Slices are the process of selecting a subset of data from a cube by pinning one or more dimensions to specific values, similar to filtering in a traditional database.
For example, the following MDX pins the time dimension to a specific year (for example, 2022) to slice the data, querying the turnover generated by all service types.

select
{
measure.[Turnover]
} on 0,
members([Service Type], LEAFS) on 1
from [Airline Turnover]
where ([Date].[ALL].[2022]);

Dicing is also by fixing one or more dimensions to a specific value, as if a hypercube is divided into a part, and then this part is further observed and analyzed through other dimensions.
For example, the MDX below fixes the time dimension to a specific year (e.g. 2022) while fixing the aircraft type dimension to the Boeing 787 Dreamliner model, and then looks at the turnover of this aircraft type corresponding to all service types in 2022.

select
{
measure.[Turnover]
} on 0,
members([Service Type], LEAFS) on 1
from [Airline Turnover]
where ([Date].[ALL].[2022], [Aircraft Type].[ALL].[Boeing].[Boeing 787 Dreamliner]);

Slicing and dicing are powerful operations that allow users to analyze data from multiple perspectives in an OLAP cube.
If you really understand OLAP multidimensional data structures, you will find that slicing and dicing are essentially the same, and there is no difference between them.