IOBC Capital: 5 New Opportunities for Profit from AI + Crypto

Author: 0xCousin, IOBC Capital

Introduction:

With the rapid development of digital technology, AI and Crypto have become the two hottest topics. AI, as a technological revolution, represents the most advanced productive forces; Crypto, based on blockchain technology, represents the most equitable production relations. AI and Crypto are continuously changing the way we live and work. This article will explore the integration of AI and Crypto, and how they together shape our future.

AI: The Most Advanced Productive Force

AI (Artificial Intelligence) is a technology that involves enabling computer systems to mimic human intelligence and perform intelligent tasks. It encompasses several subfields, including:

1. Machine Learning: Machine learning is the foundation of AI, involving training computer systems to improve performance through data and experience. It includes different types such as supervised learning, unsupervised learning, and reinforcement learning;

2. Deep Learning: Deep learning is a branch of machine learning that simulates the workings of the human brain's neural networks. It uses multi-layer neural networks to process complex data and has achieved significant breakthroughs in fields like computer vision and natural language processing;

3. Natural Language Processing (NLP): NLP involves enabling computers to understand and process human language. It includes technologies such as text analysis, sentiment analysis, speech recognition, and machine translation.

4. Computer Vision: Computer vision aims to enable computer systems to "see" and understand images and videos. It involves technologies related to image recognition, object detection, facial recognition, and image generation.

From a fundamental perspective, the core of AI is to give computers "perceptual ability," "cognitive ability," "creativity," and "intelligence." In concrete terms, it means making computers think like humans, act like humans, think rationally, and make rational decisions.

With the development of AI technology, there are many application scenarios where AI can be used to reduce costs, improve efficiency, and enhance safety. In summary, it can better serve humanity. For example:

• Autonomous Driving: AI technology is used to develop self-driving cars, improving road safety and driving efficiency by perceiving the environment, making decisions, and controlling vehicles.

• Healthcare: AI plays a significant role in medical image recognition, disease diagnosis, and treatment planning, helping doctors provide more accurate diagnoses and personalized treatment plans.

• Financial Services: AI is widely applied in the financial sector, including risk assessment, credit scoring, investment strategies, and fraud prevention, improving the efficiency and accuracy of financial institutions.

• Smart Homes: AI is applied in smart home devices, allowing home devices to be controlled by voice or gestures, enhancing convenience and comfort.

• Natural Language Processing: AI technology enables machines to understand and process human language, including speech recognition, semantic understanding, and automatic translation, widely used in smart assistants (like Siri, Alexa, Google Assistant) and virtual robots (like chatbot customer service) to provide personalized services and support through voice and text interaction.

• Entertainment and Gaming: AI plays an important role in game development, including the design of intelligent enemies, adaptive game difficulty, and realistic graphical effects.

The most popular ChatGPT this year is a chatbot model based on Generative Pre-trained Transformer. GPT is a language model developed by OpenAI based on the Transformer architecture. The goal of ChatGPT is to learn the statistical patterns and semantic understanding of language by pre-training on a large amount of text data to generate human-like natural language responses.

The underlying design logic of GPT mainly includes two key components: the Transformer architecture and the pre-training-fine-tuning method.

Transformer Architecture: The Transformer is a neural network architecture based on the self-attention mechanism, which can establish long-distance dependencies when processing sequential data. The Transformer consists of multiple encoder-decoder layers, each composed of multi-head attention mechanisms and feedforward neural networks. The attention mechanism allows the model to focus on different positions in the input sequence when generating outputs, thereby better understanding contextual information.

Pre-training-Fine-tuning Method: ChatGPT uses large-scale unsupervised pre-training to learn patterns and knowledge of language. During the pre-training phase, the model attempts to predict missing parts of the input sequence through self-supervised learning on massive text data. This enables the model to learn knowledge such as grammar, semantics, and common sense. Then, in the fine-tuning phase, supervised fine-tuning is performed using labeled data for specific tasks to adapt the model to specific tasks, such as a chatbot.

The generation process of ChatGPT includes two stages: the encoder input stage and the decoder generation stage. In the encoder input stage, the model receives user input and transforms it into hidden representations to capture the semantic information of the input. In the decoder generation stage, the model uses the hidden representations from the encoder and previously generated tokens to generate the next response token until a specific stopping condition is met.

Crypto: Blockchain is the Most Equitable Production Relation

This is self-evident; fundamentally, the reason Crypto has developed to its current scale is that blockchain can enhance social equity, representing the most equitable production relations. Of course, fairness needs to be discussed within a relatively universal value framework to be meaningful.

Taking the current largest market cap cryptocurrencies, Bitcoin and Ethereum, as examples. Within the value framework of "to each according to their labor," Bitcoin's PoW consensus mechanism is very fair; similarly, within the value framework of "capital gains," Ethereum remains very fair even after its transition from PoW to PoS.

In summary, Crypto based on blockchain technology can optimize resource allocation and achieve community autonomy, representing the most equitable social production relations.

The Integration of AI and Crypto

The integration of AI and Crypto may lead to some interesting application explorations.

1. Crypto AI Trading Bot

As AI has matured in data analysis, processing, and model training, there are already precedents for AI in investment:

Renaissance Technologies is a hedge fund that relies 100% on large-scale data analysis and mathematical modeling through machine learning, using high-frequency trading, statistical arbitrage, and market-neutral strategies to invest, earning $100 billion during its existence. Renaissance Technologies can be seen as a financial version of AI utilizing machine learning and data analysis.

