Modeling how the brain works and replicating its flexibility and associative abilities is an ongoing research aim for artificial intelligence research. Artificial neural networks are one way that AI research is moving slowly out of the realm of theory and into day-to-day usage in certain industries and fields. Functionally, an artificial neural network emulates the neuron structure of the human brain, where each neuron acts as a gateway for differentiated data transmission. Like a human brain, neural networks adapt to the particulars of internal and external input, and they're used extensively in fields where pattern recognition is important. They're key to robotics and are a critical component of the technology used for autonomously controlled drones and cars.
Neural networks -- within the bounds of their data inputs and initial conditions -- can learn organically. They aren't limited entirely by what's been given to them in an expert system. Neural networks can generalize from their inputs, which makes them valuable for robotics and pattern recognition systems and for large-scale data analysis.
Nonlinear Data Processing
Nonlinear systems perform shortcuts to reach computationally expensive solutions and can infer connections between data points, rather than wait for records in a data source to be explicitly linked. This nonlinear short-cut mechanism is why neural networking techniques are valuable in commercial big-data analysis and is the reason IBM's Watson succeeded on "Jeopardy."
In addition to the data processing advantages, artificial networks have the potential for high fault tolerance; when scaled across multiple machines and multiple servers, a neural network is able to route around missing data or servers and nodes that can't communicate.
Neural networks can do more than route around parts of the network that no longer operate; if they are asked for data that was in part of the network that is no longer communicating, they can regenerate large amounts of data by inference and using their organic learning traits, working forward from their current state. This is also a useful trait for networks that need to inform their users about the current state of the total network and effectively results in a self-debugging and diagnosing network. As networking become more complicated and networks grow larger, this feature will gain in importance.
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