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What is NKTg AI's algorithm built on?

NKTg AI is not a generative AI model, and it is not a text summarization tool. It is a Language Decoding System built on The NKTg Law on Varying Inertia, using a mathematical algorithm to measure semantic energy and identify the Core Content of a text.

What is the NKTg Law?

The NKTg Law (The NKTg Law on Varying Inertia) is the scientific foundation of NKTg AI. According to the NKTg Law, an object's tendency of motion is determined by the relationship between position (x), velocity (v), and mass (m):

NKTg = f(x, v, m)

Where:

• x: position or displacement
• v: velocity
• m: mass

From these three quantities, the NKTg Law builds:

p = m × v
NKTg₁ = x × p
NKTg₂ = (dm/dt) × p
  

• NKTg₁: the product of position and momentum (the tendency to move away from or toward a stable state)
• NKTg₂: the product of mass change over time and momentum (supporting or resisting motion)

The Stable State is where position, velocity, and mass interact to maintain the structure of motion. Unit of measurement: NKTm (Varying Inertia Unit).

Research foundation of the NKTg Law

• The physical law "The NKTg Law on Varying Inertia"
• The mathematical model NKTg = f(x, v, m)
• Core libraries developed in C++, Rust, and Go
• REST API and gRPC interface layer
• Client libraries for multiple programming languages
• 150+ reference implementations in the examples/ directory
• Integrated into numerous software systems
• DOI research record (Zenodo: 10.5281/zenodo.15808498)
• Author profile ORCID: 0009-0002-9877-4137
• Verified on the Solana Blockchain with the Token NKTg = f(x, v, m)
• Deployed in 11 programming languages, tested against ESA data with a 0.208% margin of error
• Books and documentation published on Leanpub, Google Play Books, Amazon, Payhip, and Gumroad

NKTg AI's algorithm

Building on the NKTg Law, NKTg AI constructs a Semantic Energy model to analyze text structure:

NKTg = f(x, v, m)

p = m × v

NKTg₁ = x × p
        (Semantic Potential Energy)

NKTg₂ = (dm/dt) × p
        (Semantic Kinetic Energy)

NKTg(total) = f(NKTg₁, NKTg₂)
  

Where:

• x: Semantic Position
• v: Semantic Velocity
• m: Semantic Mass
• p: Semantic Momentum
• NKTg₁: Semantic Potential Energy
• NKTg₂: Semantic Kinetic Energy
• NKTg(total): the total semantic energy of the text

From this result, NKTg AI continues to:

• Measure the energy of each word
• Measure the energy of each sentence
• Analyze the energy structure of the entire text
• Calculate the AMP, DAMP, and STABLE ratios
• Identify the Core Content while preserving 100% of the author's original sentence