A.I. (Introduction)

Lin-Kernighan heuristic for solving the traveling salesman problem

I stumbled across artificial intelligence almost 4 years ago. I was making a personal assistant application to help me in my everyday life. I called it Alice, a play on the AI language named Alice (It was coded in three different environments: Javascript, Python and Java Android). It was kind of cool; it could turn off lights and activate my coffee machine in the morning, talk to me (kinda it’d get in a loop telling me that it wanted to “marry me”), etc. It ended up winning a hackaton, and I didn’t think much of it after that.

Then about a year ago, AI and bots started blowing up, and I started to take notice again. The resources to go out and learn AI are numerous, but that’s a daunting task. The problem is that AI is a fragmented “science”. There are hundreds of different methods for solving problems and AI encompasses optimization algorithms, Bayesian Networks, Markov Models, etc. Its a bit overwhelming and frankly even turns me off to the subject if I think of it like that. However, knowing the approaches in AI is very important to the future of programming. In this article, (which is going to be the first of many) I will attempt to explain various methods that may be helpful to your business, or everyday life.

The first article is going to be on an optimization algorithm. Its going to be a path finding algorithm. The basic premise is: I’m a salesman and I have 20 cities I want to visit, what is the most efficient route I can take to and still visit all 20 cities? This problem can be extrapolated to warehouse robots, networking applications, airline tickets, placing components on microchips, routing cables.

At the end of the article, you should be able to use this algorithm for the uses stated above and more. You are only limited by your creativity and imagination. Look forward to it in the coming days.

Abhishek Pratapa

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