Latest results of AI research indicates that simpler, smaller neural networks. These can be used to solve certain tasks even better, more efficiently, and more reliably than ever before. The idea of a neural network is inspired by the structure of the human brain that could help computers and machines more like a human would. The human brain is exceptionally complex. It is the most powerful computing machine ever known that is still not completely decoded. Science explains that the internal workings of the human brain are modelled around the concept of neurons. The networks of neurons are known as biological neural networks. It is estimated that the human brain contains roughly 100 billion neurons. These are connected along pathways throughout these networks. Hence, for a computing machine to replicate brain functions, it must use an artificial version of this complex network of neurons. This is the start of the advanced statistical technique and term known as Artificial neural networks (ANNs). Bringing artificial intelligence and neuroscience together promises to yield benefits for both fields. The progression of AI techniques in this line and its applications will certainly be very exciting to watch!
Artificial neural networks (ANNs) and their more complex model - deep learning technique are some of the most capable AI tools. These resolve very complex problems and will continue to be developed and be leveraged in the future.
By duplicating the working of the brain using silicon and wires which act like dendrites and neurons, we can make necessary connections. Like stimulus from an external environment are accepted by dendrites in a way, the input generates electric impulses that travel through the neural network.
ANN consists of several nodes which act as a neuron. The nodes are connected by wires for communication with one another. Nodes collect input data to perform small operations on trained data. The results of these operations are moved to the subsequent node that acts like neurons. The output at the node is called the node value. An ANN includes a huge number of processors working parallelly, that are arranged in multiple layers. The first layer receives the raw data as input, similar to optic nerves in the human eye processing visuals. Every successive layer then receives the raw input data as output from the previous layer. It is just like the neurons of the optic nerve receiving signals from those close to it. The final layer generates the actual final output. Neural networks include various technologies like deep learning and machine learning.
In the initial stages, neural networks (NN) are fed with huge amounts of data. Training is generally given to these networks by providing inputs and educating the networks to the desired output. For example, facial recognition is the latest technology embedded in most smartphones. Each input is gathered by identifying matching data, like the image of a person’s face, iris, various facial expressions. All these inputs are trained into these networks. Providing proper answers will train it to accommodate its internal data to learn how better it can perform. Rules must be defined in such a way that each node knows what has to be sent to the next layer considering its own inputs from the previous layer.
Considering principles like genetic algorithms, fuzzy logic, gradient-based training, Bayesian method etc. ANNs are given a basic frame of rules. An accurate decision must be taken in framing the rules.
Most business applications and commercial companies are making use of these technologies. Their main aim is to solve complex problems like pattern recognition or facial recognition, and several other applications include data analysis, weather prediction, and signal processing.
Recognition of Image was the first area where neural networks were applied. Eventually, technology expanded to areas such as translation and language generation, drug discovery and development, stock market prediction, delivery driver route planning and optimization, chatbots.
ANNs are also used in undersea mine detection, Google assistant or SIRI, recovery of telecommunications from faulty software, diagnosis of hepatitis, handwriting recognition, self-driving cars, object detection, music composition, spell checking, character recognition etc. Neural networks have a remarkable ability to retrieve meaningful data from an ample amount of imprecise data. This is used in detecting trends and extract patterns that are difficult to understand both by simple computers or humans. A trained NN can be made an "expert" in the information that has been given to analyze and can be used to provide projections. Self-organization, real-time operation, adaptive learning, redundant information coding through fault tolerance are some of the added advantages that come along with neural networks.
From the technical perspective, one of the biggest challenges is the amount of time it takes to train networks, which often require a huge amount of computing power even for simple tasks. It should also be considered that neural networks are black boxes, in which the user aligns the trained data and receives answers. The networks are allowed to tune the answers, but the drawback is that they have no access to the exact process of decision making, human errors may hit hard in this scenario. This is the reason why researchers are working relentlessly, but artificial neural networks play a very big role in changing everyday lives. Being in a highly competitive world, we have a lot to gain from neural networks. Their capability to learn through better example makes them powerful and flexible. Moreover, we do not need to devise an algorithm to perform a particular task. We do not require the internal mechanisms for that task. These are well suited for real-time systems as they respond quickly with the best computational times because of their parallel architecture.