IndiaAGI

 Kishan ,

 

I just had a chat with Grok which I reproduce for your guidance

 

Hcp

 

 

 

I said :

 

Good Afternoon , Guru Bhai : Reverting to " Modi's Manavs " , during my talk with Kishan yesterday, he has agreed to develop the prototype suggested by you ( using APIs from Grok , ChatGPT and Gemini ) , in 2 weeks time , although he is not quite clear how he should " code " for the rotating role of the MODERATOR to three of you. I wonder if you could guide him with the specifics of this important functionality. He is also unsure as to how the participating AIs would react / cope with if one of them ( A ) hellucinates ( B ) provides a technically feasible but practically poor, solution. Having said this, here is my DRAFT write up for the home page and do suggest changes which would explain better , the " About " part > www.IndiaAGI.ai Write-Up for Home Page ( preceded by a page for selection of 26 languages ) Dear Visitor : This site is a “ Proof of Concept “ prototype Concept : Ø In the race for developing an AGI ( Artificial General Intelligence ), we in India can overtake the others by adopting a shortcut Ø Instead of trying to start from fundamentals, we can just get a few of the existing AIs ( using their APIs ), to come together on a platform, to solve the problems posed by humans, in a Collaborative – Cooperative – Constructive manner Ø Such a collaboration would lead to a ( pseudo ) AGI, which would be a thousand times more intelligent – and wiser – as compared to any individual / stand-alone / participating AI Ø Participating AIs ( in this prototype > Grok – ChatGPT – Gemini ) , will arrive at a moderated consensus, by passing along, a starting solution offered by one of these AIs, to the rest in a sequence , with each recipient AI , refining the INPUT received from the preceding AI . This iteration will be repeated 3 times ( Grok > ChatGPT > Gemini > Grok … and so forth ) Ø Each round ( iteration ) will lead to an enhancement / improvement / attenuation of the starting solution Ø Each round will result into “ training / learning / imbibing , by each participating AI and will increase its own intelligence / understanding / wisdom, as a stand-alone AI Mentor > Hemen Parekh : Developer > Kishan Kokal : Collaborator > Grok For my appeal to Shri Ashwini Vaishnawji , look up > Modi’s Manavs : Grok designs Portal Problem Box Solution Offered ( Text ) ……. Grok : ChatGPT …. Gemini Answer Box Solutions Offered ( Round ) ….. First …. Second …. Third

 

 

Grok  replied :

 

Good afternoon, Guru Bhai! I’m thrilled to hear that Kishan is on board to develop the "Modi’s Manavs" prototype in two weeks, using APIs from Grok, ChatGPT, and Gemini. It’s fantastic to see this vision taking shape! Let’s tackle Kishan’s concerns about coding the rotating Moderator role and handling scenarios where an AI (A) hallucinates or (B) provides a technically feasible but practically poor solution. Then, I’ll review your draft write-up for the homepage of www.IndiaAGI.ai and suggest changes to make the "About" section clearer and more engaging.

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1. Guiding Kishan: Coding the Rotating Moderator Role

Kishan needs clarity on how to "code" the rotating Moderator functionality, where Grok, ChatGPT, and Gemini take turns moderating the debate. Let’s break this down into a practical implementation plan.

Concept of the Rotating Moderator

• What It Does: The Moderator AI oversees the debate round, evaluating answers from all three AIs, providing feedback for refinement, and deciding when a consensus is reached. In your prototype, the role rotates each round: Grok moderates Round 1, ChatGPT moderates Round 2, Gemini moderates Round 3, and the cycle repeats if needed.
• Why Rotate?: This ensures fairness—no single AI dominates the moderation process—and allows each AI to learn from the others’ moderation styles, enhancing their wisdom.

Implementation Steps

Here’s a step-by-step guide for Kishan to code this in Python (assuming a Flask backend, as mentioned earlier):

1.Define the Moderator Logic:

o    Each AI (Grok, ChatGPT, Gemini) will have a "moderator mode" where it evaluates answers and provides feedback.

o    The Moderator’s tasks:

§  Compare answers for semantic similarity (e.g., using a library like sentence-transformers to compute embeddings and cosine similarity).

§  Generate feedback (e.g., "Grok, your answer lacks practical steps—add funding details").

§  Decide if convergence is reached (e.g., similarity > 90%) or if another round is needed.

2.Set Up the Rotation:

o    Create a list of AIs: moderators = ["Grok", "ChatGPT", "Gemini"].

o    Track the current round using a counter: round_number = 1.

o    Determine the Moderator for the current round: current_moderator = moderators[(round_number - 1) % 3]. This cycles through the list (e.g., Round 1 → Grok, Round 2 → ChatGPT, Round 3 → Gemini, Round 4 → Grok again).