The Crypto market has unique advantages for supporting AI's involvement in investment: it operates seamlessly 24/7, is anonymous, has no KYC, is completely closed-loop on-chain, and requires no physical contact. If an AI Trader specifically for the Crypto market is developed, it could operate on-chain to arbitrage, quantify, and analyze trends in the Crypto market; additionally, designing machine learning and data analysis models could enable this AI Trader to continuously improve its understanding of the Crypto market, potentially creating a consistently profitable AI Trader.

Using AI to predict Crypto market trends: The price volatility in the cryptocurrency market is extreme, and AI can analyze vast amounts of market data and historical price trends to predict market trends and price fluctuations. Machine learning algorithms can identify hidden patterns and trends, helping investors make more informed decisions. For example, AI can analyze market sentiment through deep learning models to predict the upward or downward trends of cryptocurrency prices.

Using AI for automated trading: AI's automated trading algorithms are one of the important tools in cryptocurrency trading. By writing smart contracts and trading bots, automated cryptocurrency trading can be achieved. These bots can execute trades based on preset rules and strategies, reducing human interference and improving trading efficiency and accuracy. For instance, using AI algorithms, trading bots can automatically execute buy or sell operations based on market conditions to achieve the best trading results.

In this direction, we currently see Rockybot. This is a fully on-chain AI Trading bot that can predict ETH prices using on-chain AI models and make investment decisions autonomously without central authorization. Rockybot relies on StarkNet and has been trained on historical price/rate data of the WETH:USDC trading pair. Architecturally, Rocky is a simple three-layer feedforward neural network that predicts whether the price of WETH will rise or fall based on historical market price data. However, Rockybot has not started making profits yet… It may need more training (though the project team has stopped accepting donations)… and perhaps making money in the bear market of Crypto is a daunting task for AI.

2. Data Contribution and Privacy Protection

Using Crypto to incentivize more people to contribute data for AI algorithms: AI algorithms have a high demand for large amounts of high-quality data, and cryptocurrency can encourage users to share their data through incentive mechanisms. Cryptocurrency can provide economic returns to data providers, promoting data sharing and circulation. This incentive mechanism can encourage more users to contribute data, thereby increasing the training samples for AI algorithms and improving their accuracy and intelligence level.

Using Crypto to protect the privacy of AI data contributors: The encryption and anonymity features of blockchain also help protect user privacy. The data sharing and privacy protection mechanisms of cryptocurrency provide more data resources for AI algorithms while ensuring the security of users' personal information.

3. ZKML: Ensuring the Privacy and Authenticity of Machine Learning Models

ZKML (zero knowledge machine learning) is a technology that applies zero-knowledge proofs to machine learning. ZKML can address the privacy protection issues of AI models/inputs and the verifiability of the reasoning process, using zkSNARKs to prove the correctness of machine learning inferences.

ZKML can be used to train and evaluate machine learning models on sensitive data without disclosing the data to anyone else. ZKML can ensure the consistency of machine learning models, which is very important for users, as the model is crucial to the results of machine learning.

Currently, there are some exploratory applications around ZKML. In the DeFi direction, the fully on-chain AI Trading bot Rockybot has been launched, which can predict ETH prices using on-chain AI models and make investment decisions autonomously without central authorization; in the Games direction, Modulus Labs has launched a chess game called Leela based on ZKML, where all users can play against a robot supported by a ZK-verified AI model.

Additionally, there is a platform fighting game AI Arena; in the Creator Economy direction, a community has submitted an EIP proposal named zkML AIGC-NFTs#7007 (this EIP has not yet passed), proposing to use ZKML to verify whether NFTs are AI-generated, thus introducing a category of AI-created NFTs; in the DID direction, Wordcoin is exploring the use of ZKML to allow users to generate IRIS codes in a permissionless manner. When the algorithm for generating IRIS codes is upgraded, users can download the model and generate proofs without going to the Orb station; furthermore, a reputation-based token distribution platform built on StarkNet, Astraly, is creating an AI-based reputation system (using clustering models to identify user/project characteristics, badges, and historical behaviors before calculating reputation ratings in a trustless manner).

4. AI + Blockchain: Self-Optimizing Blockchain Protocols

Through transparent AI machine learning, DeFi protocols can self-optimize without trust, such as using machine learning to adjust stablecoin exchange rates/interest rates. By using multimodal biometrics/identity verification, dApps can self-manage compliance/security. Even the ZKP generation process of ZK Rollup may create a proof system focused on building for machine learning, thus constructing the world's fastest zk-AI Prover, further significantly improving the performance of ZK Rollup.

Of course, there are still many challenges on the road to the integration of AI and Crypto. For example, so far, no one has completed the work of porting existing AI operations into these automatically generated proof languages, although Giza is working on porting pre-trained ONNX models to Cairo for verifiable inference.

Conclusion

The integration of AI and Crypto may bring about an intelligent transformation in digitization. The application of AI makes Crypto smarter and more efficient, while Crypto can provide AI algorithms with more authentic, comprehensive data and a trustworthy operating environment.

Despite facing many challenges, we can look forward to deeper integration of AI and Crypto, jointly promoting the development of the digital economy and creating a better future for all humanity.

References:

https://github.com/ethereum/EIPs/pull/7007/commits

https://www.rockybot.app/

https://www.leelavstheworld.xyz/