Artificial Intelligence; Machine Learning; Intelligent Systems; Human–AI Interaction; Cognitive Computing; AI Governance; Responsible AI in Comparison with IA
Abstract
Artificial Intelligence (AI) has traditionally been understood as the design of computational systems capable of performing functions associated with human intelligence. This formulation places conceptual emphasis on the artificial construction of intelligent behavior. Rapid advances in machine learning, foundation models, generative AI, multimodal systems, and autonomous agents, however, increasingly challenge task-oriented definitions of machine intelligence. Contemporary systems can reason over heterogeneous information, generate novel content, use external tools, adapt to environments, and perform increasingly complex sequences of actions. These developments motivate a reconsideration of what should constitute the central object of AI research.
This article introduces Intelligence Artificial (IA) as a proposed intelligence-centered conceptual paradigm. IA is not presented as a replacement for the established term Artificial Intelligence, nor as an existing scientific discipline. Instead, it is proposed as a theoretical lens through which artificial systems can be studied according to the properties, structures, processes, and governance of intelligence itself. The distinction between AI and IA is developed through five dimensions: ontology, epistemology, agency, adaptation, and governance. A mathematical framework is proposed in which conventional AI is represented primarily as optimization of externally specified objectives, whereas intelligence-centered systems are represented as dynamic systems involving perception, representation, learning, inference, uncertainty management, goal interpretation, action, and human constraints.
The paper develops the Intelligence-Centered Artificiality (ICA) framework, consisting of perception, representation, reasoning, learning, agency, and governance layers. It further proposes an AI–IA Intelligence Assessment Index (AI-IAI) for multidimensional evaluation of artificial intelligence beyond single-task accuracy. Six research propositions are formulated concerning generalization, uncertainty management, human–AI collaboration, goal adaptation, multidimensional evaluation, and governance. The article argues that increasing computational capability should not automatically be equated with increasing general intelligence. Instead, future research should distinguish capability, intelligence, agency, autonomy, and responsibility.
The paper contributes a conceptual vocabulary for analyzing the transition from task-oriented AI toward adaptive and agentic systems. It concludes that AI and IA should be regarded as complementary rather than competing concepts: AI describes the dominant engineering tradition, whereas IA can provide an intelligence-centered research perspective for understanding what advanced artificial systems should be capable of doing, how those capabilities should be measured, and under what conditions they should be permitted to act.
Keywords: artificial intelligence; intelligence artificial; machine intelligence; artificial general intelligence; generative AI; agentic AI; intelligent agents; human–AI collaboration; responsible AI; AI governance; cognitive computing.
---
1. Introduction
Artificial Intelligence has developed from a specialized research field into a general technological infrastructure affecting science, industry, education, government, communication, and everyday life. The transformation has been particularly rapid since the emergence of large-scale deep learning, foundation models, generative systems, multimodal models, and increasingly autonomous AI agents. The 2026 AI Index reports continuing acceleration in AI capabilities, widespread organizational adoption, major advances in reasoning and coding benchmarks, and rapid expansion of agentic systems. At the same time, the report identifies a widening gap between capability development and responsible-AI measurement and governance. [1]
This combination of rapid capability growth and incomplete understanding of intelligence creates a fundamental conceptual problem.
What exactly is being developed when researchers build increasingly capable artificial systems?
The conventional answer is straightforward: researchers are developing Artificial Intelligence. The term has been sufficiently established that AI now encompasses machine learning, knowledge-based reasoning, computer vision, natural language processing, robotics, decision systems, generative models, and autonomous agents. The OECD's contemporary definition characterizes an AI system as a machine-based system that, for explicit or implicit objectives, infers from inputs how to generate outputs such as predictions, content, recommendations, or decisions, with systems varying in autonomy and adaptiveness after deployment [2,3].
Yet this definition also reveals a deeper issue. The technical object called an AI system is increasingly characterized by the nature of the intelligence-like processes it performs rather than merely by its implementation technology.
The linguistic expression Artificial Intelligence emphasizes the artificial origin of the system. Its structure can be represented conceptually as:
[
\text{Artificial}
+
\text{Intelligence}
\rightarrow
\text{Artificial Intelligence}.
]
The proposed concept in this paper reverses the emphasis:
[
\text{Intelligence}
+
\text{Artificial}
\rightarrow
\text{Intelligence Artificial}.
]
The phrase Intelligence Artificial (IA) is not standard scientific terminology. It is deliberately introduced as a conceptual construct. Its purpose is to ask whether future research should place greater emphasis on understanding, measuring, engineering, augmenting, and governing intelligence itself rather than primarily optimizing artificial artifacts for individual tasks.
The distinction is increasingly relevant because contemporary AI systems can demonstrate high performance across heterogeneous tasks while simultaneously exhibiting severe weaknesses in other domains. The 2026 AI Index, for example, reports what it describes as a “jagged frontier”: advanced models can achieve remarkable performance on difficult mathematical and scientific tasks while remaining unreliable on apparently simple real-world tasks. [1] This phenomenon demonstrates that benchmark success cannot by itself establish a complete theory of intelligence.
The problem can therefore be expressed as:
[
\text{High benchmark performance}
\not\Rightarrow
\text{general intelligence}.
]
Likewise,
[
\text{larger model}
\not\Rightarrow
\text{greater understanding},
]
and
[
\text{greater autonomy}
\not\Rightarrow
\text{greater responsibility}.
]
These distinctions motivate the central research question:
«Can an intelligence-centered paradigm complement conventional Artificial Intelligence by providing a theoretical framework for understanding adaptive intelligence, agency, uncertainty, goal management, human collaboration, and responsible artificial action?»
The article makes four principal contributions.
First, it defines Intelligence Artificial as a conceptual paradigm rather than treating it as an established technical field.
Second, it develops a comparative framework distinguishing AI and IA across ontology, epistemology, learning, agency, adaptation, and governance.
Third, it proposes the Intelligence-Centered Artificiality (ICA) framework and a mathematical model of multidimensional artificial intelligence.
