LianGuaintera Partners Use Cases and Existing Projects Combining Cryptography and AI

LianGuaintera Examples of Cryptography and AI Integration in Past and Ongoing Projects of Our Partners

Author: LianGuaiul Veradittakit, Partner at LianGuaintera Capital; Translation: LianGuaixiaozou

As the digital revolution continues to reshape our world, I believe there are two technologies in particular that have disruptive potential: Artificial Intelligence (AI) and cryptocurrencies. AI, with its ability to mimic human cognition and learn from data, has recently shot to the forefront of technological innovation. The applications of AI, spanning from healthcare to entertainment, are vast and transformative. Supported by robust blockchain technology, cryptocurrencies hold the promise of a decentralized financial future, empowering individuals and simplifying processes. The combination of AI and cryptocurrencies presents a powerful force, heralding the arrival of a new era of technological symbiosis.

1. Cryptocurrency Challenges that AI Can Solve

While cryptocurrencies have become a revolutionary means of transaction and investment, they are not without challenges. Market volatility is a pressing concern for investors. AI’s ability to analyze data can help sift through vast amounts of historical data to predict price fluctuations with higher accuracy.

Furthermore, as cryptocurrencies gain more mainstream adoption, security becomes paramount. The decentralized nature of these digital currencies makes them susceptible to fraud and hacking attempts. Machine learning models can be trained to detect abnormal transaction patterns and strengthen defenses against potential security vulnerabilities.

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Scalability and transaction speed are other significant challenges in the crypto world. As more people join blockchain networks, fast and seamless transactions become crucial. Advanced AI algorithms can optimize network traffic, ensuring efficient data flow and faster transaction times.

Lastly, energy consumption associated with cryptocurrency mining is a global concern. AI can play a crucial role in optimizing mining processes, reducing energy footprints, and paving the way for a more sustainable crypto ecosystem.

2. Intersections of Cryptocurrency and AI

(1) Decentralized AI Market: The Crypto Advantage

In the field of AI, platforms like Hugging Face have garnered significant attention for their role in democratizing access to pre-trained AI models. However, the emergence of decentralized AI markets within the crypto realm introduces a new dimension of democratization and decentralization, surpassing the efforts of traditional platforms like GitHub in open-source code, including web3 code and smart contracts.

• Hugging Face and Traditional AI Model Repositories:

Hugging Face, as an outstanding AI model repository, has played a pivotal role in popularizing cutting-edge AI models to a wide audience. It provides a central platform where users can access, share, and fine-tune pre-trained models. This significantly lowers the barriers to entry for AI practitioners and developers, enabling them to leverage state-of-the-art models for various natural language processing (NLP) tasks.

• Decentralized AI market driven by encryption:

However, decentralized AI markets driven by encryption, such as those built on blockchain technology, take democratization and decentralization to a whole new level. Here’s how:

– True ownership and control: In traditional code repositories like Hugging Face, although access is democratized, the control and ownership of underlying infrastructure and data are still centralized. In contrast, cryptocurrency-based markets typically use decentralized blockchain networks to ensure control is distributed among network participants. Users have a voice in decision-making, making it a more democratic and community-driven ecosystem.

– Incentive mechanisms: Cryptocurrency markets include token-based incentive mechanisms that reward efforts from data providers to model developers and other contributors. This incentivizes collaboration and innovation while ensuring fair distribution of interests. In contrast, traditional platforms may lack these direct financial incentives, making cryptocurrency platforms more attractive to participants.

– Data privacy and security: Blockchain technology ensures high transparency while maintaining data privacy through techniques like zero-knowledge proofs. This addresses concerns about data leakage, a critical consideration for AI applications. Traditional platforms may not provide the same level of privacy assurance.

– Interoperability: Cryptocurrency-based markets are often built on blockchain standards with interoperability considerations from the start. This means AI models and services can seamlessly integrate with various blockchain-based applications, smart contracts, and decentralized applications (dApps), forming a more interconnected ecosystem.

Essentially, while platforms like Hugging Face have made significant strides in the democratization of AI models, the decentralized AI market driven by encryption builds upon this by combining decentralization, true ownership, and token-based incentive mechanisms enabled by blockchain. This increased level of democratization and decentralization has the potential to reshape the landscape of the AI field by fostering collaboration, innovation, and fairer distribution of benefits among participants.

(2) AI-enhanced smart contracts:

Traditional smart contracts are pre-coded with specific conditions. By integrating with AI, these contracts can adapt and dynamically respond to external data and conditions, enabling more efficient and versatile decentralized applications.

