Sometimes, a system with a rather large level of complexity
, such as a very large number of interconnections
, that also has some level of self-organization
, or such, will suddenly start to exhibit behaviors
that were totally unpredicted
by those people who designed
These are knowm as emergent behaviors - behaviors that arose within the system itself, created from the properties within, and their interactions.
This is completely different from a "bug" found in traditional software. Regular software, such as a word processor, operating system, or the like, is a completely ordered set of instructions for a specific purpose. Any unintended behavior is a bug, can be tracked down to a problem in the code, and fixed.
However, when the software is designed to be able to change it's workings, or is designed to learn, then it is not operating in quite the same manner. For example, a neural network isn't completely hard coded in how it works - the basics are code, but the rest comes from inputs and how they are handled. The interconnections between "neurons" in the network increases much faster than the number of neurons, so as the network is made larger and larger, it becomes nearly impossible to track and plan how the network will react.
Some of the more recent networks have suddenly started exhibiting these emergent behaviors, reacting to inputs in totally unexpected ways, and leading some people to think the road to true artificial intelligence and artificial consciousness may lie down this road, to allowing the chaos and uncertainty of huge masses of connections and the like a lot of latitude.
Note that this is not always wanted. For example, a robot being developed to sweep for mines that used a large neutal net to facilitate learning started showing unexpected behaviors. This could be dangerous for such an application.
A more benign example may be the computer game series Creatures, as the "creatures" in the game, the "Norns", were goverened by a neural net "brain", which dealt with sensory input, along with internal input from simulated body systems. The creatures, even though they are based on the same structure for their neural net, begin to act uniquely, handling different input, different events, in a manner unique to each creature. (One for example, discovered it could stick an egg in the incubator, and a new Norn friend would hatch, and it would be happier. Another took up hitting other Norns, as that would make it happy. Others found ways to play with the toys together, while another Norn would refuse to share.)