ANNs, AI, and The Function of the Human Brain
Artificial Neural networks (ANNs), also known as neural networks, are defined as computational models that are based on the biological structure and functions of the human brain. ANNs are one of the most commonly used mechanisms within the field of deep learning, as these models can be trained to achieve complex and nuanced tasks in accordance with large sets of training data, much like the human brain serves as the driving force behind innovation in conjunction with the accumulation of knowledge and understanding. With all this being said, ANNs are typically used to solve problems that involve pattern recognition and signal processing, and some typical applications of these AI models in the business world include speech recognition software, stock market prediction, signature verification, and handwriting analysis, among many others.
How do ANNs work?
Just as the human brain is composed of billions of neurons that pass information through numerous layers, with each layer representing a different function that enables human senses, artificial neural networks also contain multiple layers that work together to recognize patterns and relationships with respect to a dataset. Likewise, the most basic ANNs will be composed of three different layers: an input layer, a hidden layer, and an output layer. Subsequently, the input layer within an ANN will be used to provide the deep learning model with the real-world information and data that the said model will attempt to learn and derive conclusions from.
On the contrary, the hidden layer within the ANN is where the computations will be performed on the data that has been fed into the model via the input layer. To this end, the output layer is the part of the model that a software developer will use to derive their conclusions from after all of the computations regarding a certain problem has been adequately performed. Due to the structure of artificial neural networks, these models can be configured in a number of different ways. For example, feed-forward neural networks are one of the most basic variants of ANNs, as these networks pass information through one direction to every other layer within the model. Alternatively, recurrent neural networks have a more complicated structure, as these models save the information that has been passed through the output layer of the model and feeds these results back into the model.
One of the primary advantages of utilizing artificial neural networks is the parallel processing capabilities that are embedded in such models. Parallel processing refers to a type of computation that allows for many calculations to be performed simultaneously, meaning that problems that would be considered too large for other models to handle can be solved by dividing said problem into smaller computations that can be worked on at the same time. To illustrate this point further, despite what may be commonly believed, artificial neural networks are by no means a new concept, and in fact, date back to some of the earliest days of computing. For reference, mathematicians Walter Pitts and Warren McCollough built the first circuitry system that was based on the numerous functions of the human brain in 1943.
However, despite the novelty and ingenuity of artificial neural networks, the very functions of the human brain itself are still very much misunderstood by even the brightest and most accomplished minds. To this point, the computational power and sheer amount of data that were needed to implement the concepts involved in artificial neural networks into the physical world were not fully realized until 2010, when the big data trend began to gain significant steam with respect to the training of AI and machine learning algorithms. In the following years, software developers would begin using artificial neural networks to reach new heights in the fields of computer vision, artificial intelligence, and machine learning that had never been reached before.
The challenges of artificial neural networks
On the other hand, while ANNs have undoubtedly altered the manner in which software engineers approach certain problems, there are invariably certain limitations that are associated with the approach. Most notably, artificial neural networks are perhaps the most prominent form of black box AI that is in existence today. As these AI systems come to conclusions and arrive at solutions in a manner that can be almost impossible to explain, even for the developers that create such systems, this inability to provide the general public with transparency generates an inherent lack of trust in such networks. For example, a lending company that has employed an ANN for use in approving American citizens for home loans would struggle to explain to said citizens why the algorithms they use have reached a certain conclusion.
Conversely, another major challenge that arises when looking to create an artificial neural network is ironically one of the strongest advantages of such models, the lack of rules that are involved in the creation of the structures that formulate said models. While this level of flexibility has enabled software developers to create groundbreaking technological solutions in one respect, the costs that are needed to accomplish such goals are often prohibitively high. This is due to the fact that the appropriate artificial neural network structure that is needed to solve a particular problem can only be achieved through a lengthy process of trial and error, making it difficult to approximate the resources that are needed to get such a project off the ground, even without taking other mitigating factors into account.
As the human brain is arguably one of the most powerful tools on our planet, it was only a matter of time before we tried to implement the functions of our own minds into new devices and inventions. At the current moment, these ambitions have manifested themselves in the form of artificial neural networks, as these models form the very basis of what artificial intelligence is supposed to be all about, creating machines that can make decisions in a fashion that is similar to that of human beings. This being said, the multitude of products and services that have been created by utilizing artificial neural networks have generally been accomplished within a time period of just over a decade, meaning that new advancements will surely be on the horizon.