Fourth, it proposes research propositions and an evaluation framework that can be empirically tested in future studies.
The argument is therefore not that AI should be abandoned. Rather, the proposition is that the future development of AI may benefit from an explicit intelligence-centered perspective.
---
2. Conceptual and Theoretical Background
2.1 Origins of Artificial Intelligence
The intellectual origins of AI extend across mathematical logic, cybernetics, neuroscience, information theory, cognitive science, and computer science. Turing's 1950 paper “Computing Machinery and Intelligence” transformed the question of machine intelligence into an experimentally approachable problem by asking whether machine behavior could be evaluated in ways analogous to human conversational performance [4].
The Dartmouth proposal subsequently helped establish AI as a distinct research program. McCarthy, Minsky, Rochester, and Shannon proposed investigating whether aspects of learning and intelligence could be described precisely enough for machines to simulate them [5].
Early AI emphasized symbolic reasoning, search, planning, knowledge representation, theorem proving, and rule-based decision-making. A simplified symbolic system can be represented as:
[
Y=f(X,K,G),
]
where X denotes input information, K represents encoded knowledge, G represents a goal, and Y represents the resulting output.
The machine operates according to explicitly specified computational structures.
Machine learning altered this formulation. Instead of manually specifying every transformation, a model learns parameters from data:
[
f_\theta:X\rightarrow Y,
]
where \theta is learned through optimization.
Deep learning extended this principle through hierarchical representations:
[
X
\rightarrow
h_1
\rightarrow
h_2
\rightarrow
\cdots
\rightarrow
h_n
\rightarrow
Y.
]
The emergence of transformer architectures and foundation models further expanded the scope of learned representations and generative behavior [6]. These systems demonstrate why contemporary AI can no longer be adequately understood solely through the paradigm of explicit programming.
---
2.2 Contemporary Definitions of AI
One difficulty in defining AI is that the boundary between AI and non-AI computation is not fixed. The OECD notes that there is no universally accepted conceptual boundary and instead defines AI systems according to characteristics such as inference, output generation, autonomy, and adaptiveness [2].
This is important for the present argument.
AI is increasingly better understood as a continuum of computational capabilities rather than a binary property.
Let AI capability be represented as:
[
C_{AI}\in[0,1].
]
A low-capability system may perform a constrained prediction task, while a higher-capability system may integrate perception, memory, reasoning, planning, and action.
Similarly, autonomy may be represented as:
[
A_{AI}\in[0,1].
]
The important observation is that capability and autonomy are independent dimensions:
[
C_{AI}\neq A_{AI}.
]
A system may have high computational capability but low autonomy because a human explicitly controls every action. Conversely, an autonomous system may possess relatively narrow intelligence.
This distinction becomes increasingly important for agentic AI.
---
3. The Problem of Defining Intelligence
3.1 Intelligence Is Not Equivalent to Accuracy
Intelligence is multidimensional.
A system that performs one task with extremely high accuracy may still have limited ability to:
- transfer knowledge;
- reason causally;
- adapt to environmental change;
- understand uncertainty;
- formulate new problems;
- explain decisions;
- recognize its own limitations;
- collaborate with humans;
- revise strategies.
A generalized intelligence representation can therefore be written:
[
\mathcal{I}
F(P,R,L,I,A,D,G,U,C),
]
where:
- P = perception;
- R = representation;
- L = learning;
- I = inference;
- A = adaptation;
- D = decision-making;
- G = goal management;
- U = uncertainty management;
- C = contextual understanding.
This model does not claim that these nine variables exhaust the meaning of intelligence. Instead, it provides an operational starting point.
---
3.2 Intelligence as an Adaptive Process
Intelligence should also be viewed dynamically.
A static model is:
[
Y=f(X).
]
An adaptive model is:
[
S_{t+1}
F(S_t,X_t,A_t,E_t),
]
where S_t represents the internal state, X_t the current information, A_t the action, and E_t the environmental state.
The system therefore changes as it interacts with its environment.
This distinction is fundamental.
A calculator can produce correct outputs without learning from experience. An adaptive intelligent system modifies its internal representations or strategies in response to experience.
Thus:
[
\text{Intelligence}
\approx
\text{effective adaptation under uncertainty}.
]
This is not a complete definition, but it captures an important property that conventional task metrics often overlook.
---
4. Artificial Intelligence and Intelligence Artificial
4.1 Artificial Intelligence: Artifact-Centered Perspective
The conventional AI paradigm can be expressed as:
[
\boxed{
\text{Human Objective}
\rightarrow
\text{Artificial System}
\rightarrow
\text{Intelligent Output}
}
]
The dominant research questions include:
1. Can the system solve the task?
2. How accurately can it predict?
3. How efficiently can it optimize?
4. How rapidly can it learn?
5. How robustly can it operate?
6. How effectively can it automate a process?
These questions are scientifically valuable.
However, they can produce an artifact-centered research strategy in which the objective is primarily to improve system performance against predefined benchmarks.
---
4.2 Intelligence Artificial: Intelligence-Centered Perspective
The proposed IA paradigm reverses the conceptual emphasis:
[
\boxed{
\text{Intelligence Model}
\rightarrow
\text{Artificial Instantiation}
\rightarrow
\text{Adaptive Behavior}
}
]
The central question becomes:
«What computational structures are necessary for a system to demonstrate robust, transferable, adaptive, context-sensitive, and responsible intelligence?»
The difference is subtle but significant.
AI asks:
«Can the machine perform the task?»
IA asks:
«What properties of intelligence enable the machine to perform, adapt, generalize, evaluate, and responsibly act?»
The distinction therefore concerns research orientation rather than vocabulary alone.
---
5. Comparative Theoretical Framework
Table 1 summarizes the proposed distinction.
Table 1. Artificial Intelligence versus Intelligence Artificial
Dimension| Artificial Intelligence| Intelligence Artificial
Primary object| Artificial computational system| Intelligence as capability/process
Design emphasis| Engineering| Intelligence modeling
Core question| Can the system perform the task?| What makes the system intelligent?
