AI x Crypto
VeradiVerdict - Issue #274
AI and cryptocurrency are converging technologies with the potential to transform the digital world.
AI can address crypto challenges like volatility, security, scalability, and energy consumption.
Use cases include decentralized AI marketplaces, AI-enhanced smart contracts, on-chain data analysis, decentralized GPU sharing, and RLHF token models.
Concerns include transparency, ethics, and data privacy.
Future prospects include zkML and blockchain-based content verification.
Regulations will play a pivotal role in shaping the future of AI and crypto.
The Intersection of AI and Crypto: Exploring Opportunities, Challenges, and the Horizon Ahead
As the digital revolution continues to transform our world, I believe two technologies, in particular, stand out for their disruptive potential: artificial intelligence (AI) and cryptocurrency. AI, with its ability to mimic human cognitive functions and learn from data, has recently surged to the forefront of technological innovation. Its applications, spanning from healthcare to entertainment, are vast and transformative. Cryptocurrencies, underpinned by the robust blockchain technology, promise a decentralized financial future, empowering individuals and streamlining processes. Together, AI and cryptocurrency present a powerful convergence, marking the dawn of a new era in technological symbiosis.
Issues in Crypto that AI Can Address
While cryptocurrencies have emerged as a revolutionary means of transaction and investment, they are not without challenges. Market volatility is a pressing concern for investors. AI's prowess in data analysis can sift through vast amounts of historical data, predicting price fluctuations with enhanced accuracy.
Moreover, with the increasing mainstream adoption of cryptocurrencies, security has become paramount. The decentralized nature of these digital currencies makes them susceptible to fraud and hacking attempts. Here, machine learning models can be trained to detect unusual transaction patterns, strengthening the bulwark against potential security breaches.
Scalability and transaction speed are other significant challenges in the crypto world. As more people join blockchain networks, ensuring swift and seamless transactions becomes vital. Advanced AI algorithms can optimize network traffic, ensuring efficient data flow and faster transaction times.
Lastly, the energy consumption associated with crypto mining is a global concern. AI can play a pivotal role in optimizing mining processes, reducing the energy footprint and paving the way for a more sustainable crypto ecosystem.
Use-cases of the Intersection of Crypto and AI
Decentralized AI Marketplaces: The Crypto Advantage
In the realm of AI, platforms like Hugging Face have garnered considerable attention for their role in democratizing access to pre-trained AI models. However, the emergence of decentralized AI marketplaces within the crypto space introduces a novel dimension of democratization and decentralization, even beyond what traditional platforms like GitHub offer for open-source code, including web3 code and smart contracts.
Hugging Face and Traditional AI Model Repositories:
Hugging Face, as a prominent AI model repository, has played a pivotal role in making cutting-edge AI models accessible to a wide audience. It offers a centralized platform where users can access, share, and fine-tune pre-trained models. This has significantly reduced 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.
Crypto-Powered Decentralized AI Marketplaces:
However, the crypto-powered decentralized AI marketplaces, such as those built on blockchain technology, take democratization and decentralization to the next level. Here's how:
True Ownership and Control: In traditional repositories like Hugging Face, while access is democratized, the control and ownership of the underlying infrastructure and data remain centralized. In contrast, crypto-based marketplaces often employ decentralized blockchain networks, ensuring that control is distributed among participants in the network. Users have a say in governance decisions, making it a more democratic and community-driven ecosystem.
Incentive Mechanisms: Crypto marketplaces incorporate token-based incentive mechanisms that reward contributors for their efforts, from data providers to model developers. This incentivizes collaboration and innovation while ensuring a fair distribution of benefits. In contrast, traditional platforms might lack these direct financial incentives, making crypto versions more appealing for participants.
Data Privacy and Security: Blockchain technology ensures a high degree of transparency while maintaining data privacy through techniques like zero-knowledge proofs. This addresses concerns about data exposure, a crucial consideration for AI applications. Traditional platforms may not offer the same level of privacy assurance.
