The first 3 are well defined problems, for which there are solutions and the AI just has to find them.
On the other hand, for the 4th the AI would have to become "creative".
It is very hard to define human creativity, because this is the typical example of "I recognize it when I see it".
In any case, human creativity has characteristics that have never been demonstrated by a computer program.
For instance, it is typical for a human to have a complex and detailed plan to perform some activity, but after executing half of the steps and encountering some difficulties, to have a sudden inspiration that there exists a second, simpler way to achieve the same goal, or perhaps that there are very little chances to achieve the original goal, but it is possible to adjust the goal so that the modified goal is still acceptable, but it can be achieved with less effort. Then the human starts executing successfully a very different plan from the original one.
Another example typical for human creativity is when someone has learned and known some theory for many years, at school and from various manuals or other kinds of publications from reputable sources, but then suddenly the human may have a revelation that all that has been taught for years is not really correct, but the theory has some serious flaws and then the human may think about how the theory may be corrected and eventually an improved theory can be conceived by the human.
Such behaviors are extremely far away from what an AI can do today. They might become possible some day, but for that an AI would have to store a big part of the collective knowledge of humanity, but in a much clever way than the current trained ML models. Storing that knowledge in terms of probabilities of tokens being close to each other is certainly not enough for reproducing how the associative memory of a human works during creative activities.