A.I. is ready to advise us on how to best protect Earth from deadly asteroids

asteroid day asteroid hitting earth
Image used with permission by copyright holder

When people talk about using artificial intelligence to solve humanity’s biggest problems, there are few problems bigger than our planet’s survival. That is something a new algorithm called “Deflector Selector” is designed to aid with — by weighing up different possible solutions to deal with the possibility of a deadly asteroid heading in Earth’s direction.

“Our goal was to build a tool that would help us make funding and research decisions for asteroid deflection technologies,” Erika Nesvold, formerly at Carnegie Institution for Science at Washington, D.C, told Digital Trends. “A lot of different technologies have been suggested for deflecting an asteroid on a collision course with the Earth, including the three we describe in our paper: Nuclear explosives, kinetic impactors, and gravity tractors. But none of these technologies has been fully developed and tested in space, and some of them will work better than others.”

Recommended Videos

Nesvold and team started out by simulating attempted deflections of an asteroid on a collision course with the Earth and calculating the likely success of each method of deflection. As you would expect, this involved some heavy-duty math and computer processing power — since it meant simulating the potential distance of detection for more than 6 million hypothetical objects and the velocity change that would be necessary to change their course. To speed up the process, the team used machine-learning techniques.

“We used this data to train a machine-learning algorithm that could make this determination much faster than our simulations,” Nesvold explained. “So now we can feed in the characteristics of a population of impactors, and the algorithm can tell us which technology or technologies would work best.”

The results? That nuclear weapons can help dispatch around half of the potential objects, while kinetic impactors and gravity tractors score lower. Granted, a 50 percent chance of survival for humanity isn’t great — but the idea is that tools such as this can help decide which technologies we should be focusing our finite research budgets on.

“We’re hoping that the next steps will be to work with other experts in the asteroid deflection field to improve the Deflector Selector model to get more accurate results,” Nesvold said.

You can read a paper on the Deflector Selector project here.

Editors' Recommendations

I'm a UK-based tech writer covering Cool Tech at Digital Trends. I've also written for Fast Company, Wired, the Guardian…
Why teaching robots to play hide-and-seek could be the key to next-gen A.I.

Artificial general intelligence, the idea of an intelligent A.I. agent that’s able to understand and learn any intellectual task that humans can do, has long been a component of science fiction. As A.I. gets smarter and smarter -- especially with breakthroughs in machine learning tools that are able to rewrite their code to learn from new experiences -- it’s increasingly widely a part of real artificial intelligence conversations as well.

But how do we measure AGI when it does arrive? Over the years, researchers have laid out a number of possibilities. The most famous remains the Turing Test, in which a human judge interacts, sight unseen, with both humans and a machine, and must try and guess which is which. Two others, Ben Goertzel’s Robot College Student Test and Nils J. Nilsson’s Employment Test, seek to practically test an A.I.’s abilities by seeing whether it could earn a college degree or carry out workplace jobs. Another, which I should personally love to discount, posits that intelligence may be measured by the successful ability to assemble Ikea-style flatpack furniture without problems.

Read more
Scientists are using A.I. to create artificial human genetic code

Since at least 1950, when Alan Turing’s famous “Computing Machinery and Intelligence” paper was first published in the journal Mind, computer scientists interested in artificial intelligence have been fascinated by the notion of coding the mind. The mind, so the theory goes, is substrate independent, meaning that its processing ability does not, by necessity, have to be attached to the wetware of the brain. We could upload minds to computers or, conceivably, build entirely new ones wholly in the world of software.

This is all familiar stuff. While we have yet to build or re-create a mind in software, outside of the lowest-resolution abstractions that are modern neural networks, there are no shortage of computer scientists working on this effort right this moment.

Read more
The BigSleep A.I. is like Google Image Search for pictures that don’t exist yet

In case you’re wondering, the picture above is "an intricate drawing of eternity." But it’s not the work of a human artist; it’s the creation of BigSleep, the latest amazing example of generative artificial intelligence (A.I.) in action.

A bit like a visual version of text-generating A.I. model GPT-3, BigSleep is capable of taking any text prompt and visualizing an image to fit the words. That could be something esoteric like eternity, or it could be a bowl of cherries, or a beautiful house (the latter of which can be seen below.) Think of it like a Google Images search -- only for pictures that have never previously existed.
How BigSleep works
“At a high level, BigSleep works by combining two neural networks: BigGAN and CLIP,” Ryan Murdock, BigSleep’s 23-year-old creator, a student studying cognitive neuroscience at the University of Utah, told Digital Trends.

Read more