Artificial Intelligence in Space
The first-ever black hole image was released barely a year ago. This black hole is found at the center of Messier 87 (M87), which happens to be an elliptical galaxy located 55 million light-years away from earth!! How is it that we were able to achieve such a feat? A powerful telescope called Event Horizon was used to capture the image. But, there’s a catch! Telescopes that are meant for space exploration consist of a network of supercomputers that help to decode the image. And hence, to get an appropriate result(in this case, an image) requires the use of highly sophisticated algorithms.
The algorithm required to process the image of the black hole was designed and developed by Dr.Katie Bowman along with MIT’s Computer Science and Artificial Intelligence Laboratory. Dr. Bowman and others developed a series of algorithms called CHIRP that converted telescopic data into the historic photo shared by the world’s media. This is just one example of how AI, in the form of intelligent algorithms, collaborates to process huge amounts of data, which exists millions of kilometers away from us.
Technology has shaped the overall software world. Artificial Intelligence not only helps to build intelligent machines but also discover intelligence outside our earth. This is proved by the fact that life exists in the form of microorganisms in other planets, discovered by space rovers deployed by NASA.
Can the same computer algorithms that help driverless cars to drive safely help identify nearby asteroids and find life in our huge universe? Machine Learning is the heart of AI. It describes the most frequently used algorithms that allow computers to learn from data to make predictions and categorize objects faster and more accurately than a human being can. ML is also widely used to help technology companies to detect the faces in photos or predict which movies will people enjoy. But some scientists see applications far beyond Earth. Machine learning is also used in relative spacecraft and satellite motion control. Each control action selected for spaceships or satellites requires considering and processing a huge amount of information in an extremely short time. Space missions have become increasingly frequent and complex and spacecraft travel further from Earth, there will be growing demand for super fast and self-correcting machine-learning-based navigation capabilities.
Neural Networks have been extensively used to identify the morphology of asteroids and their origin. The success of neural networks in these situations has much broader and astonishing implications. Machine learning, when applied to the field of astronomy, has reached a certain level of accuracy and sophistication that space agencies can more comfortably deploy these algorithms to process the massive collection of current and astronomical data. These algorithms now have been developed such that they are now having the capability to detect some of the weakest signals of morphology, signals that would have been missed entirely by human intervention. Space agencies have now realized the time and money which can be saved while making use of such sophisticated neural network algorithms and the signals which could never have been discovered before and now being discovered with the help of neural networks.
The future of space technology has some of the most exciting adventures involving colonization of Mars, Identifying other planets similar to earth and increasing our vision of the observable universe. Regardless of the objective of the mission, the complexity of the mission, all the space missions will always involve the use of Machine learning and intelligent algorithms.