One reason for learning APL mentioned in the interview is learning to solve problems without branching.
Conor's knowledge of solving problems in a data parallel fashion using masks and such in APL helped him with his day job of building GPU accelerated data-science tools at NVidia.
But it's not just a NVidia thing - CPUs can do data parallel operations and also get slowed down by branching. I don't think this means that you should write everything in an array language, but it means that learning how to solve problems under those constraints will teach you important lessons.
Conor's knowledge of solving problems in a data parallel fashion using masks and such in APL helped him with his day job of building GPU accelerated data-science tools at NVidia.
But it's not just a NVidia thing - CPUs can do data parallel operations and also get slowed down by branching. I don't think this means that you should write everything in an array language, but it means that learning how to solve problems under those constraints will teach you important lessons.