A Neural Networks' Artificial Intelligence

Cognitive Modeling and Data Gathering Improve Neural Nets

Sep 19, 2009 Victoria Nicks

A neural network's artificial intelligence, with cognitive modeling and structured learning algorithms, bring neural nets closer to independent reasoning than earlier AI.

Artificial neural networks are modeled after the neural networks in the brain. The neural net is a network of tiny processors that gives a computer the ability to sort through input, and arrive at conclusions by analyzing patterns in the data. Neural networks can, for example, learn to play a game, and improve their skills each time they play the game through cognitive modeling. There are also many business applications for a neural networks' artificial intelligence, particularly in business intelligence sectors, despite the limitations on neural network technology, and learning requirements.

Natural Neural Networks

A natural neural network is composed of neurons, each connected to thousands of other neurons with synapses and dendrites. When a threshold of input is reached, an electrical signal is passed across by a firing synapse. The more times a particular path is followed, the stronger the path becomes. An artificial neural network mimics this organizational structure by connecting small processors together in the same fashion. Each artificial neuron has threshold similar to a biological neuron, and produces output based on the sum of the input.

Cognitive Processing in Artificial Neural Networks

The neurons in artificial neural networks are interconnected so that each result depends on the output level reached by other neurons. In this way, neural networks act essentially like a very complex, self-activating flowchart. If a certain neuron does not reach the threshold, the answer to that path is, "No", and another path is followed. If the neuron does reach its threshold, the answer is, "Yes", and the path continues to the next decision point. In this fashion, the neural network is able to reach a conclusion based on the information given as input. With more information, the network is better able to reach accurate conclusions.

Cognitive Modeling Improves Business Intelligence

Neural networks are becoming very useful in the business arena, due to their ability to reach independent conclusions based on data provided combined with 'learned' patterns. The medical applications of neural networks include the use of diagnostic programs, which apply all known symptoms to the knowledge base, and arrive at a diagnosis based on previous patterns. In business, neural networks can vastly improve the output resulting from Text Analytics in Data Mining Software. Internet Searches also benefit from Neural Networking technology.

Data mining, the practice of extracting patterns from past or current activity, can be paired with neural networks to provide essential business solutions. Business intelligence uses data mining to understand patterns, project future activity, and profit from it. Neural networks are perfectly suited for this use, due to their ability to use information to project an outcome. Potential uses include marketing projections, financial projections, and even playing the stock market.

Neural Nets Require Cognitive Modeling to Learn

Neural networks can learn independently, but it is much more efficient to provide the network with learning algorithms. These algorithms essentially teach the neural network how to learn. It is necessary, also, to provide information to the neural network, and classify the information so that the network can process it correctly.

AI Reasoning by Neural Nets

The ability of neural networks to learn independently, or through structured learning, shows great promise in the area of artificial intelligence. Despite current limitations, resulting from the requirements for preliminary structuring, information gathering, and data classification, neural networks have the ability to use information to reach conclusions without clearly set-out rules, which makes neural network technology superior to other attempts at artificial intelligence.

For more information, see Neural Networks.

The copyright of the article A Neural Networks' Artificial Intelligence in Artificial Intelligence is owned by Victoria Nicks. Permission to republish A Neural Networks' Artificial Intelligence in print or online must be granted by the author in writing.

Artificial Neural Networks