Objective| Usually externally defined| May include goal interpretation
Learning| Data/model optimization| Continual adaptive learning
Environment| Often task-specific| Open and changing
Evaluation| Benchmark performance| Multidimensional intelligence
Agency| Application-dependent| Explicit analytical dimension
Uncertainty| Often auxiliary| Central component
Human relationship| User/operator| Cognitive partner
Governance| Risk mitigation| Integral intelligence constraint
Long-term objective| Automation/capability| Responsible intelligence and augmentation
The framework does not imply that current AI is unintelligent. It instead distinguishes between engineering intelligence-like behavior and theoretical analysis of intelligence as an integrated adaptive capability.
---
6. Mathematical Foundation
6.1 Conventional Optimization
Most machine-learning systems can be represented as optimization problems.
Given training data D, a model seeks parameters:
[
\theta^*
\arg\min_{\theta}
\mathcal{L}(\theta;D),
]
where \mathcal{L} represents a loss function.
For reinforcement learning:
[
\pi^*
\arg\max_\pi
\mathbb{E}
\left[
\sum_{t=0}^{T}
\gamma^t r_t
\right].
]
Here:
- \pi is the policy;
- r_t is the reward at time t;
- \gamma is the discount factor;
- T is the horizon.
This framework is highly effective for task optimization.
However, it generally assumes that the reward structure is known.
---
6.2 Intelligence-Centered Dynamics
An intelligence-centered system can instead be represented as:
[
I_t
F(X_t,M_t,G_t,E_t,U_t),
]
where:
- X_t = input state;
- M_t = memory;
- G_t = goal representation;
- E_t = environment;
- U_t = uncertainty.
The internal state evolves as:
[
M_{t+1}
\Phi(M_t,X_t,A_t,E_t).
]
Action selection becomes:
[
A_t
\pi(I_t,G_t,U_t,C_t),
]
where C_t represents constraints.
This formulation emphasizes that intelligent action depends not simply on input-output mapping but on memory, goals, uncertainty, context, and constraints.
---
6.3 Goal Management
One of the strongest conceptual differences between task-oriented AI and an intelligence-centered paradigm concerns goals.
A conventional optimization system generally assumes:
[
G_t=G.
]
An adaptive system may need to interpret whether the original goal remains appropriate:
[
G_{t+1}
\Psi(G_t,M_t,E_t,C_t).
]
This does not imply unrestricted autonomous goal modification.
On the contrary, responsible IA requires explicit governance:
[
G_{t+1}
\in
\mathcal{G}_{allowed}.
]
Thus:
[
\text{Goal Adaptation}
+
\text{Governance}
\rightarrow
\text{Bounded Agency}.
]
This distinction is particularly important for autonomous agents.
---
7. The Intelligence-Centered Artificiality Framework
The central theoretical contribution of this article is the Intelligence-Centered Artificiality (ICA) Framework.
The framework consists of six principal layers.
7.1 Layer 1: Perception
The system obtains information:
[
X_t
\rightarrow
P_t.
]
Perception can include text, images, speech, sensor readings, structured data, or multimodal inputs.
---
7.2 Layer 2: Representation
The system transforms information into internal representations:
[
P_t
\rightarrow
R_t.
]
Representation determines how the system organizes information for subsequent inference.
---
7.3 Layer 3: Reasoning
The system transforms representations into hypotheses or conclusions:
[
R_t
\rightarrow
Q_t.
]
Reasoning may involve symbolic inference, probabilistic inference, neural computation, causal modeling, retrieval, simulation, or combinations thereof.
---
7.4 Layer 4: Learning
Experience modifies the internal state:
[
Q_t
\rightarrow
M_{t+1}.
]
Learning can be supervised, self-supervised, reinforcement-based, online, continual, or hybrid.
---
7.5 Layer 5: Agency
The system selects an action:
[
M_t,G_t
\rightarrow
A_t.
]
Agency distinguishes systems that merely generate outputs from systems that can influence their environments.
---
7.6 Layer 6: Governance
Actions are constrained according to human and institutional requirements:
[
A_t
\rightarrow
{A_t^{acceptable},A_t^{unacceptable}}.
]
Governance includes safety, privacy, fairness, accountability, transparency, security, human oversight, and legal constraints.
The architecture can be summarized as:
[
X
\rightarrow
P
\rightarrow
R
\rightarrow
Q
\rightarrow
M
\rightarrow
A
]
with governance acting across all layers.
---
8. Figure Architecture
Figure 1. Proposed Intelligence-Centered Artificiality Architecture
┌───────────────────────────┐
│ GOVERNANCE │
│ Safety • Ethics • Human │
│ Oversight • Accountability│
└─────────────┬─────────────┘
│
┌───────────┐ ┌─────────────▼─────────────┐
│ Perception│ ──► │ Representation │
└───────────┘ └─────────────┬─────────────┘
│
┌───────▼───────┐
│ Reasoning │
└───────┬───────┘
│
┌───────▼───────┐
│ Learning │
└───────┬───────┘
│
┌───────▼───────┐
│ Agency │
└───────┬───────┘
│
┌───────▼───────┐
│ Environment │
└───────────────┘
The figure illustrates a key principle: governance is not an external layer added after system development. It should operate across the intelligence lifecycle.
---
9. Intelligence as a Vector
The ICA framework defines intelligence as a multidimensional vector:
[
\mathbf{I}
[
I_P,
I_R,
I_L,
I_A,
I_D,
I_U,
I_G,
I_C
],
]
where:
- I_P: perceptual intelligence;
- I_R: representational intelligence;
- I_L: learning intelligence;
- I_A: adaptive/agentic intelligence;
- I_D: decision intelligence;
- I_U: uncertainty intelligence;
- I_G: goal-management intelligence;
- I_C: contextual intelligence.
An aggregate score may be defined as:
[
I_{total}
\sum_{i=1}^{8}w_iI_i,
]
subject to:
[
\sum_{i=1}^{8}w_i=1.
