Neural networks, also called artificial neural networks, are made up of many layers of various computational units called neurons, interconnected in various layers with connections in other layers. These networks process data through various computational unit layers until they can accurately classify it into a meaningful output.
The natural human brain more loosely inspires our computer systems. Humans have built-in visual intelligence and language processing abilities; artificial neural networks work much the same way. However, unlike biological systems, artificial neural networks do not store information but instead process it. In contrast to the human brain, which stores information in long-term memory, short-term memory, and so on, artificial neural networks process information in shorter time scales; they can be trained to recognize patterns much faster and more effectively than humans could.
Interconnection of Various Neural networks layers
The three main layers of the neural networks consist of input/output pairs, a hidden variable, and a recurrent weights function. The input/output layer performs functions like changing each neuron’s weights in the network when the user inputs a mathematical formula or string into the weights container. These weights allow the computer to “spider” the formula or string to maximize the likelihood that the network generated by the network will be correct and accurate. On the other hand, the hidden variable serves as a predictive model of what the weights are supposed to be, thus allowing the network to output an answer, or a “class,” depending on the classifier that was used to generate the answer. Finally, the recurrent weights function allows the network to continue training as long as there are inputs, or gradients, that can be fed into the weights container to continue optimizing the weights.
Complexity of Artificial Neural Networks
There are many different applications for artificial neural networks, such as speech recognition, medical and business applications, and internet marketing and e-commerce applications. Because they are much more complex than biological brains, they are much more susceptible to outside influences such as training errors, improper input, and failures in the training process. Because of these potential weaknesses, programmers must take great care when developing an artificial intelligence system. The three areas of difficulty are:
Hard: Memory Cell
Hard: Memory Cell – AI’s memory cell is its internal memory. All learned data is stored inside the memory cell. If the memory cell becomes damaged or otherwise corrupted for whatever reason, then the ability to learn from that experience is lost. In a very extreme case, an AI machine may remember everything that ever happened. However, without any knowledge of how it happened, it cannot apply that memory to future situations. This is called the ‘black box’ problem and is one of the biggest problems with artificially intelligent software that can only be solved by human interaction.
Easy: Nervous System
Easy: Nervous System – In the past, the nervous system was considered the gatekeeper between the mind and the body. It controlled and regulated all bodily functions and emotions and was closely tied to memory, identity, perception, and personality. Because of the importance of the nervous system in our daily lives, it is an area that is particularly difficult to program into an AI system. However, in principle, it would be easy to program an AI’s mind to be highly knowledgeable and intelligent or have limitless intelligence. This also ties in with the issue mentioned above about the black box condition of an AI machine.
Complex: Long Memory
Complex: Long Memory – Since the nervous system is extensive, the size of a neural network is directly proportional to its age. Neurons get older as you get older until finally, your mind is nothing more than a collection of junctions and synapses. In much the same way, the longer a network is exposed to training, the more it will improve in function. Thus, very long networks such as Google’s, Twitter’s, and Facebook’s were built on very complex AI neural networks. However, while their success is impressive, the question is, do we want to give computers total control over our minds?
Deep Learning and Neural Networks
Deep Learning: The beauty of deep learning lies in the fact that it does not need to use traditional algorithms. Traditional algorithms only translate inputs into outputs. They also make assumptions about the shape and structure of the data. On the other hand, deep learning focuses on maximizing the function of deep neural networks by leveraging the benefits of their regular functions and input data.
