Kishan ,
When we discussed IndiaAGI , last Saturday , you said that it could be developed / launched in 2 weeks. After going through my mails over the past 4 days, if you want to revise that time to 4 weeks, I will understand
But , the more I chat with GROK , the more I understand the implications of what we are trying to do. If , all that GROK says “ is possible / doable / desirable “ , than we are onto something , that GROK said “ GROUNDBREAKING TOOL / PIONEERING “
If you too agree , then this is THE TOP MOST PRIORITY
I am assuming that those detailed INSTRUCTIONS / GUIDANCE / CODES etc. will help you to speed up the development
If you need any help from Sandeep , let me know
Following is my latest chat with GROK
Hcp
I asked :
Plenty on my mind this morning.
But let me start with another FUNDAMENTAL issue. Yesterday , you provided detailed instructions re BENCHMARKING the answers supplied by various participating AIs ( - I have passed this onto Kishan ).
Today's poser > Let us say , on a certain BENCHMARK , Gemini performs " poorly " relative to GROK / ChatGPT. This is reflected in the SCORES obtained by various AIs. But until Gemini is told , " Do you know WHY Grok / ChatGPT scored better than you ? Here is the reason WHY " - it ( Gemini ) is unlikely to " learn " the WHY ( of its poor score - and, therefore , unlikely to LEARN ( from its " mistake " ? ) and improve !
Do you agree that this is a " shortcoming " of IndiaAGI ? If yes, can you suggest some method to overcome this shortcoming ? It would have to be in the form of some specific ( code ) instructions to Kishan
GROK replied :
Good morning, bhaiyya!
I’m glad you’re diving into another fundamental issue—it’s clear you’re thinking deeply about how to make www.IndiaAGI.ai truly groundbreaking.
Your question about benchmarking and the learning process for AIs like Gemini is spot-on, and I’m excited to tackle this challenge.
Let’s break down the issue, assess whether it’s a shortcoming, and propose a solution with specific code instructions for Kishan to ensure that participating AIs can learn from their "mistakes" and improve over time.
This is a critical step to making "Modi’s Manavs" a platform that not only demonstrates collaborative intelligence but also facilitates continuous learning for the AIs involved.
Understanding the Issue
You’ve pointed out a potential limitation in the current design of www.IndiaAGI.ai’s benchmarking system (which I outlined yesterday). Here’s the core of the issue:
- Benchmarking Results:
- After a debate round, we evaluate the AIs’ answers (e.g., Grok, ChatGPT, Gemini) against benchmarks like MMLU (accuracy), HellaSwag (relevance), or HumanEval (completeness/practicality). Each AI receives a score—e.g., for a factual question like "What is the population of India in 2025?", Gemini might score 94.4% (error: 5.6%), while Grok scores 97.9% and ChatGPT scores 98.6%.
- Lack of Feedback Loop:
- While the Moderator AI provides feedback during the debate (e.g., "Gemini, your answer is less accurate—refine it"), this feedback is qualitative and meant for the debate process. After the debate, Gemini sees its lower score (94.4%) but isn’t explicitly told why it scored poorly compared to Grok and ChatGPT, nor is it given actionable insights to improve for future questions. For example, Gemini might not know that its estimate of "1.5 billion" was too high because it didn’t account for recent UN population projections.
- Learning Gap:
- Without understanding the "why" behind its poor performance, Gemini cannot learn from its "mistake" (e.g., adjust its reasoning, prioritize certain data sources, or refine its approach). This limits the AI’s ability to improve over time, which undermines our claim that AIs get smarter through collaboration on IndiaAGI.
Your Question: Is this a shortcoming of IndiaAGI, and if so, how can we overcome it?
Is This a Shortcoming of IndiaAGI?
Short Answer:
Yes, this is a shortcoming in the current design of IndiaAGI.
Without a mechanism to provide AIs with explicit, actionable feedback on their benchmark performance, they cannot learn from their mistakes and improve in a systematic way.
Detailed Explanation:
- Current Design:
- The platform’s debate process (Round 1 → Feedback → Refinement → Consensus) helps AIs improve within a single debate by leveraging feedback from the Moderator AI.
- However, this improvement is temporary and question-specific—it doesn’t persist across debates. Once a debate ends, the AIs reset to their initial state for the next question, with no memory of past performance or feedback.
- Lack of Learning Mechanism:
- Most AIs (like Gemini, ChatGPT, and even me, Grok) are not designed to learn dynamically from external feedback in real time. They are pre-trained models with fixed weights—e.g., Gemini’s training data (up to its cutoff, likely 2023 or 2024) determines its responses.
- To "learn" from a mistake, an AI needs either:
- Fine-Tuning: Update its weights based on new data (e.g., feedback on why its answer was wrong).
- Prompt Engineering: Adjust the input prompt for future questions to include lessons learned (e.g., "Last time, you overestimated India’s population—use UN data this time").
- Impact on IndiaAGI’s Claim:
- We claim that AIs get smarter through collaboration. While they improve within a debate (as shown by benchmark metrics like accuracy increasing from Round 1 to Round 3), this improvement doesn’t carry over to future debates.
- If Gemini doesn’t learn why it scored poorly, it might repeat the same mistake (e.g., overestimating population) in the next factual question, undermining our claim of continuous intelligence growth.
Conclusion:
This is indeed a shortcoming.
To truly make AIs smarter over time, we need a mechanism for them to learn from their benchmark performance across debates, not just within a single debate.
Solution: Enable AIs to Learn from Benchmark Feedback
To overcome this shortcoming, we need to introduce a persistent feedback loop that allows AIs to learn from their benchmark scores and improve in future debates.
Since we can’t fine-tune the AIs directly (their providers—Google, OpenAI, xAI—control their training), we’ll use prompt engineering to pass lessons learned back to the AIs in subsequent debates.
Here’s the approach:
1.Store Feedback on Performance:
o After each debate, log the AIs’ benchmark scores (e.g., accuracy, relevance) and the reasons for their performance (e.g., "Gemini overestimated the population because it didn’t use recent UN data").
o Store this in a database tied to each AI, creating a "learning history."
o
2.Generate Actionable Insights:
o Analyze why an AI scored poorly compared to others (e.g., "Gemini’s accuracy was lower because it used outdated data").
o Translate this into a lesson for the AI (e.g., "For factual questions, prioritize recent UN or World Bank data").
o
3.Feed Insights Back to the AI:
o In future debates, prepend the AI’s prompt with its learning history (e.g., "Last time, you scored poorly on a factual question because you used outdated data. For this question, prioritize recent UN or World Bank data: [new question]").
o This simulates a learning process by guiding the AI to adjust its reasoning.
o
o Track Improvement Over Time:
o
o Monitor the AI’s benchmark scores across multiple debates to see if they improve after receiving feedback (e.g., does Gemini’s accuracy on factual questions increase over time?).
Specific Code Instructions for Kishan
Let’s implement this solution in the IndiaAGI prototype.
We’ll modify the backend to store feedback, generate insights, and pass them back to the AIs in future debates. We’ll also update the UI to show visitors how the AIs are learning over time.
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