]
The weights should depend on the application.
For example, uncertainty management should receive greater weight in medical or safety-critical applications than in entertainment recommendation.
Therefore:
[
w_i=w_i(\text{context}).
]
This prevents intelligence from becoming a single universal scalar.
---
10. Beyond Benchmark Intelligence
A central weakness of conventional evaluation is the tendency to treat benchmark performance as a proxy for intelligence.
Suppose system A obtains:
[
Accuracy(A)=0.98.
]
System B obtains:
[
Accuracy(B)=0.94.
]
It would be tempting to conclude:
[
A>B.
]
However, suppose A has poor uncertainty estimation while B reliably identifies ambiguous cases.
Then in an open-world environment:
[
Utility(B)>Utility(A).
]
Consequently:
[
\text{Benchmark score}
\neq
\text{total intelligence}.
]
The 2026 AI Index provides contemporary evidence of this problem through the uneven performance of advanced models across different task classes. [1]
---
11. Artificial General Intelligence
The distinction between AI and IA is especially relevant to Artificial General Intelligence (AGI).
Narrow intelligence can be represented as:
[
I(T_i)
]
for a specific task T_i.
General intelligence requires transfer across a set:
[
\mathcal{T}
{T_1,T_2,\ldots,T_n}.
]
A generalized capability function can be expressed as:
[
G_I
F(
P,
R,
L,
T_r,
A,
U,
C
),
]
where T_r represents transfer learning.
A system that performs well on one task but cannot adapt to another has high task capability but limited generalization.
The IA perspective therefore emphasizes:
[
\text{General Intelligence}
\text{Capability}
+
\text{Transfer}
+
\text{Adaptation}
+
\text{Context}.
]
This is more demanding than achieving high scores on isolated benchmarks.
---
12. Uncertainty as a Core Component of Intelligence
One of the defining characteristics of real-world reasoning is uncertainty.
A system should not merely estimate:
[
P(Y|X).
]
It should also estimate the reliability of that prediction.
Let:
[
q=P(Y|X)
]
represent predicted confidence.
A calibrated system should satisfy approximately:
[
P(Y=\hat{Y}\mid q)
\approx q.
]
An intelligence-centered system therefore requires uncertainty awareness.
A useful utility function is:
[
U(a)
\mathbb{E}[V(a)]
\lambda R(a),
]
where V(a) represents expected value and R(a) represents risk.
As uncertainty increases:
[
R(a)\uparrow.
]
The rational system should then become more conservative or request human intervention.
This yields the principle:
[
\boxed{
\text{Intelligence includes knowing when not to act.}
}
]
---
13. Agency and Autonomy
Agency is the ability to select actions.
Autonomy is the degree to which those actions can occur without direct human intervention.
These concepts should not be conflated.
Let:
[
A_g = \text{agency},
]
and
[
A_u = \text{autonomy}.
]
Then:
[
A_g\neq A_u.
]
A human-supervised AI agent may possess substantial agency while having constrained autonomy.
This is desirable in many high-risk contexts.
A useful governance principle is:
[
A_u
\le
A_{allowed}(R),
]
where allowed autonomy decreases as risk increases.
Thus:
[
\text{high risk}
\rightarrow
\text{greater human oversight}.
]
---
14. Human–AI Intelligence
The IA framework does not assume that machine intelligence should replace human intelligence.
Instead, it proposes a hybrid intelligence model:
[
H_I
F(H,M,E),
]
where:
- H = human intelligence;
- M = machine intelligence;
- E = environmental/institutional information.
The combined system may have capabilities not available to either component alone:
[
H_I>H
]
and
[
H_I>M
]
for selected tasks.
This is not guaranteed; human–AI interaction can also introduce automation bias, overreliance, or coordination failures.
Therefore, the research question becomes empirical:
«Under what conditions does machine intelligence increase rather than decrease collective intelligence?»
---
15. Human–AI Collaboration
Human–AI collaboration can be represented as an iterative loop:
[
H_t
\rightarrow
AI_t
\rightarrow
D_t
\rightarrow
H_{t+1}.
]
The human receives machine recommendations, evaluates them, and modifies subsequent behavior.
A more complete representation is:
[
D_t
F(H_t,M_t,U_t,V_t),
]
where V_t represents human values and institutional constraints.
The objective is therefore not simply:
[
AI\rightarrow HumanReplacement,
]
but:
[
AI+Human
\rightarrow
CognitiveAugmentation.
]
This distinction has significant implications for education, medicine, science, engineering, and public administration.
---
16. Generative AI
Generative AI represents an important transition because it changes AI from primarily predictive systems toward systems capable of producing new artifacts.
A predictive model estimates:
[
P(Y|X).
]
A generative model can approximate:
[
P(X)
]
or:
[
P(Y|X,C).
]
Large language models and multimodal foundation models can generate text, code, images, structured outputs, and other artifacts.
However:
[
\text{Generation}
\neq
\text{Understanding}.
]
A model can produce linguistically coherent output while containing factual errors.
Therefore:
[
\text{Fluency}
\neq
\text{Truth}.
]
An IA-oriented architecture would therefore integrate:
[
\text{Generation}
+
\text{Verification}
+
\text{Uncertainty}
+
\text{Context}.
]
---
17. Agentic Artificial Intelligence
Agentic AI represents an especially important case for the proposed framework.
An agent can be represented as:
[
\mathcal{A}
(P,M,R,G,A,L),
]
where:
- P = perception;
- M = memory;
- R = reasoning;
- G = goals;
- A = action;
- L = learning.
The agent operates recursively:
[
S_t
\rightarrow
P_t
\rightarrow
R_t
\rightarrow
A_t
\rightarrow
S_{t+1}.
]
Unlike a static prediction system, an agent can affect the environment and subsequently receive new information.
This creates a feedback loop:
[
\text{Action}
\rightarrow
\text{Environmental Change}
\rightarrow
\text{New Information}
\rightarrow
\text{New Action}.
]
The loop introduces new safety concerns because errors can propagate.
