With advancements in artificial intelligence development services, data analysts are constantly exploring revolutionary ways of applying AI to disrupt existing systems. This blog post highlights one such revolutionary technology, artificial swarm intelligence that is a nature-inspired subject matter.
Artificial intelligence is a popular subject these days. Online ads, robots, self-driven cars, all are using AI for augmented experiences. But a more complex and hidden intelligence exists which is a subject of research but is easily visible in nature. An individual cannot own this intelligence but to a group. SI i.e. Swarm Intelligence is a complex intelligence that emerges out from the collective behavior of a large number of natural or artificial elements. This intelligence is a consequence of Stigmergy phenomena i.e. every action of every element gets back feedback through the whole system. SI is inspired by and can be seen in natural instances like ant colonies, birds flocking, fish school, etc.
A flock of birds showing patterns.
Image source page: https://arstechnica.com/science/2012/03/animal-magnetism-using-magnetic-models-to-understand-flocks-of-birds/
Artificial swarm intelligence is a really interesting subject of research. This can be applied to the areas of advertisement, sales improvement, market and stock analytics and predictions, robotics, particle science, and whatnot. Swarm robotics is an area of development where several mini robotic elements are programmed with simple rules and they all interact with each other to reveal intelligent behaviors. Altering the basic rules display a completely new intelligent behavior. Analyzing the complex response of buyers of a brand to various new products and market stimulations is another related field.
There are various types of optimization techniques and algorithms that have been inspired by nature:
1. Ant Colony Optimization (ACO).
2. Spider Monkey Optimization (SMO).
3. Particle Swarm Optimization (PSO).
Deep Swarm is an open-source library for optimizing convolutional neural networks. A deep swarm is based on the Ant Colony Optimization (ACO). Following is an example code snippet to find an optimized topology of a neural network using deep swarm.
backend = TFKerasBackend(dataset)
deepswarm = DeepSwarm(backend=backend)
topology = deepswarm.find_topology()
trained_topology = deepswarm.train_topology(topology, 30)
In the above snippet, we create a backend object by providing a dataset. It is responsible for training and validating. Then we create a deep swarm object that is responsible for optimization. After this, we find the topology for the given dataset and evaluate the discovered topology. We can further train the topology for an additional number of epochs and finally evaluate that. For further details, refer to this GitHub link: https://github.com/Pattio/DeepSwarm
As described in https://www.researchgate.net/publication/268508391_Spider_Monkey_Optimization_algorithm_for_numerical_optimization, a system's behavior can or cannot be swarm intelligent. The division of labor and self-organization is necessary and sufficient for obtaining swarm intelligent behavior.
Following are some of the examples of Artificial Swarm Intelligence: