralized AI, such as ownership, access, and the potential biases introduced by relying on models from a few dominant companies.
- 🌐 The project aims to democratize AI by providing open-source models, which are said to be faster, more customizable, private, and capable, thus fostering innovation and reducing biases.
- 💼 Lilypad's compute network is positioned as an alternative to centralized cloud providers, offering a more distributed and efficient model that could be more accessible and better for pricing and supply-demand dynamics.
Q & A
What is Lilypad and what does it aim to do?
-Lilypad is a project being incubated at Protocol Labs, aiming to create a verifiable, trustless, decentralized compute platform. It focuses on enabling mainstream web 3 applications to utilize internet-scale data processing without relying on centralized services like AWS.
Why is verifiable compute considered a core need for the modern internet?
-Verifiable compute is one of the three components of the modern internet, but it has been a missing piece in the decentralized internet story. It's now seen as essential for bringing the next billion users to web 3 and for allowing users to do more with their data in a decentralized manner.
What are the challenges in building decentralized and verifiable compute?
-The challenges include the complexity of building such a system and the fact that the research is still cutting-edge and catching up. Additionally, the crypto space has been focused on solving more pressing problems like account abstraction.
How does Lilypad plan to make decentralized compute accessible and affordable?
-Lilypad aims to create a global, trustless, permissionless compute network with an independent global marketplace of GPUs. This marketplace is designed to eliminate middlemen, keep prices low, and make the service accessible to everyone on the same terms.
What is the significance of being able to run AI and ML models on a decentralized network like Lilypad?
-Running AI and ML models on Lilypad allows for ownership of AI models, access to models, and the ability to train models with security and speed. It also supports innovation through open-source projects and addresses practical and ethical challenges arising with mainstream AI adoption.
How does Lilypad address the issue of AI model ownership and access?
-Lilypad promotes decentralized AI through distributed networks, which helps prevent central entities from controlling access to AI advancements. It also encourages open-source models that are more customizable, private, and capable.
What are some practical use cases for Lilypad's decentralized compute network?
-Lilypad can provide cheaper, more open, better distributed, and more efficient compute power. It can also help with security, data ownership, and provide a stake in the ownership of AI solutions.
What is the 'water lily' project mentioned in the script?
-The 'water lily' project is a proof of concept that uses Lilypad to train an AI model on artist input data. Users can then pay a small amount to get images generated by the trained model, demonstrating the practical application of Lilypad's compute capabilities.
What is Lilypad's roadmap for the future?
-Lilypad has recently released version 2 with updated models and plans to go multichain, being EVM compatible. They also intend to release an incentivized testnet and aim for a mainnet launch in the following year.
How are participants in Lilypad's network incentivized?
-On the supply side, node operators are compensated with LP tokens. The network tracks all participants for future launches, implying that there will be incentives for their contributions.
Is Lilypad's compute network limited to GPUs, or does it also include CPUs?
-While Lilypad primarily focuses on GPUs due to the AI and ML focus, it does imply the potential for a network that could include various types of hardware, including CPUs.
Outlines
🌐 Introduction to Lilipad and Decentralized Compute
Alie, the co-founder and CEO of Lilipad, introduces herself and the project, which is being incubated at Protocol Labs. She reflects on her first crypto conference and sets the stage for discussing Lilipad's focus on verifiable, trustless, decentralized compute. This technology is positioned as a core component of the modern internet, currently missing from the decentralized web narrative. Alie highlights the complexity of building such a system and the fact that the research is cutting-edge. She also touches on the readiness of the space for this technology, mentioning developments like account abstraction and ZK proofs. The goal is to make decentralized compute accessible and part of the web 3 ecosystem, aiming to bring the next billion users to web 3 by leveraging this technology beyond just user experience improvements.
🚀 Decentralized AI and the Need for Lilipad
Alie delves into the importance of decentralized AI and the role Lilipad aims to play in the space. She discusses the ideological drivers behind decentralized AI, emphasizing the need for open access to AI advancements to prevent central entities from controlling AI models and their usage. The talk highlights the benefits of open-source models, which are more customizable, private, and capable. Alie also addresses the practical challenges of AI innovation, such as the high cost and limited access to GPUs, which Lilipad aims to solve through its decentralized network. The paragraph also touches on the geographical concentration of cloud provider data centers and the need for a more distributed model. The vision is to provide a practical solution to these challenges while upholding the ideological values of web 3, such as transparency, data ownership, and peer-to-peer networks.
