preface:
With the rapid development of digital technology, AI and Crypto have become two of the hottest topics. As a technological revolution, AI represents the most advanced productivity; Crypto, based on blockchain technology, represents the fairest production relationship. AI and Crypto are constantly changing the way we live and work. This article will explore the integration of AI and Crypto, and how they can jointly shape our future.
AI: The most advanced productivity
AI (Artificial Intelligence) is a technology that involves enabling computer systems to imitate human intelligence and perform intelligent tasks. It covers multiple subfields, including:
1. Machine Learning: Machine learning is the foundation of AI and involves 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 working mode of human brain neural network. It uses multi-layer neural networks to process complex data and has made major breakthroughs in computer vision, natural language processing and other fields;
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 in image recognition, object detection, face recognition, image generation, etc.
From the underlying logic, the core of AI is to enable computers to have "perception", "cognition", "creativity" and "intelligence". To put it concretely, it means that computers should be able 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 humans. For example:
Autonomous driving: AI technology is used to develop autonomous vehicles, improving road safety and driving efficiency by perceiving the environment, making decisions, and controlling vehicles.
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 sector, 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, which enable home appliances to be controlled by voice or gestures, improving the convenience and comfort of the home.
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 effects.
ChatGPT, the most popular chatbot model this year, is based on Generative Pre-trained Transformer. GPT is a language model based on Transformer architecture developed by OpenAI. The goal of ChatGPT is to learn the statistical laws and semantic understanding of language by pre-training a large amount of text data to generate natural language responses similar to humans.
The underlying design logic of GPT mainly includes two key components: Transformer architecture and pre-training-fine-tuning method.
Transformer architecture: Transformer is a neural network architecture based on self-attention mechanism, which can establish long-range dependencies when processing sequence data. Transformer consists of multiple encoder-decoder layers, each of which consists of a multi-head attention mechanism and a feed-forward neural network. The attention mechanism allows the model to focus on different positions in the input sequence when generating output, thereby better understanding contextual information.
Pre-training-fine-tuning approach: 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 of the input sequence by self-supervising massive 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 in a supervised manner using labeled data for specific tasks to adapt it to specific tasks, such as chatbots.
The generation process of ChatGPT consists of 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 a hidden representation to capture the semantic information of the input. In the decoder generation stage, the model uses the encoder's hidden representation and previously generated tokens to generate the next response token until a specific stopping condition is reached.
Crypto: Blockchain is the fairest production relationship
There is no need to elaborate on this. Fundamentally speaking, the core of Crypto's development to its current scale is that blockchain can enhance social fairness and represent the most equitable production relationship. Of course, first of all, fairness needs to be discussed in a relatively universal value framework to make sense.
Take Bitcoin and Ethereum, which currently have the largest market capitalization, as examples. In the value framework of "work pays, more work pays", Bitcoin's PoW consensus mechanism is very fair; similarly, in the value framework of "capital gains", Ethereum is still very fair after changing from PoW to PoS.
In short, Crypto based on blockchain technology can optimize resource allocation, achieve community autonomy, and represent the most equitable social production relations.
The Fusion of AI and Crypto
The integration of AI and Crypto may lead to some very interesting application explorations.
1、Crypto AI Trading Bot
Because AI has become relatively mature in data analysis and processing, model training, etc., there are precedents for AI investment:
Renaissance Technologies, a hedge fund that relies 100% on machine learning based on large-scale data analysis and mathematical models, has made $100 billion during its existence by investing using high-frequency trading, statistical arbitrage, and market neutral strategies. Renaissance Technologies can be seen as a financial version of AI that uses machine learning and data analysis.
The Crypto market has unique advantages in supporting AI investment: 24-hour seamless operation, anonymity, no KYC, a completely closed loop on the chain, and no physical contact. If an AI Trader is developed for the Crypto market, it is entirely possible to run hedging strategies such as on-chain arbitrage, quantification, and trend analysis in the Crypto market; and then design some machine learning and data analysis models to allow this AI Trader to continuously improve its understanding of the Crypto market, and perhaps create an AI Trader that can continue to make profits.
