According To Artificial Intelligence, The Movie Armageddon Was Not Too Far Off

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Aliyah Kovner 13 Feb 2018, 20:53

If you’re over the age of 28, chances are you’ve seen the 1998 disaster film Armageddon. In Michael Bay’s cinematic masterpiece, ordinary Joes played by Bruce Willis, Ben Affleck, and company are sent into space with a mission to set up and detonate a nuclear weapon on a giant asteroid that is on track to destroy the planet.

Twenty years later, an advanced, self-learning artificial intelligence algorithm has decided that the controversial director’s penchant for solving problems with explosions really is the best way to deal with an incoming celestial object.

Obviously, we have not yet needed to defend the Earth from an extinction-level impact, and experts at NASA’s Jet Propulsion Laboratory keep careful tabs on any large objects that could come within striking distance in the future. According to their reports, nothing is likely to threaten Earth within the next several hundred years.

Yet because the stakes are quite high, it might be nice to be prepared.

via GIPHY

To that end, a team of astrophysicists led by Erika R. Nesvold developed and trained a computer program to analyze the current options for neutralizing an asteroid or cometThe charmingly named “Deflector Selector” looked at 6 million hypothetical scenarios of an object approaching Earth. In each instance, the program compared how effectively the object's trajectory could be altered using one of three options: 1) a projectile called a kinetic impactor that is crashed into the object, 2) a large craft called a gravity tractor that gets close enough to the object to disrupt its path, and 3) a nuclear weapon to blow it to pieces.

According to New Scientist, the simulation training took 40 hours on a cluster of 100 computers. This process and the algorithm’s results are detailed in an article in Earth and Planetary Astrophysics.

Machine learning algorithms differ from traditional AI because it can solve problems or complete tasks without human input after being trained on sample data sets. The way the program teaches itself closely mirrors how our brains do the same task – just much, much faster.

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