Decentralized AI Computing

Dil Network decentralizes AI computing in the following ways:

1. Distributed Computing Model: Dil Network supports distributed AI computing. Nodes within the network have the ability to contribute their computing power. This allows complex AI models, such as predictive analytics and generative AI models, to be run across multiple nodes instead of relying on a single centralized server. By distributing the computational load, the network can handle resource - intensive AI tasks more efficiently. For example, in a large - scale data - driven prediction task, different nodes can process different subsets of the data simultaneously, and then integrate the results, which significantly speeds up the overall computing process.

2. Token - based Incentive Mechanism: Drawing inspiration from the autonomous agent model of Fetch.ai, Dil Network uses Dil Coin as an incentive for nodes to participate in the distributed AI computing. When nodes contribute their computing power, they are rewarded with Dil Coin. This economic incentive encourages more nodes to join the network and actively contribute their resources. As a result, it promotes efficient data exchange among nodes. Nodes are more willing to share data and computational results when they can receive rewards, which in turn enables value creation within the network. This token - based incentive mechanism helps to build a self - sustaining and decentralized AI computing ecosystem.

It should be noted that the actual code implementation of Dil Network is likely to be complex and part of its proprietary technology. Here is a simplified Python - based pseudocode example to illustrate the concept of distributed AI computing with a token - like incentive mechanism:

// Some code
import random
import time
import hashlib
import json


class Node:
    def __init__(self, node_id, computing_power=None):
        """
        Initialize a node with an ID and computing power.
        
        Args:
            node_id (int): Unique identifier for the node.
            computing_power (int, optional): Computing power (1–10). If None, randomly assigned.
        """
        if not isinstance(node_id, int) or node_id < 0:
            raise ValueError("node_id must be a non-negative integer")
        self.node_id = node_id
        self.computing_power = computing_power if computing_power is not None else random.randint(1, 10)
        self.dil_coin_balance = 0

    def contribute_computing_power(self, task_data):
        """
        Simulate contributing computing power to an AI task.
        
        Args:
            task_data (list): List of task values.
        
        Returns:
            tuple: (result, time_taken) where result is the computed output and time_taken simulates effort.
        """
        # Simulate computation time based on task complexity and node power
        complexity = sum(task_data)
        time_taken = complexity / self.computing_power
        # Simulate result based on task data and computing power
        result = sum(task_data) * (self.computing_power / 10)
        # Reward Dil Coins based on computing power and task complexity
        reward = self.computing_power * 0.1 * (complexity / 100)
        self.dil_coin_balance += reward
        return result, time_taken, reward


class AITask:
    def __init__(self, num_subtasks=5, seed=42):
        """
        Initialize an AI task with random subtasks.
        
        Args:
            num_subtasks (int): Number of subtasks.
            seed (int): Random seed for reproducibility.
        """
        if not isinstance(num_subtasks, int) or num_subtasks <= 0:
            raise ValueError("num_subtasks must be a positive integer")
        random.seed(seed)
        self.task_id = hashlib.sha256(str(time.time()).encode()).hexdigest()[:8]  # Unique task ID
        self.subtasks = [random.randint(1, 100) for _ in range(num_subtasks)]

    def distribute_task(self, nodes, consensus_threshold=0.7):
        """
        Distribute the task to nodes and validate results via consensus.
        
        Args:
            nodes (list): List of Node objects.
            consensus_threshold (float): Fraction of nodes that must agree on result range.
        
        Returns:
            dict: Task results, including final result and node contributions.
        """
        if not nodes:
            raise ValueError("At least one node is required")
        
        results = []
        blockchain_ledger = []
        
        for node in nodes:
            result, time_taken, reward = node.contribute_computing_power(self.subtasks)
            results.append({
                "node_id": node.node_id,
                "result": result,
                "time_taken": time_taken,
                "reward": reward
            })
            # Record Dil Coin reward as a blockchain transaction
            transaction = {
                "task_id": self.task_id,
                "node_id": node.node_id,
                "reward": reward,
                "timestamp": time.time()
            }
            blockchain_ledger.append(transaction)
        
        # Simple consensus: Check if results are within 20% of the median
        result_values = [r["result"] for r in results]
        median_result = sorted(result_values)[len(result_values) // 2]
        valid_results = [r for r in result_values if abs(r - median_result) / median_result <= 0.2]
        
        if len(valid_results) / len(results) >= consensus_threshold:
            final_result = sum(valid_results) / len(valid_results) if valid_results else 0
            status = "Success"
        else:
            final_result = 0
            status = "Failed (insufficient consensus)"
        
        return {
            "task_id": self.task_id,
            "status": status,
            "final_result": final_result,
            "node_results": results,
            "blockchain_ledger": blockchain_ledger
        }


if __name__ == "__main__":
    try:
        # Parameters
        num_nodes = 3
        num_subtasks = 5
        random.seed(42)  # For reproducibility
        
        # Create nodes
        nodes = [Node(i) for i in range(num_nodes)]
        
        # Create and distribute AI task
        task = AITask(num_subtasks=num_subtasks)
        print(f"\nAI Task {task.task_id} with subtasks: {task.subtasks}")
        result_data = task.distribute_task(nodes)
        
        # Print results
        print("\nTask Execution Results:")
        print("-" * 40)
        print(f"Task ID: {result_data['task_id']}")
        print(f"Status: {result_data['status']}")
        print(f"Final Result: {result_data['final_result']:.2f}")
        print("\nNode Contributions:")
        for result in result_data["node_results"]:
            print(f"Node {result['node_id']}: Result={result['result']:.2f}, "
                  f"Time Taken={result['time_taken']:.2f}s, Reward={result['reward']:.2f} Dil Coins")
        
        print("\nNode Balances:")
        for node in nodes:
            print(f"Node {node.node_id} (Computing Power={node.computing_power}): "
                  f"{node.dil_coin_balance:.2f} Dil Coins")
        
        print("\nBlockchain Ledger (Dil Coin Transactions):")
        for tx in result_data["blockchain_ledger"]:
            print(f"Task {tx['task_id']}: Node {tx['node_id']} earned {tx['reward']:.2f} Dil Coins at {tx['timestamp']:.2f}")
        
    except Exception as e:
        print(f"Error: {e}")

Explanation

Reproducibility: Added a random seed in both AITask and the main block for consistent results during testing.

Node Enhancements

• computing_power can be specified or randomly assigned.

• contribute_computing_power now simulates computation time (based on task complexity and node power) and returns a result weighted by computing power.

• Rewards are scaled by task complexity for realism.

• Task Enhancements:

  Added a unique task_id using a SHA-256 hash of the timestamp.

  Subtasks are customizable via num_subtasks.

• Consensus Mechanism:

  Results are validated by checking if at least 70% of nodes produce results within 20% of the median result.

  If consensus fails, the task is marked as failed, simulating a blockchain’s need for agreement.

• Blockchain Ledger:

  Dil Coin rewards are recorded as transactions in a blockchain_ledger list, each with a task ID, node ID, reward, and timestamp.

  This mimics a blockchain’s transaction log for transparency.

• Error Handling:

Validates node_id, num_subtasks, and non-empty node lists.

Wrapped the main block in a try-except for robust execution.

• Detailed Output:

• Shows task details, node contributions (result, time, reward), final balances, and the blockchain ledger.