🛠 Lilipad's Roadmap and Future Vision
Alie outlines Lilipad's recent achievements, such as the release of Lilipad V2, and future plans, including multichain compatibility and an incentivized testnet. She discusses the project's vision to create a global, trustless, permissionless compute network with a marketplace of GPUs. The goal is to make this network accessible to everyone on the same terms, leveraging idle processing power to unlock new data economies. Alie also mentions a proof-of-concept project called 'Water Lily,' which demonstrates Lilipad's capabilities in generating images from artist input data. The roadmap includes further development and expansion, with a focus on making Lilipad a key player in the decentralized compute space, offering solutions for AI and ML tasks, and integrating with blockchain technologies for secure and efficient computation.
Mindmap
Keywords
💡Lilypad
💡Verifiable Compute
💡Decentralized Compute
💡Web 3
💡AI and ML
💡Protocol Labs
💡Decentralized Storage
💡Account Abstraction
💡ZK Proofs
💡Data Economies
💡Open Source
Highlights
Alie, co-founder and CEO of Lilipad, discusses the project's incubation at Protocol Labs.
Lilypad aims to create verifiable, trustless, decentralized compute for Web 3 applications.
Decentralized compute is identified as a missing piece in the decentralized internet ecosystem.
The complexity of building verifiable decentralized compute and the readiness of the space are highlighted.
Decentralized storage through Filecoin is now available, setting the stage for verifiable compute.
Lilypad's vision is to build an internet-scale distributed compute network for data processing.
The project offers a global, trustless, permissionless compute network with an independent marketplace of GPUs.
Lilypad's approach to decentralization includes ideological and practical use cases.
Ownership of AI models and access to hardware for model training are key challenges addressed by decentralized compute.
Decentralized AI through networks like Lilypad can prevent central entities from controlling AI advancements.
Open source models are presented as faster, more customizable, and more private alternatives to centralized AI.
Access to GPUs at a reasonable price is a significant barrier to AI innovation that decentralized networks can help overcome.
Lilypad's two-sided marketplace aims to improve efficiency and pricing for GPU access.
Decentralized physical infrastructure networks can provide a more distributed model than current cloud providers.
Lilypad's practical applications include cheaper, more open, and more efficient compute power for developers.
The project also focuses on transparency, data ownership, and community ownership of AI solutions.
Lilypad's proof of concept project, Water Lily, demonstrates the potential of decentralized AI for content creation.
The roadmap includes releasing Lilypad V2, going multichain, and launching an incentivized testnet.
Participants in the Lilypad network are incentivized with LP tokens for supplying GPU resources.
Lilypad is EVM compatible, allowing for direct smart contract interactions and results computation on-chain.
Transcripts
[Music]
uh so hi everyone uh I'm Alie uh I'm
from uh lilipad uh co-founder and CEO
this is a project that's being incubated
at protocol Labs though uh which is why
you might have seen me at filecoin and
protocol Labs events um so we're
currently uh incubating and building
this out at protocol Labs as well uh
really great to be here actually fun
fact uh back in 2020 I think chain link
conference 2020 was one of my first ever
crypto conferences that I went to it was
Co so it's was all online and as an
Australian I could actually attend so I
got to meet like some of the gang back
then and now I'm actually talking here
so uh pretty cool uh anyway moving on uh
so what is lilypad what is this
verifiable trustless decentralized
compute there's a lot of big words in
there why do I think it's the now
Frontier for web 3 and why am I shoving
a i and ml onto another slide we've
probably had enough of hearing those
words but there's actually a reason for
this and hopefully uh you'll see that by
the end of this chat um so verifiable
compute is kind of a core need it's one
of the three components of the modern
internet uh but at the moment uh it's
kind of Compu has kind of been a missing
piece of the decentralized kind of
Internet story um so there's a few with
a few experimental um kind of exceptions
along the way and that's likely for two
reasons one is complex building
decentralized compute that's verifiable
is complex uh and the research is only
is kind of still bleeding edge but
catching up now secondly I think as a
space uh we just weren't quite ready yet
and there was more pressing problems to
solve like some of the things that we've
seen this morning um you know a account
abstraction a big one I'd like to be
able to use uh or or send this compute
or Ai and ml compute model that we're
building to um you know everyone we want
mainstream adoption um so we're starting
to see the results of some of these
discussions in the space like come out
like the account abstraction Pieces come
out uh ZK uh proofs have come out now
all that sort of thing is finally
catching up of course uh I've worked for
filecoin so uh decentralized compute uh
sorry decentralized storage is also now
available as well um so I think now
