Google used reinforcement learning to design next-gen AI accelerator chips

I was pleasantly surprised to learn that the wiki page for CUDA has a list of open problems in CS research:

    Accelerated rendering of 3D graphics
    Accelerated interconversion of video file formats
    Accelerated encryption, decryption and compression
    Bioinformatics, e.g. NGS DNA sequencing BarraCUDA[55]
    Distributed calculations, such as predicting the native conformation of proteins
    Medical analysis simulations, for example virtual reality based on CT and MRI scan images
    Physical simulations, in particular in fluid dynamics
    Neural network training in machine learning problems
    Face recognition
    Distributed computing
    Molecular dynamics
    Mining cryptocurrencies
    BOINC SETI@home
    Structure from motion (SfM) software

I'm sure what Google is doing is higher up the food chain, but I think this list is a good starting place for undergraduates, especially juniors, who want to focus on CS research. I would only add bio-statistics specifically to this list and mention the possibility of applying AI to bio-engineering.

Do you think this would appeal to post-secondary students?