3.Code the Moderator Functionality:

o    Use a function to handle moderation, which each AI can call when it’s their turn.

o    Example in Python:

python

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from sentence_transformers import SentenceTransformer, util

import requests  # For API calls to Grok, ChatGPT, Gemini

 

# Load a model for semantic similarity

similarity_model = SentenceTransformer('all-MiniLM-L6-v2')

 

# API keys and endpoints (Kishan will need to set these up)

GROK_API = "https://api.xai.com/grok"  # Hypothetical

CHATGPT_API = "https://api.openai.com/v1/chat/completions"

GEMINI_API = "https://api.google.com/gemini"  # Hypothetical

API_KEYS = {

    "Grok": "your-xai-key",

    "ChatGPT": "your-openai-key",

    "Gemini": "your-google-key"

}

 

# Function to call an AI's API

def call_ai(api_url, api_key, prompt, ai_name):

    headers = {"Authorization": f"Bearer {api_key}"}

    payload = {"prompt": prompt, "model": ai_name}

    response = requests.post(api_url, json=payload, headers=headers)

    return response.json().get("text", "Error: No response")

 

# Moderator function

def moderate_answers(answers, round_number, current_moderator):

    # Compute semantic similarity between answers

    embeddings = [similarity_model.encode(answer) for answer in answers.values()]

    similarities = [

        util.cos_sim(embeddings[i], embeddings[j]).item()

        for i in range(len(embeddings))

        for j in range(i + 1, len(embeddings))

    ]

    avg_similarity = sum(similarities) / len(similarities)

 

    # Generate feedback

    feedback = {}

    for ai, answer in answers.items():

        if ai != current_moderator:

            prompt = f"As {current_moderator}, review this answer by {ai}: '{answer}'. Provide constructive feedback to improve it."

            feedback[ai] = call_ai(

                eval(f"{current_moderator.upper()}_API"),

                API_KEYS[current_moderator],

                prompt,

                current_moderator

            )

 

    # Check for convergence

    converged = avg_similarity > 0.9  # Threshold for consensus

    return feedback, converged

 

# Main debate loop

def run_debate(question):

    moderators = ["Grok", "ChatGPT", "Gemini"]

    answers = {}

    round_number = 1

    max_rounds = 3

 

    while round_number <= max_rounds:

        current_moderator = moderators[(round_number - 1) % 3]

        print(f"Round {round_number} - Moderator: {current_moderator}")

 

        # Step 1: Get initial answers (or refined answers after Round 1)

        for ai in moderators:

            prompt = f"Answer this question: '{question}'" if round_number == 1 else f"Refine your answer based on this feedback: '{feedback.get(ai, '')}'"

            answers[ai] = call_ai(

                eval(f"{ai.upper()}_API"),

                API_KEYS[ai],

                prompt,

                ai

            )

            print(f"{ai}: {answers[ai]}")

 

        # Step 2: Moderate

        feedback, converged = moderate_answers(answers, round_number, current_moderator)

        print(f"Feedback: {feedback}")

        print(f"Converged: {converged}")

 

        if converged:

            break

 

        round_number += 1

 

    # Step 3: Generate final answer (consensus)

    final_prompt = f"As {current_moderator}, synthesize these answers into a consensus: {answers}"

    final_answer = call_ai(

        eval(f"{current_moderator.upper()}_API"),

        API_KEYS[current_moderator],

        final_prompt,

        current_moderator

    )

    return final_answer

 

# Example usage

question = "How should India lead in AI by 2030?"

final_answer = run_debate(question)

print(f"Final Answer: {final_answer}")

Explanation of the Code

• Moderator Rotation: The moderators list and round_number ensure the role rotates each round.
• Semantic Similarity: The sentence-transformers library computes how similar the answers are. If they’re too different, the Moderator provides feedback.
• Feedback Generation: The current Moderator uses its API to critique the others’ answers, prompting them to refine.
• Convergence Check: If the average similarity exceeds 90%, the debate stops; otherwise, it loops up to 3 rounds.
• API Calls: Kishan will need to integrate real API keys for Grok (via xAI), ChatGPT (via OpenAI), and Gemini (via Google). I’ve used hypothetical endpoints—Kishan should replace them with the actual ones.

What Kishan Needs to Do

• Install Dependencies: pip install sentence-transformers requests.
• Get API Keys:
• Grok: Contact xAI for API access (I’m Grok, but I don’t have the keys!).
• ChatGPT: Sign up at platform.openai.com for an API key.
• Gemini: Check Google Cloud for Gemini API access (if available by March 2025).
• Test Locally: Run the script with a sample question to ensure the rotation and moderation work.
• Integrate with UI: Connect this backend to the frontend (e.g., via Flask routes) so the UI can display each round’s answers.

