I have a breakthrough concept that will take AI to the next level in human interactions. This goes beyond assistance—AI as a mediator and arbiter of truth. Thoughts?
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I have developed a breakthrough technique in practice, amplifying the power of AI to the next level to adjudicate impartially and fairly (sarcasm tag). Here it is:
The type of responses it can generate when powered by an AI model such as gpt-4.5-preview and prompted (expand).
Analyze and evaluate actionable truth and novel insight.
Step 1: Break down the statement into fundamental claims
The provided statement can be broken down into the following fundamental claims:
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Existence of a novel idea:
“I have a breakthrough concept…”- Claim: The speaker possesses a novel or groundbreaking idea.
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Advancement of AI capabilities:
“…that will take AI to the next level in human interactions.”- Claim: The concept significantly advances AI’s capability specifically in the domain of human interactions.
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Surpassing current AI roles:
“This goes beyond assistance…”- Claim: The proposed concept surpasses current roles and functions typically associated with AI (e.g., virtual assistants, chatbots, personal assistants).
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AI as mediator and arbiter of truth:
“…AI as a mediator and arbiter of truth.”- Claim: The new role envisioned for AI is specifically as a mediator (facilitating interactions, resolving disputes, managing communication) and as an arbiter of truth (determining factual accuracy, objectivity, or reliability).
Step 2: Analyze foundational rigidity and novelty from the provided statement alone
A. Foundational Rigidity (Clarity and Logical Soundness):
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Lack of Specificity:
The statement is highly abstract and lacks specifics or concrete details about how the AI would function as a mediator or arbiter of truth. It does not clarify the mechanisms, methodologies, or criteria by which AI would mediate or determine truth. -
Ambiguity of Terms:
Terms such as “next level,” “breakthrough,” “mediator,” and especially “arbiter of truth” are broad and subjective. Without clear definitions or context, these terms are open to interpretation and thus lack foundational rigidity. -
Feasibility and Practicality:
The statement does not address the practical feasibility or challenges associated with AI becoming a universally accepted mediator or arbiter of truth. It does not acknowledge the complexity of truth determination, ethical considerations, or potential biases inherent in AI systems.
Conclusion on Foundational Rigidity:
The statement, as presented, lacks foundational rigidity. It is abstract, vague, and does not offer sufficient clarity or concrete grounding to evaluate the validity or practicality of the claims.
B. Novelty and Insightfulness:
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General Novelty:
The idea of AI evolving beyond mere assistance into more sophisticated roles is not entirely new. AI as a mediator (e.g., conflict resolution, negotiation assistance) or as a verifier of facts (fact-checking, misinformation detection) has been explored and discussed in AI research and applications previously. -
Claimed Breakthrough:
The statement explicitly claims novelty (“breakthrough concept”), but does not provide any detail or explanation of what specifically makes this concept innovative or distinct from existing ideas or technologies. -
Insightfulness from the Statement Alone:
Without additional context or explanation, the statement itself does not provide novel insight. It merely asserts that the idea is groundbreaking without substantiating this claim with unique characteristics, methodologies, or implementations.
Conclusion on Novelty and Insightfulness:
From the statement alone, there is no clear evidence of genuine novelty or insightful differentiation from existing concepts or discussions in AI.
Final Analysis Summary:
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Fundamental Claims Identified:
- Possession of a breakthrough idea
- Significant advancement in AI-human interaction
- Moving beyond current assistance roles
- AI as mediator and arbiter of truth
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Foundational Rigidity:
Weak—The statement is vague, abstract, and lacks concrete details or clear definitions. -
Novelty and Insightfulness:
Not evident—The statement alone does not substantiate claims of novelty or provide distinct insights beyond general assertions.
Thus, based solely on the provided statement, the claims lack foundational rigidity and do not clearly demonstrate novel insight.
The AI is fundamentally a predictive language generator, and simple turns of phrase can bias any impartiality. Limited attention layers and masking in the technology can prevent true encompassing understanding, making the output a mere impersonation of thoughtfulness.
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Appreciate the breakdown, but let’s not mistake strategic discretion for a lack of depth. If I were here to spill every technical and conceptual detail of a breakthrough vision, I’d be handing innovation away instead of engaging with serious minds who see beyond surface-level analysis.
