What sparks will AI + Crypto collide?


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 force; Crypto, based on blockchain technology, represents the most fair production relationship. AI and Crypto are constantly changing our lives and ways of working. This article will explore the fusion of AI and Crypto and how they will shape our future.

AI: The most advanced productive force

AI (Artificial Intelligence) is a technology involving the ability of computer systems to mimic human intelligence and perform intelligent tasks. It covers multiple subfields, including:

1. Machine learning : Machine learning is the foundation of AI, involving training computer systems to improve performance through data and experience. Different types of machine learning include supervised learning, unsupervised learning, and reinforcement learning.

2. Deep learning : Deep learning is a branch of machine learning that simulates the way the human brain’s neural network works. It uses multi-layer neural networks to process complex data and has made significant breakthroughs in computer vision, natural language processing, and other fields.

3. Natural language processing (NLP) : NLP involves making computers able 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 make computer systems able to “see” and understand images and videos. It involves technologies such as image recognition, object detection, face recognition, and image generation.

From the bottom-up logic, the core of AI is to make computers have “perception”, “cognition”, “creativity”, and “intelligence”. In concrete terms, this means enabling computers to think like humans, act like humans, think rationally, and make rational decisions.

With the development of AI technology, there are many application scenarios that can achieve cost reduction, efficiency improvement, and safety through the use of AI. In short, it can better serve humanity. For example:

  • Autonomous driving: AI technology is used to develop self-driving cars, which can perceive the environment, make decisions, and control vehicles to improve road safety and driving efficiency.

  • Healthcare: AI plays an important 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 used in the financial industry, including risk assessment, credit scoring, investment strategies, and anti-fraud, improving the efficiency and accuracy of financial institutions.

  • Smart home: AI is applied to smart home devices, allowing home devices to be controlled by voice or gesture to improve home convenience and comfort.

  • Natural language processing: AI technology enables machines to understand and process human language, including speech recognition, semantic understanding, and automatic translation. It is widely used in intelligent assistants (such as Siri, Alexa, Google Assistant) and virtual robots (such as robot customer service) to provide personalized services and support through voice and text interactions.

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

This year’s most popular ChatGPT is a chatbot model based on Generative Pre-trained Transformer. GPT is a language model based on the Transformer architecture developed by OpenAI. The goal of ChatGPT is to learn the statistical patterns and semantic understanding of language through pre-training on a large amount of textual 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.

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

Pre-training-fine-tuning method: ChatGPT uses large-scale unsupervised pre-training to learn language patterns and knowledge. In the pre-training phase, the model tries to predict the missing parts in the input sequence by self-supervised learning on a massive amount of text data. This enables the model to learn knowledge such as grammar, semantics, and common sense. Then, in the fine-tuning phase, the model is fine-tuned with labeled data for specific tasks, such as chatbots.

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 converts it into hidden representations to capture the semantic information of the input. In the decoder generation stage, the model uses the hidden representations of the encoder and the previously generated tokens to generate the next response token until a specific stop condition is reached.

Crypto: Blockchain is the fairest production relationship

This goes without saying that the core reason why Crypto can develop to its current scale is that blockchain can enhance social fairness and represents the fairest production relationship. Of course, fairness needs to be discussed within a relatively universal value framework.

Take Bitcoin and Ethereum with the largest market capitalization as examples. In the value framework of “to each according to his work”, Bitcoin’s PoW consensus mechanism is very fair; similarly, in the value framework of “capital gains”, Ethereum is still very fair after transitioning from PoW to PoS.

In short, Crypto based on blockchain technology can optimize resource allocation, achieve community autonomy, and represent the most fair social production relationship.

Integration of AI and Crypto

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

1. Crypto AI Trading Bot

Since AI has developed relatively maturely in data analysis and processing, model training, etc., there are already precedents for AI investment:

Renaissance Technologies relies 100% on large-scale data analysis and mathematical models of machine learning to invest in high-frequency trading, statistical arbitrage, and market-neutral strategies, earning 100 billion dollars during its existence. Renaissance Technologies can be regarded as a financial version of AI that uses machine learning and data analysis.

