From my perspective, the difference can be easily explained with a comparison from everyday life: Today's AI is like a highly specialized expert. She can be impressively good at a certain area – like writing texts or recognizing patterns – but she doesn't really understand the bigger picture. These systems are called weak AI. They are limited to clearly defined tasks and can hardly transfer their knowledge. The idea of artificial general intelligence, i.e. AGI, goes much further. This would be more comparable to a well-educated person who can think, learn and solve problems in different areas. For example, an AGI could analyze a historical event, develop mathematical models for it, and independently draw new conclusions from it. So the main difference lies in flexibility and understanding: Weak AI is a specialist, AGI would be a generalist.
The difference between weak AI and artificial general intelligence (AGI) can be well explained by comparing it to human capabilities. Weak AI specializes in certain tasks. She can describe and solve individual problems very well. For example, it can translate language, recognize images or make recommendations on streaming platforms. These systems work quickly and often very precisely, but only understand the area for which they were designed. They have no real consciousness and cannot flexibly transfer their knowledge to completely new situations. The idea of artificial general intelligence, on the other hand, describes an AI that could think and learn in a similarly versatile way as a human. An AGI could understand various tasks, recognize connections, transfer experiences and solve new problems independently. It would not be limited to a certain area, but would be able to act and learn flexibly. For me as a teacher, this distinction is important because students often believe that today's AI systems are already "intelligent like humans". In fact, we almost exclusively encounter weak AI in everyday life. The idea of AGI currently belongs more to the field of research and visions of the future. In the classroom, it should therefore be conveyed that today's AI is powerful, but cannot replace human thinking, creativity and social responsibility.
Narrow AIs are models that are trained for a very specific purpose or scope and usually reach a very high percentage of success, most of the time even beating humans. An Artificial General Intelligence would be an AI entity which is not restricted to a scope, but rather its possibilities would be endless. It would mean that it can learn, infer, create and imagine in every topic, comparable to a human.
It is specialized for specific tasks; for example, I am training it to provide recommendations for planning production processes. AGI will need to mimic human evolution, but it is still in the planning stages.
AGI is a theoretical concept that does not yet exist (and likely won’t for some time). From the very beginning, it became clear that it was better to have specialized AIs (for medicine, engineering, programming, etc.) rather than a one-size-fits-all solution. It is possible that AGI will emerge, but its purpose will be different—namely, to serve as an everyday assistant. That is why there are now specialized AIs for programming (Claude), medicine (OpenEvidence), engineering (GP AI), art (OpenArt), and others.
Nobody knows what AGI is. The concept is constantly changing. The current stage of AI surpasses what was defined as AGI 6 years ago.
