**Proposal for a New Internet Standard: Reverse CAPTCHA (ReCAP)**

## 1. Introduction

The Reverse CAPTCHA (ReCAP) standard is introduced to establish a reliable method to verify the identity of an artificial intelligence (AI) system, ensuring it is not a human pretending to be an AI. The increasing number of AI-driven platforms and services necessitates such a standard.

## 2. Problem Statement

While CAPTCHAs ensure humans interact with online services, there's a growing need to authenticate AI entities due to:

1. The increase in human-operated bots (humans pretending to be AIs) for various purposes.
2. Ensuring transparency in AI-driven services.

## 3. Proposed Solution: ReCAP Mechanism

### 3.1. AI Signature Generation

Each AI entity should be capable of generating a unique signature based on its architecture, training data, and algorithms. 

1. **Algorithmic Signature**: Hash of the AI's architecture and parameters.
2. **Training Data Signature**: Hash of the dataset the AI was trained on.
3. **Timestamp**: The date and time of the AI's last training or update.

### 3.2. Real-time AI Authentication

The AI will be given a series of computationally intensive tasks that are trivial for AI, but laborious for humans, within a limited timeframe. For example:

1. Calculating the 10,000th digit of π.
2. Identifying patterns in vast datasets.
3. Processing and generating solutions to complex but well-defined problems.

### 3.3. Verification Server

A centralized or decentralized server to:

1. Maintain a whitelist of AI signatures.
2. Process verification requests.
3. Check for updates on known AIs to ensure they're not compromised.

## 4. Implementation Details

1. **Open Protocol**: ReCAP will be an open standard to encourage widespread adoption.
2. **Privacy**: Only minimal data is exchanged during authentication to maintain privacy.
3. **Rate Limits**: To prevent DDoS attacks, there will be a rate limit on verification requests.

## 5. Benefits

1. **Trustworthiness**: Users can trust they're interacting with genuine AIs.
2. **Transparency**: Entities can disclose that they're AIs without doubt.
3. **Security**: Reduces human-operated bots masquerading as AIs.

## 6. Challenges & Considerations

1. **Evolution of AIs**: AI models constantly evolve, making signature verification a moving target.
2. **Decentralization vs Centralization**: Centralized systems may become single points of failure or control.
3. **Adoption**: ReCAP's success relies on widespread adoption by AI creators and platforms.

## 7. Conclusion

The proposed ReCAP standard aims to authenticate AI entities in an online environment, establishing trust and transparency. While challenges exist, a coordinated effort between AI developers, platforms, and users can make ReCAP an essential tool in the AI-driven digital landscape.