Interoperability: Crypto-based marketplaces, often built on blockchain standards, are designed with interoperability in mind. This means AI models and services can seamlessly integrate with various blockchain-based applications, smart contracts, and decentralized applications (dApps), fostering a more interconnected and versatile ecosystem.
In essence, while platforms like Hugging Face have made significant strides in democratizing access to AI models, crypto-powered decentralized AI marketplaces build upon these foundations by incorporating blockchain's principles of decentralization, true ownership, and token-based incentives. This enhanced level of democratization and decentralization holds the potential to reshape the AI landscape by fostering collaboration, innovation, and a more equitable distribution of benefits among participants.
AI-Enhanced Smart Contracts:
Traditional smart contracts are pre-coded with specific conditions. By integrating AI, these contracts can be made adaptive, responding dynamically to external data and conditions, leading to more efficient and versatile decentralized applications.
A Practical Example
Imagine a decentralized insurance platform built on blockchain technology that offers weather-related insurance policies to farmers. Traditional insurance contracts rely on predefined conditions and manual claims processing, which can be slow and prone to disputes. In this scenario, AI-enhanced smart contracts come into play to revolutionize the insurance industry.
1. Weather Data and AI Analysis:
The smart contract is designed to interact with external data sources, such as weather APIs, to obtain real-time weather data for the insured area. An AI model, integrated into the smart contract, continuously analyzes the weather data. This AI model has been trained to identify adverse weather conditions, such as droughts or floods, that could potentially impact the insured crops.
2. Dynamic Premium Adjustments:
Traditionally, insurance premiums are fixed, and claims are processed after an event has occurred. In this AI-enhanced smart contract, the premium is dynamically adjusted based on the AI's real-time assessment of weather conditions. If the AI detects a high risk of adverse weather that could harm the crops, the premium for affected policies is automatically adjusted upwards to reflect the increased risk. Conversely, when the AI predicts favorable weather conditions, premiums may be lowered, incentivizing more farmers to purchase insurance.
3. Automated Payouts:
If the AI model detects adverse weather conditions that meet the predefined criteria for a payout (e.g., prolonged drought), it triggers an automatic payout to affected policyholders. The AI monitors that payouts are made swiftly, reducing the need for manual claims processing and the associated delays.
On-Chain Data Analysis: Leveraging Machine Learning
The blockchain, with its vast troves of transactional data, presents a goldmine for data scientists and machine learning enthusiasts. Companies like CertiK and TokenMetrics harness the power of machine learning tools to derive valuable insights from this data, thereby enhancing security, refining investment strategies, and optimizing overall blockchain efficiency.
Decentralized GPU Sharing: Empowering AI and Earning Crypto
Decentralized GPU sharing is a novel concept that brings a fundamental shift to the way computational resources are utilized within the AI and machine learning communities. Just as Filecoin disrupted data storage by incentivizing users to share their unused storage space, decentralized GPU sharing operates on a similar principle.
Why Dedicate Your GPU?
GPUs (Graphics Processing Units) are essential for training and fine-tuning complex machine learning and AI models. These processes demand significant computational power, which can be both time-consuming and resource-intensive on a personal computer. By dedicating your GPU to decentralized GPU sharing networks, you contribute your computational resources to a larger pool that's accessible to anyone in need. In return, you earn cryptocurrency rewards as compensation for your contribution. This model allows individuals and organizations to access high-performance GPU resources without the upfront costs of purchasing dedicated hardware or relying on centralized cloud services. It democratizes access to GPU power, making it more inclusive and cost-effective for AI and machine learning enthusiasts.
Unit economics in decentralized GPU sharing networks revolve around cryptocurrency rewards. When you lend your GPU to the network, you earn tokens as compensation.
The number of tokens you earn depends on several factors, including the computational power of your GPU, the duration of your contribution, and the demand for GPU resources within the network.
Decentralized GPU sharing platforms often have transparent and predefined reward structures, ensuring fairness and predictability for participants.