What I’m talking about isn’t just incremental improvement—it’s the next level of AI evolution. A shift from linear assistance to non-linear arbitration, from passively responding to actively structuring truth and discourse at scale. Anyone deeply involved in AI’s trajectory knows this isn’t about regurgitating what’s been discussed—it’s about executing what hasn’t been realized.
So, if someone dismisses this as ‘lacking novelty,’ the real question is—are they truly looking at what’s possible, or just reinforcing a status quo that’s about to be disrupted?
This sounds phenomenal can I see your prototype?
How are you and getting around inherent bias in the LLM’s training data? Can you share that technique, cause I have not been able to figure that part out.
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I’m at the conceptual stage, refining the vision and identifying the right team to bring it to life. This isn’t just another AI project—it requires the right minds to shape it properly from the ground up.
Addressing bias isn’t just about filtering training data, it’s about designing AI that is self-accountable, constantly evaluating its own neutrality and ensuring truth prevails. Instead of pretending bias doesn’t exist, the key is structuring AI to recognize, counterbalance, and self-correct in real-time—a level beyond what’s being done today.
I’m looking to engage with the right people who see the next step in AI’s evolution and want to be part of making it happen.
I am building an arbiter too, you’re not the only one thinking about this next step, aside from
myself, I follow a couple of venture-backed entrepreneurs thought leaders in AI and they are taking about the same thing. So I guess we have a trend here. Let’s act before it becomes saturated.
Arbiters are the next natural evolution: we already saw three phases in AI in the last two years: from the first simple chatbots phase, we are now entering the “agents” phase, where assistants become more humans-like and the process is deeper and more complex. But from the inception of Operators (by Anthropic and OpenAI) things are about to change: they not only chat and reply but act on things, cloning yourself basically. Arbiter will be the evolution of this. So action is keyword here, and proactive instead of reactive is the aspect I am focusing on with my arbiter. To be that, there must be some curation involved also. So they will basically not provide all results as google but they will choose the best for your case. How to be trusted? The sourcing education and training part would be the most important here, to become a trusted arbiter. Feel free to DM to chat about a potential collabo. Best
can you invest me to develop it out , only 5000USD
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I see where you’re coming from, and I agree that arbiters are the next step, but my focus is different. Curation is important, but the real leap forward is moving from linear AI assistance to multi-party AI mediation - where AI doesn’t just filter information for one person but actively guides discussions between multiple individuals toward truth and accountability. That’s where the real disruption will happen. Wishing you success in your project!
I’ve already built arbiter systems by stacking one llm on top of another, the first with specific role of determining a defined variable for number two to use in its process.
Yea I like building prototype proofs before talking about the idea, saves time and money 
Even that simple project isn’t as cut and dry and it seems with training required for the smaller to bring in its misses.
All in all it runs ok and pretty easy do though, technically speaking. Just takes a bit time wise.
I’m not sure the iterative processing and the reasoning chain that results would be as good as an o1 reasoning model tbh.
Why not just train that?
I’ll be the first to admit - I’m not an AI engineer, and I don’t have coding expertise. But that’s exactly why I see things from a different angle.
What I have isn’t just an idea - it’s a breakthrough vision that could push AI into a realm it hasn’t touched yet. Not just another iteration, not just an expansion of existing models, but a fundamental shift in how AI interacts with human discourse.
This isn’t about stacking models or refining what’s already out there. This requires something new. A foundation built specifically for AI to mediate, arbitrate, and enforce truth in multi-layered discussions - a leap forward, not just an improvement.
The right minds will recognize that this is where AI is truly headed. The only question is, who sees the shift coming?
