mNFT: Trustless Semantic Bridging w/ Asymmetrical Key Analogy

This blog is referencing multi-modal NFT protocol. If you haven’t read my other blogs on this topic — you might want to check out these links first:

TEN steps FWD — Multi-modal NFTs

Time Travel Unlocked (mNFT)

Otherwise, let’s dive into the fascinating world of mNFTs as Semantic Enablers.

What are mNFTs?

An mNFT is a protocol for enabling transactions or interactions that reference private user models in secure enclaves via Decentralized Confidential Compute (DeCC). These models can be trained or used for inference either in isolation or alongside other user models in a trustless environment.

What is Trustless Semantics?

Trustless semantics is a way to share meaning with another party or system as accurately as possible, without revealing any additional knowledge or data. It ensures the integrity and authenticity of the information shared, similar to digital security protocols but with key differences in confidentiality.

In digital security, confidentiality means completely hiding information from anyone other than the intended recipient. However, in trustless semantics, confidentiality means that while some information might be visible to the receiver, the deeper, internal knowledge and context of the sender remain hidden.

Key Characteristics of Trustless Semantics:

1. Confidentiality: Core details and internal context remain hidden, even if some information is visible to provide a partial understanding.
2. Integrity: The message remains unchanged from sender to receiver.
3. Authentication: The identity of the sender is confirmed.
4. Non-repudiation: The sender cannot deny sending the message.

Understanding Information Gradients and Sensitivity

To make this more accessible, let’s think of information as layers of an onion:

• Outer Layer: The visible part of the message that the receiver can see, providing enough context to understand the basic meaning.
• Middle Layers: Detailed information that might be partially visible but not fully exposed, offering additional context and depth while remaining protected.
• Core Layer: The most sensitive information, including the sender’s facts, knowledge, tendencies, and cognitive models, which remains completely confidential and hidden from the receiver.

By structuring information in this way, trustless semantics allows for effective communication without compromising the security and confidentiality of sensitive information. Even if some details are shared, the most critical and private information remains protected.

Why Are These Objectives Important?

Everything we say, do, compute, or exchange is a set of information or facts transacted between different actors over time. These processes must ultimately be understood by humans to have any value. mNFTs enable a close approximation of linking original semantic encodings between the sender and receiver, vastly improving communication.

Analogies with Asymmetrical Keys

In digital encryption, we use keys for secure communication:

• Public Key: Used by the sender to encrypt the message.
• Private Key: Used by the recipient to decrypt the message.

For instance, Alice encrypts a message with Bob’s public key, and Bob decrypts it with his private key. This model ensures secure, reliable communication.

Moving to Semantic Keys

Let’s take this concept a step further:

1. Message encoded in text/data:
   - Alice(encoding) → Encrypted message → Bob(encoding)
2. Consciousness:
   - Alice(cognition) → Message encoded in text/data [1] → Bob(cognition)

While digital exchange is assumed successful, the challenge lies in ensuring the received message triggers the same cognitive response as the sender intended. This involves decryption linked to the property of integrity in asymmetrical key protocols.

The Nature of Decryption

For a message to retain its integrity across a medium, the semantic meaning of its encoding and decoding must be equivalent. The intermediate process (digital exchange) might be successful, but the variance in human consciousness can lead to different interpretations.

Improving the Algorithm

Digital keys rely on precise, deterministic mappings between binary symbols (1,0), ensuring clear and exact communication. However, this approach can be limited for complex, nuanced exchanges.

Semantic keys are fundamentally different, operating on a stochastic and threshold-based system. This means encoding and decoding messages are influenced by probabilities and context rather than fixed rules.

Example:

When Alice and Bob use digital keys, a binary message like “01001000 01100101 01101100 01101100 01101111” (binary for “hello”) will always decode to “hello.”

Using semantic keys, Alice’s message “hello” can vary in interpretation based on context. At a concert, it might mean “let’s find our seats,” while in a library, it could mean “let’s talk quietly.”

This stochastic nature allows for flexible communication, aiming for a sufficient level of shared understanding rather than perfect agreement. Achieving this involves:

• Contextual Interpretation: Using context to infer meaning.
• Threshold-Based Decisions: Making decisions when interpretations reach a similarity threshold.

mNFTs leverage semantic keys to encode information, considering human cognition’s variability. By exposing relevant contextual hidden data using mNFTs, we can create more accurate semantic keys. This process maps Alice’s hidden state (private keys) to a message encoded with a subset of Bob’s hidden state (public key). When Bob receives the message, he can access the original meaning more precisely. This method enhances communication efficiency and accuracy by aligning the sender’s intent with the receiver’s understanding, reducing bias and increasing the impact at the cognitive-semantic level.