Let’s consider a practical example.

Imagine a decentralized insurance platform based on blockchain technology, providing weather-related insurance policies for farmers. Traditional insurance contracts rely on predefined conditions and manual claims processing, which can be slow and prone to disputes. In this case, AI-enhanced smart contracts come into play, revolutionizing the insurance industry.

• Weather data and AI analysis:

Smart contracts are designed to interact with external data sources, such as weather APIs, to obtain real-time weather data for insured areas. The AI model is integrated into the smart contract and continuously analyzes the weather data. This AI model, trained to identify adverse weather conditions that may affect insured crops, such as drought or floods.

• Dynamic premium adjustment:

Traditionally, insurance premiums are fixed, and claims are processed after the event occurs. In this AI-enhanced smart contract, premiums are dynamically adjusted based on real-time AI assessments of weather conditions. If the AI detects a high risk of severe weather that could damage crops, premiums for affected policies will automatically increase to reflect the increased risk. Conversely, when the AI predicts favorable weather conditions, premiums may decrease, incentivizing more farmers to purchase insurance.

• Automatic claims:

If the AI model detects adverse weather conditions that meet predefined claim criteria (e.g., prolonged drought), it triggers automatic claims for affected policyholders. The AI monitors the claims process to ensure it is conducted quickly, reducing the need for manual claims processing and associated delays.

(3) On-chain data analysis: Leveraging machine learning

Blockchain holds a large amount of transaction data, making it a gold mine for data scientists and machine learning enthusiasts. Companies like CertiK and TokenMetrics leverage the power of machine learning tools to extract valuable insights from this data, enhancing security, improving investment strategies, and optimizing blockchain efficiency.

(4) Decentralized GPU sharing: Empowering AI, earning cryptocurrencies

Decentralized GPU sharing is a revolutionary concept that completely changes how computing resources are utilized in the AI and machine learning community. Just as Filecoin disrupts data storage by incentivizing users to share their unused storage space, decentralized GPU sharing operates on similar principles.

• Why contribute your GPU?

GPUs (Graphics Processing Units) are crucial for training and fine-tuning complex machine learning and AI models. These processes require significant computational power, which is both time-consuming and resource-intensive on personal computers. By contributing your GPU to a decentralized GPU sharing network, you can pool your computing resources into a larger pool accessible to anyone. In return, you will receive cryptocurrency rewards. This model allows individuals and organizations to access high-performance GPU resources without the upfront cost of purchasing dedicated hardware or relying on centralized cloud services. It democratizes GPU capabilities, making them more inclusive and cost-effective for AI and machine learning enthusiasts.

• Unit Economics:

-The unit economics in decentralized GPU sharing networks revolve around cryptocurrency rewards. When you lend your GPU to the network, you receive tokens as compensation.

– The number of tokens you receive depends on several factors, including the computing power of the GPU, the duration of your contribution, and the network’s demand for GPU resources.

– Decentralized GPU sharing platforms usually have a transparent and predefined reward structure to ensure fairness and predictability in participation.

Overall, decentralized GPU sharing not only enables individuals and organizations to access powerful computing resources for AI tasks but also allows GPU owners to effectively monetize their hardware. It’s a win-win situation that promotes collaboration, cost-effectiveness, and accessibility within the artificial intelligence and machine learning ecosystem.

(5) RLHF token model: Connecting AI and incentives

The RLHF token model provides an interesting intersection between artificial intelligence and token-based incentive mechanisms. This concept is particularly effective in specialized areas where traditional financial rewards may not be feasible. Here’s a comprehensive explanation:

In the RLHF token model, the main idea revolves around using token rewards to incentivize real users to provide valuable feedback and training for AI systems. Imagine an AI system that needs specific training tasks, such as content moderation on a social media platform.

Traditionally, training AI models often require hiring human trainers to label data or fine-tune algorithms, which can be resource-intensive. However, the RLHF token model introduces a more innovative approach. They encourage users to actively engage with AI systems and receive token rewards for their contributions.

For example, Amazon’s Mechanical Turk (MTurk) is a well-known crowdsourcing platform where people complete small tasks in exchange for rewards. In the RLHF token model, users essentially play the role of “AI trainers,” providing feedback similar to how MTurk task performers provide feedback to earn rewards. The key difference is that in the RLHF token model, users receive tokens as rewards instead of traditional currency. These tokens have intrinsic value within the ecosystem, opening up avenues for their utility, such as purchasing AI-generated content or accessing advanced AI services. This token-based incentive system not only streamlines the financial process of AI training but also fosters a dynamic ecosystem where users actively collaborate to enhance AI systems.