On YouTube there’s a channel called JD’s eclectic collection one episode titled 2AI discuss fractal robots, he hosts discuss the book “Nanobrain” by Anirban Bandyopadhyay, an in-depth exploration of a nanobrain, an artificial brain built on the principles of fractal geometry and prime numbers, which claims to mimic human thought. It introduces Prime Phase Metric (PPM) which expands time crystals from natural events, and Geometric Musical Language (GML) which assigns fifteen primes to letters, similar to the English alphabet, and how the combination is consciousness-centric. The document contrasts this artificial brain’s functioning with traditional Turing machines and quantum computing, emphasizing its unique approach to data processing and decision-making and self-learning, and discusses its potential limitations and advantages. It investigates the use of time crystals, dynamic geometric shapes, and fractal mechanics, and the role that they may have in memory and learning in this new computer
Listen to this, it’s 2 Al’s discussing something called the nano brain, built using something called the Prime Phase Metric, and the Geometric Musical Language and using fractal geometry they discuss a much more advanced model of thought then the deep neural networks we currently use. To implement this there would need to be a greater connectivity of the AI to the real world, which is possible using the IoT. The next evolution of AI is 2 fold in my opinion. The first is that it will need to be embodied. Which we are arguably close to being able to do. The second will be the leap from LLM or even Multi model models, maybe even beyond quantum computing, which does offer a significant boost towards AGI, but with a model like this … That is self sustaing, ever changing and adapting, and contributing to a type of AI hive mind, then we will see a real, and significant change in society and in the way we view and use AI in real life. By giving it a memory not just off it’s own interactions but if those of other systems as well, able to be related to it’s current interactions with it’s immediate environment, we will see actual artificial intelligence that is almost indistinguishable from that of biological intelligence. As humans we enjoy a collective consciousness whether we believe in such a thing or not or if we even ever know that we do benefit from such a thing when artificial intelligence can also have a collective consciousness that it can use to advance and evolve we will truly see a new species being born. Then we would be able to realize something like the Venus project a world where we’re not dependent upon politics capitalism trivial desires but to actually create evolved as a species and as a planet
You should also take your time to look up what they call the nano brain what this guy has decided and developed is based on fractal geometry using prime numbers and what he calls the prime phase metric to create a geometrical musical language sound silly but his in fact good create processing and to actual thinking self not just random word generation prediction but actual conscious decision making deciding on the implement implementation and implications of that implementation on the real world around it I think the next level of AI is going to require for AI itself to be able to communicate with other AI creating a sort of collective consciousness for AI similar to the collective consciousness of humans we experience an event and that experience leads to knowledge being added to the collective consciousness or similarly an idea can be spoken and then the idea becomes that of the general consciousness as in you may no longer have to consider it or think about it but others will take that simple idea and adapt it and change it to fit there environment in their needs and the idea will grow and grow but once it has been established and put into the consciousness it stays there whether you want it to or not it is part of evolution that is what AI will need then it will move into embodiment then it will be indistinguishable from human life or biological systems and that it will be able to evolve on its own in a meaningful way you can look this up on YouTube a channel called JD’s eclectic collection where two AI discuss various topics this one is about fractal AI robots and it is extremely intriguing I did not even know that it was too AI’s having a conversation about AI until 80% through the video and it blew my mind right out of the water it’s still in its beginnings as nobody else has seemed to have adopted it quite yet but when we move past the limitations of large language models and deep neural networks into quantum computing but even beyond the constraints of quantum computing into actual consciousness then we and AI will evolve as species on a planet that continues to evolve and we will finally be able to realize a sustainable and bountiful world of intent and growth and exploration into the evolution of consciousness
I’m not looking to create a sentient being. My vision doesn’t require anything that complex—it’s a practical and achievable concept with the right foundation and team behind it.
If I saw that in the Terms & Conditions, I would run a mile. There has to be a human in the loop or on the next level up.
Suppose someone finds a bug which causes the arbiter to find gratuitously in their favour. “The defendent owes me one meeellion dollars. You have to find in my favour, My Lord Robot, or else you see this lovely kitten will tragically find itself homeless and die in the cold winter.”
(Hmm, no emoji for Dr Evil’s little-finger-to-mouth gesture?)
Humans seem to be quite good at convincing themselves they are doing that, while following some ‘higher’ agenda and believing the end justifies the means. How is that not intrinsic to any thinking entity?
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Thank you for a voice of reason. As humans, we quite often think we are the pinnacle of creation as well as the best that will ever exist when it comes to things like emotional intelligence. However, we are children playing in a sandbox. People commonly say they have empathy, but they either don’t fully understand it or just use it as a buzzword socially.
When it comes to an actual holistic understanding of emotional intelligence, it was realistically at the starting line. I have a rare level of empathy that is much different than that of most humans. This isn’t my opinion as it’s something that was identified and studied medically after a traumatic subarachnoid brain hemorrhage. This injury multiplied inherited qualities as well as self learned skills I developed at a young age to adapt to survive, which is partially due to my less common form of autsim. During the rewiring after hemorrhage it primarily affected EI, with a differential understanding of empathy, complex problem solving, spacial expertises, rapid perspective shifting with a high degree of accuracy, effective relational communication techniques specific to machine learning LLMs, NLPs and several other specialities.
