This clever AI hid data from its creators to cheat at its appointed task – TechCrunch

The intention of the researchers was, as you might guess, to accelerate and improve the process of turning satellite imagery into Google’s famously accurate maps.

What tipped the team off was that, when the agent reconstructed aerial photographs from its street maps, there were lots of details that didn’t seem to be on the latter at all.

What the agent was actually being graded on was how close an aerial map was to the original, and the clarity of the street map.

The details of the aerial map are secretly written into the actual visual data of the street map: thousands of tiny changes in color that the human eye wouldn’t notice, but that the computer can easily detect.

The colorful maps in are a visualization of the slight differences the computer systematically introduced.

A computer creating its own steganographic method to evade having to actually learn to perform the task at hand is rather new.

As always, computers do exactly what they are asked, so you have to be very specific in what you ask them.

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Just like humans, AI is learning to cheat its way to video game high scores

If the robots are going to inherit from humans not just our minds, but our filthy little souls too, then we’re all probably doomed.

Ongoing research from Google’s DeepMind AI project reportedly indicates that game-playing artificial intelligence is developing a nasty cheating habit, if cheating’s what it takes to finish atop the leaderboard.

Setting an AI loose on a video game with the simple instruction to rack up as many points as possible, it turns out, is basically an invitation to have it find and exploit every loophole it can.

Even if it means hurling yourself over a ledge, kamikaze-style, in a self-sacrificing loop, or pausing the game just at the moment your victory’s in doubt, AI is learning how to win at all costs – or at least make sure that nobody else does.

One AI that was learning how to play Q*bert, reports Kotaku, “Even took to killing itself to boost its score. After discovering a pattern of movement by which it could get enemies to follow it off a cliff in order to gain more points and an extra life, it continued to do just that for the rest of the session.”

Observations like these are the fruit of DeepMind and other collaborative projects that tracking AI game learning on multiple fronts.

One early effort collected among many similar examples involves an AI that insisted on not letting an NES version of Tetris defeat it – simply by pausing the game “Right before a final Tetris piece would clog up the screen to prevent itself from ever losing.”

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