Yes some transitions will be translatable, others will not. For instance if I was a cab driver now, I would be looking to improve in non related areas. Although I may retrain as a mechanic, as maintenance for the time being will be human led.
You're right to look at machine learning, this is the future and there will be lots of opportunities for humans in these areas. Especially in the 'imagineering' department, being able to match tech to human need will I think be a big area.
Out of interest, what is your internship in?
I haven't found the internship yet.
We just got back for the second year of the Bioinformatics Master this September, and we're expected to begin looking more or less now, though some of the more "go lucky" types tend to procrastinate on this, then spam a lot of university research departments and take whichever internship they're accepted to.
Personally I plan on focusing on getting an Internship either in a university or a startup working with ML on genomic or image processing.
Just had a conversation with one of our teacher's tuesday about the different places to checks, universities with departments working on ML for Bioinformatics purposes, etc...
She seemed quite surprised there could be applications in Genomics (though of course she's an Informatician and only recently began playing around with Neural Networks to work on a problem) but I think it's going to be a big domain.
I'm following Stanfords 11 week course on Machine Learning to have some bases to build up from, and one of the things we've already learned are the different kinds of problems one can apply ML to.
From just predicting what the next value of a variable is going to be based on already available data (Supervised Learning) to trying to classify or separate groups of variables/objects based on common features (unsupervised, AKA., where you don't give an explicit answer to the machine other than "separate based on what common denominators you find, using this data and these potential features of the data"). And that's just the basic algorithms.
Personally I think the Classification/Separation problems are the kind of problems that there are going to be most of in Genomics.
Darn, got a bit off topic there XD
one of my friends (#1 in the class but she's quite shy) is more or less about to land her Internship now. she spontaneously asked a research team working with ML and image processing (image processing being her passion), and she's told me the first Google Hangout with one of the researchers (A couple of hours ago) went pretty well.
Now I feel all lazy for not having started sending off emails yet :/
Wow, super awesome; I knew there was a good reason why I was following you! I think the path you've laid out is sound; another tip I would give you is to learn marketing and copywriting.
Purely because many people in your industry will not understand how to properly communicate their ideas to investors and the masses.
Secondly I would say download the cyberdust app onto your phone, and try and get an internship with Mark Cuban, I saw an interview with him not too long ago where he said that his focus was deep learning, I think he'd love to hook up with a young hungry go-getter like yourself.
Cg
Thanks :)
As for Marketing and Copywriting, they're on my to-do list already.
Science in general is Oh So Slooooowly coming to realize that we're rubbish at the whole "communicating with the masses/investors" thing. There's actually been a few scientific articles studying how bad we are at communicating, and possible ways to improve XS
That sounds like an interesting idea. I'll look into it !
Yeah, well there's an opportunity/possible new job area, right there! Yes please do look at cyberdust; a guy from a forum I'm a part of, said he downloaded it and contacted Cuban and he got back to him, I think it's worth a try :-)
Cg