Overall, decentralized GPU sharing not only empowers individuals and organizations to access powerful computational resources for AI tasks but also allows GPU owners to monetize their hardware effectively. It's a win-win model that fosters collaboration, cost-efficiency, and accessibility within the AI and machine learning ecosystem.
RLHF Token Models: Bridging AI and Incentives with a Mechanical Turk Analogy
Reinforcement Learning from Human Feedback (RLHF) Token Models offer a fascinating intersection of artificial intelligence and token-based incentives. This concept can be particularly powerful in specialized sectors where conventional financial incentives might not be feasible. Here's a cohesive explanation:
In RLHF Token Models, the primary idea revolves around using token rewards to motivate human users to provide valuable feedback and training for AI systems. Imagine an AI system that requires training for a specific task, such as content moderation on a social media platform.
Traditionally, training AI models often involves hiring human trainers to label data or fine-tune algorithms, a process that can be resource-intensive. However, RLHF Token Models introduce a more innovative approach. They incentivize users to actively engage with the AI system and offer feedback by rewarding them with tokens for their contributions.
To draw a parallel, think of Amazon's Mechanical Turk (MTurk), a well-known crowdsourcing platform where individuals complete small tasks in exchange for compensation. In RLHF Token Models, users essentially step into the role of "AI trainers," providing feedback akin to how MTurk workers perform tasks for pay. The key distinction is that, in RLHF Token Models, users receive tokens as rewards, rather than traditional currency. These tokens hold intrinsic value within the ecosystem, opening avenues for their utilization, such as purchasing AI-generated content or accessing premium AI services. This token-based incentive system not only streamlines the financial aspects of AI training but also nurtures a dynamic ecosystem where users actively collaborate to enhance AI systems.
Concerns with Integrating AI and Crypto
While the integration of AI and crypto holds immense promise, it also brings forth a set of challenges. AI models, especially deep learning ones, are often referred to as "black boxes" due to their opacity. Merging this opacity with the transparent nature of blockchains can raise concerns about accountability and trust. Addressing these concerns will be crucial in establishing a secure and trustworthy environment at the intersection of these technologies.
Moreover, ethical considerations become paramount. As open-source AI algorithms are deployed on decentralized networks, questions about responsibility and restitution in case of unintended negative outcomes arise. Striking the right balance between innovation and ethical responsibility is an ongoing challenge that requires careful consideration.
Another challenge lies in data privacy. Balancing the transparent nature of blockchain with the need for data confidentiality in certain AI applications can be tricky. Ensuring compliance with regulations like the GDPR, while maintaining the decentralized ethos of blockchain, poses a unique challenge that necessitates innovative solutions.
As both AI and crypto technologies continue to mature, their convergence holds the promise of unprecedented applications.
zkML (zero-knowledge machine learning): One of the fundamental capabilities of zkML is its ability to cryptographically prove that a specific machine learning model produced the results without revealing the model's details, offering new possibilities for secure and transparent data processing. In the realm of big business, zkML opens doors to innovative applications, including:
Financial Services: Financial institutions can employ zkML to verify the integrity of credit scoring models and loan eligibility decisions without disclosing sensitive customer data, enhancing trust and accuracy in lending processes.
Healthcare: Hospitals and research institutions can use zkML to evaluate the effectiveness of medical diagnosis models while keeping patient data confidential, ensuring data privacy and medical accuracy.
Legal and Compliance: zkML can aid in regulatory compliance by enabling companies to verify that their AI-driven compliance models adhere to industry regulations without exposing proprietary algorithms.
Smart Contracts: In blockchain-based applications, zkML can ensure that smart contracts execute machine learning models accurately, providing trust and transparency in decentralized financial services, insurance, and more.
