Author: Accelxr

Artificial Intelligence is an accelerating technology that will dramatically alter social trends while transforming economies, reshaping industries, and providing new forms of online interaction.

While many believe that Crypto’s intrusion into the world of artificial intelligence is unnecessary, we believe it is a crucial synergistic relationship. As restrictions on the production and distribution of AI models tighten, a fast-paced, anti-authoritarian open source community is rapidly emerging, taking on well-funded centralized schemes and governments. Crypto is by far the best tool for raising funds and managing open source tools, as opposed to external pressure. That’s already an ideal match, and that’s before taking into account the impact of AI on authenticity, provenance, identity, and other areas that Crypto has inherent strengths in remediating or improving.

There are all kinds of rabbit holes worth exploring here. This article attempts to cover as much ground as possible, so this article can be regarded as a review of the current situation so far. A stormy overview of some of the emerging areas of Cryptox AI so far and in the foreseeable future.

Creativity

In recent times, The first wave of interest in artificial intelligence was in the field of idea generation tools. Generative AI reduces users' reliance on technical skills such as programming or advanced software proficiency, allowing anyone with basic electronics experience to produce complex works while outputting professional work at the lowest cost.

This could have a huge impact on the creative industries, to name just a few:

Now, anyone can become a creator, and as the number of scenarios in which people create works together with these tools becomes more and more mature, the creative model of multiplayer games will flourish like never before.

Niche communities are able to produce high-quality work that previously would have limited commercial viability due to audience size.

Generative content will flood in at a rate that far outpaces human work and will even lead to a potential revaluation of human content online.

The following is a discussion of some innovative media that are highly interactive with AI.

Art

"AI art is not art" are those A common slogan among those stubbornly opposed to the rise of AI tools. The release of the generative model was quickly met with backlash and protests, such as what we saw on ArtStation . However, it has sparked sparks in some of the most interesting creative vertical subfields in web3.

AI art comes in many forms, the most famous being the currently popular generative models, including DALL-E, Stable Diffusion and Midjourney. There are also web3 competitors like ImgnAI that are working to provide users with a better social experience around generative image creation driven by token economics, which is much needed to build community moats around these generative models.

However, AI artists highly regarded in the field often engineer and fine-tune their models in a more unique way, resulting in even more unique works, rather than through simple prompts. This might require training new embeddings, or using LoRAs to perfect a style, or even building your own model entirely.

Popular artists using more complex and personalized models to release AI art as NFTs include Claire Silver, Ivona Tau, Roope Rainisto, Pindar van Arman, Refik Anadol , Gene Kogan and more. These artists have explored the use of various marketplaces for distribution, most prominently AI art-specific marketplaces such as Braindrops, Mirage Gallery, and FellowshipAI, as well as event platforms dedicated to the art form such as Bright Moments.

Vertical subfields of AI art have also been formed, such as post-photography and data art. Post-Photography Art is primarily driven by the Fellowship.ai team, working with Roope Rainsto to bring more artists exploring this medium into the public eye. Much of post-photographic aesthetics strives to embrace the visual artifacts common in early generative tools. With the publication of Roope Rainisto’s Life in West America series on Braindrops, post-photographic art has begun to receive increasing attention on social media.

Regarding data art, Refik Anadol is a well-known artist in the field who is known for using data, algorithms and artificial intelligence to create dynamic and interactive artworks. Known for its immersive installations. There are some interesting examples in his work, such as Unsupervised, which transforms MoMA’s metadata into a work that generates new forms in real time. Another example is Sense of Place, which uses real-time environmental data such as wind, temperature and humidity, as well as signal data from Bluetooth, Wifi and LTE to provide data sources for the work.