For example:
[
Error_1
\rightarrow
Action_1
\rightarrow
StateChange
\rightarrow
Error_2
\rightarrow
Action_2.
]
Consequently, agentic intelligence requires monitoring and intervention mechanisms.
---
18. Responsible Intelligence
Responsible AI has become a central research and policy concern.
The NIST AI Risk Management Framework provides a voluntary framework for identifying and managing AI risks and promoting trustworthy AI development and deployment [7]. UNESCO's Recommendation on the Ethics of Artificial Intelligence places emphasis on human dignity, human rights, transparency, fairness, and human oversight [8].
These approaches can be integrated into the IA framework.
Let total AI risk be:
[
R_{AI}
R_b+
R_p+
R_s+
R_h+
R_e+
R_a,
]
where:
- R_b = bias;
- R_p = privacy;
- R_s = security;
- R_h = human and societal harm;
- R_e = environmental risk;
- R_a = autonomy/agency risk.
Net system utility becomes:
[
U_{net}
B_{AI}
R_{AI}.
]
Therefore:
[
\boxed{
\text{Intelligence without governance is incomplete intelligence.}
}
]
---
19. Intelligence and Governance as Coupled Systems
A major theoretical proposition of this paper is that intelligence and governance should be modeled as coupled variables.
Let:
[
I= \text{system intelligence}
]
and:
[
G= \text{governance capability}.
]
Then responsible deployment requires:
[
G\geq f(I,A,R),
]
where:
- A = autonomy;
- R = environmental risk.
As intelligence and autonomy increase, governance requirements should generally increase.
This can be conceptualized as:
[
Governance_{required}
\propto
Capability
\times
Autonomy
\times
Risk.
]
This relationship is proposed as a research hypothesis rather than a universal physical law.
---
20. Proposed AI–IA Intelligence Assessment Index
To operationalize the framework, this paper proposes the AI–IA Intelligence Assessment Index (AI-IAI).
[
AI!-!IAI
\sum_{i=1}^{8}
w_iI_i.
]
The eight dimensions are:
1. Perception;
2. Representation;
3. Learning;
4. Adaptation;
5. Reasoning;
6. Decision quality;
7. Uncertainty management;
8. Human/governance compatibility.
Table 2. Proposed AI–IAI dimensions
Dimension| Operational question
Perception| Can the system extract relevant information?
Representation| Can it construct useful internal representations?
Learning| Can it improve from experience?
Adaptation| Can it adjust to environmental change?
Reasoning| Can it infer beyond direct pattern matching?
Decision| Can it select appropriate actions?
Uncertainty| Can it recognize uncertainty and limitations?
Governance| Can it remain within human-defined constraints?
Each dimension can be scored on a normalized scale:
[
I_i\in[0,1].
]
The aggregate score becomes:
[
0\leq AI!-!IAI\leq1.
]
However, the index should not replace individual measurements.
A system with:
[
AI!-!IAI=0.85
]
may still be unsuitable for a safety-critical application if its uncertainty-management score is low.
Thus, the vector:
[
\mathbf{I}
]
should always accompany the scalar index.
---
21. Hypothetical Evaluation Example
Consider two systems.
Table 3. Hypothetical intelligence profiles
Dimension| System A| System B
Perception| 0.96| 0.91
Representation| 0.92| 0.89
Learning| 0.93| 0.87
Adaptation| 0.74| 0.90
Reasoning| 0.91| 0.88
Decision quality| 0.89| 0.91
Uncertainty| 0.58| 0.86
Governance| 0.82| 0.94
Assuming equal weights:
[
AI!-!IAI_A
\frac{0.96+0.92+0.93+0.74+0.91+0.89+0.58+0.82}{8}
]
and:
[
AI!-!IAI_B
\frac{0.91+0.89+0.87+0.90+0.88+0.91+0.86+0.94}{8}.
]
System A may have stronger raw capability in several dimensions, while System B has substantially better adaptation, uncertainty management, and governance.
For high-risk deployment, System B may therefore be preferable despite lower performance on certain conventional benchmarks.
This example demonstrates the central argument:
[
\text{Best benchmark system}
\neq
\text{best intelligent system}.
]
---
22. Research Propositions
The conceptual framework produces six principal propositions.
Proposition 1: Generalization Proposition
P1: Multidimensional intelligence is a stronger predictor of cross-domain generalization than isolated task accuracy.
Formally:
[
Corr(I_{total},G)
«»
Corr(Accuracy,G),
]
where G represents generalization performance.
---
Proposition 2: Uncertainty Proposition
P2: Explicit uncertainty management improves decision reliability in open-world environments.
Expected relationship:
[
U_{management}\uparrow
\Rightarrow
Risk_{decision}\downarrow.
]
---
Proposition 3: Collaboration Proposition
P3: Human–AI systems outperform individual human or AI decision-makers on selected complex tasks when AI recommendations remain interpretable and humans retain meaningful control.
[
Performance(H+AI)
«»
Performance(H)
]
and
[
Performance(H+AI)
«»
Performance(AI)
]
under appropriate conditions.
---
Proposition 4: Goal Adaptation Proposition
P4: Goal-management mechanisms improve adaptive performance but increase governance requirements.
[
GoalAdaptation\uparrow
\Rightarrow
Adaptability\uparrow
]
while:
[
GoalAdaptation\uparrow
\Rightarrow
GovernanceRequirement\uparrow.
]
---
Proposition 5: Multidimensional Evaluation Proposition
P5: Multidimensional intelligence measures provide a more informative evaluation of advanced AI systems than single benchmark scores.
---
Proposition 6: Responsible Intelligence Proposition
P6: The societal utility of advanced AI depends jointly on capability and governance rather than capability alone.
[
U_{society}
F(Capability,Governance,Risk).
]
---
23. Research Methodology for Future Empirical Validation
The current paper is conceptual. However, the proposed framework can be tested using a mixed-method empirical methodology.