3. Concerns about the fusion of AI and encryption

While the integration of artificial intelligence and cryptocurrencies brings great promise, it also poses a series of challenges. AI models, particularly deep learning models, are often referred to as “black boxes” due to their opacity. Combining this opacity with the transparency of blockchain technology may raise concerns about accountability and trust. Addressing these issues is crucial for establishing a secure and trustworthy environment in this intersection of technologies.

In addition, ethical considerations have become crucial. With the deployment of open-source artificial intelligence algorithms on decentralized networks, responsibility and compensation issues are bound to arise in the event of unexpected negative outcomes. Striking the right balance between innovation and ethical responsibility is an ongoing challenge that requires careful consideration.

Another challenge lies in data privacy. In certain artificial intelligence applications, balancing the transparency of blockchain with the need for data privacy can be quite tricky. Maintaining the decentralized spirit of blockchain while ensuring compliance with rules like GDPR poses a unique challenge that calls for innovative solutions.

4. Future Prospects

As both artificial intelligence and cryptocurrency technologies continue to mature, their integration holds the potential for unprecedented applications.

(1) zkML (Zero-Knowledge Machine Learning):

One of zkML’s key features is its ability to cryptographically prove the results generated by specific machine learning models without revealing the details of the models. This opens doors for innovative applications in large enterprises, including:

• Financial Services: Financial institutions can use zkML to verify the integrity of credit scoring models and loan eligibility decisions without exposing sensitive customer data, thereby enhancing trust and accuracy in the lending process.

• Healthcare: Hospitals and research institutions can use zkML to assess the effectiveness of medical diagnostic models while ensuring confidentiality of patient data, ensuring data privacy and medical accuracy.

• Legal Compliance: zkML can help companies comply with regulations by allowing them to verify the compliance models of their AI-driven systems without exposing proprietary algorithms.

• Smart Contracts: In blockchain-based applications, zkML can ensure the accurate execution of machine learning models within smart contracts, providing trust and transparency for decentralized financial services, insurance, etc.

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(3) Privacy and Security Assurance:

One of the urgent challenges that businesses face today is the uncertainty about data handling when sharing proprietary data with platforms like OpenAI. This is where data privacy comes into play, including whether the data is used for training, who can access the data, and whether the data is secure throughout the entire computing process. While there are non-blockchain solutions such as local models, Azure cloud services, and legal agreements, blockchain has unique advantages in providing transparent and tamper-proof data interaction records. It allows businesses to verify that their data remains unreadable during the computing process, thus providing a higher level of confidence in data privacy and security. This assurance is crucial for industries that deal with sensitive information, such as healthcare or finance, where data confidentiality is of utmost importance.

Essentially, the ability of blockchain to create an immutable ledger can help businesses maintain control over their data and ensure privacy and security when interacting with artificial intelligence systems, allowing them to comply with data protection regulations with peace of mind.

5. Existing Projects

A visionary group of projects has already combined encryption and artificial intelligence.

(1) Together.ai

Together’s mission is to revolutionize the world of artificial intelligence by creating an open-source decentralized cloud platform that caters to large-scale fund models. They have already established a decentralized cloud platform dedicated to AI research, hosting many open models, such as Bloom, OPT, T0pp, GPT-J, and Stable Diffusion. One of their focuses in research is to create accessible alternatives to closed-source AI systems while deepening our understanding of the performance of these models. With a forward-thinking approach, Together aims to build a decentralized supercomputer that seamlessly connects the global cloud, mining infrastructure, gaming hardware, and laptops, all coordinated through blockchain technology. Together’s vision includes equitable access to artificial intelligence and computing, potentially leading to transformative impacts in the field.

(2) Bittensor

Bittensor aims to pioneer the development of decentralized AI networks, primarily focusing on enhancing data oracles for smart contracts. This cutting-edge technology utilizes blockchain to create a secure and efficient decentralized framework for retrieving real-world data and delivering it to smart contracts. Bittensor’s unique decentralized oracle approach significantly improves the accuracy, speed, and reliability of data feeds, making it a core player in the intersection of cryptography and artificial intelligence.

