Hello Mohamed el Nawawy

Apologies for the lengthy response, but this is a technically complex question that requires careful consideration.

The question of what aspects of machine intelligence are being tested is, therefore, complex; this becomes even more so when we consider what kind of tests they would be. Generally, these tests have been designed to test a machine's performance of tasks mimicking intelligent behavior, often importing human cognitive standards such as problem-solving, language processing, or pattern recognition. But the nature of human-defined criteria places an inherent constraint on such tests, which may not encompass the intricacies and breadth of intelligence itself.

In this light, one salient problem is the absence of an agreed-upon global standard for measuring intelligence common to both man and machine. In majority of cases, existing tests pass machines or fail them in terms of their performance at tasks deemed "intelligent" by human beings, as opposed to rationally comparing machine competence with human cognition. A machine might win at chess or Go, outperforming humans in these capacities, but that does not mean that it is demonstrating general intelligence or human cognition with flexibility.

Moreover, even the standards and metrics we apply usually emphasize certain abilities to solve specific kinds of problems, or a specific expertise in a domain, which is task-oriented and does not signify broader or general intelligence (which is popularly referred to as an Artificial General Intelligence or AGI). The benchmarks may reveal some replication of human behavior within constraints, but they do not specify whether the machine evinces intelligence akin to human cognition, especially in instances where human intelligence might be engaged in social, emotional, or abstract reasoning.

Thus, present tests primarily measure machines' capabilities in particular domain based on criteria set down by human designers and do not present a genuine assessment of intelligence. Here lies an extremely important distinction: current assessments of machine intelligence measure performance, and not intelligence in itself. As AI continues to mature, assessing machine intelligence will need ever-more refined and complex indicators, shifting the focus from study tasks designed around human aptitude to accommodate a wider range of cognitive capabilities and the distinct ways through which machines might process or learn.

Mohamed el Nawawy ,

While I agree with your conclusion: "Therefore, speculations about scenarios where artificial intelligence matches or exceeds human cognitive abilities become questionable"

the premises used do not entail this conclusion.

There are two different issues in your question:

1) The capability of the machine intelligence

2) How machine intelligence is measured

It is in principle possible to device a technology that performs X but not be able to measure its performance Y.

This can be due to lack of knowledge of the technology or the evaluation instrument itself. Alternatively, it can be that there is lack of consensus on the evaluation criteria but the technology has been well developed. Thus, there exist possible explanations of why one issue does not necessarily influence the other.

That said, that AI matches or exceeds human intelligence can be objected on theoretical grounds in that state of the art systems are mere sequence predictors. Defining intelligence is the other issue, and while it is hard to imagine consensus, I think that it can generally be agreed upon that intelligence is not being a mere sequence predictor.

On the issue of evaluation, I think that the post by Anil Shukla covers it.

Having worked closely with machine learning models in vehicular networks, I’d say the true test of machine intelligence goes beyond classification accuracy or benchmark scores. It’s about context sensitivity and adaptability—how well a system can respond to unpredictable real-world scenarios, like rerouting emergency vehicles in congested cities or predicting a traffic surge based on weather anomalies. For me, intelligence is less about mimicking humans and more about being operationally reliable in messy, dynamic environments—something I’ve seen firsthand in deploying predictive models for urban mobility systems.

https://medium.com/@melnawawy1980/what-does-a-test-of-machine-intelligence-actually-measure-a434bd21295c

I think an important but rarely evaluated skill is critical reasoning, including pointing out hidden assumptions in the question.

Also, I would be cautious about linking intelligence to "guessing intent" as in some AGI definitions. Even humans find it normal to ask for clarification and if a machine recognises ambiguity, it is intelligent to indicate it before answering the question.