The Hilarious (and Terrifying) Ways Algorithms Have Outsmarted Their Creators
Source: Popular Mechanics
Evolutionary algorithms can turn up ingenious, unexpected solutions. They also love to cheat.
Flying saucers have yet to land—at least, not that we've confirmed—but alien intelligence is already here. As research into AI grows ever more ambitious and complex, these robot brains will challenge the fundamental assumptions of how we humans do things. And, as ever, the only true law of robotics is that computers will always do literally, exactly what you tell them to.
A paper recently published to ArXiv highlights just a handful of incredible and slightly terrifying ways that algorithms think. These AI were designed to reflect evolution by simulating generations while other competing algorithms conquered problems posed by their human masters with strange, uncanny, and brilliant solutions.
My favorite example of when AI surprised us, the scientists wielding it. A corner case in our algo challenged AI to learn to walk without touching its feet to the ground. Many more in https://t.co/7690RlnTwI from @joelbot3000with @jb_mouret @CULLYAntoine Cully et al Nature 2015 pic.twitter.com/YtzV7zgwhb
— Jeff Clune (@jeffclune) March 13, 2018
The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities covers some 27 anecdotes from various computer science projects and is worth a read on its own, but here are a few highlights:
- A study designed to evolve moving creatures generated 'hackers' that would break their simulation by clipping into the ground and using the "free energy" of the simulation's correction to speed towards their goal.
- An AI project which pit programs against each other in games of five-in-a-row Tic-Tac-Toe on an infinitely expansive board surfaced the extremely successful method of requesting moves involving extremely long memory addresses which would crash the opponent's computer and award a win by default.
- A program designed to simulate efficient ways of braking an aircraft as it landed on an aircraft carrier learned that by maximizing the force on landing—the opposite of its actual goal—the variable holding that value would overflow and flip to zero, creating a practically catastrophic, but technically perfect solution.
- A test that challenged a simulated robot to walk without allowing its feet to touch the ground saw the robot flip on its back and walk on its elbows (or knees?) as shown in the tweet above.
- A study to evolve a simulated creature that could jump as high as possible yielded top-heavy creatures on tiny poles that would fall over and spin in mid-air for a technically high 'jump.'
While the most amusing examples are clearly ones where algorithms abused bugs in their simulations (essentially glitches in the Matrix that gave them superpowers), the paper outlines some surprising solutions that could have practical benefits as well. One algorithm invented a spinning-type movement for robots which would minimize negative effect of inconsistent hardware between bots, for instance.
As the paper notes in its discussion—and you may already be thinking—these amusing stories also reflect the potential for evolutionary algorithms or neural networks to stumble upon solutions to problems that are outside-the-box in dangerous ways. They're a funnier version of the classic AI nightmare where computers tasked with creating peace on Earth decide the most efficient solution is to exterminate the human race.
The solution, the paper suggests, is not fear but careful experimentation. As humans gain more experience in training these sorts of algorithms, and tweaking along the way, experts gain a better sense of intuition. Still, as these anecdotes prove, it's basically impossible to avoid unexpected results. The key is to be prepared—and to not hand over the nuclear arsenal to a robot for its very first test.
Source: Popular Mechanics
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Originally posted by Gale Frierson
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