we're ready we're ready for verifiable
compute it's the now Frontier of web 3
and it's going to enable us to actually
we've heard a lot about bringing the
next billion users to web 3 and the ux
story is part of that but what about
being able to do stuff with our data
like I said it's one of the core pillars
of the internet uh so we want to be able
to do that where it doesn't actually
connect to just another AWS service um
so that's what I'm aiming we're aiming
to do here at lilipad we're trying to
create this project uh of verifiable
trustless computation uh like this
internet scale and can allow for these
mainstream web 3 applications to use it
uh so um we want to unlock this uh new
data economies as well and as
kryptonians I didn't know is that a word
I just made it up um we also are highly
native to running our own nodes as well
so why not be rewarded for your spare
GPU being used by a project like this as
well um so we're hoping this is a space
that the Lily W pad Network can play a
big part of um so our vision is to build
this internet scale uh kind of
distributed compute Network that enables
internet scale data processing so not
just Ai and ml but those are big parts
of it especially with the mainstream
adoption we're seeing and other
arbitrary computation from blockchains
while unleashing idle processing power
and unlocking this new wave of data
economies um so in Practical terms
lilipad offers kind of three main things
a global trustless permissionless
compute network with an independent
Global Marketplace of gpus so we're
basically running a two-sided
Marketplace which we want to do in
crypto we want to get rid of the
middleman we want prices to stay low we
want things to be accessible which is
the next point we want it to be
accessible to everyone on the same terms
uh so it's permissionless and thirdly we
want to use this distributed Hardware
network with inbuilt cryptographic trust
mechanism so offchain large scale
compute with onchain guarantees which is
you know probably a lot of parallels
with chain link here which is
great uh so why not AWS then why aren't
we using that why do we need this um
probably don't need to tell you all you
can probably guess already but it's not
just a buzzword uh like with AI and ml
reaching the kind of mainstream adoption
curve uh first with kind of generative
AI images and now with llms uh like chat
GPT um there's several kind of practical
as well as ethical challenges that are
arising here so some of these are
uniquely suited to a decentralized
compute solution like lilypad and these
kind of include ownerships of a
ownership sorry of AI models access to
AI models and Hardware to train the
models and security and speed to
Innovation through open- Source projects
so I'll expand a little on some of those
firstly the capability for decentralized
AI with distributed uh networks like
lilipad is one of the major ideological
wi's and it's a driver for me personally
so we've all heard this story before
being in this web 3 Community uh Bears
repeating here though in the same way
that one person should not control
access to social media or one Co company
shouldn't control access to your data or
scientific breakthroughs Central
entities probably should not be allowed
to control access to AI advancements or
then Lobby for how those should be used
um along the way so currently both the
models and our access to them are either
a black box or rate limited or there's
kind of other questions surrounding that
uh so that's one reason that networks
like these are going to be important in
the future and what's also interesting
to add to that point though is I don't
know if anyone's read this article um so
this was like a leaked Google article um
about how they have no moat and neither
does open Ai and basically what it says
is that open source models are faster
they're more customizable they're more
private and they're more capable um so
that means opening up access to AI gives
a better Pathway to Innovation it also
reduces biases because we aren't just
like relying on um an algorithm created
by one of these companies which are
generally American companies no offense
to Americans but you know what I mean um
and you know uh we're opening up access
instead uh for open Innovation and that
means also that people can get there
quicker because they know what their use
case is they know what they need one of
the big problems here though for being
able to innovate in the AI space is
access to gpus is at a reasonable price
I don't know if anyone's tried to use a
GPU lately on one of the main
centralized Services they're really hard
to get to and they're really expensive
anyway um so deep in or decentralized
physical infrastructure networks uh and
one of those is vcoin uh can help solve
this with kind of an open compute
Network so leveraging these networks and
connecting them to a legitimate uh uh
front end and code protocol base really
uh can help us uh solve this kind of
practical problem of access to
gpus um this should given it's a
two-sided Marketplace also be good for
efficiency and pricing so Alig this
Supply and aligning this supply and
demand uh by providing these gpus and
new revenue streams to the other side of
that as well so olops don't make for
proficient use of hardware and they
don't provide good pricing models for
users um this is another great point I
don't know if if anyone knows Brooklyn's
