With the integration of artificial intelligence into nearly every industry and avenue of communication, it should come as no surprise that developing complementary technology to promote intuition and machine learning is the next logical step. Already, neural networks have come into being, utilizing artificial intelligence to eliminate the strain on human workers and optimize certain processes. While these networks are designed to alleviate some of the unnecessary exertion that plagues human workers, there are some potential issues that accompany this innovative technology.


Black Box Nature

One of the most prominent disadvantages to reliance on a neural network is their black box nature; put simply, black box refers to an unclear process that occurs within the network that produces an unpredictable outcome based on the input. In some regards, an algorithm or process that enables human intervention, override, and insight can allow for more straightforward and accurate deductions.

A bank, for example, will likely not incorporate a neural network to determine whether an individual is a good candidate for a loan; credit score, payment schedules, spending habits, and more can contribute to a bank’s decision on this matter, and a customer would be unhappy if a machine, however advanced it was, decided they were not deserving of a loan. Because of this, neural networks are often limited in their application as well as their clarity and accuracy.



At this point in time, neural networks and machine learning technology are still very much in development. Success with these tools largely results from trial and error which takes time, money, and other resources. Identifying the proper structure, assessing best practices, and determining the most effective applications of neural networks will take time. Naturally, all technological developments, especially ones of this scope, are demanding, and once mastered, their integration will be more effective and apt. However, we have not yet reached a point that neural networks are standardized or perfected enough for regular use.



Unlike other machine learning programs, neural networks require an immense amount of data in order to function. Because they are designed to operate independently of human technicians (with leeway for hardware issues), neural networks must have access to the most accurate and relevant information in the largest volume possible to ensure the outputs are as comprehensive as they can be. The success of the neural network in question is entirely dependent on the quality and quantity of the data it receives.


Technological advances in the field of A.I. have enabled a massive shift in communication, problem solving, and time management on the individual and societal level. However, the neural network technology is not yet perfected, so its widespread integration is still out of sight. Still, as machine learning algorithms continue to advance, the prospect of neural network usage becomes more probable.