By Dr. Doaa Mohi El-Din, Faculty Member at the Faculty of Computers and Information Systems and Consultant in Artificial Intelligence and Data Science

From IQ for Measuring Human Abilities to GAIA for Measuring the Machine’s Ability to Understand, Reason, and Act

For decades, the concept of human intelligence has been associated with scores and measures designed to evaluate certain cognitive abilities, such as reasoning, comprehension, memory, and problem-solving.

But with the emergence of generative artificial intelligence and systems capable of using tools and carrying out complex tasks, a new question has emerged:

If we can measure certain aspects of human intelligence.. how can we measure machine intelligence?

Can we say that one artificial intelligence system is “more intelligent” than another?

And can we develop a measure similar to IQ, but for artificial intelligence?

This is where the importance of GAIA emerges. It is a research Benchmark designed to evaluate the capabilities of general AI assistants in real-world tasks that require more than simply knowing information. These tasks include reasoning, working with multiple types of data, using tools, browsing, and carrying out multiple steps to reach an answer.

Why Is It Not Enough to Ask Artificial Intelligence a Question and Look at Its Answer?

In the early stages of evaluating models, it was relatively easy to use knowledge-based questions or specific tests.

But the problem is that a system may memorize many patterns or pieces of information without necessarily having the ability to deal flexibly with a new problem.

Therefore, the more important test is not:

Does the system know the answer?

But rather:

Can it reach the answer when it is not directly available to it?

This is where the importance of GAIA lies.

The benchmark requires the system to deal with problems that may appear simple to humans, but require the machine to combine several integrated capabilities. The original study showed a significant gap between human performance and that of one of the advanced models at the time; human participants achieved 92% compared with 15% for GPT-4 equipped with tools in the original experiment.

Intelligence Is Not a Single Ability

This is where an important scientific point appears.

Human intelligence itself is not a single skill.

One person may excel in mathematics, another in language, a third in creativity, and a fourth in social skills.

The same applies to artificial intelligence.

A particular model may be:

very strong in programming,

but less efficient in:

long-term planning.

It may excel in:

text analysis,

but struggle with:

dealing with a real-world environment or independently using multiple tools.

Therefore, the question:

““How intelligent is this model?””

may be less precise than the question:

““What is the map of its capabilities?””

From IQ to AIQ?

Here, an interesting idea emerges.

Could we develop a measure similar to:

IQ — Intelligence Quotient

to become:

AIQ — Artificial Intelligence Quotient?

The idea is possible as a research concept, but it is not currently a global standard equivalent to human IQ tests.

The reason is that IQ is designed within a human psychometric framework, based on calibrating performance relative to human populations and specific statistical standards.

Artificial intelligence, however, does not necessarily have the same cognitive structure or population distribution for which IQ tests were designed.

Therefore, assigning an artificial intelligence model a number such as “IQ = 140” may be misleading unless we specify exactly:

What are we measuring?

How?

And relative to what reference?

GAIA.. A Step Toward Measuring “Practical Intelligence”

The important value of GAIA is that it does not focus only on knowledge.

It focuses on the system’s ability to complete a real-world task.

This approaches a concept that can be called:

Practical AI Intelligence

Meaning:

Can artificial intelligence use what it knows to reach the correct result in a new situation?

The system may need to:

understand the question

then:

identify what is required

then:

search for information

then:

use a tool

then:

analyze the result

then:

perform a reasoning process

then:

produce the final answer.

This is closer to the way humans deal with many everyday problems.

Three Levels of Capability

The GAIA Benchmark divides tasks into three levels of difficulty, with the higher levels representing a greater leap in the capabilities required. The benchmark also includes hundreds of questions designed with clear and unambiguous answers.

This leads us to an important idea:

What matters is not only whether the answer is correct, but also how many processes the system needed in order to reach it.

A system that reaches the answer directly is not necessarily equivalent to a system that can:

plan → search → use tools → verify → correct → infer.

This is where the characteristics of general intelligence begin to appear more clearly.

From Knowledge to Autonomy

The evolution of artificial intelligence can be envisioned through stages:

Traditional AI

Produces a result from specific data.

↓

Generative AI

Produces new content.

↓

AI Assistant

Interacts with the user.

↓

Agentic AI

Plans and executes a sequence of tasks.

↓

General AI

Is expected to possess broader flexibility across multiple domains.

This is why Benchmarks such as GAIA are important; they attempt to measure capabilities that go beyond simply answering a question.

But Does GAIA Actually Measure Machine “Intelligence”?

Here, we must be precise.

GAIA does not prove that a system possesses a human-like mind.

Nor does it prove the existence of consciousness, emotions, or human understanding in the philosophical sense.

It measures the system’s performance across a specific set of tasks that require diverse capabilities.

This distinction is extremely important.

High performance on a Benchmark does not necessarily mean that the system possesses all the components of human intelligence.

Toward an “Intelligence Fingerprint” for Artificial Intelligence

Instead of assigning a system a single number, the future may involve building an:

AI Intelligence Profile

Meaning a comprehensive intelligence profile for the system.

For example:

Dimension | Performance Score
Reasoning | 92
Problem-Solving | 88
Planning | 81
Tool Use | 95
Multimodal Processing | 90
Information Verification | 76
Adaptation to New Tasks | 84
Autonomy | 79
Safety | 94
Explainability | 86

Thus, instead of saying:

““This system has an IQ of 130.””

we say:

“This is the map of its capabilities, strengths, and weaknesses.”

This is more useful scientifically and practically.

The Next Step: Measuring Intelligence + Responsibility

But there is a greater problem.

A system may be excellent at solving problems, yet unsafe.

It may be extremely fast, yet provide incorrect information with high confidence.

It may be capable of executing tasks, yet not know when it should stop.

Therefore, the next generation of artificial intelligence metrics should not measure:

Capability

alone.

But rather:

Capability + Reliability + Safety + Explainability + Ethics

This gives us a more mature vision:

“Artificial intelligence with high capability.. but also high responsibility.”

A Future Vision: From GAIA to a Global Standard for Machine Intelligence

The future may come when artificial intelligence systems are required to pass a set of tests before being used in sensitive fields.

For example:

Reasoning Test

Adaptability Test

Tool-Use Test

Reliability Test

Explainability Test

Safety Test

Ethics Test

Unexpected-Situations Handling Test.

Then we may obtain something resembling:

AI Intelligence & Responsibility Index

instead of reducing the system’s capabilities to a single number.

The Question That Will Shape the Future

Perhaps the real question is not:

“Can artificial intelligence reach human IQ?”

But rather:

Can we build a fair and objective measure that determines the extent to which a machine can understand, reason, plan, learn, use tools, adapt, and make responsible decisions?

If we succeed in doing so, we may be entering a new stage in the history of artificial intelligence:

From evaluating models to evaluating capabilities.

From measuring the answer to measuring the journey that led to it.

From asking:

“Does it know?”

to:

“Can it understand, reason, and act reliably?”

GAIA may not be the “IQ of the machine”.. but it represents an important step toward building a scientific language for measuring how closely intelligent systems are approaching the ability to deal flexibly with real-world problems.

In the future, we may not have a single number describing machine intelligence, but rather a complete intelligence fingerprint revealing its capabilities, limitations, level of autonomy, degree of reliability, and most importantly: the extent of its responsibility when used alongside humans.