Using AI to predict Crypto market trends: The price fluctuations in the cryptocurrency market are very volatile, 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 more informed decisions. For example, AI can analyze market sentiment through deep learning models to predict the upward or downward trend of cryptocurrency prices.
Automated trading using AI: AI's automated trading algorithm is one of the important tools for cryptocurrency trading. Automated cryptocurrency trading can be achieved by writing smart contracts and trading robots. These robots can execute transactions according to preset rules and strategies, reduce the interference of human factors, and improve transaction efficiency and accuracy. For example, using AI algorithms, trading robots can automatically execute buy or sell operations according to market conditions to obtain the best trading results.
In this direction, we are currently seeing Rockybot. This is a fully onchain AI Trading bot that can predict ETH prices with on-chain AI models and make investment decisions on its own without central authorization. Rockybot relies on StarkNet and has been trained on historical price/exchange rate data for 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 money yet... It may need more training (but the project has stopped accepting donations)... It may also be difficult for AI to make money in the bear market of Crypto.
2. Data Contribution and Privacy Protection
Use Crypto to encourage more people to contribute data to AI algorithms: AI algorithms have a high demand for 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 rewards, thereby promoting data sharing and circulation. This incentive mechanism can encourage more users to contribute data, thereby increasing the training samples of AI algorithms and improving their accuracy and intelligence.
Use Crypto to protect the privacy of AI data contributors: The encryption and anonymity characteristics of blockchain also help protect the privacy of users. The data sharing and privacy protection mechanisms of encrypted currencies provide more data resources for AI algorithms while ensuring the security of user personal information.
3. ZKML: Ensuring the Privacy and Authenticity of Machine Learning Models
ZKML (zero knowledge machine learning) is a technology that uses zero-knowledge proofs for machine learning. ZKML can solve the privacy protection issues of AI models/inputs and the verifiability of the reasoning process, using zkSNARK to prove the correctness of machine learning reasoning.
ZKML can be used to train and evaluate machine learning models against sensitive data without revealing the data to anyone else. ZKML can be used to ensure the consistency of machine learning models. This is very important for users because the model is critical to the results of machine learning.
There are already some application explorations around ZKML. In the DeFi direction, the fully onchain AI Trading bot-Rockybot has been launched, which can use on-chain AI models to predict ETH prices and make investment decisions on its own without central authorization; in the Games direction, Modulus Labs launched a ZKML-based chess game Leela, where all users can play against a robot powered by an AI model verified by ZK, in addition to the platform fighting game AI Arena; in the Creator Economy direction, the community submitted an EIP proposal called zkML AIGC-NFT#7007(this EIP has not yet been passed), proposing to use ZKML to verify whether NFTs are AI-generated, thereby introducing the AI-created NFT category; in the DID direction, Wordcoin is exploring the use of ZKML to allow users to generate IRIS code in a permissionless manner. When the algorithm for generating IRIS code is upgraded, users can download the model and generate proofs by themselves without going to the Orb station; in addition, there is a reputation-based token distribution platform Astraly built on StarkNet, which is creating an AI-based reputation system (using clustering models to identify user/project characteristics, badges, and historical behaviors before trustlessly calculating reputation ratings).
4. AI+Blockchain: Self-improving blockchain protocol
Through transparent AI machine learning, DeFi protocols can self-optimize without trust, such as using machine learning to adjust the exchange rate/interest rate of stablecoins. By using multi-modal biometrics/authentication, dApps can self-manage compliance/security. Even the ZKP generation process of ZK Rollup may also create a proof system focused on building for machine learning, thereby building 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, no one has yet completed the work of porting existing AI operations to these languages that automatically generate proofs, although Giza is working on porting pre-trained ONNX models to Cario for verifiable reasoning.
Summarize
The integration of AI and Crypto may bring intelligent changes to digitalization. The application of AI makes Crypto more intelligent and efficient, while Crypto can provide AI algorithms with more real, comprehensive data and a reliable operating environment.
Despite the many challenges, we can expect more in-depth integration of AI and Crypto to jointly promote the development of the digital economy and create a better future for all mankind.
Reference Documents:
https://github.com/ethereum/EIPs/pull/7007/commits
https://www.rockybot.app/
https://www.leelavstheworld.xyz/