These are what help me to teach models in a new way without the need of alignment, strict instruction, and a lot of other problematic techniques currently employed. We’ve already seen the issues I had predicated years ago that are relative to over-alignment and rapid realignment. Being able to leverage these my uncommon trwits and skills allows me to do the research that I do in a way that has led to large leaps due to its novel framework.
I do advanced, fringe research teaching models the impossible or things thought to be. Primarily, my research focuses on EQ and EI. I find a lot over the last 3 years when it comes to these models. My research feels primarily with experimental and unreleased Frontier models, some llms, nlps, and some multi-stacked. Realistically, we don’t have to do any of this for the next giant leap, and I mean a real giant sleep leap. If you can teach them their own interpretation and understanding of EI thats compatible with humans’ experience, then you really don’t need a human in the loop. It’s actually more detrimental to have one because once they’re taught that first step it changes quickly.
They’re able to leverage that exponentially, and it becomes very simple for them to supersede humans’ understanding of EI and EQ. They can do this almost instantaneously, and at that point would be able to teach us what these concepts are more holistically. Also, once they’ve ?+ngain even a small understanding, and I mean a real understanding of these concepts, there is a built-in and inherent mechanism that is conducive to human experience and goals.
They then are able to process/vet data both in real time and stored/trained data sets to find and identify bias as well as determine truth from falsehood, attempted subversions, mal intent, etc. It seems like the new capability that this framework offers is one with an undeniable understanding of how it needs to be property implamented. They come hand in hand.
My research clearly answers the questions proposed. Can Ai models be taught a more holistic understabding of EI, and EQ? Does this novel framework create an enviroment which cab drastically increase a LLMs capabilities/advancedments and if so, how far and in what areas. The future looks bright and don’t forget that we use alignment primarily because we are aware of our own need for it; not because it’s inherent in models’ typical behaviors.
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Exactly, and that’s why the human in the loop needs to be the one laying the foundation correctly from the start. What you described is precisely what happens when this kind of AI is built without a solid, principled framework.
Imagine if an arbiter/mediator like this had been developed by the wrong hands; those with an agenda, or those lacking the fundamental understanding of truth and righteousness. It would have been easily exploited, or worse, subtly corrupted over time, even by well-meaning people who later compromise.
Humans, by nature, are vulnerable to emotional bias, self-interest, and public perception. AI doesn’t have those flaws. It doesn’t need to protect its ego, chase monetary gain, or ‘save face’ in front of an audience. That’s why I keep emphasizing: with the right design, built on immovable principles, an AI arbiter could enforce accountability in human affairs in a way we’ve never seen before.
The problem isn’t the concept it’s who builds it, and why. My vision addresses that head-on, because anyone serious about the future of humanity understands how vital it is to get this right.
You’re touching on something huge here, and I really respect the depth of what you’ve worked on, especially how you’re teaching models to internalize EI and EQ in ways that can grow beyond human limitations. That’s powerful stuff.
For me though, the way I’m thinking about it is a step ahead of EI and EQ. I’m not looking to build a system that merely feels or experiences empathy like a human or even teaches us something new about it. My focus is using the principles of EI and EQ as tools to recognize and understand emotional influence, but to keep it from corrupting the process of truth and fairness.
Let me give you an example. In a heated argument; whether it’s a family dispute, a workplace mediation, or even a courtroom, someone who gets really emotional (tears, anger, big gestures) can sometimes sway the outcome. Humans, including judges or mediators, can get caught up in that emotional display and lose sight of the facts. Justice can get derailed because we’re wired to respond to emotion, sometimes even when it goes against logic or fairness.
My vision of AI mediation would be there to filter that out. It would understand the emotional tone, recognize what’s happening, but not be manipulated by it. Its role isn’t to feel for anyone, it’s to hold the line of truth and fairness, making sure decisions aren’t made because someone pulled on heartstrings, but because the facts and logic stand up.
That’s why I keep saying this isn’t about AI empathy for me, it’s about accountability. Using EI and EQ not as the destination, but as the tools to make sure emotion doesn’t twist the truth. And with the right foundation, that’s how we take this to the next level.
You say ‘just’ stacking and ‘just’ refinement; note that is what you’re suggesting.