Though we know that we can use programming and setup rules to let the machines work for us, but learning is really based on algorithms which can learn from data. And this needs to happen without the need to setup rules in Programming, when we’re talking about Machine Learning.
It’s often however, misunderstood that this may mean that machines can learn like humans do. This isn’t the case. A great perspective to understand this is to consider the way you yourself learn. Do you refer the Chapter of addition which you learned in school - each time you need to add up numbers? No! You may crunch those numbers in head, use a calculator, or simply ask Google. Using a calculator, or asking Google was not a part of those rule set defined in the chapter to understand. But you eventually learned.
However, Machine learning is not the same learning which we do as humans. On contrary, deep learning use large scale neural networks that can contain millions of simulated “neurons” structured layers. The most common networks are called convolutional neural networks (CNNS) and recurrent neural networks (RNNs). These neural networks learn through the use of training data and backpropagation algorithms - McKinsey
One of the many challenges it poses is data labeling. Google’s self drive is intended to be AI and Machine learning based - but they still need to capture footage of real people driving cars, for thousands of hours, and then feed it to systems. Talk about human brain!
Here’re key areas where the concept is already started for application.
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