Using artificial intelligence to understand nature/evolution

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phase 2
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Using artificial intelligence to understand nature/evolution

Post by phase 2 » Mon Aug 24, 2009 8:23 pm

Interesting article I read just now. Whilst programming AI-based machines has been common for quite some time, the slant on this experiment has incidentally provided a glimpse at the "unglimpseable" in evolution and biological conditioning.

It tells of robots that have evolved to cheat in order to further their own achievement against a given yardstick.

http://www.wired.com/wiredscience/2009/ ... -to-cheat/
In an experiment that sounds like a deleted scene from one of the Terminator movies, robots with artificial animal-inspired brains quickly evolved to deceive one another.

The robots — soccer ball-sized assemblages of wheels, sensors and flashing light signals, coordinated by a digital neural network — were placed by their designers in an arena, with paper discs signifying “food” and “poison” at opposite ends. Finding and staying beside the food earned the robots points.

At first, the robots moved and emitted light randomly. But their innocence didn’t last. After each iteration of the trial, researchers picked the most successful robots, copied their digital brains and used them to program a new robot generation, with a dash of random change thrown in for mutation.

Soon the robots learned to follow the signals of others who’d gathered at the food. But there wasn’t enough space for all of them to feed, and the robots bumped and jostled for position. As before, only a few made it through the bottleneck of selection. And before long, they’d evolved to mute their signals, thus concealing their location.

Signaling in the experiment never ceased completely. An equilibrium was reached in the evolution of robot communication, with light-flashing mostly subdued but still used, and different patterns still emerging. The researchers say their system’s dynamics are a simple analogue of those found in nature, where some species, such as moths, have evolved to use a biologist-baffling array of different signaling strategies.

“Evolutionary robotic systems implicitly encompass many behavioral components … thus allowing for an unbiased investigation of the factors driving signal evolution,” the researchers wrote Monday in the Proceedings of the National Academy of Sciences. “The great degree of realism provided by evolutionary robotic systems thus provides a powerful tool for studies that cannot readily be performed with real organisms.”

Of course, it might not be long before robots directed towards self-preservation and possessing brains modeled after — if not containing — biological components are considered real organisms.
While we obviously can't literally observe billions of years of evolution (thus spawning the necessary prefix "Theory of..."), computer simulations provide a very useful tool when mimicking uncertainty in beings attempting to pull themselves up by the bootstraps and work towards a goal.

We can see even 20 years ago, on relatively elementary hardware/awareness of practice, there were computer experiments done to show how Natural Selection takes place, and why it's an accepted theory in science.

Opposition to NS (invariably based on personal faith, rather than scientific reasoning) suggests that such complexity in nature cannot come without an initial design or even panspermia, but this documentary elegantly shows how self-created structure can give rise to attempts to better an organism; the failed attempts being hidden by statistical inference. It's the exact opposite of chance, given the gauge enforced by the surrounding environment.

This is part 1 - please click on the suggested other parts in order to watch sequentially.

Richard Dawkins - The Blind Watchmaker:

http://www.youtube.com/watch?v=3fgf-g8jJ7Q
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Post by kay » Mon Aug 24, 2009 8:46 pm

Quality

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Post by bandshell » Mon Aug 24, 2009 8:51 pm

Cheers for posting this, interesting stuff.

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Post by magma » Tue Aug 25, 2009 8:27 am

Brilliant. I'd missed this... thanks!
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Post by NilsFG » Tue Aug 25, 2009 2:29 pm

Interesting.

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Post by alien pimp » Tue Aug 25, 2009 6:07 pm

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Post by mawltea » Tue Aug 25, 2009 7:56 pm

Interesting, but I don't quite get it. I mean, nothing in their circuitry changed "biologically" (I mean, they didn't GROW new wires or anything), so how did they "learn" to turn off the signal? Seems reasonable if it was pre-programmed to do just that, if the territory it was occupying was being too crowded. But I can't for the life of me see how it developed intelligence to turn the signal off, unless it was pre-programmed.
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Post by alien pimp » Tue Aug 25, 2009 8:13 pm

i guess this is where the human race will experience the shock of realizing "soul", "intelligence", "self awareness" come naturally after a certain complexity threshold
or not...
gonna be fine to watch the developments in this field either way
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Post by slothrop » Tue Aug 25, 2009 10:26 pm

mawltea wrote:Interesting, but I don't quite get it. I mean, nothing in their circuitry changed "biologically" (I mean, they didn't GROW new wires or anything), so how did they "learn" to turn off the signal? Seems reasonable if it was pre-programmed to do just that, if the territory it was occupying was being too crowded. But I can't for the life of me see how it developed intelligence to turn the signal off, unless it was pre-programmed.
It's basically a genetic algorithm based neural networks project, with a cool robotics based application. It's not particularly new afaict - people have been doing this sort of stuff since the 80s - but it is kind of cool.

The basic point is that you have a neural network - which is an uber simplified model of sort of how a neuron-based brain kind of might deal with a problem - by taking certain inputs and using them to fire virtual neurons which connect in certain ways. The output comes from the virtual neurons at certain points in the network - as a brain has sensory neuron inputs, fires stuff through a network and then has motor neuron outputs which trigger activity.

The clever bit is then taking a measure of how well the neural networks perform, and wiring up your next generation of networks to be similar to the ones that performed best. Although I'm not convinced of how solid the genetics / evolution analogy is - there are a load of roughly equivalent methods and they're mostly essentially very hairy optimization algorithms with different analogies to explain how they work.

What's cool about it, imo, is that you can end up using a comparatively simple process to design a processor that does a task very efficiently but in a way that you don't neccessarily understand. I read a thing once (no reference, sorry) about a genetic algorithms project that assembled a bunch of electronic components to make a primitive timer. The algorithm managed to produce one that used thirteen components where the most efficienct directly-human-designed one with the same pool of components used seventeen or something. One component in the computer designed one wasn't actually wired into the circuit, but when the engineers running the project tried removing it, the device stopped working - presumably because it was providing some very slight capacitance or something, but they basically weren't sure...

OTOH, I think you'd need quite a lot more power than they've got going on here before you start getting Skynet / Hal / The Matrix...

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Post by alien pimp » Tue Aug 25, 2009 10:48 pm

Slothrop wrote:One component in the computer designed one wasn't actually wired into the circuit, but when the engineers running the project tried removing it, the device stopped working - presumably because it was providing some very slight capacitance or something, but they basically weren't sure...
and they left the mystery unsolved :?: :o
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