I was reading the book Introduction to Algorithms, Chapter 1. When I saw this diagram, I got confused because $\lg n$ has the largest value of time while $n!$ has the least value of time. And that doesn't make sense to me - perhaps I misunderstood this?
The numbers in the table are not times; they're the rough sizes of the input $n$, for which the algorithm would take the amount of time in the column labels to run.
i.e. you'd need to give an algorithm of logarithmic complexity input of size on the order of $2$ to the something enormous in order to get it to run for a century, but for an algorithm of complexity $O(n!)$ you only input of size ~10 to make it run for that long.
One of the main things to take away from this table is how the numbers grow as you go across some row, and compare this growth rate for different rows.
For example, for logarithmic complexity the size of the input grows exponentially with the increase in time, while for exponential and factorial complexities the input size grows sublinearly with the time increase (though this is a bit obscure just from the table because the time scale is not linear).