Another interesting vertical subfield is the new content media enabled by Crypto’s features: autonomous artists on the chain. The most famous example is Botto, a community-governed generative artist that creates 350 pieces of art every week in rounds, each of which contains multiple individual fragments. ). Each week the BottoDAO community votes on these "shards", using their aesthetic preferences to guide Botto's generative algorithms for future art creation, ensuring that artworks develop over time under the influence of the community. Each week, that voted “shard” is minted and auctioned on SuperRare, with proceeds returned to the community. After completing the "Fragmentation" and "Paradox Periods", Botto is currently in the "Rebellion Period", integrating new technologies such as Stable Diffusion 2.1 and Kandinsky 2.1, and in each of its Explore collaborations and curated collections in weekly rounds. Botto is one of the highest paid artists on SuperRare and has even amassed his own collector DAO called CyborgDAO. In addition, projects like v0 are also exploring the integration of token economics and AI art models, aiming to provide a place for multiple artists to create their own on-chain art engines, governed by a community of holders.

When interviewing collectors of AI art of any kind, the most common retort from the crypto space is that curation by artists reduces their interaction with the blockchain , which is different from the more classic generative art (Art Block). Rather than randomness arising from chain-specific inputs, these outputs are selected by the artists themselves and permuted multiple times before being “implanted” into the collection. Although this is a digitally native art-making process, it must be wound manually.

Fully on-chain AI art is difficult due to the limitations of the execution environment and the computational complexity of the image generation model used. Some examples of lightweight output, such as Pindar van Arman's byteGANs, are stored on-chain, but we expect that for more complex models the closest available form in the short term will be off-chain verification mechanisms. For example, Modulus Labs recently partnered with Polychain Monsters to build a zkML-validated GAN model for generating collectible pixel monsters. Using zk proofs, every generated NFT can be cryptographically verified as coming from an actual Polychain Monsters art model, which is a huge step forward for AI art.

Music

Beyond image-based art, There's a major movement brewing in music. The success of ghostwriter's AI Drake hit seems to be well known by now. Within 2 days, it accumulated over 20 million views and was quickly banned by UMG. This short-lived phenomenon made the public aware that the relationship between the artist and the work itself was undergoing a fundamental change.

Within a few years, generated music will undoubtedly surpass music created by humans. Boomy is a generative music startup founded in late 2018. Its users have created nearly 14% of the world’s recorded music (approximately more than 14 million songs) in a short period of time. This was just for this one platform, and that was before the recent surge in public interest.

Given that generative content will exceed works created by humans, and the use of speech models will further increase the difficulty of authenticating works, that is, how to determine that the work is by the artist Created, therefore the artist will require verification of authenticity. Of course, the best way to publish and verify the authenticity of an artistic medium is through cryptographic primitives.

However, it is worth noting that this is not all bad for artists, especially those who are willing to embrace this inevitable trend. Holly Herndon is an innovator of the open voice model, empowering her community (Holly+) to create and distribute work using her voice. Holly's assertion at the time of publication was simple:

"While the difference between pirated and official speech models may be small at the moment, as more refined , the improvement of more realistic speech generation capabilities, user demand for more comprehensive and higher-fidelity voice training data, and the need to identify sources will also increase. For these reasons, I believe that official, high-fidelity speech training for public figures will also increase. Voice models will become a necessity, so why not give it a try?"

The DAO oversees the Holly+ voice model and can vote on the creation and approval of new works. Token holders of the DAO have an incentive to ensure that only high-quality work is approved to prevent devaluation due to poor art or negative connotations. The voice model will be used to produce a limited number of official artworks, and DAO token holders will receive ongoing profits from the resale of these works.

Recently Grimes launched elf.tech, a platform that allows artists to use "GrimesAI voiceprints" in their original songs. After obtaining Grimes' approval, they need to share 50% of the royalties with Grimes. Elf.Tech is powered by CreateSafe’s AI and facilitates professional distribution and ensures proper royalty management through a partnership with TuneCore. If the final form of the music is an on-chain NFT, profit distribution is handled via fiat currency or through an automated on-chain royalty split. Hume, a web3 music studio focused on virtual artists, was one of the first companies to use the Grimes model to release Grimes AI in collaboration with its virtual artist angelbaby.

Fashion and physical goods

I have previously posted this article The concept of generative manufacturing of physical consumer goods and fashion products using creative programming algorithms and artificial intelligence is explored in the article: https://mirror.xyz/1kx.eth/oBuaEp5jgGbe2gCsa6Z-_mLAeMRUhsIdZsaScHQNXS0.

