Internship for Students

Writing this after a while. 

I was thinking of doing something like this for a very long time (many years probably) but took while. Dr. Sankaran's post is one of the reasons [Thank you Sir]

Hope you guys still remember OpenGyan. I am planning to do something similar but for folks who are really want to take programming to next level. The thinking is to come up with real world problems and TRY solving it and on the way you will learn some cool things. It will be like internship (of course with no stipend).

Program Structure:

1. Identify folks who are interested to do this. Ideally someone with good (this is purely relative) programming skills and willing to put efforts to learn something new. Should be doing graduate or post-graduate course in a college (and interested in computers)
2. Willingness to put 4-5 hours of efforts per week. I think this will be a big differentiation. 
3. Willingness to work in latest technologies - cloud/containers/tools/http/REST (some of these are highly talked about in industry)
4. Has Google hangout or similar tool for interaction
5. Talking to geeks and learning from them (expands your learning)
6. Planning to do for two teams each containing not more than two people. The members of team are co-located (either from same college or live near by each other or connected by Google hangout in the order of preference)
7. Finally a demo of your work (a write-up on what you have done and learnt) and move on to next problem (if you are still interested)

What you will probably get (no guarantee):

1. Problems that the world is trying to solve (tough part)
2. How some of the problems are solved (easy part once tough part is found)
3. Get hooked to problems (disciple)
4. Apply what you learnt. If you have not learnt anything, learnt it first and apply it (consistency)
5. Learn tools that professional use to debug/solve problems (smart work)

What you pay:

• Your fee is through your commitment and valuing the folks' bandwidth
• No exchange of money or favour to be involved

If you are interested - write to me GRABYOURFREEDOM AT GMAIL DOT COM

If you feel it will help someone that you know (or don't know) - Just Share.

Simplification and Generalization in Problem Solving

You scatter toy towers of varying heights on the floor and ask some five old kid to arrange the towers in increasing order of heights. Most likely, he is going to knock of the puzzle. His first problem (and yours) will be understanding the problem - what needs to be done. Once he understands, he will complete the puzzles with your help here and there.After sometime, you ask him to sort toy circles in increasing order to circumference. Most likely he is going to finish off this taking lesser time than his previous. 

Both the instances are specialization of sorting - a basic algorithm that is known to human being. Human beings were sorting objects in life much before sorting algorithms were put forth. All those sorting were merely specializations and they work in specific scenarios but not all. Rather than saying it won't for all scenarios it is better to say that it was not tried having "all scenarios" in mind.

It is when we think about "all scenarios" we forget the specifics and the properties that is unique to the given problem and start to think about the "commonalities". When we think about "commonalities" we abstract ideas/concepts and bring many levels of abstraction based on the "commonalities". The specifics that are known at the time of abstraction forms as a boundary condition for abstraction. So, it is also very important to think widely about a specialization so as to make the abstraction more flexible & accommodative. Moving from specialization to a broader generalization is possible only when we simplify the problem domain and think about "many scenarios".

In order to understand the problem, you have to travel towards specialization and in order to solve it after understanding it requires simplification and generalization (and for achieving this you have travel further in time, by time i mean experience).

In next post, we will see some of the ideas on generalization from neuroscience's point of view.