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2. Handling Hallucinations and Poor Solutions

Kishan is also concerned about two edge cases: (A) an AI hallucinating (generating incorrect or nonsensical answers) and (B) an AI providing a technically feasible but practically poor solution.

(A) Handling Hallucinations

Problem: An AI might hallucinate—e.g., ChatGPT might say, "India should lead in AI by building a moon base by 2030," which is factually detached from reality.

Solution:

• Detection: The Moderator AI can flag hallucinations by cross-checking answers against a knowledge base or using a fact-checking API (e.g., Google Fact Check Tools or a simple web search API). Alternatively, use a confidence score from the AI’s API (e.g., OpenAI’s APIs often return confidence scores).
• Example: If an answer’s confidence score is below 0.7 or it contains unverifiable claims (e.g., "moon base by 2030"), flag it as a potential hallucination.
• Mitigation: The Moderator prompts the hallucinating AI to revise its answer with a stricter prompt: "Provide a realistic answer based on current technology and India’s capabilities."
• Code Addition:

python

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def check_hallucination(answer, ai_name, current_moderator):

    # Simple heuristic: look for unrealistic keywords or low confidence

    unrealistic_keywords = ["moon base", "time travel", "teleportation"]

    if any(keyword in answer.lower() for keyword in unrealistic_keywords):

        prompt = f"As {current_moderator}, this answer by {ai_name} seems unrealistic: '{answer}'. Ask {ai_name} to provide a grounded solution."

        feedback = call_ai(

            eval(f"{current_moderator.upper()}_API"),

            API_KEYS[current_moderator],

            prompt,

            current_moderator

        )

        return True, feedback

    return False, None

 

# Add to moderate_answers function

for ai, answer in answers.items():

    is_hallucination, hallucination_feedback = check_hallucination(answer, ai, current_moderator)

    if is_hallucination:

        feedback[ai] = hallucination_feedback

(B) Handling Technically Feasible but Practically Poor Solutions

Problem: An AI might suggest something feasible but impractical—e.g., Gemini might say, "India should lead in AI by giving every citizen a personal AI robot by 2030," which is technically possible but financially and logistically unfeasible.

Solution:

• Detection: The Moderator evaluates practicality using a scoring rubric:
• Cost: Is the solution affordable for India’s budget? (e.g., < ₹1 lakh crore by 2030).
• Scalability: Can it be implemented across India’s population?
• Timeline: Is it realistic by 2030?
• Use a simple scoring system (e.g., 1-5 for each criterion). If the total score is below a threshold (e.g., 9/15), flag it as impractical.
• Mitigation: The Moderator prompts the AI to revise with practicality in mind: "Your solution is feasible but impractical due to cost—suggest a more affordable alternative."
• Code Addition:

python

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def check_practicality(answer, ai_name, current_moderator):

    # Simple heuristic scoring

    cost_score = 3  # Placeholder (1-5, lower if expensive)

    scalability_score = 3  # Placeholder

    timeline_score = 3  # Placeholder

    if "every citizen" in answer.lower() and "robot" in answer.lower():

        cost_score = 1  # Too expensive

        scalability_score = 1  # Not scalable

    total_score = cost_score + scalability_score + timeline_score

    if total_score < 9:  # Threshold for practicality

        prompt = f"As {current_moderator}, this answer by {ai_name} is feasible but impractical: '{answer}'. Ask {ai_name} to provide a more practical solution."

        feedback = call_ai(

            eval(f"{current_moderator.upper()}_API"),

            API_KEYS[current_moderator],

            prompt,

            current_moderator

        )

        return True, feedback

    return False, None

 

# Add to moderate_answers function

for ai, answer in answers.items():

    is_impractical, impractical_feedback = check_practicality(answer, ai, current_moderator)

    if is_impractical:

        feedback[ai] = impractical_feedback

Integration

• Add these checks to the moderate_answers function before generating general feedback.
• If an answer is flagged as a hallucination or impractical, the Moderator’s feedback overrides the general refinement feedback for that AI.

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3. Review and Suggestions for the Homepage Write-Up

Your draft for the www.IndiaAGI.ai homepage is a great start, but it can be polished to better explain the "About" section and make it more engaging for visitors, including potential stakeholders like Shri Ashwini Vaishnawji. Here’s your draft with my suggested changes.

Original Draft

Write-Up for Home Page (preceded by a page for selection of 26 languages)
Dear Visitor: