The trend in machine learning is using streaming data — and attempting to perform analytics on that data as it flows back to Earth, rather than waiting for all of it to arrive before doing the processing. “You get faster sorts of alerts and dashboarding on that data coming in from the devices to [the programmer], who determines where the big trends are going,” Brooks said.
This theory of compressing and dealing with data will be particularly applicable for programs with Webb that seek a lot of information, such as those that seek signs of life. The Search for Extraterrestrial Intelligence (SETI) Institute, for example, sees a potential partnership Webb might engage in, with a ground-based radio telescope.
While the radio telescope on the ground looks for sky-based, narrow-band signals that move at the same rate as the Earth’s rotation — showing that the signal is coming from the sky — Webb might be able to send back information about oxygen, nitrogen, or other elements that indicate a planet may host life as we know it, said the institute’s senior astronomer, Seth Shostak.
“It’s a clear case in which if you have machine learning, and you trained the software to recognize an actual signal and to reject all the ones you pick up that are not correct, that just speeds up the search,” he said.
That of course assumes Webb might be able to see planets close to the size of our own, which is not a guarantee; most researchers say the telescope will be better situated to see huge, Jupiter-sized planets.
Cloudera’s Brooks points out that space-based AI has numerous applications for companies seeking to have organized information as quickly as possible, likening the process to having a “Star Wars”-like drone on a potentially habitable planet using AI to steer its way.
“You’re trying to pick a needle in a haystack. You’re just zeroing in on an object better … it’s a massive kind of a concept,” Brooks said of the filtering tools in place today. The right AI, he added, will assist telescope users with using the knowledge they have to move forward on the results turned up by machine learning, whether it’s an interesting black hole or a potential life-friendly world.
Back on Earth, it’s not just astronomers and astrophysicists who benefit from streaming data and AI. In healthcare, for example, doctors are starting to leverage ML for real-time analysis of data to improve medical care. As do many other industries, from retail and logistics to banking and insurance.
No matter what industry, organizations like yours are likely to encounter large amounts of streaming data too. Learn how to tackle all of this data and use AI for your business.