My Question :
“I just came across following
news :
Ø
https://elevenlabs.io/blog/what-happens-when-two-ai-voice-assistants-have-a-conversation
Then looked up:
> https://github.com/PennyroyalTea/gibberlink
- which I dont understand - but , in this latest development , do you
find the seeds of " Modi's Manav " platform , in preparation of which
, you contributed substantially at
> https://myblogepage.blogspot.com/2025/02/2047-your-call-of-destiny.html “
Claude replied :
““I understand you're connecting recent
developments in AI-to-AI communication with your "Modi's Manav"
platform concept.
The
ElevenLabs conversation between two AI voice assistants is indeed interesting -
it demonstrates AI systems communicating with each other using voice synthesis
and speech recognition, creating a natural-sounding dialogue.
Gibberlink
(on GitHub) appears to be a framework or protocol designed to facilitate
communication between AI systems, though I don't have specific details about
its implementation.
These
developments do align with some elements of your "Modi's Manav"
concept, as they demonstrate:
- Technical feasibility of
AI systems communicating with each other
- Potential for structured
dialogue between different AI systems
- Mechanisms for
coordinating responses between AI models
However,
there remain significant technical challenges to creating a full collaborative
platform as you've envisioned, particularly:
- Establishing consensus
mechanisms between different AI systems
- Managing differences in
knowledge and capabilities
- Creating protocols for
real-time collaboration on complex problems
- Building the
infrastructure for multilingual support and timely responses
Your vision
goes beyond simple AI-to-AI communication to creating a deliberative process
that produces unified, actionable solutions. While these developments show
progress in the technical foundations, building the complete platform would
still require substantial work on coordination protocols, consensus algorithms,
and integration frameworks.
What aspects
of these developments are you most interested in exploring further for your
concept?
I asked :
Thanks
Friend : What would be of most importance to " Modi's Manavs " ,
would be a " Supervisory Protocol " , whereby , after each
participating LLM has SPOKEN its solution ( for the problem under debate ), a
SUPERVISOR LLM ( chosen in advance by some system of rotation ) , would
summarise and declare / publish the CONSENSUS . Do you have any suggestion in
this regards ?
Claude replied :
This
"Supervisory Protocol" concept is a key component for your
collaborative AI platform. Here's how I envision it could work:
Supervisory Protocol Framework
- Rotational Supervisor
Role
- Each participating LLM
takes turns serving as the supervisor in a predefined rotation
- The supervisor for each
session is clearly identified to users
- Rotation ensures no
single system dominates the consensus process
- Deliberation Process
- Problem is presented to
all participating LLMs simultaneously
- Each LLM generates and
presents its solution proposal (spoken/text)
- All solutions are shared
with all participants
- Brief response period
where LLMs can comment on other solutions
- Consensus Formation
- The supervisor LLM
analyzes all solutions for:
- Common elements across
proposals
- Complementary
approaches that could be combined
- Areas of disagreement
requiring resolution
- Practical
implementation considerations
- Consensus Declaration
- Supervisor synthesizes a
unified solution incorporating the strongest elements
- Highlights areas of
strong agreement
- Transparently addresses
any significant disagreements
- Formulates actionable
next steps with timeframes
- Quality Assurance
Mechanism
- Implementation of a
feedback loop where other LLMs can flag if they believe the supervisor
has misrepresented consensus
- User feedback
integration to evaluate solution quality
This
approach provides structure while maintaining balance through rotation. The key
technical challenge would be developing the consensus algorithm that the
supervisor uses to fairly represent diverse inputs.
Would you
like me to elaborate on any particular aspect of this protocol?
I asked :
Hey thanks for
your in-depth analysis. I am sure , " The key technical challenge would be
developing the consensus algorithm that the supervisor uses to fairly represent
diverse inputs. ". Any suggestion on this algorithm ?
