What If AI Models Lived Onchain? Exploring a Bold New Frontier with Arbitrum Stylus

Proposals
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again @cp0x

Great question! When we mention “identifying the most suitable lightweight AI models for deployment,” we’re focusing on selecting AI models that are efficient, require minimal computational resources, and can operate effectively within the constraints of a blockchain environment like Arbitrum Stylus. Here’s a detailed explanation of the options and how we can use AI models in our case.

Options for Lightweight AI Models

1. Linear Models

  • Linear Regression
    • Use Case: Predicting continuous numerical values.
    • Why Suitable: Involves simple mathematical operations; low computational overhead.
    • Example Application: Predicting future asset prices based on historical data.
  • Logistic Regression
    • Use Case: Binary classification problems (e.g., yes/no decisions).
    • Why Suitable: Computationally efficient; outputs probabilities.
    • Example Application: Determining the likelihood of a transaction being fraudulent.

2. Decision Trees

  • Use Case: Classification and regression tasks.
  • Why Suitable: Based on simple conditional (if-else) statements; depth can be limited to control complexity.
  • Example Application: Automated decision-making processes, such as credit approvals.

3. Naive Bayes Classifier

  • Use Case: Classification tasks, especially with text data.
  • Why Suitable: Relies on calculating probabilities; computationally light.
  • Example Application: Categorizing messages or transactions as spam or legitimate.

4. Rule-Based Systems

  • Use Case: Systems based on predefined rules.
  • Why Suitable: Highly transparent; easy to implement with basic logic.
  • Example Application: Smart contracts enforcing regulatory compliance.

5. Simple Neural Networks

  • Use Case: Basic pattern recognition and function approximation.
  • Why Suitable: Small networks (e.g., one hidden layer) can be manageable onchain.
  • Example Application: Simple predictive models for user behavior.

How We Can Use AI Models in Our Case

Our goal is to develop a framework that allows developers to deploy and interact with AI models on Arbitrum Stylus. Here’s how we can use these AI models:

1. Onchain Inference

  • What It Means:
    • Running the AI model directly within a smart contract to make predictions or decisions.
  • Application:
    • Smart contracts can automatically execute actions based on model outputs.
  • Example:
    • An insurance contract that assesses claims automatically using an onchain model.

2. Automated Decision-Making in Smart Contracts

  • What It Means:
    • Embedding AI models to handle conditional logic more intelligently than static rules.
  • Application:
    • Dynamic adjustment of interest rates in lending platforms based on risk assessment.
  • Example:
    • A lending protocol that adjusts collateral requirements based on borrower profiles.

3. Enhancing NFTs with AI

  • What It Means:
    • Integrating AI models into NFTs to enable dynamic behavior.
  • Application:
    • NFTs that evolve over time or in response to certain conditions.
  • Example:
    • A virtual pet NFT that “learns” and changes based on owner interactions.

4. Verifiable Computations

  • What It Means:
    • Performing computations where the correctness can be verified by anyone.
  • Application:
    • Using zero-knowledge proofs to verify offchain model training results onchain.
  • Example:
    • A prediction market that verifies the integrity of submitted predictions.

Use Cases Specific to Our Proposal

1. Decentralized Finance (DeFi)

  • Risk Assessment:
    • Use logistic regression to evaluate the risk level of loan applicants.
  • Price Prediction:
    • Linear regression models to forecast asset prices for automated trading strategies.

2. Supply Chain Management

  • Verification Systems:
    • Decision trees to verify product authenticity and traceability.
  • Automated Compliance:
    • Rule-based systems to ensure all transactions meet regulatory standards.

3. Gaming and NFTs

  • Dynamic NFTs:
    • Simple neural networks to enable NFTs that evolve based on gameplay or user interactions.
  • Game Mechanics:
    • AI models to control non-player characters (NPCs) behavior onchain.

4. Identity and Reputation Systems

  • Trust Scores:
    • Naive Bayes classifiers to compute user reputation scores based on onchain activity.
  • Access Control:
    • Decision trees to grant or restrict access to certain features or services.
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