Everyone here mentioned Data Science and honestly, I see it happening but makes me pretty sad nonetheless. ML has been able to do some pretty cool things but the question I keep asking is: has it been worth it? Google has been one of the biggest proponents of ML and I'll be honest; apps like Maps being able to give directions and projects like Watson at IBM are revolutionary and fantastic. However, on the other hand there are problems the industry has "solved" that didn't need solving in the first place imo. Things like YouTube recommendations and super targeted advertising. A lot of my problem with ML comes down to the data itself. Users are using a service with the intent for the service to do its job, not decide what we want to do. The rise of ML has put the algorithms in control instead of the user, and that's not tech I like using.
Besides buzzwords in tech these days, I may be wrong but, I guess you understand that differences between Data Science(along with Data Mining/Info retrieval), ML/Deep Learning, NLP and classical applied statistics are razor thin. Which means having a good training in one of those fields can get you a job in another.
Unfortunately, Machine Learning.
Everyone here mentioned Data Science and honestly, I see it happening but makes me pretty sad nonetheless. ML has been able to do some pretty cool things but the question I keep asking is: has it been worth it? Google has been one of the biggest proponents of ML and I'll be honest; apps like Maps being able to give directions and projects like Watson at IBM are revolutionary and fantastic. However, on the other hand there are problems the industry has "solved" that didn't need solving in the first place imo. Things like YouTube recommendations and super targeted advertising. A lot of my problem with ML comes down to the data itself. Users are using a service with the intent for the service to do its job, not decide what we want to do. The rise of ML has put the algorithms in control instead of the user, and that's not tech I like using.
Thanks Sean for this input.
Besides buzzwords in tech these days, I may be wrong but, I guess you understand that differences between Data Science(along with Data Mining/Info retrieval), ML/Deep Learning, NLP and classical applied statistics are razor thin. Which means having a good training in one of those fields can get you a job in another.
Shameless self-quote:
Dumb algorithms make us dumb
Rémy 🤖
Not to mention that in order to learn our preferences they have to gobble up lots of data about us.