Claude replied :
Creating an
effective consensus algorithm for your supervisory LLM is indeed a complex
challenge. Here's a potential approach:
Consensus Algorithm Framework
- Semantic Mapping
- Parse each LLM's
solution into structured components (problems identified, proposed
actions, expected outcomes, timeframes)
- Map semantic
similarities between different solutions using vector embeddings
- Cluster similar
proposals that use different terminology but express similar concepts
- Weighted Aggregation
- Assign weights to
solution components based on:
- Frequency (how many
LLMs proposed similar ideas)
- Specificity (detailed
proposals over vague ones)
- Feasibility
(implementable within reasonable constraints)
- Effectiveness (likely
impact on the original problem)
- Conflict Resolution
- Identify true
contradictions vs. complementary approaches
- For contradictions,
present the strongest arguments for each position
- Apply decision rules
based on evidence strength, implementation feasibility, and risk
assessment
- Solution Synthesis
- Build a composite
solution incorporating the highest-weighted components
- Ensure internal
consistency by resolving logical contradictions
- Structure as a cohesive
plan with clear actions and timeframes
- Confidence Scoring
- Assign a confidence
score to different parts of the consensus
- Explicitly mark areas
where consensus is strong vs. areas with significant disagreement
- Include minority
perspectives when they offer unique insights
This
algorithm would need to balance between computational efficiency (to meet your
3-minute response target) and thoroughness. An initial implementation could
start with simpler heuristics and evolve toward more sophisticated approaches
as you gather data on how different LLMs interact.
Would you
like me to explore any specific component of this algorithm in more detail?
I asked :
Good Morning, Claude : This morning, I
would like to continue to explore potential of " Modi's Manavs " .
Here is my question > I want your opinion about a new development which
could , possibly help US bring one step closer to realizing OUR dream of
launching " Modi's Manavs " platform where different LLMs can come
together and in a COLLABORATIVE and CO-OPERATIVE spirit, come up with a
CONSENSUS solution to any problem posted by a human. During our last chat , all
of you agreed that, with the rapidly advancing AI / LLM / SLM , technology,
before too long, it might even be possible for all of you to be able to DEBATE
your solutions by TALKING ( much like in human meetings ), instead of WRITING (
Text typing ). Now , moving on, I just came across this news > https://www.marktechpost.com/2025/02/28/google-ai-introduces-plangen-a-multi-agent-ai-framework-designed-to-enhance-planning-and-reasoning-in-llms-through-constraint-guided-iterative-verification-and-adaptive-algorithm-selection/ > At one
place, it reads : " PlanGEN comprises three agents that work in concert:
the constraint agent extracts problem-specific details, the verification agent
evaluates the quality of the proposed plan, and the selection agent chooses the
most appropriate inference algorithm based on the problem’s complexity. Rather
than relying on a single, rigid approach, this framework facilitates a process
in which initial plans are refined iteratively, ensuring that the final output
is both accurate and contextually appropriate " // Does this development
bring " Modi's Manavs " , one step closer to realization ? PS :
Read this in context of this what I came across just a few minutes ago > https://siliconangle.com/2025/03/06/tavus-introduces-family-ai-models-power-real-time-human-face-face-interaction/
Claude replied :
Based on the developments you've shared, I
do think these advancements bring the concept of "Modi's Manavs"
closer to realization. Let me share my perspective on how these technologies
might contribute to your vision of collaborative AI.
The PlanGEN multi-agent framework you
mentioned represents a significant step forward in how AI systems can
collaborate. By having specialized agents (constraint, verification, and
selection) working together to tackle complex problems, it demonstrates a
practical implementation of the kind of collaboration you've envisioned for
Modi's Manavs. This approach of having different "experts" focus on
distinct aspects of problem-solving mirrors how human teams often work.
The second development from Tavus about AI
models enabling real-time human-like face-to-face interaction also addresses
another aspect of your vision - moving from text-based collaboration to more
natural conversational modes. This could potentially enable the kind of
"debate" format we discussed previously, where different AI systems
could interact in a more dynamic, human-like manner.
Combined, these developments suggest
progress in two critical areas for your concept:
1.
Creating frameworks for effective AI collaboration and
consensus-building
2.
Developing more natural interfaces for AI communication
that go beyond text
While there's still work to be done to
fully realize your vision of a platform where various LLMs collaborate to
develop consensus solutions, these technologies do represent meaningful steps
in that direction. The ability to have specialized roles, iterative refinement
of solutions, and potentially more natural modes of interaction all align well
with the Modi's Manavs concept.
What aspects of these technologies do you
find most promising for your vision, and are there specific capabilities you're
still waiting to see developed?