The debate around artificial intelligence today revolves around a central question: is the advanced performance we observe a form of thinking, or the result of highly complex probability calculations? This question framed the introduction given by Dr. Mohamed Ismail, a researcher and trainer in digital transformation, in his talk with Annahar, where he emphasized the need to distinguish between the impression created by these systems’ outputs and their actual nature, which is based on statistical learning rather than human consciousness or understanding.

From strict programming to pattern learning

Ismail explains that traditional software relied on pre-written logic, where the programmer defines every possible step for the computer: if a certain event occurs, the system executes a specific action.

Today, the model has changed radically. Modern systems are not provided with fixed rules but are trained on enormous amounts of data, enabling them to learn patterns and statistical relationships within it.

In practice, artificial intelligence does not seek an answer through a defined logical path; instead, it predicts the next outcome based on probabilities derived from its training experience. For example, when generating text, the system does not “feel” the next word but calculates which word has the highest probability of appearing in the given context.

Ismail points out that this approach highlights an important aspect of human intelligence itself, as much of what we consider “intuition” or quick thinking in humans also relies on recognizing patterns accumulated through experience, with a fundamental difference: human consciousness and awareness.

Real Learning or Advanced Statistics?

One of the most common questions in this field revolves around whether artificial intelligence actually “understands.” Ismail emphasizes that the scientific answer is clear: these systems do not possess understanding or consciousness.

However, they are capable of generalization, meaning they do not merely memorize data but learn the internal relationships between it and apply them to new problems they have not encountered before.

This gives rise to what is known as “emergent abilities,” where the system succeeds in performing tasks it was not explicitly programmed for, such as summarizing complex texts or solving multi-step problems. Ismail explains that this performance does not come from understanding concepts but from discovering reusable mathematical and linguistic patterns.

Thus, the true distinction is not between understanding and statistics, but between rote memorization and the ability to generalize and reason probabilistically.

The era of digital acceleration and its impact on daily life

Ismail observes that artificial intelligence today stands at the core of an unprecedented phase of digital acceleration, with direct effects on many aspects of life.

In the labor market, automation is no longer limited to manual work but has extended to intellectual tasks, such as data analysis, report preparation, and solution design, with intelligent systems now capable of completing them in seconds.

In decision-making, reliance is increasingly placed on analytical and recommendation tools that process massive amounts of data. However, according to Ismail, this reality requires a new human skill: critical thinking and reviewing the system’s outputs rather than following them mechanically.

Alongside the growing computational abilities of machines, the human role is reinforced in areas that cannot be reduced to numbers, such as ethical evaluation, setting priorities, deep contextual understanding, and making decisions with a human dimension.

Dr. Mohamed Ismail concludes that what we are developing today are not “minds” in the human sense, but systems capable of statistical learning and pattern extraction from collective human data. These systems do not think or feel, but they simulate some outcomes of thinking through advanced mathematical models.

Ultimately, artificial intelligence reminds us of a basic truth: intelligence does not always rely on explicit logic and clear steps; much of it consists of the ability to spot patterns and connect the dots quickly. The difference is that humans do this with awareness and life experience, while machines do it through probabilities and algorithms.