Hilarious (and Terrifying?) Ways Algorithms Have Outsmarted Their Creators (popularmechanics.com)

"Robot brains will challenge the fundamental assumptions of how we humans do things," argues Popular Mechanics, noting that age-old truism "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... 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...

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.
The paper (available as a free download) contains 27 anecdotes, which its authors describe as a "crowd-sourced product of researchers in the fields of artificial life and evolutionary computation. Popular Science adds that "the most amusing examples are clearly ones where algorithms abused bugs in their simulations -- essentially glitches in the Matrix that gave them superpowers."

Comments

Outsmarting Mother Nature

[mentil]

If an evolutionary algorithm is pitted against real life, and 'outsmarts' it, that's one measure of evolutionary progress. The real issue is the same as in 'teaching to the test', or even the 'kobayashi maru solution': the metrics are gamed once the one being tested realizes what they are, and then the metrics no longer hold meaning.
Replace 'metrics' with 'simulation parameters' and it's the same thing. The simulation has to be as intelligent as the uncontrolled agents operating inside of it, or else these types of things will happen. Self-modifying simulations perhaps?

Re:Stupid local minima

[NoZart]

My favorite is the tetris bot that just presses pause before he loses

Re:Stupid local minima

[Kjella]

So it sent itself off the virtual edge of the simulation area, ending the run and minimizing it's negative score as best as possible. By accident someone created a suicidal bot, yay! (...) But a lot of the time it does something stupid.

Who did something "stupid"? The bot achieved its goal, but the programmed goal completely failed to achieve the intended goal. This is basically "The code did what I said, not what I meant" taken to a new level. The problem is that you can't easily inspect a neural network's logic in human terms the way you trace through code, it's more like another person. I think this is a cat, you think this is cat, the AI thinks this is a cat but we can't exactly quantify exactly what makes this a cat or non-cat which means the model can break down unexpectedly in ways you can't possibly predict, like you show it a one-eyed cat and suddenly the AI thinks it's a cyclops. And that's going to be a problem as we start relying on AI, like this self driving car thinks you're a pedestrian until one day for some inexplicable reason you don't qualify.