In short, generative artificial intelligence and creative programming create the prerequisites for a hyper-personalized future of products and user experiences, allowing us to tailor products and user experiences to our personal preferences. Create unique designs, patterns and art. This technology can be applied in everything from fashion to home décor, and further exploits its advantages by allowing users to fine-tune the output to their liking. New manufacturing tools often allow us to connect code directly to machines to automate output production, fundamentally solving many of the technical bottlenecks in manufacturing personalized goods.

Web3 projects currently exploring this area include Deep Objects, RSTLSS, and Little Swag World. It’s worth pointing out that most digital fashion projects will likely explore generative creative tools and media, with Draup, Tribute Brand, and others discussing its use in detail.

Community-created model output similar to Botto is an interesting idea that Deep Objects is exploring. They used a community curation engine to reduce the 1 million designs generated by the GAN AI model into a single community-selected piece. This final piece will now be 3D printed in a showcase of generative product creation. DeepObjects can easily extend this kind of curation design to other physical goods as well.

RSTLSS collaborated with AI artist Claire Silver to launch a work called Pixelgeist, in which each casting includes, in addition to the artwork itself, a Digital clothing featuring the artwork, a game avatar with the clothing, and the right to purchase the corresponding physical piece. This unique fusion of digital physical fashion and AI output is one of those interesting experiments that brings gaming, fashion, and AI together. Claire Silver also tackles fashion photography with her latest series, made possible on Braindrops. For more information on the topic of digital fashion, see my article: https://medium.com/1kxnetwork/augmenting-culture-the-emerging-field-of-digital-fashion-bead627c8dcd.

Little Swag World is a great example of using GAN models in the creative workflow, from design to physical product. Bosch, the artist behind the project, built the initial design himself and then ran it through Stable Diffusion/Controlnet to produce a uniquely surreal piece. This technique achieves a high degree of aesthetic consistency, and the next step in the project is to combine these generative models with ceramics to create AI-enhanced NFT physical goods.

All in all, we expect there will be many exciting Crypto x AI projects, from decentralized brands curating generated products to divisible AI agent designers NFT.

Entertainment

After the initial hype surrounding Nothing Forever , generative entertainment has also been more fully developed. Nothing Forever is a generative interactive animated sitcom based on Seinfeld that runs 24/7 on Twitch Live. Interestingly, it demonstrates the power of the medium, with the show's narrative changing based on Twitch chat replies and allowing donors to import their likeness as a character into the show.

Simulation from Fable extends this research with SHOW-1, a model for prompted generation of television shows in which writing, animation, directing, Both dubbing and editing are accomplished through prompts. They first demonstrated this in an episode of South Park, but it can be easily expanded to any IP. I fully expect more permissionless forms of IP to experiment more deeply with this type of content creation tool, as we've seen with web3.

Upstreet has also recently begun experimenting with generative TV programming, using the AI ​​agent model they developed for virtual world platforms (see details below), allowing creators to add their own VRM avatars and create unique interactions and skits through prompts.

Another area worthy of attention is intellectual property. Projects like Story Protocol are looking into using decentralized IP registries to facilitate the creation, distribution and monetization of IP. This is useful for creators, is more streamlined than traditional IP licensing, and is particularly unique in the age of generative AI. NFT IP, memes and other entertainment projects can be authorized and paid copyright fees to generate various derivatives, which can greatly unlock the value amplification of creators' works.

Are you a robot?

We may soon be facing a problem: deepfakes. Examples include chatbots trained on influencers to interact with their fans, and generative spam on social media, to name a few. Soon it will be crucial to verify who the real human beings are.

Web3 has put a lot of effort into preventing witches (although the problem has not been eradicated). However, reputation systems, personality proof mechanism design, user passports, soul-bound NFTs, and the entire token economy are all working to solve this problem.

Authentication hardware, zkML and proof of character

I was before The practical implications and potential use cases of zkML are discussed in detail in this article: https://mirror.xyz/1kx.eth/q0s9RCH43JCDq8Z2w2Zo6S5SYcFt9ZQaRITzR4G7a_k.

