gene editin vs machine learning

idk but check this shit out:
https://arstechnica.com/science/2017/03/new-technique-efficiently-stores-error-resistant-data-in-dna/

>215 PBytes/gram
>infinite reads
>possibility of self-replication

I am a molecular biologist currently doing my phd in the field of cancer research. I use crispr routinely in my experiments, and it is already a well established method. One problem is the administration into adult organism. The other being the inability to fully rule out potential off target effects. In model organisms this isnt much of a concern, but makes human applications quite dangerous. Remember the pioneer experiments with gene editing in humans - most of the subject developed some form of cancer or autoimmune diseases. It will get much more accurate of course.
I think machine learning is much more of a brekthrough than crispr, simply because of one techical point - it is not the only gene editing technique in use today, only the simplest one, so it would be more accurate to pit machine learning against gene editing in general.

>>60258407
crispr

>>60258407
CRISPR is very useful technique.
Deep Learning is a buzzword for some kind meme technology.

>>60258407
Deep learning CRISPR editing. Learns the best way to change your DNA into a 12 year old girl.