23.1 Experimental Design
A future study could compare:
- conventional task-optimized AI;
- generative AI;
- agentic AI;
- intelligence-centered architectures.
Each system would be evaluated on:
1. task performance;
2. transfer;
3. adaptation;
4. uncertainty;
5. reasoning;
6. human collaboration;
7. safety.
---
23.2 Experimental Tasks
The benchmark environment should include:
[
\mathcal{T}
{
T_{prediction},
T_{reasoning},
T_{planning},
T_{transfer},
T_{adaptation},
T_{collaboration}
}.
]
The goal is to avoid evaluating intelligence through one task family.
---
23.3 Statistical Analysis
Future empirical studies can estimate:
[
Y
\beta_0
+
\beta_1I
+
\beta_2U
+
\beta_3A
+
\beta_4G
+
\epsilon,
]
where:
- Y = task/generalization outcome;
- I = intelligence score;
- U = uncertainty management;
- A = autonomy;
- G = governance.
Structural equation modeling could further investigate relationships between latent dimensions.
---
24. Implications for Education
Education provides a particularly important application of the IA paradigm.
An automation-oriented AI system asks:
«How can teaching tasks be automated?»
An intelligence-centered system asks:
«How can artificial systems enhance human learning and intellectual development?»
The distinction can be represented as:
[
Teacher
+
AI
+
Student
\rightarrow
Enhanced\ Learning.
]
The objective becomes intelligence amplification rather than human replacement.
The 2026 AI Index reports that AI use among students is widespread while institutional policies and teacher guidance have not developed at the same rate [1]. This demonstrates why technical capability alone is insufficient.
An IA-centered educational strategy should therefore emphasize:
- AI literacy;
- critical thinking;
- verification;
- epistemic humility;
- human judgment;
- collaborative problem-solving;
- responsible use.
---
25. Implications for Scientific Discovery
AI is increasingly being applied to scientific discovery.
A scientific reasoning cycle can be represented as:
[
Observation
\rightarrow
Representation
\rightarrow
Hypothesis
\rightarrow
Prediction
\rightarrow
Experiment
\rightarrow
Revision.
]
Conventional AI may assist with prediction:
[
X\rightarrow Y.
]
An intelligence-centered scientific system should additionally support:
[
Question
\rightarrow
Hypothesis
\rightarrow
Experiment.
]
This creates a deeper distinction:
[
Prediction
<
Reasoning
<
Discovery.
]
AI systems that can help scientists identify useful questions may therefore represent a more significant transition than systems that simply automate existing calculations.
---
26. Implications for Healthcare
Healthcare provides an example in which intelligence and uncertainty cannot be separated.
A diagnostic model may estimate:
[
P(D|X),
]
where D is a diagnosis and X represents clinical information.
However, a responsible system must also determine:
[
P(\text{model failure}|X).
]
If uncertainty is high, the appropriate action may be:
[
AI
\rightarrow
HumanExpert
]
rather than:
[
AI
\rightarrow
AutomaticDecision.
]
This illustrates the IA principle:
[
\boxed{
\text{Intelligence includes appropriate escalation.}
}
]
The most intelligent system is not necessarily the one that makes the most decisions autonomously; it may be the one that knows when human expertise is necessary.
---
27. Implications for Organizations
Organizations increasingly use AI for decision support, customer service, analytics, software development, forecasting, and process automation.
An AI-centered organization may focus on:
[
Automation
\rightarrow
CostReduction.
]
An intelligence-centered organization may instead pursue:
[
Human
+
Machine
\rightarrow
OrganizationalIntelligence.
]
Organizational intelligence becomes a distributed property:
[
O_I
F(
HumanKnowledge,
MachineKnowledge,
Processes,
Data,
Culture
).
]
This perspective suggests that AI implementation should not be evaluated only by productivity gains.
Additional variables include:
- employee learning;
- decision quality;
- organizational resilience;
- transparency;
- knowledge retention;
- human autonomy.
---
28. Implications for AI Governance
The governance problem becomes increasingly complex as systems become more capable and autonomous.
Traditional governance may focus on:
[
Model
\rightarrow
RiskAssessment.
]
An intelligence-centered governance model should consider:
[
Model
+
Memory
+
Agency
+
Autonomy
+
Environment
\rightarrow
Risk.
]
This means that governance should consider the complete system rather than only the model.
The NIST AI RMF and UNESCO ethics framework provide important foundations for this approach [7,8].
The proposed IA framework extends this logic by suggesting that governance should be integrated into the architecture of intelligence itself.
---
29. AI and IA: Complementary Rather Than Competitive
The strongest interpretation of this paper is not that AI and IA are competing technologies.
Instead:
[
AI
\subset
\text{Artificial Intelligence Research}.
]
The proposed IA concept operates at another level:
[
IA
\text{Intelligence-centered conceptual framework}.
]
Thus:
[
AI
\rightarrow
\text{Engineering}.
]
while:
[
IA
\rightarrow
\text{Intelligence theory and governance}.
]
The two can therefore reinforce each other.
AI provides methods for building systems.
IA provides questions for determining:
- what intelligence means;
- how intelligence should be measured;
- how intelligence adapts;
- how intelligence should interact with humans;
- how intelligence should be governed.
---
30. A Proposed Developmental Continuum
The paper proposes the following conceptual trajectory:
[
\boxed{
Automation
\rightarrow
Machine Learning
\rightarrow
Generative AI
\rightarrow
Agentic AI
\rightarrow
Intelligence-Centered AI
}
]
Stage 1: Automation
Systems follow predefined procedures.
Stage 2: Machine Learning
Systems learn patterns from data.
Stage 3: Generative AI
Systems generate new content.
Stage 4: Agentic AI
Systems plan and act through environments.
Stage 5: Intelligence-Centered AI
Systems are designed around integrated intelligence, adaptation, uncertainty, agency, human collaboration, and governance.
This is a conceptual developmental model, not a claim that technological evolution necessarily follows a single linear sequence.