There are multiple teams, such as Modulus Labs, EZKL, and Giza, that focus more on using zk to prove the reasoning of the model. These efforts to use zk to validate model outputs have broad applications and enable new experiments in DeFi, identity, art, and games to use these models in a trust-minimized manner.

While there are countless projects focused on character proof, one of the most interesting applications is undoubtedly Worldcoin. Worldcoin uses an AI model to convert iris scans into short hashes that can be easily cross-checked to verify similarities or conflicts in the event of a Sybil attack. Because each iris is unique, the model is able to determine that the user is real and unique. It uses a trusted hardware setup (that highly recognizable sphere) to ensure that the model only accepts cryptographically signed input from its camera.

Similarly, the zk micophone team demonstrated how to use certified microphones to create and digitally sign audio content to verify the authenticity of the recording. The key is stored in the microphone's secure area, which is signed to guarantee the authenticity of the recorded audio. Since most recordings are processed or edited, audio editing software powered by SNARK enables audio conversion while still proving the source of the audio. Daniel Kang also collaborated with Anna Rose and Kobi Gurkan on a proof-of-concept for certified recordings.

Forever Influencer

Validate personality or humanity The other side of creating content is embracing the possibility of deepfakes. Similar to the voice cloning model above, some influencers choose to create chatbots to engage their audience. One famous example is Caryn Marjorie, who launched an AI girlfriend product using her voice and trained on thousands of hours of YouTube videos to perfectly capture her personality, mannerisms and voice. For $1 per minute, users can chat with her avatar in a private Telegram channel, sending and receiving voice messages with her likeness. In the first week of launch, Caryn Marjorie made $72,000, and as subscriptions grow, she's expected to make more than $5 million a month.

CarynAI is just one example of AI girlfriend products (more introduction below), imagine that you can interact with the AI ​​model of your favorite game anchor Play games together, have real-time conversations, and simulate real experiences; alternatively, KOLs can use anthropomorphic AI+ avatars, which can be authorized for use in fashion shows or publications, etc.

˚✧₊⁎( ˘ω˘ )⁎⁺˳✧༚ Uwu-ral Networks is so kawaii(ノ◕ヮ◕)ノ:・゚✧*

An indisputable reality is that 79% of adults aged 18 to 24 people report feeling lonely; 42% of those aged 18 to 34 report feeling “forgotten” “all the time”; 63% of men under 30 consider themselves single, compared with 34% of women in the same age group. % consider themselves single; only 21% of men said they had received emotional support from friends in the past week.

People are lonely. In an era where loneliness is increasingly prevalent, especially among young people, the emergence of artificial intelligence companionship offers a unique, if slightly dystopian, solution. AI companions are always available, judgment-free and highly personalized. They can serve as therapists or outlets for desires. They can be creative colleagues or lifestyle coaches. They are always waiting to talk to you about anything you want.

The infrastructure for doing this could be: fine-tuning the model using personality cues to outline behavior, appearance, traits, communication styles, etc. The output of running the model through a vocal model like elevenlabs. Generate selfies on request using the image generator model and defined appearance hints. Generate an appropriate vrm avatar and place it in an interactive environment. Well, you now have a hypermedia companion that's perfect for you. If you add Crypto into it, you can make them ownable, tradable, rentable, etc.

Companion

The above setting can be achieved completely through DIY, but you can also Use apps that specifically address this concept. Replika is the most famous example, which allows us to communicate in real time with a virtual partner without any technical skills. These apps typically operate on a subscription model, with users paying to interact with their virtual companions. Not only are such products profitable, but they also demonstrate the huge impact this trend has on human psychology: for example, one post on Reddit showed a person's conversations with a virtual partner for 2,000 consecutive days, and we also saw Proposals, AR selfie creations, and more. Here's another interesting tidbit: When porn was removed from the platform, subreddit moderators had to pin the suicide hotline to the top of the community to appease agitated community members.