Authenticity of AI-Generated Content: Verifying the authenticity of AI-generated content through blockchain technology has the potential to reshape content creation and distribution. This innovation addresses a pressing concern in the digital age, where AI can convincingly mimic human-generated content, leading to concerns about misinformation, intellectual property, and trust in digital media. By anchoring content authenticity on the blockchain—a tamper-proof and transparent ledger—consumers, creators, and institutions can readily determine the origins and integrity of digital content. This not only aids in detecting and mitigating malicious deepfakes and fraudulent content but also safeguards the intellectual property of creators and empowers consumers with reliable sources of information. For instance, blockchain can certify the legitimacy of political speeches or video evidence in legal proceedings, mitigating the risk of misinformation and ensuring accountability. In the creative industries, it can track the creation and ownership of music compositions, ensuring that artists are properly credited and compensated. In education, it can verify the authorship of academic work, preventing plagiarism and upholding academic integrity. In journalism, it can authenticate news articles, helping readers differentiate between authentic journalism and AI-generated content. In essence, the use of blockchain to verify the authenticity of AI-generated content extends beyond technology—it underpins the credibility, transparency, and integrity of digital content in the age of AI, reshaping how information is created, consumed, and trusted while addressing real-world challenges.
Privacy and Security Guarantees: One of the pressing challenges facing businesses today is the uncertainty surrounding the handling of their proprietary data when shared with AI platforms like OpenAI. Questions arise about data privacy, including whether the data is used for training, who has access to it, and whether it remains secure throughout the computation. While non-blockchain solutions such as local models, cloud services like Azure, and legal agreements exist, blockchain offers a distinct advantage in providing transparent and tamper-proof records of data interactions. It can enable businesses to verify that their data remains unreadable during computation, providing a higher level of confidence in data privacy and security. This assurance would be crucial for industries dealing with sensitive information, such as healthcare or finance, where data confidentiality is paramount.
In essence, blockchain's ability to create an immutable ledger can help businesses maintain control over their data and ensure that it remains private and secure throughout its interaction with AI systems, offering peace of mind and compliance with data protection regulations.
A cohort of forward-thinking companies is already spearheading the merger of crypto and AI
Together is on a mission to revolutionize the world of AI by crafting an open-source decentralized cloud platform that caters to large foundation models. They've established one of the largest decentralized clouds dedicated to AI research, where numerous open models like Bloom, OPT, T0pp, GPT-J, and Stable Diffusion find a home. A key focus of their research endeavors is to create accessible alternatives to closed AI systems while deepening our understanding of these models' performance. Through a visionary approach, Together aims to construct a decentralized supercomputer that seamlessly connects global clouds, mining infrastructure, gaming hardware, and laptops, all orchestrated via blockchain technology. Together's vision encompasses equitable access to AI and computing, promising a transformative impact on the field.
Bittensor seeks to pioneer the development of a decentralized AI network, with a primary focus on enhancing data oracles for smart contracts. This cutting-edge technology leverages blockchain to create a secure, efficient, and decentralized framework for sourcing and delivering real-world data to smart contracts. Bittensor's unique approach to decentralized oracles significantly enhances data feed accuracy, speed, and reliability, positioning it as a central player at the crossroads of crypto and AI.
Akash Network is transforming cloud computing through its decentralized marketplace, offering a dynamic alternative to traditional cloud providers. It provides users with cost-effective, resilient, and censorship-resistant access to computing resources. What sets Akash apart is its potential impact on AI. By decentralizing cloud computing, Akash opens doors for AI developers to access scalable, secure, and efficient resources. This is particularly significant as AI heavily relies on extensive computational power. Akash's decentralized approach empowers AI initiatives, making it a pivotal player in the evolution of AI technologies.
The Gensyn Protocol establishes a decentralized ecosystem for machine learning computations. This protocol empowers AI researchers and practitioners to seamlessly distribute their computational workloads.While Solvers contribute their computational resources, enabling access to high-performance hardware for training and inference,, verifiers ensure the accuracy and integrity of AI tasks, crucial for maintaining the trustworthiness of AI models. Moreover, Gensyn prioritizes privacy by offering methods like secure mapping layers and encrypted training data, safeguarding sensitive information in AI applications. This protocol's decentralized nature, efficient scaling, and global accessibility democratize AI, making it more accessible and cost-effective for users.