The Crypto market has unique advantages in supporting AI to participate in investment: 24-hour seamless operation, anonymity, no KYC, complete on-chain closed loop, and no physical contact. If an AI Trader tailored to the Crypto market is developed, it can completely operate on the Crypto market on-chain arbitrage, quantitative analysis, trend analysis, and other hedging strategies. Then, design some machine learning and data analysis models to let this AI Trader continuously improve its understanding of the Crypto market, and it may also create a profitable AI Trader.

Use AI to predict Crypto market trends: The price fluctuations in the cryptocurrency market are extremely violent, and AI can predict market trends and price fluctuations by analyzing a large amount of market data and historical price trends. Machine learning algorithms can identify hidden patterns and trends to help investors make wiser decisions. For example, AI can analyze market sentiment through deep learning models to predict the upward or downward trend of cryptocurrency prices.

Use AI for automated trading: AI’s automated trading algorithm is one of the important tools for cryptocurrency trading. By writing smart contracts and trading robots, automated cryptocurrency trading can be realized. These robots can execute transactions based on preset rules and strategies, reduce human interference, and improve trading efficiency and accuracy. For example, using AI algorithms, trading robots can automatically execute buy or sell operations based on market conditions to obtain the best trading results.

In this direction, what we currently see is Rockybot . This is a fully on-chain AI trading bot that can use on-chain AI models to predict ETH prices and make investment decisions without central authorization. Rockybot relies on StarkNet and has been trained on historical price/exchange rate data for the WETH:USDC trading pair. In terms of architecture, 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 yet started making money… it may need more training (but the project has stopped accepting donations)… and it may also be a difficult task for AI to make money in the bear market of Crypto.

2. Data Contribution and Privacy Protection

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

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

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

ZKML (zero-knowledge machine learning) is a technology that applies zero-knowledge proof to machine learning. ZKML can solve the privacy protection problem of AI model/input and the verifiability of inference process, using zkSNARK to prove the correctness of machine learning inference.

ZKML can be used to train and evaluate machine learning models for sensitive data without revealing the data to anyone else. ZKML can be used to ensure the consistency of machine learning models. This is important for users because the model is crucial for the results of machine learning.

There are already some applications exploring ZKML. In the DeFi direction , fully onchain AI Trading bot-Rockybot has been launched, which can predict the price of ETH using onchain AI models and make investment decisions without central authorization; in the Games direction , Modulus Labs has launched a chess game Leela based on ZKML, where all users can play against a robot supported by a ZK-verified AI model, and there is also the platform fighting game AI Arena; in the Creator Economy direction , the community submitted an EIP proposal named zkML AIGC-NFTs#7007 (which has not been passed yet), proposing to use ZKML to verify whether NFTs are AI-generated, thus introducing a category of AI-generated NFTs; in the DID direction , Wordcoin is exploring the use of ZKML to allow users to generate IRIS code without permission. When the algorithm for generating IRIS code is upgraded, users can download the model and generate proof without going to the Orb station; in addition, there is a reputation-based token distribution platform Astraly established on StarkNet, which is creating an AI-based reputation system (using clustering models to identify user/project features, badges, and historical behavior before distrustful computation of reputation ratings).

4. AI+Blockchain: Self-Improving Blockchain Protocol

Through transparent AI machine learning, DeFi protocols can self-optimize without the need for trust, such as using machine learning to adjust stablecoin exchange rates/interest rates. By using multimodal biometric/authentication, dApps can self-manage compliance/security. Even the ZKP generation process of ZK Rollup may create a proof system dedicated to building machine learning, thus building the world’s fastest zk-AI Prover and 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, no one has yet completed the task of porting existing AI operations to these automatically generated proof languages, although Giza is working on porting pre-trained ONNX models to Cario for verifiable reasoning.


The fusion of AI and Crypto may bring about intelligent transformation to digitization. The application of AI makes Crypto more intelligent and efficient, while based on Crypto, it can provide more real, comprehensive data and trusted operating environment for AI algorithms.

Although facing many challenges, we can expect deeper integration of AI and Crypto to jointly promote the development of the digital economy and create a better future for all mankind.

Reference documents:




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