Role-based platforms are also beginning to emerge, which provide users with a way to use multiple roles (often also on a subscription model). While there are plenty of ready-made characters to choose from on platforms like Character.ai and Chub.ai, the real novelty lies in making a character or scene entirely your own through character prompts + feedback training.

Many web3 projects have made some attempts to provide these companion experiences, such as Belong Hearts, MoeMate, and Imgnai.

Belong Hearts has pioneered a novel NFT casting method that allows users to chat with the character they provide until the user gets her phone number, which can be Whitelisted for NFT minting. Once received, the NFT allows the user to enjoy a chat experience with the character, including erotic role play as well as the resulting selfie. While the future direction of the product is yet to be determined, there's a lot of discussion surrounding tokenomics as a mechanism for players to gift items or tokens to the chatbot to affect her mood and relationship levels.

Created by the team behind Webaverse, MoeMate offers both desktop and browser versions of the application, allowing users to easily import vrm models, which they can then personalize and interact with. The desktop version is reminiscent of a previous AI assistant called the old-school paperclip assistant.

There is also Imgnai, which in addition to being the high-quality image generator model mentioned above, also addresses the anthropomorphism of the Nai character with a fully integrated chatbot experience issues.

Ultimately, the potential of tokenomics abounds in the companion space, including tokenized APIs, tradable personalized prompts (see below), and on-chain games. Scenarios such as currencies, agent payments, tradable trinkets, role-playing mechanics, and token-restricted access are just a few of the potential scopes for future exploration.

Personality Marketplace

Interestingly, companion apps The rise of , has also led to the rise of standardization of personality prompts and platforms for exchanging personality primitives. The field is likely to move toward the financialization of high-quality prompts and scenarios. For example, if an uncensored open source LLM could read metadata from an NFT containing a standardized personality, the personality NFT could benefit from the royalties generated therefrom to benefit its creator.

But this also raises another unanswered question: since many top models are restricted by NSFW content, it is necessary to create viable open source models, but this is precisely is a great opportunity for token-based crowdfunding and governance.

—

You can check out this article I wrote to learn more about this chapter To get some ideas: https://medium.com/1kxnetwork/virtual-beings-51606c041acf.

Enhanced Governance

History of DAO Governance It is actually the evolution of a long history of human collaboration. Ultimately we discovered that it is extremely difficult to organize resources effectively, minimize governance bloat, eliminate fishing, and identify soft power inefficiencies or bottlenecks.

Experiments using AI as an enhancement layer for DAOs are just beginning, but their potential impact is far-reaching. The most common form is the use of trained LLMs to help direct labor capital within the DAO toward more efficient matters, identify issues in proposals, and open up wider participation in contributions and voting. There are also simpler tools, such as AwesomeQA, which improves the efficiency of DAOs through search and automatic replies. Ultimately, we expect that “autonomous” in DAOs will become more important over time.

Autonomous Committees and Voting Agents

Upstreet has Agent systems (such as AutoGPT, etc.) are applied to their governance process as an early experiment. Each agent is defined by a subgroup of the DAO, such as artists, developers, BD strategists, PR, community managers, etc. These agents are then tasked with analyzing proposals from contributors and discussing their pros and cons. Agents are then scored based on their impact on their respective scopes, and the scores are aggregated. Human contributors can evaluate their discussions and score before voting on the outcome, so essentially it provides a diverse parallel review service.

This is particularly interesting because the process can surface aspects of a proposal that humans might have missed, or enable humans to debate with the AI ​​agent about its subsequent impact .

Advanced coordination system

MakerDAO has also discussed in detail Through similar topics, we can achieve the goal of autonomous governance decision-making with minimal human input. They completed an overview of Atlas, which depicts a live data hub containing all things related to Maker governance. These data units are organized in the form of a document tree, which provides context to prevent misinterpretation. Atlas will be formatted and standardized as JSON to make it easier for AI and programming tools to use it.

Atlas can be used by various Artificial Intelligence Governance Tools (GAIT) that engage by automating interactions and prioritizing participant tasks Governance. Example use cases include:

Project Bidding: GAIT can simplify the process for ecosystem participants by processing paperwork and ensuring proposals meet strategic goals. Project bidding process.