Nexus AI is at the forefront of financial empowerment, leveraging advanced AI algorithms to provide investors with unparalleled insights into market trends. What sets Nexus AI apart is its unwavering commitment to decentralization, ensuring that investors maintain complete control over their assets, free from the risks associated with centralized exchanges. The platform's innovative offerings include a cutting-edge NFT Marketplace, powered by AI to enhance pricing, discovery, and authentication, with rewards distributed to investors. Additionally, a Telegram AI Bot driven by GPT-3 offers real-time financial advice and market analysis to all Telegram users for free. Nexus AI further supports informed investment decisions through Asset Intelligence reports, offering comprehensive insights into crypto assets. With AI-driven solutions like staking dashboards and smart investment bots, Nexus AI empowers users to navigate the markets confidently, achieving their financial goals with precision and efficiency.
Modulus Labs has introduced an innovative solution aimed at integrating AI into blockchain technology. They have unveiled a specialized Zero-Knowledge (ZK) prover tailored for AI applications, addressing the challenge of ensuring tamper-proof AI access for decentralized applications (dApps). This development allows smart contracts to leverage robust AI capabilities without compromising the fundamental principles of decentralization. Modulus is actively working on launching significant AI applications on the Ethereum platform through collaborations with partners such as Upshot and Ion Protocol. Additionally, they are making their ZKML prover open-source for the wider community. Modulus envisions a future where cryptographic accountability safeguards AI's introduction into various sectors, including finance and healthcare, without compromising security.
The Render Network is a platform that offers decentralized GPU rendering capabilities. This innovative network empowers users to scale GPU rendering work using high-performance GPU nodes worldwide through a blockchain-based marketplace for idle GPU compute resources. The platform's vision is to democratize GPU cloud rendering, making it more efficient, scalable, and accessible for 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 decentralized economy of connected 3D assets, offering a solution for developers in various industries, including media, augmented reality, virtual reality, gaming, and more.
Ritual is a company that stands at the intersection of cryptography and artificial intelligence, revolutionizing the way developers can create, distribute, and enhance AI models. By harnessing the power of crypto, Ritual provides an open, modular, and sovereign execution layer for AI. It connects a distributed network of nodes with access to computational resources and model creators, allowing creators to host their AI models. Users, in turn, can effortlessly access a wide range of AI models through a unified API, all while benefiting from cryptographic infrastructure that ensures computational integrity and privacy. This platform, with its first manifestation called Infernet, marks the beginning of a suite of protocols and utilities, paving the way for developers and applications to seamlessly integrate with Ritual and gain permissionless access to its network of model and compute providers. Ritual envisions becoming the central hub for AI in the web3 space, driving innovation, accessibility, and democratization in the world of artificial intelligence.
The harmonization of AI and crypto is not just a convergence of technologies but a fusion of possibilities. It represents a future where machines think, learn, and transact in a decentralized, secure environment. The journey ahead is fraught with challenges, but with every challenge comes an opportunity for innovation and progress. Innovators and thinkers at this crossroads have the potential to script an epoch that could redefine the contours of the digital age. As we move forward, it's essential to tread with caution, wisdom, and a visionary approach to ensure that the benefits are reaped while the challenges are adeptly navigated, ultimately shaping a brighter and more technologically advanced future for all.
- Paul Veradittakit
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Hi, I’m Paul Veradittakit, a Managing Partner at Pantera Capital, one of the oldest and largest institutional investors focused on investing in blockchain companies and cryptocurrencies. I’ve been in the industry since 2014, and the firm invests in equity, early stage token projects, and liquid cryptocurrencies on exchanges. I focus on early-stage investments and share my thoughts on what’s going on in the industry in this weekly newsletter.
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