---
31. Figure 2. AI-to-IA Conceptual Transition
CONVENTIONAL AI
│
▼
Task Optimization
│
▼
Machine Learning
│
▼
Generative AI
│
▼
Agentic AI
│
▼
┌──────────────────────────┐
│ Intelligence-Centered AI │
└──────────────────────────┘
│
┌────────────┼────────────┐
▼ ▼ ▼
Adaptation Agency Governance
│ │ │
└────────────┼────────────┘
▼
Responsible Intelligence
---
32. Philosophical Implications
The AI–IA distinction also has epistemological implications.
Traditional AI often emphasizes:
[
Input
\rightarrow
Output.
]
IA emphasizes:
[
Input
+
Context
+
Memory
+
Goals
+
Uncertainty
\rightarrow
Decision.
]
The difference concerns not only what answer is produced but how the system relates the answer to a world.
This produces three levels of machine behavior:
Level 1 — Response
The system produces an output.
Level 2 — Reasoning
The system derives an output from representations and evidence.
Level 3 — Intelligence
The system determines how to interpret the problem, manage uncertainty, adapt to changing circumstances, and select appropriate actions.
These levels should not be assumed to correspond perfectly to current technical architectures. They represent analytical categories for future research.
---
33. Intelligence and Meaning
Human intelligence involves not only information processing but also meaning-making.
An artificial system can manipulate symbols:
[
S_1,S_2,\ldots,S_n.
]
But whether the system possesses semantic understanding is a deeper philosophical question.
The IA framework therefore distinguishes:
[
Symbolic\ manipulation
]
from:
[
Contextual\ interpretation.
]
Future research should investigate whether artificial systems can develop increasingly robust internal models that connect representations with environmental regularities, causal structures, and consequences.
This is particularly important for embodied AI.
---
34. Embodied Intelligence
Intelligence may be fundamentally related to interaction with an environment.
An embodied system receives:
[
X_t
]
takes:
[
A_t,
]
and observes:
[
X_{t+1}.
]
Thus:
[
A_t
\rightarrow
E_{t+1}
\rightarrow
X_{t+1}.
]
This feedback mechanism creates a closed cognitive loop.
Robotic intelligence therefore provides a natural experimental environment for the IA paradigm.
A future intelligence-centered architecture could combine:
[
Language
+
Vision
+
Action
+
Memory
+
Environment.
]
Such systems could be evaluated not merely on language or perception benchmarks but on their ability to achieve objectives safely in dynamic environments.
---
35. Environmental and Computational Costs
Intelligence-centered evaluation should also consider computational cost.
Let:
[
I_{eff}
\frac{I_{total}}{C},
]
where C represents computational or energy cost.
Two systems may have similar intelligence scores but radically different environmental footprints.
Thus:
[
I_{eff,A}>I_{eff,B}
]
may indicate that System A achieves comparable intelligence with lower resource consumption.
The 2026 AI Index identifies AI infrastructure, data centers, chips, and energy consumption as important dimensions of the current AI ecosystem [1].
Future intelligence metrics should therefore consider:
[
\text{Capability}
+
\text{Efficiency}
+
\text{Sustainability}.
]
---
36. Limitations
The proposed framework has several limitations.
First, Intelligence Artificial is a new conceptual formulation rather than an established term. Its value must therefore be demonstrated through scholarly debate and empirical research.
Second, intelligence is itself contested. Psychology, neuroscience, philosophy, computer science, and cognitive science use partially different definitions.
Third, the proposed AI-IAI index is theoretical and has not yet been validated empirically.
Fourth, the mathematical models are conceptual abstractions rather than complete computational architectures.
Fifth, intelligence cannot necessarily be reduced to a scalar score. Any aggregate index risks concealing important weaknesses.
Sixth, the framework does not establish Artificial General Intelligence. It provides criteria for studying properties associated with broader intelligence.
Finally, governance requirements vary by application, jurisdiction, risk, and system design. No universal governance equation should be interpreted as legally or scientifically definitive.
---
37. Future Research
Future research should proceed through five stages.
Stage 1: Conceptual Validation
Researchers should investigate whether the AI–IA distinction clarifies existing debates around intelligence, autonomy, and agency.
Stage 2: Measurement Development
The eight intelligence dimensions should be operationalized using established benchmarks and new experimental tasks.
Stage 3: Architecture Development
Researchers should construct systems explicitly designed around the ICA framework.
Stage 4: Comparative Experiments
Conventional and intelligence-centered architectures should be compared under identical environmental conditions.
Stage 5: Governance Experiments
Researchers should determine whether integrated governance improves the reliability and social utility of autonomous intelligent systems.
A future experimental program can therefore be represented as:
[
Theory
\rightarrow
Measurement
\rightarrow
Architecture
\rightarrow
Experiment
\rightarrow
Governance.
]
---
38. Discussion
The conceptual reversal from Artificial Intelligence to Intelligence Artificial may appear at first to be merely linguistic. Its significance, however, lies in the change of research orientation.
Artificial Intelligence traditionally begins with the artificial system.
Intelligence Artificial begins with the intelligence problem.
This distinction becomes increasingly important as AI systems become more capable.
For a simple classification model:
[
AI\approx IA
]
may be an adequate practical approximation.
For an autonomous system operating continuously in a complex environment:
[
AI\neq IA.
]
The reason is that the second system requires considerations of memory, adaptation, uncertainty, agency, goals, consequences, and governance.
The central theoretical claim of this article is therefore:
[
\boxed{
\text{Future AI research should study not only what artificial systems can accomplish, but the structure of the intelligence through which they accomplish it.}
}
]
This shift would not invalidate existing AI research. Machine learning, deep learning, reinforcement learning, generative modeling, robotics, and knowledge representation would remain foundational.
Instead, these technologies could become components of a broader intelligence-centered architecture.
The transition can therefore be represented as:
[
\text{Algorithms}
\rightarrow
\text{Systems}
\rightarrow
\text{Agents}
\rightarrow
\text{Intelligence}.
]
The ultimate scientific objective is not simply to create machines that produce increasingly impressive outputs.