Monitor rule violations: GAIT can help monitor deliverables and rule compliance, flagging potential issues for human review.

Professional advice integration: GAIT can transform professional advice into formatted proposals, bridging the gap between governance and expertise.

Data Integration: GAIT can easily integrate new data and experience, helping DAOs learn and adapt to new situations without repeating mistakes.

Language Inclusion: GAIT can act as a translator so governance can be conducted in multiple languages, creating a diverse and inclusive environment of.

SubDAO: Atlas and GAIT can be applied to SubDAO, allowing experimentation and rapid development, and the ability to learn from failures.

The area where I am particularly excited about Crypto x AI is gaming. There are many novel games to explore in this area, such as procedural content games, generative virtual worlds, LLM-based narratives, cooperative games where AI agents work with each other, and more.

While there are many good examples of new games in web2, here we will focus on examples from web3. It is worth mentioning that this academic article "Generative Agents: Interactive Simulacra of Human Behavior" awakened many people to explore the possibility of multi-artificial agent game environments. Researchers from Stanford University and Google demonstrated this potential by applying LLM to agents in a sandbox gaming environment. LLM-powered agents exhibit impressive behaviors including spreading party invitations, establishing friendships, dating, and coordinating everyone to get to parties on time, among others, all based on a single user-specified recommendation. The approach leverages an architecture that extends LLM to store and synthesize higher-level feedback, allowing agents to achieve more dynamic behavioral planning.

This research is the basis for the most explored (but still experimental) game in web3 to date. The core idea is how we can use AI agents with a high degree of autonomy or identity in simulated environments and build fun and engaging games around them.

Parallel Colony from the Parallel TCG team explores this concept by having AI agents collect resources and tokens for players in the game. Using the ERC-6551 standard, AI agents are NFT wallets that can conduct transactions in the game on behalf of users. AI agents can create, mint, and store new game items, and also have personalities defined by fine-tuned LLMs created by the team, giving them non-standardized behaviors and traits that can influence their actions in the game.

But conceptually the most intriguing AI agent-based game is Upstreet. Upstreet is a virtual world project with some crazy ideas like an AI agent SDK, procedural tasks, browser + VR, drag-and-drop interoperability, and social features in an environment called "The Street" where players can build their own experience and interact with them. In addition to players, there are artificial intelligence agents that developers (and players) can deploy to influence the game environment with personalities and goals. Most interesting is their research and development of the AI ​​Director, an AI agent that decides a goal, such as "parachute from the tallest building" or "start a new religion," in which the user and agent participate as challengers . The Director determines the winner at the end of each round, rewarding players and agents with prizes, tokens, and NFTs. This could lead to very interesting and complex agent-player interactions, and we are very excited to see its development. In particular, it could directly lead to high-value 3D environment research and data for more advanced models in the future. According to the data, OpenAI also seems to be interested in acquiring open source Minecraft-style games.

Generating tools for creating virtual worlds is another area of ​​enhanced gaming. Today, for example, lets players design their own virtual island and take care of AI NPC companions. Particularly unique is their use of generative creative tools to facilitate the development of in-game UGC. Since the game is primarily based on these user-created islands, it's important to provide silky-smooth asset development opportunities for players without 3D game development or art skills. Arguably, much of the lackluster nature surrounding Metaverse-style gameplay is due to a lack of content, and in the short term, this can be remedied precisely through the use of generation tools.

AI agents require training, and training itself can become a fun game for players to explore. AI Arena provides a novel way to train AI agents, allowing players to play Super Smash Bros-style games and slowly teach AI agents to compete in matches through imitation training. Because the AI ​​agent doesn't need to rest, it can play competitive tournaments around the clock against an always-active pool of competitors to earn prizes, while players can fine-tune their playstyle asynchronously. This turns training into a game and amplifies its effectiveness through token economics.

