Hey my friends, this is the article 87 / 1000! For those who don’t know me, my name is Selim Chehimi and I’m an engineering student. I’ve been programming for 7 years now so you can tell that I really enjoy that. Those 7 years of programming lead me to Artificial Intelligence. My dream is to build an AI Startup so that’s the reason why I’m sharing this article with you. I hope that you’ll enjoy reading it as much as I’ve enjoyed writing it.
What’s up guys! I hope that you are doing well 🙂 My Articles are really popularized so anyone can get to know more about Artificial Intelligence. In my opinion, this is a good thing because AI is going to fulfill a large part of our lives in the future. I’m starting to get more into the details of Machine Learning and AI. I’m learning with the Coursera lecture made by Geoffrey Hinton. It’s very interesting so I wanted to make a short Article about the first lecture called “Why we need Machine Learning”.
At the risk of oversimplifying, I’ve always defined Machine learning as: “A subfield of AI where machines can learn without explicitly programmed instructions”. Hinton also defines it this way, arguing that it’s very hard to write programs that solve problems like recognizing 3D objects because we don’t know how it’s done in our own brain. (Even if we knew it, it might be tremendously hard to write these kinds of programs) Another decent example that he’s giving is card transaction fraud. It’s impossible to write a program able to recognize that a credit card transaction is fraudulent because there is no reliable rules and fraud is a moving target so the program needs to keep changing.
This is the reason why we need Machine Learning. Instead of writing a program, we collect a lot of examples that specify the correct output for a given input (e.g. this picture is a cat, this one is a dog, this transaction is a fraud). A machine learning algorithm takes these examples and produces a program that does the job. The program can change without rewriting anything – all we need to do is to retrain it on new data). In other words, if the data change, the program can change too. Machine Learning (ML) has proven to be extremely efficient and useful. Especially for recognizing patterns, facial expressions, predictions or speech recognition. AI and ML are the number 1 priority of all Tech Giants so we should all keep an eye on its advancements.
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