Semantic Keys

Compared to fixed-length digital keys, semantic keys are variable in length, density, and capacity. Each instantiation has different reconstructive values. The more information derived from the private key, the more accurate the reconstruction. Ideally, semantic public keys should have high contextual salience, low redundancy, and minimal information leakage.

How We Use Semantic Keys

We generate semantic public keys by:

1. Identifying goals (mind)
2. Understanding counter-party’s goals (knowledge, theory of mind)
3. Planning strategy or actions (mind, linguistic)
4. Translating internal encoding to counter-party encoding (perception)
5. Delivering the message (motor-linguistic)
6. Awaiting feedback (sense, theory of mind)

The Role of mNFTs

mNFTs augment traditional communication methods with enhanced memory, sensing, computation, and production capabilities. They work beyond the limitations of human cognition, where inferences are delegated, and humans act as initiators and validators. The process involves:

1. Identifying goals using mNFT chained models.
2. Understanding counter-party goals through encrypted mNFT models.
3. Combining strategy/action through DeCC TEEs for optimal outcomes.
4. Inferring and distributing information for understanding or confirmation.
5. Executing the transmission as understood most effectively.
6. Awaiting and processing feedback through mNFT models.

Compounding Value Chains

The increased efficiency in decoding semantics using mNFTs, DeCC, and AI has significant ramifications. By accurately interpreting hidden contextual data, we can vastly improve personal and business interactions. Let’s quantify this improvement using a tangible business objective.

Imagine a company working on a collaborative project, such as developing a new product. The project involves daily communications among team members, including designers, engineers, and marketers.

Assume the pre-mNFT/DeCC communication efficiency is 70% (0.70) due to misunderstandings and data loss. With mNFTs, imagine this efficiency increases to 90% (0.90) — due to context re-enforcement and modal encodings.

Using these efficiencies, we can model the compounded value of communication over time. For simplicity, assume the value of a single effective communication is $100.

Equation

• Vn: Value after n communication rounds
• Vo: Initial value ($100)
• E: Efficiency increase factor
• n: number of communication rounds

Without mNFTs:

With mNFTs:

Over 10 communications, the value increases from approximately $2.82 to $34.87. This represents an exponential gain in efficiency and effectiveness.

Hypothetical Enterprise Scenario

If a business conducts 1000 such communications daily, the value pre-mNFT would be 1000 x 2.82 = $2,824.75, while with mNFT that would deliver 1000 x 34.87 = $34,867.84; for a daily gain of ($34,867.84 — $2,824.75) = $32,043.09.

Annual Gain:

Assuming 250 working days a year: 32,043.09 x 250 = $8,010,772.50

Real-Life Application

Let’s say this company is a tech firm developing a new smartphone. The project involves intricate coordination among different departments:

• Design Team: Conceptualizes the look and feel of the phone.
• Engineering Team: Develops the hardware and software.
• Marketing Team: Plans the product launch and promotion strategies.

Using pre-mNFT communication, important details might get lost or misunderstood, leading to delays and rework. However, with mNFT-enhanced communication:

1. The design specifications are clearly understood by the engineers.
2. Marketing messages align perfectly with the product features.
3. Feedback loops are faster and more accurate.

This improved communication leads to faster project completion, reduced costs, and a better product, ultimately translating into higher revenues and market success.

By implementing mNFTs, the company could see an annual gain of approximately $8 million, showcasing the immense monetary value of increased communication efficiency.

Conclusion

Although it might seem complex, mNFTs essentially create an encoding package with high contextual salience, low redundancy, and minimal information leakage. This reduces bias and variance, increasing the efficiency and impact of communication at the cognitive-semantic level.

Many of the initial use cases for mNFTs focus on shared experiences and the value derived from collection, trading, and the perception of subjective experiences related to standard NFTs and real-world assets (RWA). However, as described in this blog, mNFTs combined with decentralized computing and communication (DeCC) generalize to an efficient protocol for all types of communication.

The compounding value of enhanced communication extends to any series of interactions over time, including person-to-person, agent-to-agent, and system-to-system. Each turn of communication compounds in value and consensus, enhancing the overall process across all domains. TEN.xyz is at the forefront of these innovations, bridging both traditional and DeFi markets and protocols. Ultimately, the primary goal of blockchain and web3 is to enable frictionless transfer of value and consensus, and the best way to achieve this is by understanding and optimizing the human component.

By implementing mNFTs, we can significantly improve the efficiency and accuracy of communications, leading to exponential gains in productivity and value across various sectors. This transformation not only enhances individual interactions but also drives collective progress in our increasingly interconnected world.

Alch3mist, (aka Anthony Nixon) is a web3 engineer with a passion for cognitive science, AI, and information theory. Currently contributing to TEN.