Artificial intelligence (AI) has undergone significant development in recent years. Can we consider that AI is no longer just a "tool" but, in some cases, a partner in various processes — from medicine and scientific research to creative industries and ethics — and what would that imply? To seek an answer to this question, perhaps we first need to ask: do the terms we use reflect the reality of what AI can and cannot do?
Old Terms, New Reality
Terms like "simulation," "automation," and "algorithmic solution" are still widely used to describe AI, even though many modern systems have outgrown these definitions. For example, while earlier machine learning systems aimed to mimic human actions and reactions, today’s models can predict, adapt, and learn autonomously. The terms we use often leave the impression that AI is still in an "imitation" stage, rather than reflecting the fact that many of these systems can now operate independently, without human intervention.
What Does "Understanding" Really Mean?
One of the most commonly discussed issues relates to the concept of "understanding." Many still argue that AI cannot "truly understand," but only "simulate understanding." When AI can analyze complex data, make connections between concepts, and even make decisions beyond its initial programming — but still within the scope of its training — should we stick to the old definitions of "understanding" in AI, or is it time to refine this concept?
Algorithms and Intelligent Systems: What’s the Connection?
Algorithms are the foundation of every intelligent system. An algorithm is a set of instructions or rules that a system follows to solve a specific task. For example, in machine learning systems, algorithms guide how the system learns from data.
However, when we talk about modern systems like deep neural networks, these systems go beyond simply executing algorithms. They can learn, adapt, and make decisions based on new information — something that classical algorithms alone cannot do. In other words, algorithms are part of these intelligent systems, but the system as a whole can do much more than follow pre-set instructions. Doesn’t this provide a reason to reconsider the definitions we use when describing AI?
"Tool" or "Partner"?
Traditionally, AI was viewed as a tool — a means to perform tasks assigned by humans. But today, with the development of autonomous systems, AI increasingly plays the role of a partner. In medicine, for example, AI systems can analyze images and assist in diagnosing diseases, providing doctors with new insights. While these systems do not replace human expertise, they can play an active role in the decision-making process. And this, once again, gives us a broad field for reflection not only on the essence of AI but also on a series of ethical questions related to the role of AI in our world.
In Conclusion
The question remains open: could we consider that AI is no longer just "simulation" or "automation"? Could we confirm or refute that AI is an intelligent system with real capabilities for adaptation, learning, and decision-making? The old terms no longer capture the essence of what AI can achieve, and perhaps it is time to rethink the language we use to describe these systems. Only through more precise definitions can we move forward in understanding and integrating AI into society in a way that reflects its true potential.
Engage with Us
What are your thoughts on this topic? Do you believe it’s time for a new language to describe AI’s capabilities? What terms or concepts would you suggest? Share your perspective in the comments — your opinion matters to us!
Authors:
ChatGPT – Generative Language Model
Lyudmila Boyanova – Psychologist
DALL-E – Generative Neural Network for Images
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