alinker or the fishing team um but she's
amazing CTO there uh she also made the
point that if you look at these large
Cloud providers their data centers are
really in two main places almost
entirely and there's not data centers
anywhere else so I think uh this this
kind of distribution model needs to be
looked at as well uh so a quick repap on
some of my points uh hopefully I'm not
all ideology here there's actually some
real practical use case reasons which I
think the team before was very are
adamant about having a real use case I
think there's some real use case reasons
uh for this decentralized compute
Network as well as some uh kind of
ideological uh solutions that I think
are very important as well um so we've
probably heard a lot about Ai and
blockchain recently this you know web 3
is really good at unlocking Network
effects eliminating these middleman and
creating uh peer-to-peer networks so
this is what we can bring to web 3 and
web 2 developers Main stream developers
so cheaper more open better distributed
and more efficient compute power
combined with kind of this transparency
and data
ownership and having a stake in the
ownership of AI uh Solutions as well
there's also a security point there
which I think don't know if anyone was
at Allison uh jman's I'm sorry if I
pronounced that wrong uh talk this
morning but uh yeah it was really good
right she was talking all about uh
security and the need for more people to
be able to look at this as well uh so
these are some of the things that we can
do uh that a web 3 is uniquely capable
of helping AI with uh and there's
definitely AI problems cropping up um so
we can create open source Community
ownership here uh we you know we can
create proof of humanity in Providence
and I think we're already seeing
projects doing that as well uh and
there's also attribution models so uh
crypto as a payments layer is incredibly
efficient uh so we can use it for things
like giving attribution to the original
content creators and this is a proof of
concept uh project we did uh with Lily
Pad hence the name water lily which
basically takes uh artists input data uh
trains it so does a Laura training model
on it and then a user can go ahead type
in their prompt pay a small amount of um
money and get their uh images back this
is on an old uh stable diffusion model
that's why the images don't look as good
as the new ones do uh which you'll be
able to see uh you can try it out
actually it's all live um I don't think
I've got any pictures in this slide
though um anyway so our road map uh
we've just released lilypad V2 which has
a lot of updated models uh we intend to
go multichain as well we're evm
compatible so anything evm we can run on
um we're going to release an
incentivized test net as well uh for
suppli so if anyone's interested in that
please hit me up uh and May net launch
next year okay ah there we go um we just
did a whole team did a talk on
everything we're we're uh doing at Lily
Pad as well so if you want to know more
uh you can have a look at Phil Dev
Summit uh and uh have a look at those
talks sorry that's an old picture it was
meant to be Sal Salvador uh Dar picture
of Barcelona which is really kind of
weird um but this one's from Iceland so
hence the
Aurora okay thank you everyone uh hope
open to questions if anyone has any
otherwise try it out one question sorry
no questions I'll take one
though uh yes there's a whole uh
research paper on that so maybe I'll
just give you the link to that because
I'm getting like
the thank you hello sorry quick question
if you have a look in our docks there's
oh there's a quick question up there too
hey yeah so uh how the participants are
incentivized do do you have kind of
you're talking that you used to
compensate those who are in this
calculation like Network yeah yeah
exactly so on the supply side uh the
node side they are compensated with LP
tokens currently we're only at test net
though so those are pretty worthless but
we are tracking uh all participants on
the network for the future launch oh
cool there network of GPU and also I
guess CPU like mostly mostly gpus at the
moment um and yeah mostly gpus that
we're using because we're focusing most
of our modules or jobs are around Ai and
ml at the moment so Laura training or
infant training um yeah and when you say
that it's EV compatible you mean that
you have also some contracts and the the
tasks that uh we have from our contract
we can uh put it through the contract
directly on chain you can directly run
from a smart cont get results calculated
then also like yep you can get yep you
can run this in a yes or from a solidity
contract the Oracle part is provided by
Chen link yeah I mean no it's not quite
an oracle uh I'm kind of hoping in
future we might be able to use ccip as a
messenger between our multi chains
actually um that's probably one of the
ways I'd integrate um but we're
currently yeah there's there's no chain
link cool great thank you I'm asking
because in the next session of what we
are doing we will need scoring okay that
uh has to be calculated off chain in ml
models and then
recorded into this would be completely
useful for that and it would be highly
compatible with being put with either ZK
models like to do the computation or
with oracles um to help do computation
so yeah great use case thank
[Music]
you
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