(3) Akash Network

Akash Network is changing cloud computing through its decentralized marketplace, offering a dynamic alternative to traditional cloud providers. It provides users with cost-effective, elastic, and censorship-resistant access to computing resources. What sets Akash apart is its potential impact on artificial intelligence. Through decentralized cloud computing, Akash opens doors for AI developers to scalable, secure, and efficient resources. This is particularly important as artificial intelligence heavily relies on extensive computing power. Akash’s decentralized approach supports AI projects, making it a key player in the development of artificial intelligence technology.

(4) Gensyn Protocol

The Gensyn protocol establishes a decentralized ecosystem for machine learning computation. The protocol enables seamless distribution of computing workloads for AI researchers and practitioners. When a solver contributes their computing resources to access high-performance hardware for training and inference, verifiers ensure the accuracy and integrity of AI tasks, which is crucial for maintaining the trustworthiness of AI models. Additionally, Gensyn prioritizes privacy and security by providing a secure mapping layer and encryption of training data to protect sensitive information in AI applications. The decentralized nature, efficient scalability, and global accessibility of this protocol democratize AI, making it more accessible and cost-effective for users.

(5) Nexus AI

Nexus AI is at the forefront of financial empowerment, leveraging advanced AI algorithms to provide unparalleled market trend insights to investors. What sets Nexus AI apart is its unwavering commitment to decentralization, ensuring investors have complete control over their assets without being affected by risks associated with centralized exchanges. One of the platform’s innovative products is a cutting-edge NFT marketplace driven by AI, which enhances pricing, discovery, and authentication, rewarding investors. Furthermore, a Telegram AI Bot powered by GPT-3 offers real-time financial advice and market analysis to all Telegram users for free. Nexus AI further supports wise investment decisions through Asset Intelligence reports, providing comprehensive insights into crypto assets. With AI-driven solutions like the staking dashboard and intelligent investment robot, Nexus AI empowers users to confidently navigate the markets, efficiently achieving their financial goals.

(6) Modulus Labs

Modulus Labs introduces an innovative solution that integrates AI into blockchain technology. They have launched a zero-knowledge (ZK) prover specifically tailored for AI applications, addressing the challenge of ensuring tamper-proof AI access in decentralized applications (dApps). This solution enables smart contracts to leverage powerful AI capabilities without compromising the decentralized principles. Modulus is actively working towards launching significant AI applications on the Ethereum platform through collaborations with partners such as Upshot and Ion Protocol. Additionally, they are open-sourcing the ZKML prover to a wider community. Modulus envisions a future where cryptographic accountability ensures the safe integration of AI into various domains, including finance and healthcare.

(7) Render Network

The Render Network is a platform that offers decentralized GPU rendering capabilities. This innovative network allows users to expand their GPU rendering work globally through a blockchain-based marketplace for idle GPU computing resources. The platform’s vision is to democratize GPU cloud rendering, making it more efficient, scalable, and applicable to next-generation 3D content creation. It addresses challenges related to network scalability, efficient infrastructure utilization, GPU power optimization, and digital rights management for immersive media and AI applications. The Render Network transforms GPU power into a interconnected decentralized economy of 3D assets, providing solutions for developers in industries such as media, augmented reality, virtual reality, and gaming.

(8) Ritual

Ritual is a company that stands at the intersection of cryptography and artificial intelligence. It completely revolutionizes the way developers create, distribute, and enhance AI models. By harnessing the power of encryption, Ritual provides an open, modular sovereignty layer for AI. It connects a distributed node network with computational resources and model creators, allowing creators to host their AI models. In turn, users can easily access a variety of AI models through a unified API, while benefiting from an encrypted infrastructure that ensures computational integrity and privacy. The first product of this platform is Infernet, marking the beginning of a full suite of protocols and utilities that seamlessly integrate developers and applications with Ritual, granting them permissionless access to its model and computation provider network. The vision of Ritual is to become the AI hub in the web3 world, driving innovation, accessibility, and democratization in the realm of artificial intelligence.

6. Conclusion

The collaboration between AI and cryptography represents not only a fusion of technologies but also a fusion of possibilities. It signifies a future where machines can think, learn, and transact in a decentralized and secure environment. The path forward is challenging, but every challenge is an opportunity for innovation and progress. The innovators and thinkers at this intersection have the potential to write a chapter that redefines the digital age. As we move forward, we must be cautious, wise, and visionary, ensuring that we reap the benefits while skillfully addressing the challenges, ultimately shaping a brighter, more technologically advanced future for everyone.

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