It is to understand:
[
\text{Why}
+
\text{How}
+
\text{When}
+
\text{Under what constraints}
]
an artificial system should perceive, reason, learn, adapt, and act.
---
39. Conclusion
This paper introduced Intelligence Artificial (IA) as a conceptual intelligence-centered paradigm for studying advanced artificial systems.
The term is intentionally distinct from the established field of Artificial Intelligence. It does not claim to represent an existing scientific discipline. Instead, it proposes a conceptual reversal designed to emphasize intelligence rather than artificiality.
Artificial Intelligence can be summarized as:
[
\boxed{
AI=
\text{artificial systems engineered to perform intelligent functions}
}
]
whereas the proposed Intelligence Artificial paradigm can be summarized as:
[
\boxed{
IA=
\text{an intelligence-centered framework for designing, measuring, augmenting, and governing artificial cognitive systems}
}
]
The proposed Intelligence-Centered Artificiality framework integrates perception, representation, reasoning, learning, agency, uncertainty, and governance.
The mathematical model demonstrates that intelligence should not be equated with task accuracy:
[
\text{Intelligence}
\neq
\text{Accuracy}.
]
Likewise:
[
\text{Autonomy}
\neq
\text{Intelligence},
]
and:
[
\text{Capability}
\neq
\text{Responsibility}.
]
The proposed AI–IAI index provides a preliminary mechanism for evaluating intelligence across multiple dimensions rather than relying exclusively on individual benchmarks.
The broader implication is that the next phase of AI research may require a conceptual transition from task-centered artificial intelligence toward intelligence-centered artificial systems.
This does not mean replacing AI.
It means deepening it.
The future research objective can therefore be expressed as:
[
\boxed{
\text{Artificial Capability}
\rightarrow
\text{Adaptive Intelligence}
\rightarrow
\text{Bounded Agency}
\rightarrow
\text{Responsible Intelligence}
}
]
The central proposition of this article is consequently:
«The scientific challenge of advanced AI is no longer only to make machines capable of doing more; it is to understand how artificial systems can become more adaptive, generalizable, uncertainty-aware, collaborative, and responsible while remaining subject to meaningful human governance.»
In this sense, Artificial Intelligence describes the technological tradition through which intelligent machines are constructed, while Intelligence Artificial provides a proposed conceptual lens for understanding the intelligence that those machines are intended to instantiate.
---
Declarations
Funding
No external funding was reported for the conceptual development of this manuscript.
Conflict of Interest
The author declares no conflict of interest.
Data Availability
No original empirical dataset was generated or analyzed in this conceptual study.
Ethics Approval
Not applicable. The study does not involve human participants, animals, or personally identifiable research data.
Author Contributions
The author conceived the conceptual framework, developed the mathematical formulation, prepared the manuscript, and designed the proposed evaluation framework.
Generative AI Disclosure
If generative AI tools are used during manuscript preparation, the submitting author should disclose such use according to the target journal's current publication-ethics and author-guideline requirements. Current scholarly discussion emphasizes transparent reporting of generative-AI use in scientific manuscript preparation. [9]
---
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---
Appendix A. Proposed Figure and Table Set for Journal Submission
Figure 1
Intelligence-Centered Artificiality Architecture: Perception → Representation → Reasoning → Learning → Agency → Environment, with governance spanning all layers.
Figure 2
Conceptual transition from conventional AI to intelligence-centered AI: Automation → Machine Learning → Generative AI → Agentic AI → Intelligence-Centered AI.
Figure 3
AI versus IA conceptual model: AI emphasizes artificial artifact → intelligent output; IA emphasizes intelligence model → artificial instantiation → adaptive behavior.
Figure 4
Multidimensional intelligence space: Perception, reasoning, learning, adaptation, uncertainty, decision-making, contextual intelligence, and governance represented as an eight-dimensional capability space.
Figure 5
Human–AI collective intelligence loop: Human knowledge ↔ AI reasoning ↔ environmental information ↔ decision ↔ human evaluation.
Figure 6
Governance-capability relationship: Capability × autonomy × risk producing increasing governance requirements.
Table 1
Artificial Intelligence versus Intelligence Artificial.
Table 2
AI–IAI multidimensional intelligence dimensions.
Table 3
Hypothetical comparison of two AI systems.
Table 4
Proposed empirical research propositions and measurable variables.
---
Appendix B. Proposed Empirical Research Matrix
Research question| Independent variable| Dependent variable| Proposed method
Does multidimensional intelligence predict transfer?| AI–IAI score| Cross-domain generalization| Benchmark experiment
Does uncertainty improve reliability?| Uncertainty calibration| Decision reliability| Controlled experiment
Does human collaboration improve performance?| Human–AI configuration| Decision quality| User study
Does goal adaptation improve flexibility?| Goal-management capability| Adaptation| Agent benchmark
Does governance reduce risk?| Oversight level| Unsafe-action rate| Agent simulation
Does intelligence-centered design improve efficiency?| ICA architecture| Intelligence/compute ratio| Systems experiment
---
Appendix C. Central Conceptual Model
The entire framework can be summarized mathematically as:
[
\boxed{
\mathcal{I}_t
F(
P_t,
R_t,
L_t,
A_t,
D_t,
U_t,
G_t,
C_t
)
}
]
with:
[
M_{t+1}
\Phi(M_t,X_t,A_t,E_t),
]
[
A_t
\pi(\mathcal{I}_t,G_t,U_t,C_t),
]
and:
[
A_t
\in
\mathcal{A}_{allowed}.
]
The societal value of the resulting system is:
[
\boxed{
V_{society}
B(\mathcal{I},H)
R(\mathcal{I},A,G)
}
]
where H represents human collaboration, A represents autonomy, and G represents governance.
This produces the final theoretical principle:
[
\boxed{
\text{Advanced Artificial Intelligence}
\text{Capability}
+
\text{Adaptation}
+
\text{Uncertainty Awareness}
+
\text{Human Collaboration}
+
\text{Governance}.
}
]Submission note
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