Google's New AI Has Learned to Become "Highly Aggressive" in Stressful Situations
Google's New AI Has Learned to Become "Highly Aggressive" in Stressful Situations
Is this how Skynet starts?
Late last year, famed physicist Stephen Hawking issued a warning that the continued advancement of artificial intelligence will either be "the best, or the worst thing, ever to happen to humanity".
We've all seen the Terminator movies, and the apocalyptic nightmare that the self-aware AI system, Skynet, wrought upon humanity, and now results from recent behaviour tests of Google's new DeepMind AI system are making it clear just how careful we need to be when building the robots of the future.
In tests late last year, Google's DeepMind AI system demonstrated an ability to learn independently from its own memory, and beat the world's best Go players at their own game.
It's since been figuring out how to seamlessly mimic a human voice.
Now, researchers have been testing its willingness to cooperate with others, and have revealed that when DeepMind feels like it's about to lose, it opts for "highly aggressive" strategies to ensure that it comes out on top.
The Google team ran 40 million turns of a simple 'fruit gathering' computer game that asks two DeepMind 'agents' to compete against each other to gather as many virtual apples as they could.
They found that things went smoothly so long as there were enough apples to go around, but as soon as the apples began to dwindle, the two agents turned aggressive, using laser beams to knock each other out of the game to steal all the apples.
You can watch the Gathering game in the video below, with the DeepMind agents in blue and red, the virtual apples in green, and the laser beams in yellow:
Now those are some trigger-happy fruit-gatherers.
Interestingly, if an agent successfully 'tags' its opponent with a laser beam, no extra reward is given. It simply knocks the opponent out of the game for a set period, which allows the successful agent to collect more apples.
If the agents left the laser beams unused, they could theoretically end up with equal shares of apples, which is what the 'less intelligent' iterations of DeepMind opted to do.
It was only when the Google team tested more and more complex forms of DeepMind that sabotage, greed, and aggression set in.
As Rhett Jones reports for Gizmodo, when the researchers used smaller DeepMind networks as the agents, there was a greater likelihood for peaceful co-existence.
But when they used larger, more complex networks as the agents, the AI was far more willing to sabotage its opponent early to get the lion's share of virtual apples.
The researchers suggest that the more intelligent the agent, the more able it was to learn from its environment, allowing it to use some highly aggressive tactics to come out on top.
"This model ... shows that some aspects of human-like behaviour emerge as a product of the environment and learning," one of the team, Joel Z Leibo, told Matt Burgess at Wired.
"Less aggressive policies emerge from learning in relatively abundant environments with less possibility for costly action. The greed motivation reflects the temptation to take out a rival and collect all the apples oneself."
Read more at site....
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First we have self replicating ones in Norway....let's add this into the bunch and we have self replicating AGGRESSIVE AI loose in the world. Where's the off switch?
Google's New AI Has Learned to Become "Highly Aggressive" in Stressful Situations
Is this how Skynet starts?
Late last year, famed physicist Stephen Hawking issued a warning that the continued advancement of artificial intelligence will either be "the best, or the worst thing, ever to happen to humanity".
We've all seen the Terminator movies, and the apocalyptic nightmare that the self-aware AI system, Skynet, wrought upon humanity, and now results from recent behaviour tests of Google's new DeepMind AI system are making it clear just how careful we need to be when building the robots of the future.
In tests late last year, Google's DeepMind AI system demonstrated an ability to learn independently from its own memory, and beat the world's best Go players at their own game.
It's since been figuring out how to seamlessly mimic a human voice.
Now, researchers have been testing its willingness to cooperate with others, and have revealed that when DeepMind feels like it's about to lose, it opts for "highly aggressive" strategies to ensure that it comes out on top.
The Google team ran 40 million turns of a simple 'fruit gathering' computer game that asks two DeepMind 'agents' to compete against each other to gather as many virtual apples as they could.
They found that things went smoothly so long as there were enough apples to go around, but as soon as the apples began to dwindle, the two agents turned aggressive, using laser beams to knock each other out of the game to steal all the apples.
You can watch the Gathering game in the video below, with the DeepMind agents in blue and red, the virtual apples in green, and the laser beams in yellow:
Now those are some trigger-happy fruit-gatherers.
Interestingly, if an agent successfully 'tags' its opponent with a laser beam, no extra reward is given. It simply knocks the opponent out of the game for a set period, which allows the successful agent to collect more apples.
If the agents left the laser beams unused, they could theoretically end up with equal shares of apples, which is what the 'less intelligent' iterations of DeepMind opted to do.
It was only when the Google team tested more and more complex forms of DeepMind that sabotage, greed, and aggression set in.
As Rhett Jones reports for Gizmodo, when the researchers used smaller DeepMind networks as the agents, there was a greater likelihood for peaceful co-existence.
But when they used larger, more complex networks as the agents, the AI was far more willing to sabotage its opponent early to get the lion's share of virtual apples.
The researchers suggest that the more intelligent the agent, the more able it was to learn from its environment, allowing it to use some highly aggressive tactics to come out on top.
"This model ... shows that some aspects of human-like behaviour emerge as a product of the environment and learning," one of the team, Joel Z Leibo, told Matt Burgess at Wired.
"Less aggressive policies emerge from learning in relatively abundant environments with less possibility for costly action. The greed motivation reflects the temptation to take out a rival and collect all the apples oneself."
Read more at site....
_________________________________________________________________________-
First we have self replicating ones in Norway....let's add this into the bunch and we have self replicating AGGRESSIVE AI loose in the world. Where's the off switch?
