Narrow AI, or ANI, is AI designed for specific tasks. It excels within its limited scope but lacks general intelligence. Think voice assistants, facial recognition, or recommendation systems. Unlike general AI, it can't reason or problem-solve broadly. It's highly specialized, focusing on doing one thing well, not mimicking overall human cognition.

In addition to the response by Rahul Goel , development of computer systems that perform specific tasks requiring human-like intelligence without possessing general cognitive abilities.

If you don't understand artificial intelligence, ask yourself if I'm right because others don't understand it either.

There is no generally applicable definition of artificial intelligence [1].

The field is so vast that its actors will always have to develop a more precise definition of their own objectives in their context.

Artificial intelligence is broadly defined as the simulation of human intelligence by machines programmed to learn, reason, and solve problems.

Therefore, AI encompasses several subfields, including machine learning (ML), deep learning (DL), robots, natural language processing (NLP), and computer vision, etc., each with distinct applications and limitations.

And yet, AI can be classified into two broad categories:

i-Narrowly defined AI (concrete definition of AI)

Which is simply defined as an automated algorithm operating on the basis of statistical transition matrices, which allows it to increase the amount of existing information.

ii- General AI (broad definition of AI):

such as machine learning (ML), deep AI (DAI), image and voice recognition, etc.

We believe that the term AI should be reserved for software capable of increasing the amount of information that is the function of only humans (and much less other creatures) and statistical transition matrices.

A hypothetical AI with human-like cognitive abilities, including reasoning, writing and reading rules, pronunciation, specific tasks, such as facial recognition, speech processing, and more.

These systems operate within predefined parameters and many input data cannot be generalized beyond their designated functions.

These systems obviously cannot increase the amount of existing information and therefore cannot be called artificial intelligence.

We can conveniently call these systems storage intelligence SI systems rather than artificial intelligence systems.

Concrete and narrow AI remains a theoretical and practical construct with many existing examples in physics and mathematics as well as in real daily life.

1-Theory and Practice of artificial intelligence, RG,RSIJST review, Feb 2025.

A program having ability to analyse (complex) data like human and make decisions (predictions) like human do.

B-transition matrix chains derived from the Cairo technique for numerical statistical solutions have been successfully applied to the statistical solution of time-dependent partial differential equations in classical physics.

This Q&A examines the extension of B-transition matrix chains to the numerical statistical solution of the time-independent Schrödinger equation.

Note that both the classical physics transition matrix and the QM transition matrix are SMART matrices in the sense that they operate statistically in 4D x-t unitary space and can therefore create more information than exists in PDEs operating in 3D geometry plus real-time space.

However, extending physical B-transition matrix chains to solve the time-independent Schrödinger equation is not complicated, but it is quite time-consuming and requires compliance with certain limitations of fundamental principles, which we briefly explain in this answer [1,2].

Furthermore, it requires a vivid imagination.

Here, we present the numerical solution of the B-matrix using two illustrative examples: the one-dimensional infinite potential well and the quadratic potential well, where the numerical results are surprisingly accurate.

Extending physical transition matrix chains B to solve the time-independent Schrödinger equation requires some basic knowledge, which we briefly explain below:

i- Square matrices are a subset of mathematical matrices, and physical square matrices with physical meaning (such as the B transition matrix) are a subset of square matrices.

ii- Statistical transition matrices and chains of statistical transition matrices exist for situations in classical and quantum physics. Modeling these situations in transition matrix mechanics is more efficient for solving the corresponding partial differential equations (without going through the PDEs themselves) in a numerical statistical procedure.

Currently, we only know two: the mathematical statistical Markov transition matrix and physical transition matrix chains B, the subject of this Q&A.

iii- Not all matrix equations resulting from solving a PDE via the transition matrix are eigenvalue equations. For example, the matrix of the numerical solution to the heat diffusion equation produces a system of inhomogeneous first-order linear algebraic equations, while the matrix of the numerical solution to the Schrödinger equation is homogeneous and produces a multiple-eigenvalue eigenvalue problem.

What is the significance of a matrix with zero determinant?

First, if a matrix has a zero determinant, can it have an inverse or not?

The answer on Google is:

If the determinant of a matrix is ​​zero, then it has no inverse; the matrix is ​​therefore said to be singular. Only non-singular matrices have inverses.

Contrary to the Google answer, the author assumes that a singular matrix can have an inverse that is another singular.

Consider the homogeneous system of n independent first-order linear algebraic equations:

a11 x1 + . . +a1n xn = 0

a21 x1 + . . +a2n xn = 0

. . . .

an1x1 + . . +ann xn = 0

In matrix form,

A . x = 0 . . . (2)

This system has a solution if and only if the nxn matrix A (ai,j)

satisfies two seemingly contradictory conditions:

i- The determinant of matrix A = 0.

ii- The inverse of A (A^-1) exists.

In this case, the QM solution for vector x is given by:

x=A^-1 (b+S)

b is the assumed Diriclet boundary condition vector and S is the source/sink vector (S=S1+S2 where S1 is the spontaneous or intrinsic source and S2 the extrinsic applied source).

Both S1 and S2 are subject to the authors' AB equation.

Therefore, if a matrix has a zero determinant, it can describe the transfer matrix of the isolated QM system Q.

This means that the QM energy vector can be easily found as an eigenvector of Q with eigenvalue = 1, as shown below.

Ultimately, it is true that Cairo's intelligence techniques = natural intelligence = artificial intelligence in the strict or narrow sense = unified field theory.

1-BOOK-Fundamentals of Artificial Intelligence-Theory and Practice-ISBN- 978-969-8092-20-7

2-A statistical numerical solution for the time- independent Schrödinger equation, ResearchGate, IJISRT Journal, November 2023