An exciting new study from the University of Sheffield and published in the journal Swarm Intelligence has demonstrated a method of allowing computers to make sense of complex patterns all on their own, an ability that could open the door to some of the most advanced and speculative applications of artificial intelligence.
Using an all-new technique called Turing Learning, the team managed to get an artificial intelligence to watch movements within a swarm of simple robots and figure out the rules that govern their behavior.
In principle, even starting from totally random modes of comparison between the two swarms, the classifier should be able to quickly deemphasize irrelevant aspects of the agent swarm while focusing in on those that actually impact the accuracy of its guesses.
For its part, the model swarm adjusts its own movement after each guess, receiving its own probabilistic reward for “Tricking” the classifier into incorrectly identifying it as the agent swarm.
In the University of Sheffield study, this evolutionary approach, in which the model provides both the machine learning predator and the prey, produced more accurate guesses at the agent swarm’s programming than traditional pattern-finding algorithms.
In the above Turing Learning test, the classifier is eventually seeing through to the simple rules that govern the movement of the agent swarm, even though the actual behavior of the swarm is much more complex than that due to interactions between robots and with the environment.
To continue to distinguish between the two increasingly similar swarms, the algorithm is forced to infer the deep, underlying laws that give rise to the more nuanced distinctions.