Facebook AI Research Team Open Source DeepMask and SharpMask

The answer I got was rather unpleasant and won't please everybody. No you cannot contribute. At least not in a direct meaningful fashion. You can be a waterboy. So that's what I did for 2 years. I wrote a lot of ETL jobs using custom Scala DSLs, that fed the input to these ML jobs. It was a total waste of time. Sure I learnt map reduce and Hadoop and all that jazz, but end of the day, I wasn't doing ML. I was doing ancillary tasks. These tasks no doubt have some economic worth, because I was getting paid. But no company is going to let you do the ML when they have 100s of ML PhDs on their payroll and you aren't one of them. So you just do the data prep, or do ETL, or do data viz, or crunch some numbers aka BI, and convince yourself you are doing real ML. This went on for a while. Finally I couldn't put up with this farce and quit. What worked for me personally was finding a very small company with a tiny data science department, that was headed by an ML PhD who was ready to mentor me, tell me which papers to read, get me to start working on my own papers, get me to build ML systems for image recognition on company time, get me to read textbooks and present topics...it was all quite painful and very humbling, but I learnt a shit ton of stuff. So my frank suggestion is to be brutally honest with yourself. You aren't going to get from here to there by hanging around on coursera or writing ETL. This stuff is seriously hard. If you want to make a genuine contribution, be willing to put in serious time - and that means literally stopping whatever shit you are doing now as a webdev/back-end dev/ETL dev/data-eng etc. Those ancillary tasks won't get you anywhere. Buckle down and do the real deal. You'll thank yourself one day if you did.

What really helped advance my understanding from zero to knowledgeable novice was rewriting some existing code line by line (using expanded variable names and comments), and thinking about each line and what it does as you go. It's the software development equivalent of Hunter S. Thompson re-typing The Great Gatsby just to get the feel of writing a great novel. Here's one I did based on Denny Britz's tutorial:

Britz's Original: http://www.wildml.com/2015/09/implementing-a-neural-network-...

My version: https://gist.github.com/sthware/c47824c116e6a61a56d9

HTH

A Noob question:

If it reaches the "breadth" of human brain, how close will that to the "Skynet becomes self-aware" moment.

What would it tell us about human, our society after it study, analyze millions, billions hours of FB, youtube videos?

If you want to make actual contributions to the machine learning part of machine learning (i.e. not building infrastructure and pipelines), then you need to get your math into a really good state. That comes first before you can even hope to begin to learn ML. That means acing probability and linear algebra, which is most of what ML is based off of.

If you have no experience with probability or linear algebra, it'll take you at least a year of solid studying to get up to speed.

Then, you can start taking ML courses. That will take you another year to really understand well.

At that point, as dxbydt said, you should find a ML PhD who is willing to mentor you. Otherwise, you will not be able to make meaningful contributions, since there are thousands of ML PhDs out there who are much better skilled than you are, and companies would rather hire them than you.

So the ultimate answer is: yes, with a ton of sacrifices and years of work. The only question for you to answer is whether or not you want to make those sacrifices and put in that work.