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If you start a new job, you’re handed a stack of books, the latest paper, and an empty coffee pot. You’re left with a problem: you need to build a compiler. By the end of your first week, you will either have implemented a lexer and parser or you won’t. The choice is yours.
The same is true of AI.
In November 2017, Dr. Sergei Karavaev, a post-doc researcher at McGill University, explained how he used the open-source project Trinary to solve a difficult problem: to compute the degree of a polynomial. His resulting project can be used to compute the value of a binary neural network, the optimal reply to a question with three possible answers, and much more.
Rather than explain how he built his app, let me tell you why it’s important for AI:
Computing the degree of a polynomial is a common task, and takes about 3 lines of code. However, solving a problem simply by building an app is not interesting, it’s only useful if it can be used to solve other problems. It’s the beginning of an AI, not the end.
The basic idea is to build an ML model which outputs a binary decision. For example, a binary decision may choose the app for iPhone over the app for Android. Then, you can train your model using data and a metric (fitness) based on the degree of the polynomial, the success rate, or any other metric you want.
In Dr. Karavaev’s case, he trained a neural network using the degree of the polynomial.
If Dr. Karavaev’s algorithm succeeds, it’s because it’s a very specific task that’s easy for a computer to answer (the degree of a polynomial is an integer) and a machine learned model is unlikely to be able to solve it (this problem could not be solved by a toy model and is also hard for human) at an acceptable rate
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