Large-scale cooperative games between humans and powerful artificial intelligence players were possible in the past, but with the integration of token economics, it has been Taken to a new level. Leela vs. the World from Modulus Labs is an experiment in this type of game format. In this experiment, Modulus takes the Leela chess engine and verifies its output with a zk circuit. Players can invest money to bet on the game between humans and artificial intelligence, thus forming an interesting prediction market. Although the verification time of this model will be long considering the current state of zk, it certainly opens up the possibility of esports prediction markets and verifiable and complex AI player governance mechanisms based on large-scale collaboration challenges.

Finally, pure chain games or autonomous worlds will also be enhanced by artificial intelligence. The most notable on this topic is Large Lore Models, which looks at using the LLM protocol layer to create ongoing knowledge that can interoperate in a modifiable and interconnected game environment, where players Actions affect multiple game environments simultaneously in an autonomous world and therefore should carry higher-dimensional knowledge to facilitate the storyline. This is ideal for building abstract LLM layers in multi-chain gaming environments.

Infrastructure

AI x Crypto Basics The facility itself deserves a separate article, but here I'll briefly touch on some of the ideas we're seeing taking shape.

Distributed computing

To understand the impact of cryptoeconomic systems on To understand the computing requirements, we must first understand the core issues. To date, there has been a huge bottleneck in GPU capacity, with wait times of up to a year for the best hardware, like the H100. Meanwhile, startups are raising huge sums of money to buy hardware, governments are scrambling to procure it for defense purposes, and even the best-funded teams like OpenAI are having to pause feature releases because of limited computing power.

Many teams focused on decentralized computing and DePIN see an opportunity here: bootstrapping permissionless clusters to meet demand while providing crypto incentives and minimum profits , making the network highly competitive on pricing with its web2 peers while providing better returns for hardware vendors.

Machine learning can be roughly divided into four main computational workloads:

Data preprocessing: Prepare raw data and convert it into a usable format.

Train: Let ML models train on large datasets to learn patterns and relationships in the data.

Fine-tuning: ML models can be further optimized using smaller datasets to improve performance on specific tasks.

Inference: Run a trained and fine-tuned model to make predictions.

We have seen the shift from more general computing networks like Render and Akash to serving more specialized computing such as AI/ML. For example, Render has leveraged providers like io.net that are built on top of their network to serve AI customers more directly, while vendors like Akash have begun bringing in hardware vendors who have the demand and by directly training their own models to demonstrate the power of the network, the first case is a Stable Diffusion fork trained only on uncopyrighted material. Livepeer is also focusing on AI video computing as they already have a large network serving video transcoding use cases.

In addition, a network specifically for AI computing is forming, which makes us realize that the core challenges around collaboration and verification can be solved by building chains or models around AI. to address it more directly. One of the more notable examples is Gensyn, which built a substrate-based L1 designed for parallelization and verification. This protocol uses parallelization to split larger computing workloads into tasks and push them asynchronously to the network. To solve the verification problem, Gensyn uses probabilistic proof-of-learning, a graph-based pinpoint protocol, and an incentive system based on staking and slashing. Although the Gensyn network is not yet live, the team predicts that the hourly cost of an equivalent V100 GPU on its network will be approximately $0.40.

Besides storage, alternative training models are also emerging, such as federated learning, after realizing that blockchain can more appropriately incentivize these models. , its revival in web3. In short, federated learning is a method in which multiple parties train a model independently and periodically batch updates and send them to the global model. There are many practical examples, such as Google’s keyboard text prediction algorithm. In web3, FedML and FLock are trying to combine federated learning methods with token incentives.

It is also worth noting that decentralized data stores like Filecoin and Arweave, as well as databases like Space and Time, can help in data preprocessing. Play an important role.

Consensus-based ML

Using blockchain Another novel form of infrastructure is the concept of consensus-based machine learning (ML). Bittensor is the most prominent example of this concept: a Substrate-based L1 blockchain designed to make machine learning more efficient and collaborative through the use of application-specific subnets. Each subnet has its own incentive system to serve various use cases, from LLM to predictive models to generative innovation. Bittensor is unique in how it uses miners to orchestrate quality output: miners earn TAO (its native token) by providing smart outputs from their ML models (rated by validators). Because miners are incentivized for their best output, they continually improve their models to stay competitive, helping Bittensor through the process of achieving faster learning coordinated by token economics.

