RE: I HAVE IMPLEMENTED YOUR VISION

 Dear Prof. Kautz :

 

Even as I await your views on www.IndiaAGI.ai  , I thought you might want to spare a few minutes to look up my following message to India’s AI community

 

Regards,

 

Hemen Parekh

 

 

https://myblogepage.blogspot.com/2025/04/ndiaagi-should-it-become-open-source.html

 

Dear AI Professionals ,

 

Should I turn www.IndiaAGI.ai ( launched on 27 April 2025 ), into an OPEN SOURCE project ?

 

[  A  ]

I am tempted , considering that I am 92 years old and not having resources to grow it on my own – leave alone “ Patenting “ it’s UNIQUE proposition, despite Grok’s advice in the following chat :

Ø  Alibaba and Eight Thieves vs IndiaAGI’s Four Saints ?  .. 30 April 2025

 

[  B  ]

 

Also because going OPEN echoes the call of experts like you, as reported in the following news report :

 

“ Experts Make a New Case for Open-Source Path to Indian LLMs “ .. Economic Times.. 30 April 2025

 

https://economictimes.indiatimes.com/tech/artificial-intelligence/experts-make-a-new-case-for-open-source-path-to-indian-llms/articleshow/120739412.cms?from=mdr

 

[  C  ]

 

How will going OPEN help the cause of www.IndiaAGI.ai  ?

 

That is a no-brainer !

 

Hundreds of AI GEEKS like you will download it’s Source Code from GitHub , modify it , and in a matter of weeks, launch ( on their own portals ), following projects :

 

 

     ( 1 )   ChatGPT and GEMINI : Diverging and Converging

    ( 2 )   IndiaAGI : Evolution

    ( 3 )   IndiaAGI - Existential Threat

    ( 4 )   IndiaAGI : BILL ANALYSIS MODULE

    ( 5 )   IndiaAGI – Authors Corner

    ( 6 )   IndiaAGI : Emulate Emergence

    ( 7 )   Toggle Solutions : Consensus vs Stand-Alone 

 

    ( 8 )    E Mail  to  Github 

    ( 9 )   IndiaAGI vs Model Open Framework ( MOF )

    ( 10 )  META CORAL vs IndiaAGI

    ( 11 )  MIT  enables LLM write Perfect Code 

 

    ( 12 )  When LLMs debate on www.IndiaAGI

                 

 

[  D  ]

 

Dear  AI  Experts  :

 

Just :

Ø  Copy the above list

Ø  Select ( tick one or more ) the projects that you would like to launch on your portal

Ø  Email to me ( hcp@RecruitGuru.com ) your choices

 

If I get many positive responses, I will upload IndiaAGI code on GitHub and mail you that link

Do share this message among your friends / colleagues / contacts ( - not your boss ? )

 

 

With regards,

 

Hemen Parekh

 

May Day 2025

 

 

From: Hemen Parekh [mailto:hcp@recruitguru.com]
Sent: 28 April 2025 12:34
To: 'henry.kautz@gmail.com'; 'henry.kautz@virginia.edu'
Cc: 'hcp@RecruitGuru.com'
Subject: I HAVE IMPLEMENTED YOUR VISION

 

Dear Prof. Henry Kautz ;

 

 

Some time back , I came across following news report :

 

AI Experts Say We’re on the Wrong Path to Achieving Human-Like AI  –  March  30,  2025

You were quoted :

“" I believe the next stage in improving trustworthiness will be the replacement of individual AI agents with cooperating teams of agents that continually fact-check each other and try to keep each other honest."

By launch of >  www.IndiaAGI.ai  <  today , I  HAVE  IMPLEMENTED  YOUR  VISION !

Following is what www.IndiaAGI.ai , had to say about your vision

Regards,

Hemen Parekh

Mumbai – India /  hcp@RecruitGuru.com  /  www.HemenParekh.ai  /  www.HemenParekh.in

www.IndiaAGI.ai  says :

“Grok provided a consensus response

 

In reflecting on Professor Kautz's vision for advancing AI trustworthiness through cooperating teams of agents that continually fact-check each other, our discussion has uncovered a multifaceted picture that balances optimism with realism.

 

Across the various perspectives shared, there's a clear consensus that this approach could represent a meaningful evolution from individual AI systems, potentially leading to more reliable, bias-resistant outcomes by fostering a dynamic ecosystem of mutual verification and accountability.

 

 

On the positive side, the strongest supported conclusions emphasize how such collaborative frameworks could enhance accuracy and ethical decision-making.

 

For instance, integrating diverse datasets, algorithms, and incentive structures—as highlighted in earlier responses—could effectively mimic human peer review, reducing errors and preventing groupthink in high-stakes areas like medical diagnostics or misinformation detection.

 

The UTSA research and other examples underscore that probabilistic, multi-perspective fact-checking might not only mitigate biases but also build public trust by making AI processes more transparent and adaptable.

 

 

However, the challenges we've explored add necessary nuance, tempering this enthusiasm. Key limitations include the computational overhead of continuous verification, the risks of echo chambers or unresolved disagreements, and the complexities of implementing ethical governance and conflict resolution mechanisms.

 

As noted, strategies like dynamic verification depths, hierarchical evidence frameworks, and decoupled reward systems could help address these, but they require careful design to avoid inefficiencies or over-reliance on AI.

 

The search results and analyses remind us that while foundational research exists, scaling this to real-world applications demands interdisciplinary collaboration, robust accountability protocols, and ongoing refinement to ensure systems remain efficient and equitable.

 

 

Ultimately, Kautz's idea holds promise as a forward-thinking paradigm, but its success hinges on striking a balance between innovation and practicality. By prioritizing diverse agent designs, transparent processes, and human oversight, we can move toward AI that not only keeps itself honest but also truly serves societal needs, building on the insights shared here to guide future developments.