Socializing is an odd term. People in Computer Science are usually regarded as socially incompetent, or at least plain out weird and introverted. Funny, though, that most of the modern tools (Twitter, Facebook, Google+) people use to interact were created by these socially impaired persons.
But, then again, people from CS are usually the ones that post more actively and have many friends in Facebook and Twitter. So, they do socialize.....
Yet, we are not here to point out this oddities, but to speak on the importance of socializing (which is different from team work) in a research environment. Also, I'll address and recommend some particular sites fro the ML community.
I've found that I feel motivated when I discuss different topics with different people. And even if the problem is different, you still feel good. How many times have you discussed with your adviser or a colleague and found out something you did not consider. Sometimes our thoughts are different when we externalize them. A good idea may seem dumb when spoken out-loud, or a bad idea may have a touch of genius when others hear it.
Teaching something you like is another way to socialize. Prepare a presentation on some random topic, and you'll see you can learn a lot from that topic (if presented to the right audience). I highly endorse making the student present their work or a random topic to an audience. It forces them to understand the material, and in the process they learn a bit on expressing their ideas.
But in this modern age, we have a myriad of tools to achieve these interactions without getting out of our desks (which I have yet to decide whether is a good or a bad thing). We have social networks, where we can find good researchers, we have online lectures, were we can socialize with other people watching the lecture, and thankfully machine learning has been an early adopter as well as an active player.
On social networks, you can have different interactions depending the network you use. I find Twitter lists to be a great source of Machine Learning researchers, feel free to follow mine. Google+ recently releases a feature that allows the users to share their circles, and I have a somewhat good circle of Machine Learning Researchers (Andrew Ng, Nir Friedman and Yan LeCun among others). The important thing about these lists and circles are not only the researchers, but the enthusiastic community of grad students that post, discuss and share ideas and innovations.
Now, I've found that while great, these networks are not really suited for a deep theoretical discussion, and with the advent of QA sites like Quora, I think we have a better forum to externalize doubts and consultations on ML and other topics.
I think the top QA venue in Machine Learning is Metaoptimize (lets plug the ad here). It is a place, where most of the people are devoted Machine Learning students (unlike Reddit's Machine Learning sub-reddit, where most of the people are ML enthusiasts).
In Metaoptimize, you usually will get good answers for most of your questions, and if you don't, you get at least a link and a starting point to keep looking for an answer. People there have their fundamentals right, so if you have specific questions on the intuition and maths of a problem, you'll get your answer there. Most of the top contributors in Metaoptimize are also in my Twitter and Google+ lists.
Metaoptimize is definetily a step in the right direction, but sill, I think more can be done to share and to create a broader community, Andrew Ng and Norvig have recently opened free courses to the world, in which they teach ML and AI. They haven't started yet, but a hefty amount of students are rallying to them.
Our world is open, do not think that doing research in a desk involves only reading books and papers, socializing is a very important part of it, and one you should embrace.
That's everything for tonight
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