The lecture offers a look at humans and neural networks without sensational noise - through questions that truly concern us today. Why does artificial intelligence already know a lot, yet still does not resemble us? Where does it impress, and where is it powerless against tasks that a child handles almost effortlessly? The conversation about artificial intelligence has long moved beyond the technical environment and become part of everyday life. We correspond with smart systems, assign them texts, searches, analyses, and increasingly catch ourselves thinking that we seem to have a conversational partner capable of thinking almost like a human. But this very sensation requires careful discussion: if a machine seems intelligent, does that mean it is structured similarly to us? And what do we even call intelligence when we speak of a human and an algorithm in the same sentence? This lecture revolves around one of the most intriguing comparisons of our time - natural and artificial intelligence. The focus will not be on a set of loud predictions, but on the differences in the very way of thinking, perception, and problem-solving. Listeners will be invited to look at AI without the usual extremes - without awe at the miracle and without fear of the fantastic enemy. Such an approach is especially valuable now, when neural networks seem omnipotent and remain a black box for most. The interest of the event lies in its connection between philosophical and practical perspectives. The discussion will address what intellectual actions a person performs almost unnoticed, while machines struggle greatly with them. Conversely, it will explore where artificial systems open up possibilities that are fundamentally inaccessible to humans. Such a conversation helps to better understand not only the technologies but also ourselves: memory, attention, intuition, the ability to generalize, make mistakes, and find unexpected solutions. The speaker Alexander Khomyakov works at the intersection of AI and philosophy, meaning he can discuss complex topics not only from a technical standpoint but also in a broader intellectual context.