Source: Deep Learning on Medium
The field of Machine learning is experiencing exponential growth today, especially in the subject of computer vision. Today, the error rate in humans is only 3% in computer vision. This means computers are already better at recognizing and analyzing images than humans. What an amazing feat! Decades ago, computers were hunks of machinery the size of a room; today, they can perceive the world around us in ways that we never thought possible.
The progress we’ve made from 26% error in 2011 to 3% error in 2016 is hugely impactful. The way I like to think is, computers have now evolved eyes that work. — Jeff Dean
Now this achievement — made possible with advancements in machine learning — isn’t just a celebration for computer geeks and AI experts, it has real-world applications that save lives and make the world a better place. Before I blab about a life-saving application of computer vision, let me illustrate to you the power of computer vision.
Let’s say I give you 10,000 pictures of dogs and I ask you to classify them into their respective species, are you able to do that? Well, you can, but you have to be a dog expert and it’ll take days by the time you’re done. But for a computer (with a GPU), this takes mere minutes. This incredible capability of computer vision opens up a profusion of applications.
Application of computer vision
One quintessential application for computer vision given by Jeff Dean is in diabetic retinopathy — which is a diabetes complication that affects the eye. Now to diagnose it, an extensive eye exam is required. In third-world countries and rural villages where there is a paucity of doctors, a machine learning model that uses computer vision to make a diagnosis will be extremely beneficial. As with all medical imaging fields, this computer vision can also be a second opinion for the domain experts, ensuring the credibility of their diagnosis. Generally, the purpose of computer vision in the medical field is to replicate the expertise of specialists and deploy it in places where people need it the most.