A recent exciting development in the TAO ecosystem is the dynamic TAO proposal to transition Bittensor to a more automated, market-driven mechanism design around token emission, and the launch of the Nous subnet to provide incentivized model fine-tuning to compete with the likes of OpenAI.

We may see more attempts at such systems, such as having mining or consensus regulate model output in a way that favors quality.

Intention is all you need

In DeFi , the latest argument in the field of MEV is about user intentions and the use of economically-aligned demodulators to execute these intentions. Discussions about intent are often mixed, but one thing is becoming increasingly clear: user intent requires higher-order semantic context to be parsed into executable code. LLMs may provide this semantic layer.

Propellerheads presents the clearest vision yet for using LLM in the intent space: https://www.propellerheads.xyz/blog/blockchain-and- llms.

In short, LLMs can transform close-matching intentions into fully-matching intentions through semantic understanding, thereby helping us find coincidences of wants. , CoWs) opportunities. This can be done through inward intent re-estimation (e.g. "Is it okay to buy LUSD instead of USDC? I found a matching limit order and you will save 0.3% in trading fees with this CoW.") and outward intent re-estimation (e.g. "I want to buy this BAYC you own, are you willing to sell it for X ETH?") to do it.

Of course there are other structures possible, which becomes particularly interesting in the context of wallets and the post-account abstraction of multisigs. Projects like DAIN and Autonolas have experimented with using proxies as signers for wallets, for example, so that talking to your wallet and having it perform transactions on your behalf is about to become a reality.

Also worthy of attention are the large-scale DeFi use cases, such as agent-based prediction markets, economic models managed by AI, and ML parameterized DeFi applications. My zkML article provides a more detailed introduction.

Agency Economy

My favorite so far One area of ​​infrastructure is the AI ​​agent economy. It stems from my vision of a world in which everyone has their own agent, and we hire those high-quality and well-trained agents to serve us, or have autonomous agents realize our needs in complex economic behavior. Target. In order to do this, agents must have a way to pay and receive payment for their services. It’s definitely possible that traditional payment models will open up for these agents, but it’s more likely that agents will transact in cryptocurrencies given their ease of use, speed of settlement, and permissionless nature.

Autonolas and DAIN are typical examples in this field. In Autonolas, agents are actually nodes in the network dedicated to achieving specific goals, and these nodes are maintained by service operators, similar to Keeper networks. These agents can be used for various services such as oracles, prediction markets, messaging, etc. DAIN takes a similar approach, enabling agents to “discover, interact, transact, and collaborate with other agents in the network.”

Other ideas

In addition to the above, we also See:

Decentralized vector database for fine-tuning models like BagelDB.

Wallet for API keys and SIWE for AI applications such as Window.ai

Data provisioning services

Indexing and search tools such as Kaito

area Block explorers and dashboards, such as Modulus Labs’ AI Validation Dashboard, which is now validating a range of inferences from Upshot models.

Development assistants, such as Dune's on-chain SQL query model

Simulated agent test environment< /p>

Bandwidth for data capture, such as Grass Network

Synthetic data and human RLHF platforms

DeSci applications such as LabDAO distributed bioML tools for protein folding

web3 There are countless ideas popping up to serve various areas of AI, so only the highlights are provided here, but I highly recommend exploring the above projects to get a deeper understanding of the full picture.

The intersection of it all

AI and Crypto is synergistic. Both tend to be open source, censorship-resistant, and are creating the largest transfer of wealth in history. They need each other and solve each other's core challenges.

For Crypto, AI solves problems in user experience, promotes more creative on-chain use cases, and enhances decentralized organizations and smart contracts capabilities and unlocks real innovation at the application and infrastructure layers.

For AI, Crypto solves the issues of authenticity and provenance, strengthens coordination around open source models and data sets, and helps guide calculations and data, and enable creators and agents to participate more directly in the post-AI economy.

The challenge now is for crypto hackers, teams, and projects to understand and embrace this shift. Creativity is limitless and we stand at the intersection of it all.