It is said that machines will never be able to compete with the human brain. Machines are programmed to run specific tasks and they can only process the information programmed in them. If you give them an unfamiliar task, they will fail to do it. However, the evolution of Artificial Intelligence (AI) has changed the way it used to be. Now machines can think like humans and they can respond to unfamiliar situations too. AI is the fifth generation of computers and scientists are studying it in depth to make it the best sophisticated technology ever.
AI technology has found its way in the field of image recognition, also known as computer vision. Using this technology, engineers have come up with software, which can describe the content in videos and photos. Earlier, image recognition was limited to recognizing only discrete objects in an image. Now, researchers at Google and the Stanford University have found new software that can describe the entire scene in an image.
In April 2017, the CEO of Facebook, Mark Zuckerburg, outlined the social network’s AI plans to create machines which will be better than humans in perception will. He also demonstrated a new image recognition technology that was designed for the blind, and is capable of identifying what is going on in the picture and explains it aloud.
The application of AI in sectors such as natural language procession, bioinformatics, and gaming has been taken to an all-new level. Today, image recognition has to a great extent benefited from the deep learning technology, open-source data base, and superior programming tools. Even though the headlines refer AI as the next big thing, how it will work to provide better image recognition technology to the world is yet to be addressed.
A massive amount of data is needed by the computers to quickly and accurately identify the picture. These databases contain millions of keyword tagged image that describe the object in the picture. For example, computers identify “cats” in an image by analyzing several images tagged with the word “cats”.
The next step is to prepare the machine to learn from these pictures. For this purpose, free frameworks such as open source libraries are available and can serve as the starting point for training purpose. They provide many image recognition functions like facial recognition, emotion, medical screening, large obstacle detection, etc.